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 http://hplusmagazine.com/wp-content/uploads/2013/05/lysenko.jpg the picture of the stamp with Vavilov is from Wikipedia https://en.wikipedia.org/wiki/Nikolai_Vavilov

  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:

https://www.theguardian.com/commentisfree/2017/mar/28/arctic-researcher-donald-trump-deleting-my-citations

http://www.newyorker.com/tech/elements/donald-trumps-war-on-science

http://www.vox.com/science-and-health/2017/1/25/14370712/trump-science-gagging-explained

https://www.scientificamerican.com/article/trump-administration-orders-epa-to-remove-its-climate-change-web-page1/

and some good news….http://www.sciencemag.org/news/2017/01/trump-officials-suspend-plan-delete-epa-climate-web-page

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 doi.org/10.1101/051722. (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 doi.org/10.1101/104539 (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: http://www.biologydiscussion.com/gene/genes-types/genes-types-top-6-types-of-genes-genetics/67413

 

References

1. Poulson, D. 1940. The effects of certain X-chromosome deficiencies on the embryonic development of Drosophila melanogaster. J Exp Zool 83: 271325.

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: http://asapbio.org/event/preprints-biomedical-science-publication-in-the-era-of-twitter-facebook. 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’   (http://onlinelibrary.wiley.com/doi/10.15252/embj.201695531/full); 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?’ (http://www.ascb.org/on-publishing-and-the-sneetches-a-wake-up-call-november-december-2016-newsletter/ ). 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:

http://amapress.gen.cam.ac.uk/?p=1635

http://amapress.gen.cam.ac.uk/?p=1415

http://amapress.gen.cam.ac.uk/?p=1434

http://amapress.gen.cam.ac.uk/?p=1239

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 (https://www.broadinstitute.org/research-highlights-human-cell-atlas) 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 (http://amapress.gen.cam.ac.uk/?p=1765) 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” (https://briscoelab.org/2016/10/02/the-three-billion-dollar-question/). 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 (https://www.broadinstitute.org/news/international-human-cell-atlas-initiative-gets-underway) 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 (http://www.plm-symposium.org/programme.html). 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!

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It’s the Magnesium!

Slide1How times change!

In an episode of the making of molecular biology, Sydney Brenner was lying on a beach in California thinking about why the experiments he had been doing were not working. He had gone to California with F. Jacob to try to isolate messenger RNA, an elusive entity at the time which genetics and theory had predicted should be there. The days were passing by and mRNA kept on escaping their clutches. In the recollection of Jacob in his autobiography (The Statue Within):

That is why, thanks to the solicitude of the biochemist Hildegaard, we found ourselves lying limply on a beach, vacantly gazing at the huge waves of the Pacific crashing onto the sand. Only a few days were left before the inevitable end. But should we keep on? What was the use? Better to cut our losses and return home. Curled in on himself, Sydney exhibited the closed mask of a bulldog. From time to time, one of us repeated the litany of the failed manipulations, trying to spot the flaw. A good woman, Hildegaard tried to tell us stories to lighten the atmosphere. But we were not listening. Suddenly, Sydney gives a shout. He leaps up, yelling, “The magnesium! It’s the magnesium!” Immediately we get back in Hildegaard’s car and race to the lab to run the experiment one last time. We then add a lot of magnesium. In my haste, I miss a tube with my pipette which then spills a huge quantity of radiophosphorus into Weiglf’s bain-marie. A short time later we tiptoe down to the basement to conceal the contaminated bain-marie behind a Coca-Cola vending machine. Sydney had been right. It was indeed the magnesium that gave the ribosomes their cohesion”.

