Numbers, data, lists: the end of Biology or the dawn of a new era?

These are notes were the reference from a lecture given at the Danby Society of Downing College on 14 February 2018 under the title ‘Any questions? The challenges of Science in a data driven world’

The era of the History of Science ushered by the publication of Newton’s Principia Mathematica is coming to an end; we need to take stock of this and consider the possibilities that a new, data centered and driven reality offers to us. The inflexion point that Newton represents does not mean that there was no Science before then; there was a lot that we would recognize as Science; it just had a different fabric and was called Natural Philosophy, but Newton’s Magnum Opus brought together observations, theory, deduction, generality, prediction- the elements of modern Science- in a manner that had not been done before, and showed the way to the future. That future is starting to feel like the past now, being substituted by a different way of practicing Natural Philosophy. We can now either surrender to the trend that data rules or try to marry the tradition that has brought us so much intellectual and practical progress with the beast that ‘big data’ represents. We have a choice.

One of the achievements of Newton was to build on the work of his predecesors but particularly of Johannes Kepler, an astrologer with a deep interest in deciphering the laws of planetary motion. He was a copernican i.e. believed that the planets orbited around the sun and not in the prevalent view of the time that they, including the sun, revolved around the earth.  He also had a gift for geometry and calculus which he used in his day job: horoscope making. Kepler’s legacy to Newton was the derivation of empirical laws for the movements of the planets that proved not only right but a match to the fire Newton was to ignite.  But the reason for Kepler’s achievement was not only his insight but the data he had access to, the data he sought. At the time the scientific community –in so far as there was ‘Science”- was small and Kepler knew where to find data. He knew about Copernicus ideas and that at the time this was more of a view than a fact anchored in observation- in fact people still fitted the data to the geocentric theory with dire consequences of difficult to comprehend models. Thinking about the possibility that the sun was at the center of the then known Universe, he set out to think about it, with data. And the best data, he knew, belonged to one Tycho Brahe, a colourful danish character living in exile in Prague who had made the most accurate observations of planetary motion, in particular of Mars, at the time. It was when he put together these data with his intuition and geometrical talent that he produced the three celebrated laws that helped Newton make his case.

Many years later, in 1859 Darwin published ‘On the Origin of species’ which represents a cornerstone of the biological sciences and many believe, with reason, the beginning of modern Biology. Behind that famous book there are over twenty years of work, patiently gathering data, corresponding with breeders and naturalists, pouring over collections and observations of plants and animals, threading all together around an idea, with its seed laid on board of the Beagle, that there is a unity to the natural world that spans millions of years, that what we see today is the result of descent by modification: Evolution by Natural Selection. The statement ‘I think’ in the 1837 sketch of the first phylogenetic tree is a witness to the fact that there was an idea at the inception of the theory. And so, like Kepler’s laws, what Darwin delivers twenty years later uses data to shape an idea and not to beget the idea. The data is sought, screened, used selectively, forged to shape the idea and, of course, the idea is in turn shaped by the data. Mendeleev and the periodic table, Watson and Crick and the structure of DNA, Schrodinger and Heisenberg and quantum mechanics, all examples of the same. This has been the way of Science for the last 400 years. But this is changing and is changing fast.

Advances in computer science and social developments have ushered in an unprecedented ability to gather and store data from any source in enormous quantities. It has also unleashed a deeply hidden human hunger for data. Google is but one reference for what this is but there are many others. When you go to your supermarket, through your loyalty card you are being tracked, analyzed and this is why in the back of your receipt you will find that the discounts you are offered match your pattern of consumption; this is why, if you bother in looking at your junk email box, you will find advertisements subtly tailored to your internet search patterns. And it is not just the supermarkets and the companies that want to know about you, also the governments and social media giants, screening emails and internet searches; sometimes with reason, sometime without. You’ld be naïve thinking that you are reading this and that nobody (well not a human but a machine) is watching. And we are curious too; remember Google processes 40,000 searches per second. Google is becoming our surrogate memory and where the skill used to be to remember, today is to ‘know how to search’. We also take advantage of this ‘data wind’ to create and to expose ourselves: there are 300 hours of video uploaded to YouTube every minute and your phone is full of pictures that you will not have time to look at in the next year. We live in the era of data and with this comes the need to analyse it, to use it, perhaps to understand it i.e, to turn it into information. But with so much we need special mechanisms that trawl through it and produce patterns.

Today we hear about Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI) and much more in the way that we use computers without understanding how they work. These terms and what they represent are permeating our lives. I don’t know much about it -though people in my lab do and try to teach me- but what I know tells me that these are powerful methods to analyse data and to find patterns in a volume of data that a human being could not go through in a life time. And thus it is this: the combination of our ability to collect large amounts of data and the methods developed to tread through it, that is changing the fabric of Science.

Often I find myself wondering whether if one could feed all the data available to Kepler (and I mean ALL, as he had to discern which data he used and which he did not, this is why he sought out Brahe) to some ML algorithms, whether it would come up with Kepler’s laws, particularly the third one, which is the result of a careful selection of data and the answer to a simple but precise question. We could ask the same question of Darwin. If we expected a positive outcome, in both cases we would have to consider that data can abolish the influence of the history of intellectual thought and personal intuition, which was so important in both cases and also others. Kepler built on Brahe but also on Copernicus and Galileo, while Darwin did so on his grandfather and also on Lamarck, Malthus and Chambers. A different and equally useful question is to ask whether, giving all the information we can gather today about celestial mechanics or species around the world to Kepler and Darwin, whether they would come up with their views. My suspicion about the second is no, because they might become overwhelmed. A key element of their epiphanies was the slow cooking of the ideas built piecemeal from small and incremental pieces of selective data, turning data into information, information into principles, principles into laws.

Why am I talking to you about this? After all, you know in one way or another most of it. I guess I am doing two things. On the one hand, I am reflecting on the obvious: that we are moving into a data driven society in which what we do and how we go about it is determined, shaped and fostered by data, big data. On the other hand, as a scientist I feel –and I am not the only one- that this is changing the essence of Science and the method that has been prevalent since the XVII century. Sometimes it is possible to use the data creatively for a good purpose as the work of the late Hans Rosling shows (see his Gapminder website Notwithstanding this example and the more sinister commercial uses of data and statistics, the question for those of us who do Science is: what about it? And nowhere is the significance of data more obvious than in the Biomedical Sciences for it is here that because of the nature of biological systems, numbers and data bloom in unsuspected manners.

Data? Numbers? Don’t look at the sky, look at the earth!. There might well be 10 trillion stars in the Universe but, from the moment of inception a human being grows about 37 trillion cells i.e. with a population of about 7 billion, there are 10^21 human cells kicking around on the plane. Ironic that there is a project “the human cell atlas’ which aims to create a directory of this…… to the human panoply you may want to add other animals In addition, there might well be 200 different cell types but, for example, the reason why you are here and able to listen to me is because you have one hundred billion neurons each of which making, on average, 7000 synapses!. There is an important difference between stars and cells that bears on these numbers: pretty much those 10 trillion stars are the same whereas, as we are starting to see, cells are different from each other and will make more that will be different. A three year old child has 1 quadrillion synapses and they are all doing different things!. These numbers are mind-boggling but you should not be surprised: that is what makes any animal what it is. More numbers, as it turns out that our trillions of cells share their existence with one trillion bacteria and don’t forget that your body right now, as you listen to me, is making 200 million new cells every minute, of which 30 million are red blood cells. What a machine!  We can go on: there are about 8 million species in the earth (give or take one million) and, at an average gene content of 40,000 genes per species (not including or getting philosophical about how much is coded in a gene) this gives again a trillion, maybe a quadrillion, figure for the number of total genes in the biosphere. A recent fad comes from the technical development that enables us to look at gene expression at the level of individual cells and what we find is that cells are very different from each other in terms of the genes that they express, that the trillions of cells that we have, use combinations of the 70,000 odd genes that we have to create their own identities, that during evolution living systems explore the combinatorial that results from having 70,000 elements at work, a canvas of 70,000 colours to create a living system.  As it happens, most likely than not the 200 million cells that you are making every minute will have with different patterns of gene expression from each other and from those that will come out in the next hour. Exhausting, but also intriguing. Does all matter?

There may we 10^80 atoms in the Universe but the dynamic nature of living systems has easily surpassed this number in terms of the numbers of genes scrutinized by Natural Selection in the course of Life History up to now. As Stephen J. Gould puts it:

For sheer excitement, Evolution as an empirical reality, beats any myth of human origins by light years. A genealogical nexus stretching back nearly a billion years and now ranging from bacteria to the highest Redwood tree, to human footprints in the moon. Can any tale of Zeus or Wotan top this? When truth value and visceral thrill thus combine then, indeed, as Darwin stated in closing his great book, there is grandeur in this view of life!

But the global view that Gould invites us to admire might is being lost in the fog of its details, the numbers and structure of genes, of cells, of organisms. We are losing sight of the picture and the questions it raises.

Are there no more questions? Is our obsession with data a reflection that we know all we need to know? Perhaps, surely, there are realities lurking behind these numbers that we cannot fathom them or we are to lazy to figure them out. Perhaps, as Umberto Ecco says it is that ‘When we cannnot provide a definition by essence for something and so, to be able to talk about it, to make comprehensible or in some way perceivable, we list its properties’. We have been doing this for centuries but now this is done in a manner that escapes our comprehension; this, to me, is the way that projects like the “human cell Atlas’ feels; a catalogue not a map. Projects like this, to which we can add the ‘many genomes’,  ‘all species genomes” or  the ‘prehistoric genomes’ are expensive, bean counting exercises; no doubt with some value but expensive counting, listing, classifying without a reason; exercises that use ML and DL to try to grasp a meaning. Something might and I don’t doubt will come out of it. Somebody, somewhere, sometime will have a question. In the meantime we should not forget how we have got here, that we have laid down a basis to understand this complex web of cells and genes without knowing its details, the microscopic details that we are so keen on at the moment. In turn, lets face it, these details have, yet, to give is something more than lists. They will but only if we do not forget that there are questions that require an answer.

So, how do those of us who have been brought up in the classical tradition deal with this? What is the future of biomedical sciences? Importantly, is there room for the ‘old way”? Or is it that we do not need any more Feynmans or Keplers or Darwins, that there are no more important questions. What follows is a personal view, which may well be wrong but that tries to rationalize what is happening before it devours some of us.

Biomedical sciences is, slowly, becoming the science of big data and one can envision two streams emerging. One is the gathering, classification and, where possible, analysis of lists. The 1000 genome project, the cancer genome project, the species genome project and the human cell atlas are examples of such endeavours. They are more technical than intellectual and in the long run will deliver useful information, though it is likely that most of it, particularly in the transcriptomics, will have to be redone in the future when technology and analysis settle down. At the moment we feel we do not have the means to cope and analyse this information but, the main reason for this is that we have not thought of the questions that this can answer. There might already be enough data to answer some questions but we need to think of questions. Gene ensembles will be linked to diseases and this information will be used to create cures and medical treatments. We shall slowly define what is an individual and in the already evident variation we shall discover much. These projects, basically the development of utilitarian approaches, are becoming the realm of Biology because its nature lends itself to this. In some ways this reflects the state of a collective that has run out of ideas or that not having any, wants to make a project out of lists and lists are many to do in Biology. Today much funding goes into these projects. Accepting that this is the way it is or, at least the substrate for the future, I can see three activities in the future biomedical sciences:

  1. Large Institutes and institutions that pool resources and provide exceptional working environments for young(ish) scientists where a redefined biosciences will develop. These institutions will accumulate funding –at the expense of research in Universities and small research groups- and will enable some people to pursue questions of merit but for the most part what they will be doing is producing what we now call ‘papers/publications’, occasionally adding elements of value to the data mountain. For the most part they will add quality data. Hype will be an important element of their production and too big too fail will be their motto. Occasionally there might be something significant but it will be difficult –as it is now- to distinguish the signal from the noise.
  2. A different strand will have a very applied slant to it and will thrive on data, some of the substrate for this work will come from the large institutions (1) but, principally it will rely on a new very applied Biology, where a third generation biotech –now emerging- and the introduction of engineering approaches will lead to very exciting and novel approaches and results to biological problems. Society will profit a lot from this and I suspect that this will be where the most interesting developments will happen. The interesting element of this strand will be that the value of the research will be determined, principally, by its practical value and not by its publication impact.
  3. Finally, there will also be room for a more classical approach, but with a twist. There will be room for people interested in questions –some of them will be able to work in the institutions summarized in 1 as long as they don’t fall into the many traps that such places contain- and they will have a chance to work on those questions much as mathematicians do today with problems like Fermat’s theorem and Riemann’s hypothesis. Somebody will define such questions, perhaps as David Hilbert did in mathematics, and this people will work in isolation or small groups to answer them. The ability to understand and access data will be essential here. On occasion, as it happens in Physics, these questions might lead to consortia to test hypothesis or predictions.