…and thus the mRNA was caught and seen. Can you imagine this scene today? A middle-aged biosciences researcher on a beach, obsessing about their (failed) bench work and springing into action on the spur of a brainwave? If not, is it because there are no big questions in Biology? Is it because when you go to the beach you don’t like to think about experiments? Or perhaps it is because the fabric of science has changed? The spirit maybe has not changed, after all the one thing that keeps many of us going is the pursuit of the unknown, the search for logic in what seems unfathomable. But something has changed in the execution of this interest and the context in which it happens. Where once there was a scientist, there is now (particularly in the biomedical sciences) a “PI”. We do know, or think we know, what is to be a scientist but, what is it to be a PI? This is not an easy question to answer. Where Brenner and Jacob were thinking about experiments and concepts in a beach and how to implement them in the lab, today the figure tinker, tailor, soldier, spy comes to mind in the form of administrator, mentor, writer, speaker, politician…many tasks but not those one associates with an old fashioned scientist. A postdoc thinks more about positions and papers than about discovery, a young PI’s main concern is with grants, meetings and journal editors; science only comes into the picture in these contexts. Surely, you and I know exceptions, individuals that are true to the time-honoured tradition of bygone times. However, the truth of the matter is that for the most part, the PI is, as I have suggested before, more a manager of a small business or the CEO of a large cooperative than someone tinkering in a lab or in their heads. Nowadays, success seems to be measured by how many conferences and lectures one is invited to, papers in HIF journals and international collaborations than by what one finds (and too often I see people bragging about this). This leads to scientists spending more time in airports than talking to postdocs and students (doing experiments is out of the question); we all know the phenotype. I hasten to add that there is nothing wrong with this. A bit like evolution: if it works, it will stay for a while. And this is the case with this phenotype. There are two problems, though, with this development – at least from my perspective-. The first one is that this is not widely known yet and that this ignorance creates an aspirational image for young researchers which is different from that which brings them into science in the first place. To avoid frustrations, it is important that this is made clear. The second one is that in the transformation from a craft to business/industry we might miss ‘the magnesium’.

Nowadays, grants are start ups, labs are small companies, students and postdocs employees trying to move up a corporate ladder and Science just a means to an end which amidst a lot of publications (the main currency of the business) sometimes makes something useful or what we used to think of as a discovery (though I would argue that everything is useful in Biology, after all it is information). Peter Lawrence has written eloquently about unintended consequences of this situation e.g the bureaucratization of the enterprise and how it affects the development of young people and the progress of science. It is difficult not to agree with him but, unfortunately, there is a point missing in his arguments and it is that the situation has not been designed by some mean group of administrators intent in benefitting themselves on the back of scientists. The situation is an inevitable consequence of the increased numbers of scientists (or I should say of practitioners of science), the devaluation of the scientific enterprise as techniques and data gathering substitute (sometimes justifiably) thinking, the exchange of content (ideas and real discoveries) for publications and the need to find a way to control all this. Lawrence’s solution is to get back to the good old days when one would tinker away in a corner, as he did in a well funded and stimulating institute. This, today, is not possible. Doing science then was a privilege and today, when such privilege is placed at the fingertips of large numbers of people, we see its cost and the need to manage it. I also like letters and pens and old photographs and one month long holidays in the small fishing villages in Spain, sometimes, think with some nostalgia about all that. But those days are gone and the post office is changing delivery schedules not because it doesn’t like letters but because the way we communicate has changed, and one month long holidays are not workable (and lazy fishing villages do not exist anymore in Spain). Solutions to the problems that we have created have to come from looking, creatively, at the future not to a past which is not fit for current purpose.

With the biosciences becoming so expensive, interdisciplinary, and therefore collaborative, with the demands to justify tax-payers’ money, and large numbers of people to manage, it is not possible to go back to a system that catered for a few working on a small number of defined problems. Where in the 70s and 80s a postdoc had a more than 70% chance of getting a job and more than 50% of getting a first grant as a new investigator, today because of sheer numbers trying to enter the trade at the highest level, the chances of both are low. What we need to do is to face the situation, which is what begets the problem in the first place, and find solutions that fit the status quo because as has been said and I agree wholeheartedly “the root cause of the problem is the fact that the current ecosystem was designed at a time when the biomedical sciences were consistently expanding, and it now must adjust to a condition closer to steady state”( http://www.pnas.org/content/112/7/1912.full and see also http://www.pnas.org/content/111/16/5773).

NIGMS-Age-DataNB: the data in the Figure on the left is from http://drugmonkey.scientopia.org/2012/02/14/nigms-data-on-the-timeline-from-ba-to-phd-to-asst-prof-to-r01/ and is US based, though it would be interesting to see the same for Europeans.