In the long term and with developing limitations in funding, it is very likely that 1 will be pushed into various forms of 2 or the mega-projects of 3.A devaluation of the current notion of publication (which is undergoing a transformation) will help this shift as this is an important component of 1. In the long term there is a chance that the classical scientific method will be restored in the biomedical sciences, though it will be in a new form.

As Bob Dylan said

Come gather ’round people
Wherever you roam
And admit that the waters
Around you have grown
And accept it that soon
You’ll be drenched to the bone.
If your time to you
Is worth savin’
Then you better start swimmin’
Or you’ll sink like a stone
For the times they are a-changin’.

Not only there is nothing wrong with these changes but rather they bring up challenges and opportunities. If we are honest, people of my generation should not be pining (as some do) for a foregone time -much of the problem of todays’s biomedical sciences is that it has not adapted to the times, that the system of decision making, attribution and research orientation is the one that operated in the 80s and 90s –, a system largely operated by the people that were successful at a very different time with  a very different system who do not cater for a constituency that has grown up with different needs and aspiration and, importantly, in a different world. Those of us from that era should be jealous of the possibilities that have opened up for people with an interest in Science and the background to tackle it, rather than nostalgic for a period and ways, which have already made their contribution. What is important, though, is no to forget that data helps answer questions and make progress, that we should not forget that the power of what we can do today is not in the making of lists, but in answering Questions which even if we can’t formulate today, we should strive to find. Let’s not be complacent in the midst of the embarrassment of data we live in. And let us not forget that these data revolution has not yet delivered anything that matches  the discoveries that people made with less data: the laws of genetics, the structure and function and DNA, the fundamentals of genetic circuits……It is disingenuous to think, as some people do these days, that one can reach fundamental principles just by looking at data or by throwing data into ML or DL algorithms.

The important bit in computational biology is ‘Biology’ and the need to know what to compute. Questions are missing in todays changing times and it is important that we start making the list that matters most, the list of important questions in Biology. Let’s do this before we forget that we can do it.

In defence of Embryonic Stem Cells as a new model system for Developmental Biology

Over the last year a trend has emerged, highlighted by several articles, to defend model systems, specifically in Developmental Biology. The reason for this is a perceived (and real) threat in the funding of research in yeast, C. elegans, Drosophila, sea urchins and other systems e.g ascidians or arabidopsis (the issue of the animalcentric view of developmental biology is an interesting one) despite their research potential supported by many and obvious substantial contributions to our understanding of biological systems. One of the latest in this series of statement was published recently (1: The authors do an excellent exposition of the contributions of several model systems to basic Biology and the impact that they have had on biomedical research, as a way to justify the need to continue the support of their research base. Surprisingly, the authors don’t include vertebrate models and Xenopus workers might query this omission. However, in addition to defend the aforementioned systems, they take issue with emerging research in Stem Cells, stating at the outset that “those who do not know the history of yeast, flies and other non-vertebrate organisms and their contributions to biomedicine may consider such studies as unworthy of participation or of funding. Given the growing excitement about human stem-cellbased cell culture systems, particularly organoids, some may argue that organisms that do not recapitulate all of the complexity of humans will cease to be useful in the near future. This line of thought is not only incorrect but could hamper scientific progress” (1). In a rather barbed comment towards the end of the essay, the authors state “We would go further to argue that organoids will not and should not replace non-vertebrate model organisms as discovery tools” (1) Here he authors refer directly to an article in the special issue of Development on organoids (2) in which it was argued exactly what is the theme of this post, that there is no way to separate the two fields and that putting them together promises much for the future.

As someone who is aware of that history and also works with Embryonic Stem Cells (ESCs) (DOI) I say that these statements are wrong and that to pitch research with Stem Cells and Organoids in contrast to that with model systems is not helpful. The issue is not about Stem Cells/Organoids versus model systems but about good and bad Science; and there is a lot of both in model systems and in Stem Cells, though I would say that from a strictly Science based perspective, right now, there is a lot of very good Science with model systems (DB) and a lot of bad science with Stem Cells at the moment. Issues of significance are a different matter. When making these arguments we should bear in mind that in comparison with model organisms, Stem Cells/Organoids are barely at the start of their historical life. In some ways Stem Cells, as an experimental system, are having the same disruptive effect that, say, Drosophila had when it erupted into a field dominated by marine invertebrates at the beginning of the XX century (3) and yet, history proofs that Drosophila, once it found its bearings, made very significant contributions and insights (3, 4). Difficult to compare historical periods but we should wait for time to make its mark in new fields of research. The discussion is not only about the quality of the Science that these areas of research can produce, it is also a matter of questions and systems  – will expand below. Notwithstanding these considerations, I want ti insist that it is not helpful to create confrontations, particularly when research in Stem Cells has always been an integral part of Developmental Biology and therefore, to try to present it as something new and competitive is to deny an essential part of the field and systems that the authors try to defend: Developmental Biology. Our understanding of cell lineages in many organisms e.g leeches, and systems e.g the nervous system in all metazoan, of gametogenesis, homeostasis and regeneration is founded on the biology of Stem Cells. We should celebrate and welcome this. It is true that over the last ten years the field of Stem Cells has undergone a renaissance (as did Drosophila in the 1980s after a hiatus of almost 40 years) and that some people want to present it as something new, but it does not work. Research with Stem Cells is Developmental Biology and what has changed is that in their renaissance, Stem Cells and, in particular Embryonic Stem Cells (ESCs), have become a new model system for Developmental Biology.

Nobody will deny that the model systems highlighted by the authors have played a key role in modern Biology. In particular they have been instrumental in revealing the elements needed for biological systems to build themselves and to function. The genetic and molecular screens of the 1980s and 90s uncovered a set of agents that, although we did not know at the time, turned out to be elements of a universal tool kit for the construction of living systems, and many have been shown to be central targets of disease (5, 6). This work has led us to appreciate the critical role of transcription in cell fate assignments, that of cell biology in morphogenesis and how the combination of the two contributes to the building of tissues and organs. In learning about the universality of many of these elements we are uncovering that they form conserved modules whose functioning we still do not understand; continuing work on these systems is making progress to understand these modules. This is the basis for much of the arguments as those presented here (1) and it is right. However, Science, even today is an issue of questions and systems i.e. one should not work on a system simply because it is experimentally sound, because it yields results, because many experiments can be done or because it has a glorious past. One should work on a system because it is suited to ask, and maybe answer, specific questions and one should match question and experimental system. In this regard, invertebrates have many features in common with vertebrates but there are features of vertebrates that do not figure in the Biology of invertebrates. For example, the large scale cellular rearrangements that characterize most vertebrate embryos do not find a correlate in Drosophila or C. elegans, not to mention issues of homeostasis which only arise in the life spans of vertebrates. The issue of Dlp and Hippo that the authors raise in their essay (1) echoes the actions of hormones and chelons, but it is clear that the time and length scales characteristic of vertebrates require studying them in vertebrates. Interestingly, the issue of the regulation and function of Hippo –very much a flavor of the times- might not be the best example since it appears that Hippo can be linked to just about anything and everything and that one would always have to make such studies in a specific context.

At a most significant level, the fundamentally genetic work with model organisms, does not address the question of how cells build tissues and organs and this is because a gene-centric approach can’t do that; I hasten to add that the fact that there is a gene involved in a specific process and that we might be able to interpret gain and loss of function phenotypes does not count in my book. To understand how cells (genes is more difficult) build tissues and organs requires a very different approach from the one we have been using until now. You don’t understand how a car works simply by removing pieces and running it –sometimes it will crash, others it will not start or run slower- you learn how it works by building it and testing what you have built or are building the necessity and sufficiency of every piece (see the famed ‘Can a biologist fix a radio’ ref 7)-. The case of yeast and the cell cycle is an interesting one in this regard. Some of the most salient insights in this field have not come only from mutagenesis and epistasis but from  a combination of modelling and engineering in which the order of the system is challenged and reorganized (8). This has led to discovering minimal systems, the functional meaning of redundancies and of modules with functional sense i.e going beyond genes. Behind these findings lurk principles and designs (see the interesting discussion presented in 9 and 10). Reengineering or engineering a system from the bottom up tells us much that Genetics cannot do. This kind of work is very difficult to do with model systems because they are not suited to this. In the case of some invertebrate models systems because they are too hardwired and one cannot create them from scratch. In the case of vertebrates because of their complexity challenges this type of approaches. Enter Stem Cells and, in particular ESCs as a new model system in Developmental Biology one in which we can ask how cells build tissues, organs, embryos.

ESCs are a derivative from early mammalian embryos with a huge potential to enlighten our understanding of how cells build embryos, tissues and organs. It is true that it is early days, that there is a lot of poor work and a great deal of salesmanship of mediocre science at the moment, but this should not hide the fact of what they offer if they are used correctly, with high standards and a critical eye. And organoids, mentioned in (1) are an excellent example of what these cells (and also Adult Stem Cells (ASCs)) can do and how much we can learn from them about details of Biology that are not accessible in any other way. But, we need to use them judiciously and critically. Some of the arguments for this have been made before ( and )  and I shall not repeat them here, but will emphasize that Duronio et al are right that Stem Cells and organoids not a substitute for Model Organisms, they are a new experimental system with possibilities that are just beginning to be explored and whose full potential remains to be explored. They are not going to replace vertebrate model systems because the kind of questions that they ask are different and complementary but they sure will change the way we view vertebrate and, in particular mammalian development. Look at the work of a small group of people and their collaborators -J. Briscoe, S. Lowell, V. Wilson, J. Brickman, P. Zandstra, A. Brivanlou and E Siggia, A. Warmfklash, Fu, M. Lutolf, A. Grapin-Botton amongst others- that are exploring this avenue. There are also people who are using developmental biology to pioneer the development of regenerative medicine with ESCs, the work of J. Wells and J. Spence comes to mind. Here it is this dialogue between Developmental and Stem Cell Biology that matters. And in the area of adult organs, optic cups, minibrains and intestinal organoids have been leading a revolution in our understanding of precisely this question of how cells build organs and how we can harness this potential for biomedical research (11, 12). If one looks at this work, one will see that there is nothing to fear and much to be gained by promoting research with Stem Cells IN the context of Developmental Biology. To paraphrase a well known quotation: Stem Cells without model systems are lame, Model systems without Stem Cells are blind.

And as long as we don’t hype the issue and provide perspective, we should not shy away from the statement that understanding the make up and development of human beings is an important aim of Developmental biology. The reasons for this could be sheer curiosity or the thought of applications to disease or regenerative medicine. However, there is no ethically sound model system to study human development. The last few years have shown that while mice are a good proxy for much that is human, the only way to study humans is with humans: the work with minibrains and intestinal organoids, highlights this. And it is here that, hopefully on sound grounds, ESCs and ASCs will become a most important tool in this endeavour because what we are learning from the work with mouse cells is that if properly used, they teach is things. Not everything that happens in an embryo happens in the dish but all that happens in the dish can happen, and often happens in the embryo.

It seems to me that what Duronio et al grapple with is not really Stem Cells and Organoids but something more mundane but perhaps also more fundamental: the squeeze in funding, the large number of researchers and the average high quality of research that makes striving for that funding a challenging task. From this perspective, we should not lose sight that Stem Cells face the same problems than Drosophila. The reason is that funders often want to hear that one is going to cure a disease and not to find something fundamental; this applies to model systems as well as to Stem Cells. Now what is important is that we shall not convince funders by denying what is obvious (the possibilities opened by Stem Cells and Organoids) but by highlighting how Stem Cells and Developmental Biology are not only very related but are, as discussed above, complementary. We should also remember that the discoveries Duronio et al highlight were made in a very different environment than the one we worked on today when, perhaps (I believe certainly) it was easier to discern what was important from what was anecdotal or simply interesting. This is difficult today when every week we are confronted by ‘great’ discoveries which strive to be seen in a rain of technical prowess.

Stem Cells as a model system are here to stay and should be seen as a complement and not a competitor in our current arsenal to understand Nature. Is is interesting to reflect on the observation that one of the great achievements of Development journal has been to create a highly successful section on Stem Cells! What we need to do, though, is raise our game, find good, difficult questions and ways to address them. We should not be complacent and think of experiments simply because we can do them or of systems because we can use them. We should be bold and look for new avenues an systems. Also, we need to admit that much has changed and is changing in the environment in which we do Science and that what worked in the past, does not work today. We need to change and adapt. We need to accept that we work in a very different environment with very little room for the scientist/craftsperson of the XIX and XX century ( What we need to do is to find ways to use much that is good in this environment and invent new ways of doing Science. In the context of the essay that triggers this post (1), we need to avoid justifying classical model organisms on what they have done in the past and find ways to make sure that they contribute to the future. Biological systems are such that there will always be a new gene, a new organelle a new relationship to be found, like the collections of the XVIII and XIX centuries were putting together animals and plants. What we need to make sure is that we find the ‘finches’ and have the eye to recognise what they tell us.



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  2. Huch, M., Knoblich, J., Lutolf, M. and Martinez Arias, A. (2017) The hype and hpe of organoids. Development 144, 938-941
  3. Kohler,RE. (1994) Lords of the fly. The University of Chicago Press.
  4. EA Carlson “Mendel’s legacy’ Cold Spring Harbor 2004 .
  5. Anderson, KV and Ingham, P. The transformation of the model organism: a decade of developmental genetics. Nature Genetics 33, 285-293.
  6. Ugur, B., Chen, K. and Bellen, H. (2016) Drosohila tools and assays for the study of human disease. Dis, Model and Mech. 9, 235-244.
  7. Lazebnik, Y. (2002) Can a biologist fix a radio? Or what I learnt whie studying apoptosis. Cancer Cell 2, 179-182.
  8. Coudreuse, D. and Nurse, P. (2010) Driving the cell cycle with a minimal CDK control network Nature 468, 1074-1079
  9. Atay, O and Skotheim, J. (2014) Modularity and predictability in cell signalling and decision making. Mol. Cell Biol 25, 3445-3450
  10. Mellis, IA and Raj, A. (2015) Half dozen of one, six billion of the other: What can small- and large-scale molecular systems biology learn from one another? Genome Res. 25, 1466-1472.
  11. Sasai, Y. (2013) Next-generation regenerative medicine: organogenesis from stem cells in 3D culture. Cell Stem Cell 12, 520-530.
  12. Bredenoord, AL., Clevers, H. and Knoblich, J. (2017) Human tissues in a dish: the research and ethical implications of organoid technology. Science 355, DOI: 10.1126/science.aaf9414

Developmental Biology: the cauldron of the biological sciences

Disclaimer: I am not a historian of Science and these views are, simply, sketches from the fringes of my interest in the subject as a practitioner of Developmental Biology.

Sometimes it is possible to draw some sort of a straight line in the history of Science. For example, one can build a sensible narrative across an axis Galileo-Copernicus-Kepler-Newton….or, sort of, as most of the time a scientific discipline is a pidgin of varying questions, thought traditions, data and, importantly, visions of different scientists and surprising experimental results. Any attempts to trace a scientific discipline to a specific root will be difficult. Visions and technical advances will always collide in unpredictable manners to answer old questions and find new ones. It would be easy to say that the combination of hindu-arabic numbers and modern Calculus is at the root (the stem cell?) of Physics and yet, the babilonians and the greeks did very well without either. The history of Science is, first and foremost, a history of progress through change, a blend of experiments, ideas and reasoning. Furthermore, it is usually when different languages and visions come together that something interesting happens. One can trace paths but there are no unique roots.

I am saying this while reflecting on an article published in PLoS Biology by Scott Gilbert, claiming that Developmental Biology is the ‘stem cell of biological disciplines’ ( ). The reason for this musing is the feeling that Developmental Biology, specifically Developmental Biology, is under threat as a discipline (a widespread thought in the field these days) and that this kind of revelation should lead us to place the discipline in its rightful place and make funders and other scientist give it a credit which many people think is overdue. I do not see any of this to be the case. Developmental Biology is no more under threat than other branches/areas of Biology (talk to prokaryotic biologists or virologists!). What is under threat (more change than threat) is a certain way of doing Science –but I shall leave this to another time, as here I want to focus on some specific aspects of the piece by Gilbert. All I will say for now about this is that we need to do is to adapt and to change. The point I would like to make here is that, even if the drama underpinning Gilbert’s statement were true, one does not help the cause by rewriting history and shoehorning facts. The author states at the beginning that it is good to create myths to defend a realm and while this could well be true, mythologies are not history and one should be careful with the consequences that go with creating myths which can mislead current generations of students, and pay lip service to the cause it pretends to help.

Let me go to the two specific claims I take issue with: that developmental biology is the birth place of Genetics and Neurobiology (I would add here immunology and cell biology but then this would be too long). Let me start with Genetics, something I have worled with, and make things clear from the beginning: the questions that led to the emergence of Genetics had nothing to do with Embryology (Developmental Biology did not exist at the beginning of the XX century), absolutely nothing. The fact that embryos of different species might have been used to answer some questions posed by the gene based theory of inheritance does not mean that Embryology was at the roots of Genetics. The questions that concerned Mendel, and later de Vries, Tschermak and Correns, had to do with hybrids, plant hybrids to be more specific, and the laws that regulate the inheritance of some of their phenotypic characteristics. If one goes back in history, one will find that the forerunners of Mendel, plant breeders in Germany like Kolreuter, were not interested in questions of Embryology. This bit is easy to demonstrate and I suggest that anybody with some interest in the matter reads the little book by Robert Olby ‘Origins of medelism’ (Schoken books New York 1966) .

The intervention of TH Morgan in the story lends itself to misconstruction as he was, first and foremost, an embryologist. However, at the time, it was not rare –as it is the case today- that people were interested in Evolution and, in particular, in matters of the inheritance of characters (not the genetic basis of development). Morgan had an interest in these matters which were of paramount interest to biologists at the time, with a particular focus in the nature of mutation. It is this that led him to start working with Drosophila and, in an inadverted manner, to lay down the foundations of Genetics in Columbia University with a small group of superb students led by Sturtevant, Bridges and Muller. All this without a single thought for embryology. It is true that Morgan did refer, particularly in his Nobel lecture, to the connections between Genetics and Embryology, but he did not see how to do it and, actually, in his latter years in Caltech he returned to embryology without even attempting to bridge the gap (see the excellent biography of Morgan by G. Allen, Princeton Univ. Press 1978).

With his work, Morgan starts what Sydney Brenner has called the ‘great deviation”, a proces in which Genetics and Cell Biology need to get on good footing before Embryology can answer the questions it had at the start of the XX century. In many ways and with the exception of the work of Spemann and his collaborators, Embryology enters into suspended animation until the emergence of developmental biology when the consolidation of a number of disciplines –in particular Genetics and Biochemistry- allows a new vista of old questions. Thus Morgan pioneers the Genetics branch of this path and does so in a somewhat unconscious manner. In this regard, Boveri’s work is interesting and important but is a thin (though important) strand in the cell biology stream which also develops –for the most part- answering specifc questions (the residence of the particles of inheritance). Let me repeat, the work of Sturtevant, Muller and Bridges under Morgan –and on their own- has nothing to do with and owes nothing to Embryology. Like classical physics lies in the axis mentioned above (Galileo-Copernicus-Kepler-Newton) one can draw an axis for Genetics: Kolreuter-Mendel-Bateson-Morgan-Avery-Watson and Crick-Nirenberg; will led the reader to decipher (of degoogleized) some of those names. There is not a whiff of Embryology here which at the time was concerned with other questions, many of which were difficult to tackle.

Once Developmental Biology emerges as a discipline, an offshoot of the Entwincklungsmechanick and Embryology, it has something to do with Genetics, particularly in the latter part of the XX century, but Embryology –as such- has very little to do with Genetics. An interesting observation about this lack of a relationship between Genetics and Embryology in most of the first half of the XX century can be seen in what came to me called “the modern synthesis which tried to reconcile Genetics and Evilution. There are two disciplines that are left out of this endeavour: Embryology/Developmental Biology and Paleontology. The reasons are different but in the case of what is the object of this discussion, the reason was that at the time the connection between Genetics and Developmental Biology is, for the most part, non existent. Later on, in the 80s there is much of a relationship, but it is not the one expressed by Gilbert, it generates Evo-Devo.

The application of Genetics to questions associated with the development of plants and animals, changes the way we practice the discipline and, most significantly, provides insights that we are still digesting. Thus, while Genetics does not owe much, if anything, to Developmental Biology -and much less to Embryology, Developmental Biology owes a huge amount to Genetics. The reasons for this are many and are made very clear every day but the most important one is that IF there is one discipline that is stem of biological sciences, this is Genetics. However, Genetics is not the stem cell of Biology (I think this is the wrong way of looking at history of Science) but the unifying language, the unifying discipline and the one that ever since its emergence provides a clarity of thought and argument to old biological questions and this includes those related to development.

I shall be brief on the second issue: that of the ‘historical lineage’ relationship between Developmental Biology and Neurobiology. The claim is not very difficult to challenge as Neurobiology is not just the study of the development of the nervous system but, more significantly, its function. The origin of Neurobiology lies in the discovery of electricity. Volta and Galvani, Hodgkin and Huxley had no interest in Embryology nor developmental biology but it is them and their work that creates the roots and the basis of Neuobiology. There is little one can add to this well established fact. Ross Harrison, referred to in the Gilbert article,  is a pioneer of Developmental Neurobiology and, in particular, of tissue culture and ex vivo approaches to biological questions. He should be celebrated (as he has) for that. To place him at some root of such an advanced, complex and insightful branch of Biology as Neurobiology, is to reduce Neurobiology to Developmental Neurobiology, a somewhat narrow view of he field. Even on the example given by Gilbert, it is true that certain aspects of Neurobiology have their roots in embryos –but NOT EMBRYOLOGY- for it was the vision of Ramon y Cajal to use embryos to look at the structure of the nervous system, where he thought that a simpler system would yield answers to the disputes with Golgi raging at the time about the individuality of neurons. Thus, I think this is a better example to relate Developmental Biology to Neurobiology but, again, Ramon y Cajal was not interested in developmental questions but in very specific issues of cell biology associated with the nervous system.

In Biology, as in Science, one needs to use specific systems to find about the world through experiments but this does not mean that certain disciplines associated with those systems are at the root of further developments. Furthermore, I don’t believe that there are stems to whole branches of Science. Particular branches: quantum mechanics, astronomy, organic chemistry, Cell Biology and, of course, Genetics are pidgins of many ideas and languages and in the blends that they represent lies their greatness and their value.

I appreciate the enthusiasm of the piece in PLoS Biology but we should be careful not to distort history for the younger researchers. In the context of the theme of the essay we should not create weak phylogenies but rather thrive in the diversity that gives so much to Science and encourage interactions between disciplines as it is here that discoveries lie.  Developmental Biology is, today. a pidgin of  many disciplines: Genetics, Biochemistry, Cell Biology, Evolution, where the  recent application of Physics and computations methods is stimulating the development of these fields and providing, as it should, new vistas of old problems in Developmental Biology. Rather than a stem cell, I encourage you to see Developmental Biology as an alchemic cauldron where the blend of disciplines produces exciting and interesting findings which, slowly, will find their way into practical applications. Cell Biology and genomics are good examples where the application of these disciplines to developmental systems has revealed issues that cannot be addressed, because they don’t exist, in different contexts e.g transcriptional states during cell fate transitions or the emergence of tissue level behaviours from individual cells. More recently, the application of Physics and Maths is changing our view of developmental systems, providing intriguing new insights and also transforming the fields (Physics and Maths) in surprising and interesting ways

As for the real problem that the article identified and tried to address, it is not the lack of recognition of Developmental Biology . Furthermore, it is not specific to Developmental Biology. The issue is deeper and it has more to do with the way we do Science today as opposed to a few years ago, the fabric and form of Science. I have mentioned some of the issues before. By creating a mythology of the past we do not solve this. What we should be doing is identifying the exact challenges that we face and rising to them by looking at the future in search of creative solutions.