It does bother me how, at some meetings, sessions are staged on how to develop a successful career. In these sessions, older scientists tell the tale of how they became ‘successful’ twenty -thirty, forty- years ago, of how ALL THAT MATTERS is to do good science and that if you do that, the rest will follow. Really? Sometimes it works, and I have seen cases, but this is luck. The overwhelming reality today is very different from the one many of us experienced as postdocs: success –which today is to get a job and a grant- does not follow from just doing good science. The recipe is fuzzy and involves strategy and luck. What worked for us (over 50s) will not work for young people today because the environment, the goals and, importantly, the form and content of the biological sciences have changed. One example of this change is in the structures that are emerging in the UK with a number of intermediate positions between a postdoc and a tenured position: career development awards (of various kinds) and senior fellowships being two stages which most postdocs look at with hunger.  The most important thing to do right now is not to pine for older times but to face the situation and see how we can change it in a useful manner (NB. I am aware that many organizations are trying).

Do I have any practical thoughts on how to go about this? Difficult question but there is one thing that comes to my mind: a need for radical thoughts on the nature of our enterprise and the career structure. This at two levels, the first one is to face the realization that there are no PI jobs for everybody and that not everybody that has a paper in Nature, Science or Cell can have a job (many discover this to their surprise). Importantly, although many people involved in a lab like doing science, not everybody wants to be a PI. The fact that nowadays so many people get their first job in their mid late 30s (and increasingly nearing 40) should be a sign of alarm. Maybe we should face the reality of labs as small business and promote groups with established scientists, beyond PIs, as a solution (the much berated French system has something like this but it would need some tinkering). The second all important fact refers to education, to a revolution that is upon us and impinges on the first one. Biology is becoming analytical and quantitative and people need to be trained in the computational arts. The future in Biology belongs to those who can deal with large data working together with those who generate the data and, importantly, the questions. A significant impact from this development will be the increase in employability of graduates. If a physicist does not want to do Physics, they have many doors open. Biologists these days linger in labs late into their 30s doing technicians jobs (for this is what screens are), with low pay and morale and few opportunities. If they had a proper quantitative training not only they would increase their market value in the biological sciences, they could look beyond.

Science the way we have known it, is gone and we should not fool ourselves, and less our students and postdocs. Today, rather than ‘it’s the Magnesium’ and back to the lab, the thought that crosses the mind of a PI is “It’s Thurdsday, it must be Heidelberg or…is it Boston?’ and then, rather than the lab, goes to the airport.

CODA: I am sure that, even if one is so far removed from the bench as modern PIs are, that one could think about important issues while travelling but, one is too concerned about grants, paper revisions and visibility to worry about such things.

Science is Global

following the Science is Global campaign we have had a chance to look back at our history and have realized that the mantra of  the campaign is true and that, indeed, our science has always been international, something we have enjoyed and valued. These are the nationalities that through the bonds that link generations of students and postdocs, are the fabric of our group.

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Of codes and machines in Biology I; elements for a discussion

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NB. As I was writing this post, a couple of comments came up on Twitter on whether the machine metaphor was a useful one for biological systems. The discussion did not change the view that is expressed here: a machine is a good metaphor and one that, to a large extent, remains untested.

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I am no fan of science fiction but there is a novel which I have always liked –for the philosophical background more than the plot- and that has come to my mind when recently thinking about genes and cells: Carl Sagan’s ‘Contact’. The key element of the story is the reception of an extraterrestrial message by scientists which, after some toiling, is revealed to contain instructions for building a machine. The instructions do not contain any hint of what the machine is for and only vague ideas of how its detailed shape will look like; both emerge as it is built. The story has many twists and turns, deals with some of the preoccupations of Sagan and, in some ways, the recent ‘Interstellar’ touches in a more saccharine manner, with some of the same issues. But the bit that has grabbed my attention over the last few weeks has been the notion of having a set of instructions without a clear picture of what they are for (self assemble furniture instructions feel like this sometimes) that, when followed, generate a machine and only when the machine emerges, one can think of what it is for.