  1. I shall not discuss the uses of the words ‘evolution’ and ‘development’ in the late XIX century which are raised in the article. This would require more time, space and study that I am able to provide now. The issue is typical of nomenclature and the use of words in Science which acquires large dimensions in Biology where definitions are loose. It is very similar to what is happening with ‘epigenesis’ nowadays.
  2. There are many excellent texts about the history of Genetics but I like, specially EA Carlson “Mendel’s legacy’ Cold Spring Harbor 2004 . There are less single books on the history of Developmental Biology –though there are many collections. I suggest two: J. Oppenheimer “Essays on the history of embryology and biology’ MIT press 1967 and “A history of embryology’ edited by T. Horder, J. Witknowski and C. Wiley Cambridge University Press 1985.


Thinking About Science in Trump’s USA: A Warning from History

The old Chinese curse, “may you live in interesting times”, has been thrust upon us; interesting times, indeed. Well, more than interesting, surreal. Brexit and Trump are just what is closest to us but let us not forget the puzzling situation in Syria and the mystery that Russia always is (the famous adage of Churchill about the Soviet Union always comes to mind: “a riddle wrapped in a mystery inside an enigma”). One of the most surprising features of these times is the disregard for logic and sense, the creeping influence of “the falsehood” and a plump disregard for objective, fact based scientific reasoning. There is so much going on that it is easy to be desensitized, to not give importance to things that matter and to forget that actions have consequences. At this, there is one area where the discussion has been going on for so long that we can easily overlook its importance. The news that Trump is revoking the Obama legacy on climate change, reactivating drills and the coal industry in ways that put in peril the environment have been headlines news. People have complained and engaged into reasonable debate but, like so much these days, this has just fallen into the flow of news. Perhaps the most painful aspect of the New White House trend is the disregard for scientific evidence in favour of ideologies and false economic reasoning without thinking of the long term consequences.

We have seen this before. Hegel said that history repeats itself, first as tragedy then as a farce and it seems to me that this is happening today. Farces also have consequences though. The denial of climate change for ideological and social reasons has been going on for a while but the Trump administration, like in many other things, is taking it to another level. Not only there is an open denial of clear facts and evidence, there are also attempts to destroy evidence. Files are being deleted and scientists linked to the administration banned from raising their voices. The reasons behind this are the selfishness of certain elites that bargain the (our) future to feed (their) short term gains. We have seen this before, and perhaps it is worth remembering one such episodes which echoes in the climate change discussion.

A plot of Science at the service of Ideology developed in the Soviet Union during the 1930s and 40s and led to the destruction of Soviet Biology, in particular Genetics, and henceforth to the poor state of this field today in Russia and its satellites. The name associated with this achievement is Trofim Lysenko and a doctrine called Lysenkoism. The plot is simple (well, not that simple but will try to summarize) and characteristic of those times and place. Even in the complicated environment of Russia in the 1920s, Science was still (just) workable and Genetics, as it was on the rise, became a normal staple of soviet Science under the influence and organization of Nikolai Vavilov (Russian stamp at the top). The importance of agriculture in a country the size of the Soviet Union with its large peasant population was obvious, so perhaps it is not surprising that much effort was focused on plant genetics and agriculture (the main specialty and interest of Vavilov). Fruit flies also had a good representation and Hermann Muller (a declared socialist) spent time in Moscow teaching and consolidating the laws and practice of transmission Genetics. The great Nikolay Timoffef-Ressovsky, was a product of this period.

It is not surprising that in a deeply ideological society even Science has to bend to the prevalent social and political thoughts; see Galileo’s trial or the attitudes of the Nazis towards ‘Jewish’ science, but also in a more benign version the role that capitalism has played in the development of modern scientific culture. Thus in the Soviet Union, the collectivization of agriculture in the 1920s had led to great famines and deaths. This was the result of a mixture of poor agricultural practices and the weather, always harsh and unpredictable in most of the territory. With this in mind, it is not surprising that Genetics would be at the centre of any discussion to tackle this problem and that Vavilov, as the leading figure in the field, would take a leadership role and worked hard to address the problem. He travelled the world collecting seeds and trying to fit them to specific locations where they could grow and yield in the complex soviet geography. But Nature has its rules and if you want to change them you need to let Natural Selection work over long periods of times. The soviets did not have time.

Enter Lysenko (left), an agricultural biologist of peasant extraction who became interested in understanding why some crops needed to be planted in Winter and some in Spring. This was not a new question, not without scientific merit and for this reason Vavilov himself had been interested in it and supported this research. Building on experimental work of several years, Lysenko observed that exposure of Winter seeds to humidity and extreme temperatures in ‘laboratory conditions’ would allow them to germinate and to be planted in Spring when they would grow as the natural plants. In this manner the laboratory conditions could bypass difficult winters and ensure harvest. He called this process ‘vernalization’, a term that already existed but which has become synonymous with Lysenko. Furthermore, he suggested that this acquired resilience and ability would be transmitted to the offspring which then would not need to be treated. If this neolamarckism were true and properly done this would avoid the vagaries of the Soviet climate and increase the yields. And of course, if this were true, it would be possible to control at will the development of the plants and the yields of crops. In an increasing climate of Ideology over Reason, much of this fitted well with premises of the emerging soviet socialism in which even the laws of Nature had to be at the service of the people.

A complex web of Science, Politics and Social Engineering was woven around Moscow in the 1930s. This led, almost imperceptibly to a political interest in shaping Science. Surprisingly Mendelian genetics, slowly but surely, become a focus of these activities. The rules and vagaries of random assortment of characters, the notion of ‘the gene’ and the Darwinian principles that underpin much of Biology were put into doubt. Suddenly facts became servants to ideology and the gene was first questioned, then doubted and finally ridiculed, expunged from teaching and research. It is difficult to believe that scientific knowledge would be a dangerous asset in the early part of the XX century but as heliocentrism in the 1400s, Mendelian genetics could take you to the gallows. Muller had to leave Moscow in a hurry when it the notion that “biologists are fly lovers and people haters” (as quoted in Wikipedia Lysenkoism) started to spread. Over time, 3000 Soviet scientists were either killed or interned in camps during the 1930s and 40s, mainly for espousing or being seen to be associated with scientists linked to Mendelian genetics. Amidst this the biggest casualty was Vavilov who tried to make his clout count to help Genetics but who, in the end, succumbed to the conspiracies and power in fighting. In the ascendancy of Lysenko and the complex web that was being built between Science and Soviet ideology, Vavilov’s links with the West provided the catalyst that the State needed to build the case against him. He died in a camp in 1943. In 1948, Lysenko was appointed director of the Institute of Genetics within the USSR’s Academy of Sciences and Genetics was declared a pseudoscience. He reigned supreme over Soviet Biology until the death of Stalin.

It is not difficult to see traces of this piece of history in the recent official decision of Trump administration to undo Obama’s climate change policies. The attack of the evidence of man made climate change is decried as pseudoscience and the reason behind this consideration is none other than an ideological one: to favour a kind of backwards industrial development for the people, a pro-business, pro-money, anti-science attitude of the administration. Of course, unlike the closed Soviet Union, this is happening in a well connected world where we are aware of news and events and in a country with a free press and without the risks that were so prevalent in Stalin’s USSR. However, let us not be fooled by these differences. The point is not whether Trump is like Stalin, he is not. The point is to remember that when Ideology takes charge of Science there are likely to be long term consequences. The USA has had, and still has, its issues with ideologies and science, creationism and intelligent design come to mind, but never before has been an administration leading the ideological charge. For the attitude towards climate change is mirrored by other national institutions like the Department of Agriculture, and the Center for Disease Control and Prevention. Nowadays, the cuts to the NIH and the appointments at the higher level of individuals with little knowledge of, respect for and belief in Science do not bode well for a Society which has given and gives so much to Science.

The US academic and scientific community has enough power and foundation to resist but it will need help and this is where a reminder of history matters. Lysenkoism affected the USSR and never spread (though it tried) but the damage to the culture of that country was immense and long lasting. While Mathematics and Physics come to mind when thinking about Soviet of Russian Science, Genetics and Biology do not. In fact these are represented by what often is called the Lysenko affair. Such events unfold over long periods of time so lest we forget and make sure that we heed the warnings from History.

The image of Lysenko is from the picture of the stamp with Vavilov is from Wikipedia

  1. This post emerged while reading Simon Ings’ “Stalin and the Scientists: A History of Triumph and Tragedy 1905-1953” which reminded me that those of us who are scientists have a responsibility to defend the proper use of scientific evidence. Further reading, if you are interested, on the recent events in the USA:

and some good news….

On the Value of Imitating Nature (imperfectly)

COI: our group has an interest in these matters and the post reflects this and contains references to our work.

The idea of making a human being from natural or unnatural parts has been more than a curiosity for centuries. The story is told in a little known book by Philip Ball (“Unnatural: the heretic act of making people” Vintage books, London 2012) and has many a fascinating angle, though the bit I really enjoy is the development of in vitro fertilization by Patrick Steptoe and Robert Edwards. So much Biology behind what today is a routine clinical procedure! This is an important point: whatever technology there is around, it is underpinned by basic Science. No application can be developed without the support and inspiration of a scientific question and this applies to making cars or humans. But, why would one want to make a human being in a lab?

If you want to make a human all you have to do is to combine an egg with sperm and let it run. The entry of the sperm into the egg triggers a process that generates an embryo which, in turn, lays down the templates for tissues and organs and their relative organization in space. Leaving aside parthenogenesis (the activation of the process without fertilization), this is the way you make any animal and, in many ways, this is what Steptoe and Edwards did for the human by recreating the event in laboratory conditions. However, the process of activating development does not tell you much more than what you knew already since, once the process is triggered you are back to just watch how form unfolds over time i.e. Life, if you want to be dramatic, requires Life and all its pieces working together harmoniously. Can we do better? The advent of Embryonic Stem Cells (ESCs) has given the age old challenge of creating organisms in the laboratory a new lease of life, though very few people would look at it from the perspective of the actual making of a human.

ESCs are clonal derivatives from mammalian blastocysts that can be grown in culture and differentiated in a controlled manner into most cell types of an organism.  Recently it has been found that these cells have the ability to organize themselves into tissues and organs and that we have the capacity to steer (but not to control) these processes. Thus disembodied brains, eye cups, intestines and muscles emerge from culture dishes under so called ‘defined conditions’. These structures are imperfect, often functionally wanting and, for the most part, just happen (rather than are created) and promise much more than they can deliver at the moment. Importantly, amplified by the media, they create dangerous hype and false hope. Intriguingly, they also raise questions about what is an organism that we have not thought much about yet.

A different approach to the problem is to go beyond the organs themselves and ask the question of whether ESCs can be used to build an organism or, for starters, an embryo. There are two reasons for this. Here I shall only state them and hope to find some time to expand on them in the near future. The first one is the sheer scientific value of what one can learn from such an experiment about how organisms are made. The second one, probably more practical, suggests that if you want to build tissues and organs, you better try to copy the embryo, to imitate Nature. Perhaps by starting with embryos one can endow cells with the properties of the real objects, improve the yield of organs and tissues and get them to a functional state. An interesting consideration arises from these musings: is it not what Steptoe and Edwards did an imitation of Nature? Did they not get an embryo out of a dish? Sure, in the end the embryo, the mammalian embryo, will need the mother but they did create a human embryo in the lab. But what do we learn from such imitations about the emergence of form? If one wants to learn how cells make embryos and we rely on the properties of the system to create embryos, what have we learnt? By counterfeiting a painting you don’t learn about the process that created it. Science teaches us something from decomposing, from asking about minimal conditions. A good example can be found in the roots of Developmental Biology (the branch of Biology that studies the emergence of an organism from a fertilized egg). For many decades in the XIX century biologists looked at and described the embryonic development of different organisms and thus developed a good roadmap of what happens after fertilization. It was the agenda laid down by Wilhem Roux and its dramatic execution by Hans Driesch with his embryo splitting experiments, that teaches us something, namely the regulative power of embryos and, in doing so, it lays down the agenda for Experimental Embryology and later Developmental Biology in the XX century ( see A New Sort of Engineering I and II).