The reason I was thinking about ‘Contact’ has to do with a preoccupation to figure out what it is that we want to know. I am well aware of things that we need to know, but given that there is always limited time, one needs to decide what it is that one would like to know; understanding is ambitious and, probably, out of reach. Modern cell and molecular biology have placed in front of us a formidable technical arsenal which, in principle, allows us to explore any question we may have. As I have hinted at before, it is unfortunate that a collusion of editorial and career interest are giving precedence to classifications and listings, sometimes one at a time, over real questions but…….. it is also true that questions are difficult to find and, more importantly, to answer. And it is in trying to understand what are important tractable questions, that something caught my eye, something which I am sure some of you either know or have thought about in a different manner. The problem of Developmental Biology is how the information in the DNA is decoded and transformed into the tissues and organs that configure an organism. Most seminars and reviews on the subject start like this only to then proceed with genetic or molecular screens. Nonetheless, as a consequence of this work, classical and popular genetics have created the mirage that there are genes for this and that i.e. that the DNA harbours instructions for digits, eyes, hair colour or height and, in the worst twist often promoted by newspapers, that there are genes for diseases. This is the basis of our current understanding of how the information in the DNA is interpreted and turn into an organism but……. if one thinks about it and thinks from first principles, genes are ONLY instructions to build machines (M in the figure below): ribosomes, transcription and replication enabling machines, membranes, cytoskeletal devices, etc….their remit does not go beyond this. These machines, once built (through the central dogma), become assembled, much like a 747 or a transatlantic liner, into a larger product, a device with the capability to process information, react to it, do work, sense the environment. This device is what we call A CELL. In multicellular animals, subtle variations in the composition and performance of the component machines, lead to different cells that can be further assembled into tissues and organs and these into an organism. Thus, don’t forget that the instruction in the DNA do not code for much that is 3D, and certainly lack any information about the function of the machines or the devices they code for. Like the extraterrestrial message in Sagan’s novel, the instructions are for a machine whose purpose only becomes clear once it is built.

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Fig1. Top, A general principle whereby functional interactions between machines built by instructions from a blueprint, configure a device that works according to the laws of Physics and Chemistry. Bottom, a translation of the principle to a biological system.

Something interesting happens once the machines and their suprastructures, the cells, are assembled: space arises (the information in the DNA does not contain, convey or encode space, maybe time, but not space). As a consequence of the generation of space (surfaces and volumes), mechanics makes an appearance and it does so at two levels: the molecular one (the machines) and the cellular one (the device).  Thus, the emergence of space leads to the emergence of mechanics which feeds-back on the processes of decoding and assembly and, in more than one way, tissues and organs and ultimately organisms, are the outcome of these feedbacks and the interactions they create. The decider of what the output of these interactions should be is function, as dictated in a blind manner by Natural Selection.

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Fig 2. Tissues and organs do  not arise from instructions but result from the feedbacks that the emergent properties of the performance of the machines assembled in cells. More explicitly from the feedbacks that generate new activities and behaviours in those machines and cells.

Much has been made of the selfish gene hypothesis (and I hasten to add that I am not an evolutionary biologist and know little about evolution) which suggests that the machines and the devices have the aim of reproducing the genes which encode them. I find this naïve, deterministic and anthropocentric. There might me no purpose in the assembly of those machines, maybe all is a game of molecular flaunting and selection is not simply looking at the replication of the genes but at the performance of the machines (I realize that I shall need to expand this here but, like Fermat, for that I need more space than the one I can afford here). Perhaps it is a widespread unconscious focus on the genes that for a long time has placed our emphasis on the decoding, which misses many of the important points and problems the need to be addressed. In this regard, it is interesting that while cell biologists have focused on the machines and, sometimes, how they are connected, developmental biologist have focused on the decoding in a kind of naïve manner: a gene for each season. I would surmise that the important problems in developmental biology require, at least for now, thinking about the machines, the devices they configure and their functioning rather than in their component parts. Only in this manner we shall be able to go beyond genes/instructions and start to look at tissues and organs from the same perspective as Nature does: the ability of the cell to integrate and process information. Work on the decoding of gradients in developmental systems (James Briscoe, Marcos Gonzalez Gaitan, Thomas Gregor, Johannes Jaegger, and Arthur Lander) or of oscillators during somitogenesis (Alexander Auhlehla and Andrew Oates) do just that and, to my mind, are some good examples of the way forward, of how to integrate quantitative cell biology, hypothesis testing and modelling. These efforts gauge the information processing ability of cells rather than in the micromanaged organization that most molecular biologist do. Perhaps phage and E. coli supervise closely every step of their biology but, while not impossible, this is not probably the way multicellular developmental systems operate. They are likely to use coarse graining, space and time averaging of molecular events, system level strategies that we can easily miss by focusing on the details of the molecular events.