The initial question behind the experiments of Driesch and what followed was to find out the minimum number of elements that can give rise to an organism but, from there and because of the answers that emerged, this approach has been rich in contributions to our understanding of biological systems. The reductionist/deconstructionist approach that underlies Experimental Embryology generated a very successful research program that was further underpinned by Genetics: instead of using scalpels and needles, use mutations to understand the system. Genetics has shown not only the processes but has identified the parts. Its combination with molecular biology has proven most effective in the dissection of the entrails of the System and has revealed that the making up of an organism is not what it seems. A classic favourite of mine is the organization of the early Drosophila embryo into stripes, beautifully choreographed in their emergence and spatial organization but which experimentation shows are individually controlled elements of an evolutionarily fitted system. The visible coordination, the obvious symmetry is an illusion created by Natural Selection; the system is built piecemeal with the whole being a collection of parts, seamlessly woven into each other for, probably, function, over millions of years of tinkering. This should not come as a surprise. After all, how are Diptera (flies) made but from disconnected parts, called imaginal discs, which develop autonomously and independently from each other inside the larva (a feeding machine that provides the raw energy and matter for the growth of the parts of the adult). Although the imaginal discs do not raise eyebrows, particularly for those that work with them, it is a strange thing that legs, eyes, genitalia and wings develop independently from each other only to come together in an assembly line during metamorphosis in the ultimate example of self-assembly.

Drosophila has taught us much but there is one feature which is very revealing and is always overlooked as trivial, namely this coming together of the adult organism from parts, that the whole that we see flying around is an illusion created by the convergence of chance and necessity, of teleology and Natural Selection working together for reasons and benefits that we do not understand.  What the development of Drosophila tells us is that Biology is not intuitive and that behind a seamless whole there is much patchwork. But what does this have to do with making humans?  Allow me to rephrase the question, rather than making humans -which I would argue has already been achieved- what we want is ways to learn how humans make themselves, how cells make humans. At this, perhaps we should aim lower first and start with a system we can work with and which is sufficiently similar to teach us the ropes. How about mice and, at even at a humbler level, mouse embryos? If we can make the seed, for that is what an embryo is, maybe we can learn about the organism. As usual, there are many ways to ask questions and we and others are trying 1 but while some aim to counterfeit the embryo, some of us have decided to see what cells can do if we set them free or if we constrain them. For example, arranging ESCs in constrained spatial arrangements and asking them to differentiate is teaching us about the connection of specific genetic circuits and the spatial self-organization of differentiating ESCs 2, 3. On the other hand, the development of 3D gastruloids has revealed many surprises as it looks as if, against the established wisdom of the field, the development of the axes of a mammalian embryo does not require the extraembryonic tissues 4-6. Furthermore, this work (still in its early days) reveals what could be interpreted as the equivalent of the imaginal discs of the mouse: that the head and the trunk can emerge independently of each other and that depending on the signals the cells receive at a particular time, they will assemble into specific and different parts of the embryo 7. I can hear the embryologists raising their voices and claiming that this is an artefact of the experimental system and this echoes much of what was in the air when Drosophila started to reveal the modular nature of its inner engineering. It may be, but I suspect that they are telling us something and that these contraptions are real.

What this says is that we are moving into an era in Developmental Biology in which Engineering is going to play a central role and will do so in two ways. One, in the manner that we usually understand engineering i.e. the development of efficient and reproducible ways towards a practical end. This has already had an impact in Biology and Biomedical research: genetic engineering, as well as artificial hearts, bones and limbs bear witness to this. Now we are starting to apply this to the activities of ESCs, though we should be careful to control the hype that can surround this research. In addition, we should look at engineering as a discovery tool. In some ways and inadvertently, we have already since when Driesch and his followers start chopping the embryo into pieces, they are using some engineering principles. They are doing it in a crude way (can a biologist fix a radio?) but they are. The analysis of the stripes in the early Drosophila embryo is another more sophisticated example of this and one that has revealed some profound principles of the link between the molecular logic of a cell and how it is used to build the blueprint of an organism.

I do not see a reason to build an embryo of a mouse or of a human. This is a relatively interesting headline-grabbing challenge.  On the other hand, I do see many reasons to ask questions about how cells build organisms or how much of an organism can arise from the autonomous behaviour of cells. In doing so we are learning interesting facts. For example that there are length scales in the organization of the basic cell populations (germ layers) that make up an embryo and, significantly, as pointed out above that mammalian embryos are, like Drosophila, piecemeal entities put together by evolution. In some ways, the emergence of organs in a dish hints at this but the reliability and reproducibility of this event might need the generation of the components of an embryo. I suspect that in understanding this there are prizes that will help us in the long term goal of engineering tissues and organs. Rather than imitating Nature we should aim to break the mirage that Nature and our minds have created in this wonderful balanced and continuous unit that we call an organism.

Note. The figure at the top is “the human condition” from R. Magritte.

  1. Simunovic, M. & Brivanlou, A.H. Embryoids, organoids and gastruloids: new approaches to understanding embryogenesis. Development 144, 976-985 (2017).
  2. Warmflash, A., Sorre, B., Etoc, F., Siggia, E.D. & Brivanlou, A.H. A method to recapitulate early embryonic spatial patterning in human embryonic stem cells. Nat Methods 11, 847-854 (2014).
  3. Etoc, F., Metzger, J., Ruzo, A., Kirst, C., Yoney, A., Ozair, M.Z., Brivanlou, A.H. & Siggia, E.D. A Balance between Secreted Inhibitors and Edge Sensing Controls Gastruloid Self-Organization. Dev Cell 39, 302-315 (2016).
  4. Turner, D., Glodowski, C., Alonso-Crisostomo, L., Baillie-Johnson, P., Hayward, P., Collignon, J., Gustavsen, M.W., Serup, P., Schroter, C. & Martinez Arias, A. Interactions between Nodal and Wnt signalling Drive Robust Symmetry-Breaking and Axial Organisation in Gastruloids (Embryonic Organoids). bioRxiv (2016).
  5. Turner, D., Alonso-Crisostomo, L., Girgin, M., Baillie-Johnson, P., Glodowski, C., Hayward, P., Collignon, J., Gustavsen, M.W., Serup, P., Steventon, B., Lutolf, M. & Martinez Arias, A. Gastruloids develop the three body axes in the absence of extraembryonic tissues and spatially localised signalling. bioRxiv (2017).
  6. van den Brink, S.C., Baillie-Johnson, P., Balayo, T., Hadjantonakis, A.K., Nowotschin, S., Turner, D.A. & Martinez Arias, A. Symmetry breaking, germ layer specification and axial organisation in aggregates of mouse embryonic stem cells. Development 141, 4231-4242 (2014).
  7. Baillie-Johnson, P., van den Brink, S., Balayo, T., Turner, D.A. & Martinez Arias, A. Generation of aggregates of mouse ES cells that show symmetry breaking, polarisation and emergent collective behaviour. JOVE doi: 10.3791/53252 (2014).

A Short Tale about Brachyury

Once upon time….somebody said that Genetics is Ariadne’s thread of Biology, the only way to guide us out of the labyrinth that is a biological problem. Nowhere has this been more true than in the analysis of development, the processes underlying the emergence of an organism. In this spirit, it is the systematic application of genetic analysis to Drosophila melanogaster and Caenorhabditis elegans spearheaded a deluge of information that started modern ‘Developmental Biology’ (the study of the dynamics of pattern and form in embryos, as opposed to ‘Embryology’, the detailed description of the different stages associated with embryos). The success of these two invertebrates took its time to permeate the mammalian embryo. There are good reasons for this. Two possibly important ones come to mind: the challenge that is to study an embryo that develops inside the mother and, not unrelated, the difficulty of applying Genetics to such an object. In retrospect we can see that most of the mutations that have given us insights into developmental events are embryonic lethal and it has been the pathological, or if you don’t want to sound so dramatic say phenotypic, analysis of those mutations that has yielded a view of the process. Notwithstanding husbandry and breeding details, the mammalian embryo, small and hidden in the confines of the uterus, is not an ideal target of systematic screens (the mammalian genetics papers) but with patience and focus, Genetics was applied and the usual combination of serendipity and method have yielded their fruits.

It could be said that there are many kinds of Ariadne’s threads. For example, mammals have an interesting aspect of their make up that is not a bad one into the labyrinth and that, in my view, has not been exploited as much as it might: haploinsufficiencies. Dominant phenotypes caused by the loss of one allele that are related to the function of a gene and it is with a haploinsufficiency that mammalian Developmental Biology started with the discovery of the mutation that lends its name to the gene Brachyury. In some ways the story (or the path if you want to stick to the mythology) of Brachyury is a story which highlights the highs and the lows of the connection between Genetics and Development, that reveals how easy it is to be distracted by the underlying complexity of biological processes, how molecular biology can simplify things but also how we should not be complacent and forget that genes are not causes but simply tools and elements to understand a process. It is also an example, as it unfolded, of the power of the blend of Genetics and Embryology that forms the core of XX century Developmental Biology. Also, perhaps surprisingly to some, Brachyury paved the way for the genetic analysis of development as it was the first mutation (genes are related to mutations but are not the same) associated with a developmental defect, many years before Poulson’s work with Drosophila Notch (1).

170213 T CrossWe do know today that Brachyury, also known as T or T/Bra, is a gene that encodes a transcription factor of a family called the T-family because of their structural relatedness, that play crucial roles in early development (2). We also know that it has a central role in the processes of gastrulation and axial extension in chordates and that its existence extends to invertebrates where it has a variety of roles in early patterning of embryos (3).

The origin of Brachyury lies in the 1920s Paris where Nadine Dobrovolskaia-Zavadskaia, a Russian emigrée from the revolution, was working at the Institute for Radium (an interaction between the Curie Laboratory and the Institut Pasteur) looking for the ability of X-rays to induce mutations in mammals (4). In the course of a screen involving 3000 crosses of mice across three or four generations she found two mutations that bred true, one of them she called T. The mutation had a dominant phenotype reflected in the length of the tail, hence the name T for taillessness (the capital for the dominant phenotype) but was, also, embryonic lethal (5).

An obsession with T starts and, in parallel with the discovery of recessive alleles, t, and interacting mutations (in laboratory and wild mice), descriptions of the phenotypes of different genetic combinations suggest an involvement of T, whatever it was, with the development of spinal cord and of the vertebral column (6-8 and then, if you want to follow on, read the more general accounts in refs 9 and 10). One thing is to describe a mutant phenotype –a forensic exercise- and a different one to wonder whether it tells something general about the connection between genes and developmental processes; I am, not sure that we have yet resolved this problem. It was, probably, Salome Gluecksohn-Schoenheimer who began to do the second through her analysis of T and t mutants (8, 10). There had been descriptions of the defects of the mutants (6-8) but she went further. Having worked in Spemann’s laboratory (11) the defects in the development of the notochord, neural tube and somites associated with T mutants, made her think of the effects of the organizer and of a possible relationship between T and its activity. These thoughts were no doubt encouraged by her discussions with, particularly C. Waddington, and the parallels she drew with mutations affecting the patterning of the Drosophila wing, suggested that T was saying something of how genes related to developmental processes (10, 11). But these were early days to see, let alone study, this relationship. Ariadne’s thread was in a badly tangled ball and the genetics of T did not help. Mutants with T like phenotype, some allelic, some not but which acted as modifiers were isolated in wild and laboratory strains, and their relationship to the original T mutant proved genetically complex (see brief discussion of these matters in refs 9, 10 and, more extensively, 12). The notion that these genetic interactions were linked to the function of T, led to a labyrinthine situation.

The problem was, and still is, that the genetic analysis of a biological process has its limitations; it is a bit like palm reading. It requires interpretation and, in the case of complex processes –like Development- it is not the way to unravel Mechanism. To quote a very prescient thought from Gluecksohn-Schoenheimer  “A mutation that causes a certain malformation as the result of a developmental disturbance carries out an “experiment” in the embryo by interfering with the normal development at a certain point. By studying the details of the disturbed development it may be possible to learn something about the results of the “experiment” carried out by the gene. However to discover anything about the nature of the action of the gene is a much harder task. It is necessary for this purpose to be able to trace back all the results of the action to certain original causes” (8). Furthermore, Genetics for its own sake (the analysis of the complexity of interactions, alleles, pseudoalleles, complex complementation associated with the breeding of a trait) can intervene, capture our imagination and lead us astray. A bit like maths without Physics; elegant and fun but lacking bite.  However, Genetics was what there was and the genetics of T proved a bit of a tangle (12), particularly when it came to the question of the link of the mutants (and at the bottom the wild type gene) to the generation of the embryo.

The question of what was the relationship between genes and development was an important issue in the 60s and the 70s. It would emerge from the molecular and cellular analysis of T, and in parallel of the bithorax complex (BX-C) in Drosophila by Ed Lewis. How genes controlled development did not look a simple affair at the time. Being interested in the topic as a graduate student in the late 70s and the early 80s I remember reading the seminal –but somewhat arcane- 1978 paper by Lewis on the BX-C (13) and a profoundly puzzling one on T (14). I must admit at having been simultaneously fascinated and befuddled by both but particularly by the mouse one. The complexity and the challenge to unravel them was part of the excitement to solve the puzzle. The Drosophila case seemed easier to unpack, perhaps; and it was. The reason probably lies in the rather Cartesian organization of the embryo –which was rather well laid out at the time- and the linear manner in which events unfold that allowed a rather rapid connection between genes and specific developmental events. It has interested me recently, how much hard work was going on to outline the battleground to apply Genetics to mammalian embryos when the buzz was about the genes of Drosophila. But T was not forgotten as it held a key to how mammalian embryos were built and therefore needed to be addressed.

170213 T ExpressionFrom here, the story gathers pace and the riddles find their solutions. And so it was that the era of molecular cloning clarified matters. In the 80s the systematic application of DNA cloning and analysis to the harvest of mutants screens from Drosophila and C. elegans, paved the way for similar work in other organisms, particularly mammals, and started to lift the fog that Genetics has laid on T. The BX-C turned out to encode three transcription factors and a complex regulatory region (15,16), while T/Bra encoded one transcription factor (17,18). The nature of the devilish genetics of T/t still lingered in the background but the brutal simplicity of molecular analysis delivered its verdict: T was, IS, a transcription factor expressed in the notochord as well as the progenitors of the spinal cord and the mesoderm (see Figure). The question then was, not how this related to the complex genetic analysis revealed by the multiple alleles and crosses –this is, apparently, still work in progress- but how this related to the activity of the protein, encoded by the gene and revealed by its loss of function. And this was just the beginning (in developmental biology, knowing what kind of protein is encoded by a gene –that we have come to know from a mutant phenotype- often opens more questions than it answers). The question was now the original one and again Gluecksohn-Schoenheimer, presciently posed it;  in 1938 thinking about how Spemann’s group approached the problems associated with embryonic development she mused about how in an ideal world to approach mammalian development: ‘The events that take place in the development of the mammalian embryo have not been subjected to an extensive causal analysis so far. The reasons for this are to be found mainly in the lack of suitable methods. It is not possible yet to use transplantation, isolation or vital staining techniques on mammalian embryos as they have been used on amphibian embryos. In the course of time it probably will be possible to analyze the mammalian embryo by transplantation and isolation just as thoroughly as has been done with the amphibian. For the present, however, the experimenter is not able “to take an active part in the course of events that take place during the embryogeny of the mammalian embryo,” nor “to alter the course of events at a chosen point in a chosen manner and draw conclusions on their relations from the resulting changes.” (Spemann 1936.)’ (8). An amazing paragraph for the time. She was more right than perhaps she thought and the molecular biology of T was going to pay high dividends that would make her musings true. The 80s gold rush of gene cloning revealed that T/Bra was conserved across phyla and its discovery in Xenopus led to a fruitful link with mesoderm induction that Jim Smith and his colleagues pursued in an enlightening manner for many years, establishing how T/Bra worked at the molecular level and established a connection between T/Bra and gastrulation (see e.g 19, 20). In parallel, Rosa Beddington began to apply the techniques that were being developed to study mouse development to the molecular insights and reagents thus unravelling the connections between T/Bra and mammalian development that lurked behind the genetics for so long (21-23). This work was soon picked up by one of her collaborators, Valerie Wilson, who has been pursuing the intricacies of the relationship between T/Bra and the mammalian body plan for the last many years answering many of the questions that had been posed by the early Genetics of the mutant. There is still much to do because, thought we have learnt much, the question of the relationship between gene and effect, mutant and phenotype, remains. But now we have the tools and the framework to try to answer it. We just need not be distracted by the ease to gather facts and remember the questions.

The story of T/Bra is a good example of the way in which the blending of Genetics. Embryology and Molecular Biology have enlightened the relationship between genes and development. History can be anecdotal but it is also informative. The recent history of developmental biology is very focused on Drosophila and C. elegans and it may come as a surprise to many that T/Bra, as a question and as a reality, was there before Bicoid and Wnt and Notch, highlighting the questions that needed an answer. History, in this case, also highlights the perils of the purely Genetic analysis of a biological process and the need to remember that Genetics is a language, a formal language, to ask questions. In the analysis of Development, it leads us to the elements of the system but might not be the element of choice to see how they come together to make an organism. The position of Genetics to Biology is a bit like Mathematics to Physics: a language that allows one to formulate a question in formal terms which then provides a machinery to work towards the answer but the output of this operation needs to be interpreted.

The history of Brachyury also has an interesting element in that it highlights the important contribution of women to the field; most of the important breakthroughs and insights in the story come from women: Nadine Dobrovolskaia-Zavadskaia, Salome Gluecksohn-Schoenheimer, Virginia Papaioannou, Rosa Beddington, Val Wilson. An influence worth emphasizing as we celebrate Women in Science week.

In the end, the tale continues. Rather than the English and “they lived happily ever after’, we could quote the French “ils vécurent heureux et eurent beaucoup d’enfants” (they lived ever happily after and had a lot of kids), the kids being the myriad of questions that have been raised by the great discoveries about T over the last twenty years. Discoveries which open the door to answer to the questions that T/Bra leads us into: about Development, Genetics, Evolution, about Stem Cell biology and, in the near future, of the engineering of living systems.


NB One appreciates that this piece just skims through the surface of the story and its implications. Still, one hopes that this will inform at some level and, also, encourage thinking about the connections between Genetics and Developmental processes. I am grateful to Peter Baillie-Johnson for the suggestion of the title. The image on the genetics of T is from:



1. Poulson, D. 1940. The effects of certain X-chromosome deficiencies on the embryonic development of Drosophila melanogaster. J Exp Zool 83: 271–325.

2. Papaioannou VE. (2014) The T-box family: emerging roles in development, stem cells and cancer. Development 141, 3819-3833.

3. Technau, U. Brachyury, the blastopore and the evolution of the mesoderm. BioEssays 23, 788-794

4. Korzh, V. and Grunwald, D. (2001) Nadine Dobrovolskia-Zavadskaia and the dawn of developmental genetics.  Bioessays 23, 365-371.

5. Dobrovolskia-Zavadskaia, N., 1927 Sur la mortification spontane´e de la queue che la souris nouveau-ne´e et sur l’existence d’un caracte`re (facteur) he´re´ditaire “non viable.” C. R. Seances Soc. Biol. Fil. 97: 114–116.

6. Dobrovolskia-Zavadskaia, N, Kobozieff N, and Veretennikoff S. Etude morphologique et genetique de la brachyourie chez les descendants de souris a testicules irradies. Arch de Zool Exp 1934;76:249±358.

7. Chesley P. (1935) Development of the short-tailed mutant in the house mouse. J Exp Zool 1935;70:429±459.

8. Gluecksohn-Schoenheimer, S. (1938) The development of two tailless mutants in the house mouse. Genetics 23: 573–584.

9. Pappaioannou, V. (1999)The ascendency of developmental genetics, or how the T complex educated a generation of developmental biologists. Genetics.151. 421-425.

10. Gluecksohn-Schoenheimer S. (1989) In praise of complexity Genetics 122, 721-725.

11. Gluecksohn-Schoenheimer S. (1992) The causal analysis of development in the past half century: a personal. Development 1992 Supplement

12. Silver L.M. (1985) Mouse t haplotypes. Annu. Rev. Genet. 19, 179-208.

13. Lewis, EB (1978) A gene complex controlling segmentation in Drosophila. Nature 276, 565-570.

14. Artzt, K., McCormick, P. and Bennett, D. (1982) Gene mapping within the T/t complex of the mouse. I. t-lethal genes are nonallelic. Cell 28: 463–470.

15. Bender W, Akam M, Karch F, Beachy PA, Peifer M, Spierer P, Lewis EB, Hogness DS. (1983) Science 221, 23-29.

16. Gehring, WJ (1992) The homeobox in perspective Trends in Biochem. Sci. 17, 277-280.

17. Hermann, B. G., S. Labiet, A. Poustka, T. King and H. Lehrach,  1990 Cloning of the T gene required in mesoderm formation in the mouse. Nature 343: 617–622.

18. Kispert A, Koschorz B, Herrmann BG. (1995) The T- protein encoded by Brachyury is a tissue specific transcription factor. EMBO J. 14, 4763-4772.

19. Smith JC, Price BM, Green JB, Weigel D, Herrmann BG. (1991) Expression of a Xenopus homolog of Brachyury (T) is an immediate-early response to mesoderm induction. Cell 67, 79-87.

20. Saka Y, Tada M, Smith JC. (2000) A screen for targets of the Xenopus T-box gene X-bra. Mech Dev. 93, 27-39.

21. Beddington RS, Rashbass P, Wilson V. (1992) Brachyury, a gene affecting mouse gastrulation and early organogenesis. Dev Suppl. 1992:157-65.

22. Wilson V, Rashbass P, Beddington RS. (1993) Chimeric analysis of T (Brachyury) gene function. Development. 1993 Apr;117(4):1321-31.

23. Rashbass P, Cooke LA, Herrmann BG, Beddington RS. (1991) A cell autonomous function of Brachyury in T/T embryonic stem cell chimeras. Nature 353, 348-351.

Engineering the Future of Developmental and Stem Cell Biology

Final poster.001A meeting was held recently at the Pasteur Institute on the topic “Engineering the embryo: beyond Systems Biology”. The event brought to my mind a question I pose to the final year undergraduate class: how should we approach a biological problem? like physicists or like engineers?

The relationship between Physics and Biology has a long and very distinguished history, strewn with technical contributions that have often changed the direction and pace of biological research. Microscopy and X-ray crystallography would not have happened without the intervention of the physicists. To see this you don’t need to go further than the 2014 Nobel prize to E. Betzig, S. Hell and W. Moerner for the deep developments in superresolution techniques that are having a most dramatic impact in cell and developmental biology. However, there are also profound and long lasting conceptual contributions. Neurobiology has benefitted enormously from the input of physicists and molecular biology was shaped by Schrodinger’s “What is life?” and Max Delbruck’s Phage School. More recently, over the last twenty years, a cadre of young physicists have led a renaissance of the relationship between Physics and Biology. Single molecule and single cell techniques have opened up our eyes to the statistical processes underlying molecular and cellular Biology and taught us the beginning of how to deal with them. For those of us who have been lucky to be part of this feast, it has been both fun and insightful. If you have missed it, I suggest you catch up.

The fact that a biological system integrates multiple variables was never in doubt but it has been our ability to access those variables that has changed the game. Faced with a deluge of information of specific processes, we have learnt to measure and to use models to understand those measurements and, in turn, perform precise experiments. In many ways we are at the beginning of this game but the input of the physical sciences forged over the last ten years has already left an important imprint permeating much of forefront biological research. There is much to do and we now know how. Why then mulling over engineering?

The word ‘engineering’ has many meanings but, in general, evokes images of machines and blueprints. If you belong to the last century, like some of us do, you might think of steam engines, bridges, chemical plants, of belts and braces. If you belong to this century you might think of jets, computer chips, speed trains and clean technology, of electronics. Engineering is, in many ways, applied Physics. It is about toiling with materials to do something useful in a reproducible manner. Where a physicist uses an equation to understand/explain a process, an engineer uses it to explore a practical solution to a problem. Where a physicist uses a phase space to explore the behaviour of a system, an engineer will look at it seeking the domain that might work for a specific process in the real world. A phase space may often be the end game of the physicists work, for the engineer it is the starting point. While much of engineering deals with the physical world, there have been many inroads into Biology and, as in the physical world, there are many kinds of engineering in Biology: fermenters, biochemical reactors, bacterial circuits, mechanical organs are examples that come easily to mind. A good example of bioengineering you can relate to is beer making (yeast engineering in disguise). However, over the last few years, two fields -developmental and stem cell biology- have been coming together unknowingly with a common nexus through Engineering, a new kind of Engineering that was the motivation of the Pasteur meeting. Let me explain.