Much of what we have done so far to understand higher order organization of biological systems (and there is work to do) deals with temporal aspects of the system: simple gene regulatory networks that try to capture sequences of gene expression linking them to specific cellular events. The challenge comes when we face the nature and details of the feedback that space creates on the decoding, the assembly and performance of the machines and their coming together into the devices that we call cells. It is the feedbacks involved in these processes that we need to understand and represent. Turing when thinking about the problem of development in his 1952 paper, was well aware of the challenge when he wrote:

“……one proceeds as with a physical theory and defines an entity called ‘the state of the system’. One then describes how that state is to be determined from the state at a moment very shortly before. With either model the description of the state consists of two parts, the mechanical and the chemical. The mechanical part of the state describes the positions, masses, velocities and elastic properties of the cells, and the forces between them.(….) The chemical part of the state is given (in the cell form of theory) as the chemical composition of each separate cell; the diffusibility of each substance between each two adjacent cells rnust also be given….(…). The interdependence of the chemical and mechanical data adds enormously to the difficulty (of understanting the state of the system) and attention will therefore be confined, so far as is possible, to cases where these can be separated”

This is a pre-molecular way of looking at the problem but remember that Turing was trying to think about Development and doing this in a very prescient manner. The interdepence he refers to is the essence of the feedbacks mentioned above which turn cells into tissues and organs. Along similar lines, a few years ago F. Julicher pointed out something to me which highlights the essence of the problem and, at the same time, the difference between Physics and Biology. It can be stated simply: whereas in a physical system, Chemistry generates mechanics and the reverse is not true, in biological systems both work (Physics generates Chemistry and Chemistry generates Physics) and it is in this feedback and interactions that, probably, lies the essence of biological systems.

The current focus and craze on screens and single cell transcriptomics (proteomics is coming) should not make us forget that these endeavours only address the parts. Moreover, as I have discussed before (http://amapress.gen.cam.ac.uk/?p=1094) the system we are trying to understand has evolved to respond to selection (and remember that this is what screens are about, a highly selective selection of the ability of cells to respond to a stimulus) and the response is based on dynamic heterogeneities that we still do not understand but which, one suspects, have something to do with the feedbacks I have outlined. The exciting thing (to me) in ‘Contact’ is the machine, what it does and how both its structure and purpose unfold as the system is built. The parts that configure the system could have been used to build anything but the instructions turn them into a space-time machine. In Biology, right now there is too much emphasis on the parts. This would not be a bad thing were it not because we endow parts with functions that correspond to the wholes they are part of. How do we avoid this? The real aim of Systems Biology is to avoid falling into this trap. It is therefore unfortunate that in most realms, Systems Biology has become a proxy for data analysis. Cell and Developmental biologists should embrace a proper version of Systems Biology because right now it is the only way to get out of the describe-the-parts loop and move towards understanding on the way to re-engineer cells and tissues.

Acknowledgement: I want to thank F. Julicher and S. Grill for very enlightening and inspiring discussions on the subject of this post. Also, note that there is a I in the title. There will be a II which aims to deal with ‘questions in Biology’.

Epilogue: The Twitter feed I mentioned above led to many interesting suggestions. They all described different kinds of machines. Even the notion of City, much liked in the thread, can be construed as a machine. What this reflects is that machines are ways to describe (and then engineer) assemblies of parts with some functional aim. It is difficult not to see this in the fabric of a cell and, in many ways, it is a useful, working notion. If properly used it could be helpful as in its day was the vision of the central dogma, as information transfer and decoding.