It is about five years that the tragically late Yoshiki Sasai surprised everybody by showing that Embryonic Stem (ES) cells could be coaxed into forming eye cups and forebrains1,2. His work coincided with the observation that intestinal stem cells would build crypts on their own3 and was followed by a plethora of other reports showing that (but not how) embryonic and adult stem cells can give rise to tissues, organs and structures. These observations are often hailed as the dawn of a new field, organoid biology, though one wonders if this is not, really, Developmental Biology by another name and with one significant difference: embryos, the realm of Developmental Biology, are reproducible, organoids more often than not, aren’t; this has consequences and sets a target.

Sasai was, at heart, a developmental biologist and, having worked with Eddy de Robertis (but probably from before) he was aware of classical experiments in which when animal caps from frogs are left to their own devices, they will make eye cups and forebrains (see Hurtado and de Robertis for a practical review 4). Furthermore, as shown in an insightful review in Development 5 he clearly knew the early development of the eye cup and the lens and thus understood the necessity to relate whatever happens in an ES cell culture to what happens in embryos. Thus he had a good sense for the fact that the autonomous potential of ES cells to organize into tissues autonomously was neither new nor unusual, it reflected what those cells are meant to do. The autonomous gastrulation of frog tissue (exogastrulae), the ability of cells from limb buds to to self organize into digits and the remarkable but much forgotten experiment 6 in which when frog animal cap cells are jumbled up, reagregated and exposed to signals they organize in space as the normal animal caps do, are some examples of this. If you allow me,  ‘organoid biology’ is a rebranding of ‘tissue and organ morphogenesis’ with, perhaps, the added spice that it is making use of our ability to differentiate stem cells rather than the material provided by embryos. It also carries a lot of hype.

The sight of disembodied organ-like structures in culture dishes easily captures the imagination. Probably it is for this reason that the report that a human brain had been grown in a dish 7sparked a huge amount of attention and interest amidst researchers and the public. The press fuelled this interest with headlines such as “Mini brains allow scientists to study brain disorders” or ‘lab grown minibrains aid Alzheimer disease researchers’ that created expectations. What had been achieved did not live up to the billing. What had been observed was not that different from what had been achieved in the mouse system but the fact that it was human, as these things tend to be, and that it had been achieved from iPS cells (genetically engineered ES cells) was an irresistible combination. There was also a sideline about microcephaly and the possibility of modelling brain diseases but one wonders how much of this had to do with journal headline and how much with real science. We certainly look forward to further reports on this front.

There is little question that this was an achievement but what the media forgot to say is that the events that led to those structures are the creations of cells over which we have little control. Furthermore, that in the v1.0 there were no brains as such but rather whimsical structures with a mixture of elements from a brain (forebrain, midbrain and eye tissue), that the system was hardly reproducible, that we had little understanding over what had happened. Feynman famously left on his final blackboard the statement “that which I cannot build, I do not understand’. If in ignorance we extrapolate this to the organoids that grow in culture dishes we would have to say: that which we can build we do not understand. The reason is, of course, that we have not built anything ourselves, that it is the cells -with rules that at the moment we cannot fathom- that have done it (as they are programmed to do), that we are at the mercy of the cells, privileged spectators of their productions. Can we change this? What is missing? Enter the engineers.

Figs for Paris.002If the system was engineered it would be reproducible and this would allow us to learn. Reproducibility is a first and most important target of the organoid game at this stage and it is where the interaction with engineers is key. Embryos are reproducible systems and it is because they are reproducible that we can use them to learn, that we can detect small changes in patterns to obtain clues of how genes relate to the building of tissues and organs. As F. Jacob pointed out, organisms are the result of evolutionary tinkering, bricolage, a more rudimentary form of engineering and so, perhaps, a way to understand them is to look at them from the point of view of engineering. The goal is not to GUESS how an organism is made, BUT to KNOW how it is made and for this only if we obtain reproducibility shall we understand. If we want to use organoids, we need to strive to make them reproducible and then, not only we shall be able to use them but, they will also teach us about the processes and interests that they represent. Recently there has been some progress in some of the organoid cases and the possibilities are clear. Thus an engineering of the minibrain system has allowed insights into the working of the Zika virus and promises more 8,9, and an engineering of the already robust intestinal organoids, has created the conditions for a sophisticated degree of reproducibility that will contribute to ongoing studies10. These are examples to follow because for the most part the field (if we admit that it is a field) remains hostage to the vagaries of the interactions between cells and culture.

Figs for Paris.003If we agree that the solution is to engineer the process, we need to use the Physics of the system, which, like in any engineering process, underpins the events. This means that in our attempt to engage cells into building tissues and organs we need to engage developmental biologist. If stem cells are the materials, developmental biology is to organoids what Physics is to Engineering, and we need to use it like that. And by Developmental Biology I do not simply mean the garden variety that dabbles in genes and cells but the more quantitative systems one that strives to integrate the rapidly emerging data into models that identifies parameters and tells us how the different variables interact. Just like Evolution tinkers, we shall tinker. At this, as Peter Zandstra exhorted us at the meeting, we need to use what we know in a realistic manner. Build models that contain real dimensions and time scales and use them, like engineers do, to improve, to build, to understand.

Organoids, in its many varieties (embryonic and adult stem cells, micropatterned cell arrays, scaffolding of cells from different tissues) promise much but we need to accept that the process is more important than the end, that understanding their beginnings and how they unfold will allow us to understand and improve the end product. One intriguing feature emerging from the current studies and that we shall have to address, concerns the differences between the events that we see in the dish and what we observe in an embryo (see refs 5 and 11 for discussions). It is too early to say whether these differences are important but, it might be that as in engineering there are many ways to make a bridge or a jet or a computer, the same might apply to a tissue and an organ; to put it clearly, there are many molecular solutions to an organ or a tissue and the embryo uses one but we might be able to use others that are simpler. Much interesting and unknown lies ahead but one of the most exciting prospects is the possibility to explore human developmental biology and, in the end, to create a proper organ and tissue engineering as a first step for a regenerative medicine. But we should not cut corners.

Much of this was discussed on and off stage at the Pasteur meeting which had a sense of first encounter between engineers, developmental and stem cell biologists and of expectation of what can be achieved by working together. There will be a report of the meeting in the Development journal, but importantly interesting things will happen. Stay tuned.

1.         Eiraku, M. et al. Self-organized formation of polarized cortical tissues from ESCs and its active manipulation by extrinsic signals. Cell Stem Cell 3, 519-32 (2008).

2.         Eiraku, M. et al. Self-organizing optic-cup morphogenesis in three-dimensional culture. Nature 472, 51-6 (2011).

3.         Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262-5 (2009).

4.         Hurtado, C. & De Robertis, E.M. Neural induction in the absence of organizer in salamanders is mediated by MAPK. Dev Biol 307, 282-9 (2007).

5.         Sasai, Y., Eiraku, M. & Suga, H. In vitro organogenesis in three dimensions: self-organising stem cells. Development 139, 4111-21 (2012).

6.         Green, J.B., Dominguez, I. & Davidson, L.A. Self-organization of vertebrate mesoderm based on simple boundary conditions. Dev Dyn 231, 576-81 (2004).

7.         Lancaster, M.A. & Knoblich, J.A. Generation of cerebral organoids from human pluripotent stem cells. Nat Protoc 9, 2329-40 (2014).

8.         Qian, X. et al. Brain-Region-Specific Organoids Using Mini-bioreactors for Modeling ZIKV Exposure. Cell 165, 1238-54 (2016).

9.         Xu, M. et al. Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen. Nat Med 22, 1101-1107 (2016).

10.       Gjorevski, N. et al. Designer matrices for intestinal stem cell and organoid culture. Nature 539, 560-564 (2016).

11.       Turner, D.A., Baillie-Johnson, P. & Martinez Arias, A. Organoids and the genetically encoded self-assembly of embryonic stem cells. Bioessays 38, 181-91 (2016).

Preprints in the Biomedical Sciences: The Future is Here

IPPA few days ago, fittingly in the context of Open Access week, we had an event to explain what are preprints and how they have the potential to change communication and career development in the biomedical sciences. You can follow the recording here: The event counted on the participation of publishers, funders and users; a summary has been posted in The Node and I encourage you to look at it and contribute to the discussion. There is much to talk about in the wake of the event. Here I shall concentrate on a few issues in the context of publications which highlight the momentum for change.

Preprints are not peer-reviewed papers but importantly, they are nothing new as they have been tried and tested in the Physical sciences for over twenty years, with much success. Interestingly, at the time of the event EMBO J published an article from P. Ginsparg, the founder of arXiv, the Physics preprint server, called  ‘Preprint deja vue’   (; I recommend everybody to read it and reflect on what it says about the biomedical sciences for it shows us up (my reading, I hasten to say) as a small minded community, with a narrow notion of competitiveness and an overreliance on commercial publishing for the evaluation of our work.

Preprints and preprint servers are a rising culture and are emerging as a good first solution to some of the problems. But we have to get it right. There are two concerns that are often levelled to preprints: the worry about their lack of peer-review and the fear of being scooped. Both fail to recognize the essence of Science and the crisis of the peer-review process in the biomedical sciences. For example, on the first one, it would be foolish not to recognize that an important element fuelling the rise of the preprint culture is the degradation of the peer-review process which, up to now, has been the cornerstone of modern science. Nobody can deny that papers improve with peer-review but it is obvious that from a system of checks and balances and with the connivance of journals, the peer-review process has become –particularly at the high end of the market- a device to delay publications and, in the process, to give reviewers the power to determine the content of the work and, in some instances, use the anonymity of the process to produce unfair allegations and decisions. Much has been written about the anonymity (blind, double blind, open reviews) but it has always worried me that one of the arguments to preserve the status quo is said to be the protection of young PIs from retributions that established peers might launch in the face of the criticisms that might be levelled to them. How come we cannot own what we say and think? What this says really is that the reason for the anonymity is FEAR, a typical situation in totalitarian systems. How come fear is a justification for anonymity? It should give us pause for thought. Where have we taken our scientific culture: people afraid of signing what they believe in? It would be good if we could change the scientific culture, if we could encourage and practice more an open discussion of our work (which let us not forget, goes on in private, in journal clubs and cafeterias). Journals give you the option of commenting and discussing but only after publication. A preprint is there for discussion, so you can comment, openly, and influence its shape and help, rather than hinder, the authors.

But, in the end, we have to ask what is the purpose of peer review? It has been suggested that it is validation of the research and yet, if you ask (and we did at the event) how many people have failed to reproduce a published piece of work, the answer will be loud and clear: many, most. So, peer-review is not validation. It is a form of certification of the quality of work, like that of the rating agencies on credits and, as it was the case in the financial crisis, there are too many subprime papers AAA rated because they are bundled in HIF journals with a few quality ones. We have lost our bearings and it is unfortunate that we value our work not for its intrinsic merits but because of where it is published.

The issue of scooping cannot be separated from the strange contraption that the peer-review process has become. Here, again, we biologists have a very different understanding of a notion (scooping) that highlights our small mindedness. As Ginsparg puts it (see EMBO J above) ““scooping” in the context of biology research appears to mean the race between laboratories working on overlapping” and herein we highlight again that the form matters more than the content. There is little question that posting your work, whether in preprint or peer-review form, gives you priority… if there is anything to give priority for………… In Physics there is no question, again Ginsparg: “Posting work on arXiv gives authors a datestamped priority claim, which is accepted by the community, and gives immediate visibility to authors’ work”. I and many agree. The large number of papers with 0 citations in HIF journals is a sad comment on the huge amount of work that in the biomedical sciences goes into useful pieces of information but largely irrelevant pieces of science. How much money and pain would those authors have saved if they had posted their work in a preprint server! I shall leave you to take it from here but in my mind, there is little to fear about posting your work in a preprint server and much to be gained. In many ways, preprints are the “ultimate open access”. Preprints can create a more democratic, cost effective way of managing science and at the event it was excellent to see Journals promoting them and seeing their value.


There is much to work on to get the community to embrace preprints. We were lucky to have exceptional speakers at the event where the Wellcome Trust, as a funder, expressed their support for the culture and explained their own efforts towards it (Wellcome Open Research & the Open Science Prize). Interestingly, shortly afterwards we learnt that EMBO will allow for preprints to be cited as evidence of output (as long as they are accompanied by at least one peer-reviewed paper). I am aware of efforts along these lines in the US where many institutions already encourage applicants for jobs to cite preprints. This is good news because it does unshackle students and postdocs from the handicap of not being able to refer to their toilings when they are looking for fellowships and jobs. Preprints are here to stay. The reasons are many and being increasingly discussed and if you wanted a particularly one, this one was stated by Richard Farndale (Dpt of Biochemistry, University of Cambridge): preprints give us a way to let funders know that the work that they gave money to do, has been done. This simple statement alone, makes the point of what preprints are for and should lead funders not only to encourage preprints but to demand them. It should be the funders and not the journals, who decide whether the work has been done.

There is much being said and written about the current situation which really is an expression of movement. A particularly thoughtful piece by P. Walter and D. Mullins appeared recently in the ASCB: ‘on publishing and the sneetches: a wake up call?’ ( ). Much to mull over here and many arguments for preprints. The article ends up on a note “The end goal seems obvious: The knowledge that we produce in our publicly funded works belongs to humankind and must not be locked up behind pay-walls— newly submitted papers should be open-access and older ones open-archive. Our real challenge is to find the paths that get us there.  But major change can happen, even if it seems impossible to imagine now” I say, let us use preprints. Not only use them but work, together, to shape the future of biomedical science communication.

Further readings on preprints from this blog:

The Launch of the Human Cell Atlas Project: A Catalogue of Trillions of Cells Without a Question?

A catalogue is not a map and if you want to transform a catalogue into a map, you probably need to understand what the map is for.

The launch of the Human Cell Atlas Project ( has caught the eyes and the ears of newspapers, the mind of some scientists and, obviously, the imagination of the leaders of the project. If you see the headlines, it might tempt you. After all, the prospect and challenge of learning the secrets of 35 trillion cells (3.5 x 10^12) is an impressive undertaking. The number reminds us of Avogadro’s number and, of course, there is the challenge and the excitement for the technologists and the computer scientists associated with such large numbers. There is a lot of money and human power at stake and, in so far as I can tell, very little forethought. Yes, there are the challenges of the logistics behind such projects but, one can think of many projects that aim to collect and organize data from biological systems but do not make it to the headlines. This one, I suspect, builds on an ongoing trend for genomic data gathering, organization and analysis inspired by our technological developments and also on the fact that, when it comes to data, Biology rules because biological data are about the generation, control, selection and maintenance of variability at different scales from molecular to organismal. This does not mean that all in it is meaningful and this, I think, is the main oversight of a project which has more cloth than substance. One has often the feeling that biologists are searching for their own Manhattan project. First it was the human genome (then any genome and many genomes), the connectome (or connectomes), then in a more quiet manner there is the human proteome (it is more complicated) and when we are getting tired of –omes, we begin to talk about Atlases.

Don’t get me wrong, there is something useful here but, are there not better investments of talent and money in the same or related fields at this point in time? I have followed the single cell field from its emergence and have recently commented on its current status ( so will not repeat myself since much of what I said there applies to this project. However, let me point out the surprising naïvité behind this project and the lack of understanding of what maps, Atlases if you want to give them a name, mean in Biology. The idea of this project is to obtain information about the transcriptome of individual cells and use this as a reference base for……what? What is the map for? We are told that “Without maps of different cell types, where they are located in the body, and the genes they express, we cannot describe all cellular activities and understand the biological networks that direct them” and, with the usual references to health and medicine, that “A cell atlas has the potential to transform our approach to biomedicine. It would help identify markers and signatures for different diseases, uncover new targets for therapeutic intervention, and provide a direct view of human biology in vivo, removing the distorting aspects of cell culture”. At first sight nothing wrong with this, and a lot of promise, except that we already have experience of similar projects at smaller scale and, when you look at them, these projects pose many questions which we have not answered yet. And many of those questions have a common denominator: what kind of questions can one ask to such data beyond cataloguing, organizing? Forget the hype, what is this for? What is this about? If there are no clear answers to these questions, why going bigger blindly?

A significant problem with this idea of cataloguing (a catalogue is not a map) trillions of cells into classes based on their identities lies in what we already have learnt from similar smaller scale experiments: populations of cells in a similar state exhibit large heterogeneities in gene expression which bedevil their identity (however we define it), that will vary between individuals and certainly with age. We use these heterogeneities to classify but we don’t understand their meaning. So, which age shall we look at? Which individuals? DNA is constant, RNA is not. Furthermore, at a technical level we are still not sure how deep we have to go into the sequence analysis of the cells to get meaningful information, we still do not know how to read this information. Most of the projects already out there seem to be more about sequencing more cells, faster, deeper, about new algorithms to organize the data, about data visualization than about Biology. The Biology tends to be left out, as if the structure of the data will yield the Biology for free. Sure there are biologists in most projects. In several years of maps at a smaller scale than what is proposed here, I have not seen much of ‘sense’ in this field and the questions that it poses are left unanswered or, worst, unuttered. What is more worrying, in a field like the hematopoietic system, which appears to be a test bed for this field, there are two or three papers a month with little cross referencing and few general messages beyond slight reshuffles of the maps that years of classical Biology had produced and, occasionally, a few new markers for cell types. And yet, I am sure that there is much that can be learnt from a proper look at the data which, in turn, would determine how we gather the data. There is something to ponder here, in particular on question of what do we want these catalogues for?

By the mid 1800s physicists knew that the macroscopic variables of a physical system depended on the atoms and molecules that made it up. So, for example, the Temperature and the Pressure of a gas were/are a consequence of the velocities and relative positions of the constituent molecules. The question was how to relate the two. If they had had much of today’s technology, they (some) might have opted for measuring the momentum and position of every molecule and through lengthy calculations work out the answer. There are about 10 trillion molecules in a nMole of a gas so, you can see the headline: Physicists engage into a Molecular Atlas that will transform physics and engineering: Trillions of molecules to be mapped in terms of their velocities and relative positions. Furthermore, we would be told that this will be done for a couple of the fundamental molecules in the Universe thus ushering in a new era of technology. This, of course, does not make much sense and it was the prescience of JC Maxwell and, in particular, L Boltzmann that showed the way to deal with the problem. In this manner they created Statistical Physics which in addition to producing very exciting Science did transform Physics and Engineering. Now, Biology of course is not Physics, and the identity of a cell is not the state of a molecule but there are analogies that can be used in the analysis of transcriptomes and maybe we should engage deeper with those analogies rather than going for the trivial. It is possible that what we want to know is not the ‘identity’ of every cell (though I would argue that our view of this is, still, primitive, superficial and inaccurate) but what those individual identities average to, what is it that is being read at the higher level of organization by the cells. After all, experiments (experiments, not cataloguing exercises) tell us that the heterogeneities that are observed in the analysis of single cells in populations are dynamic, though we do know or understand what the meaning of those heterogeneities are. This observation, alone, suggests that if you want to transform a catalogue into a map, you probably need to understand the meaning of these dynamic heterogeneities. Moreover, for all we know (or ignore) we still do not understand what the macro-variables that cells measure are, what is being represented in those heterogeneities. Doing a map of the trillions of cells of an organism (this being human cells only to reinforce an anthropocentric view which while justifiable is narrow minded) without knowing what these are related to is not that useful or clever. And if you want to compare this to the Human Genome Project, just remember one thing, the genome is the same in every cell and, for the most part in every species, it is this conservation that make SNPs and polymorphisms useful. However, with transcriptomes there will be surprises and we need to think what we want to measure before we go too far.

James Briscoe has written about the semi-comic statement of Microsoft to ‘solve’ cancer within ten years and pointed out how biological systems have this habit of ‘fighting against anything we try to do” ( Nowhere is this best exemplified than in the single cell transcriptomics field, where the intrinsic tendency to generate heterogeneities, can fox the best experimental designs. We still have much to learn.

One suspects that, if and when this project gets under way it will become one of those ‘too big too fail’ exercises that are becoming frequent in Biology. Projects which take funding away from hypothesis-driven or hypothesis-seeking projects which might provide a good focus for these accounting exercises. I would argue that, so far, the field of single cell analysis of transcriptomes has yielded some information but little insight, it has revealed the presence of heterogeneities in expression which are dynamic and pose some questions which are not often discussed but which need to be addressed before going too far, perhaps, in the wrong direction. We may take a page from the history of Physics and think that, perhaps, we should understand Pressure, Volume, Temperature and their relationships, before we attempt to deduce them from the molecules which, probably, underlie them. Because, as I have said before, a catalogue is not a map and their purposes are very different: a map helps you navigate, a catalogue puts order into a collection of items but does not in principle, have a defined purpose.

In the end the project will generate data and the brief of the Broad Institute ( contains some interesting statements: “By making the Atlas freely available to scientists all over the world, scientists hope to transform research into our understanding of human development and the progression of diseases such as asthma, Alzheimer’s disease and cancer. In the future, the reference map could also point the way to new diagnostic tools and treatments”. We have heard that before, haven’t we?

I still miss the Biology in the project. If you want a big project, might it not be better to think and apply the current technology to a system or a problem which can teach us how to approach, one day, a human cell Atlas?

11th Symposium on the Physics of Living Matter; a brief report

And that was PLM11, the 11th Symposium on the Physics of Living Matter in Cambridge. We started this 11 years ago to draw the attention of local physicists, chemists, engineers and biologists to an exciting and then emerging interface: physics and biology. At the time, this interface echoed the notion of Biophysics, which has a long and distinguished past and still is used in certain areas of research, but this new interface is not Biophysics; we could call it ‘Physical Biology’. Biophysics looks for Physics in biological systems, looks at cells and sometimes tissues and identifies questions and problems that can be treated with Physics. It is close to mechanics and has given, and still does, a great deal to neurobiology and cell biology but Physical Biology is something different: looks at biological problems the way a physicist looks at a problem and finds a solution in terms of the elements of the problem. The analysis of processes from the perspective of single cells or single molecules and how they relate to ensemble behaviours and mechanisms are examples of this. One would say that Physical Biology is close to statistical mechanics and soft matter physics.

But this is not about definitions but about PLM. The Symposium has been growing and this year we had over 170 participants and an excellent and very impressive group of speakers ( The Bragg lecture (established to honour scientists who have made significant contributions to the field) was given by Eric Wieschaus who gave an account of a remarkable trip from the genetics of Drosophila to the biomechanics of gastrulation. You could hear a pin drop during the talk, in which he dealt with novel notions of the source of the energy required for morphogenetic processes and the fluid mechanics of tissue movements. It is interesting how much the picture changes when one moves from the qualitative to the quantitative, when we realize that cells throw numbers at us and that we need to explain those numbers. It would have been unusual for Wieschaus to give a talk without mutants and he did mention them but at the end and in passing. In many ways, and particularly in Drosophila where we know much about the connections of many proteins and specific functions, nowadays mutants are ways to perturb the system by targeting some of its components. For example, if we suspect that actin is involved in a process, we have a large arsenal of tools to modify actin structure and dynamics to learn about the process.

The Bragg lecture was the cherry on the cake of the Symposium but a look at the program highlights a stimulating and interesting set of topics ranging from the dynamics of cell ensembles during morphogenesis to the control of engineered tissues in culture. It would take too long to highlight individual talks and I do not want to be (rightly) accused of bias so, will leave this one out. The high attendance, the fact that people stayed until the end, the number of posters and the intensity of the coffee breaks speak for what, once more, happened at PLM and what the meeting means to people. Somebody asked me whether I saw something change since the first Symposium. The answer is very simple: PLM1 was about drawing the attention of people to what was happening, PLM11 is about a very active field. The Physics of Living Matter is not anymore the future, it is a very rich and exciting present full of interesting challenges.

At the end, as some of us were leaving Eric Wieschaus stated the obvious: the youth of the audience, that the room was filled by young people who, rightly, see their future in this field. I don’t blame them. These are exciting times if you are prepared to move away from classical biology and embrace its quantitative, dynamic and physical aspects. In his book ‘What A Mad Pursuit’ F. Crick mulls over Max Delbruck’s search for ‘new physics’ in Biology. Crick points out, in his crisp and logical manner, that what molecular biology proves is that Biology is about Chemistry not about Physics. He may well have been right that as long as one stays within the realm of a cell, of its internal machinery and of the transfer of genetic information, maybe (just maybe) all one needs is Chemistry. However it is now clear that when we try to reconcile that chemistry from the perspective of single molecules and single cells to cell ensembles, tissues and (if you so dare) of organs, to their dynamics, one has to think about Physics and, most likely, in a manner physicists have not thought before. The Physics of Living Matter presents interesting challenges to physicists and biologists alike. A peculiar component of this area of research is that in PLM, Genetics – that classical component of biological research- becomes a perturbation tool that works hand in hand with the maths. In some way both are languages to formulate problems.

See you all at PLM12!