Challenging Questions


Initial discussion (8 Jan, Week 1)


Below are images of the blackboard following our discussion on topics and questions of interest to the group in the first week. Rough transcriptions of the board content follows underneath the images. Please add/edit as you see fit.

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1. Volvox Questions
  • James Glazier is interested in simulations of Volvox, keeping an eye on the germ line:soma ratio, cell fate choices based on cell size, roles of environmental variability in these events, etc.
  • this is a nice system because quite a lot of biological data are available

2. Cancer Questions

  • Why do the emergent selection pressures in solid tumors acting on the simultaneous undirected variation of all cell behaviors lead to the appearance of a regular sequence of appearance of particular cell capabilities known as cancer progression?
    • the genetics underlying these cell behaviours are essentially unknown in detail (no 1:1 map of gene(s) to behaviour), so toy models focus on physical behavioural parameters (Glazier happy to introduce others to his tools for simulations)
    • it would be useful for us to obtain some current specific expertise on what data are available for some of these parameters in biological systems
  • What are the roles of spatial, temporal and spatio-temporal gradients in determining the rate of progression?
  • Can we predict, for a specific tumor type, the spatial and temporal gradient structures that will maximize the rate of progression?
    • how can we apply physical theory to data on normal/disease state cell communication?
    • "genetics" has not been helpful for solving this problem (but see some work by Friedl)
  • Do tumors operate near these optima?
  • Using this information, can we design non-toxic therapeutic interventions which move the tumor away from these optima and thus slow the rate of progression?
  • do tumors represent a loss of cooperation or merely a different kind of cooperation?

3. Gradient Questions
  • what is the role of gradients in the speed of somatic evolution?
  • within the space of spatiotemporal variability are there optimalities with predictable evolutionary effects?

4. Common Tools, Assumptions, Framework and Approaches Questions
  • it would be ideal to establish a common ground/bedrock for approaching properties of "complex" (vs "simpler") systems - can we discuss/agree on answers to questions like
    • what is a theory? considering historical trajectory/baggage of theories is important (c.f. Wilson/Nowak wars, group/kin selection wars)
      • what should a theory be able to do?
    • what are models for?
    • what modelling tools are available? useful?
    • where do assumptions underlying our theories and models come from? (e.g the role of additivity - see baggage above)
  • can we agree on
    • terms
    • how to approach biological problems
    • what constitutes (reliable) "data" or "evidence" ?
    • what is falsifiable
    • the (potential) contributions of different theoretical/experimental frameworks?
  • to this end, it would be helpful to establish a survey/compendium of the history and substance of historical debates (might help us to avoid re-enacting old battles)

5. Cellular selection/division of labor Questions
  • what ideas and predictions can we generate about these that are experimentally testable?
    • what tools are available or should be developed to perform these tests?
  • germ line/soma predictions are related to cancer questions
  • what is the relative importance of change (mutation, variation) that occurs
    • during mitosis (no escape)
    • absent cell division (might escape)

6. Network Questions
  • what do we know/can we learn about entropic information flow?
    • c.f. cooperation in Death Valley
  • networks of any kind are of interest, including gene regulatory networks (consider getting Eric Davidson here from Caltech to give us the state of the art on his GRN data)

Second general discussion (15 Jan, Week 2)


Below are images of the blackboard following our discussion on additional topics and questions of interest to new members of the group in the second week. Rough transcriptions of the board content follows underneath the images. Please add/edit as you see fit.

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1. Relationships/inter-utility of different approaches
  • what is or should be the relationship between different approaches? each have pros and cons:
    • Experiments: in the field
      • con: limited to extant organisms
      • con: bias of experimental design choices/lens - same as theory biases or not?
      • pro: we can measure performance of features in the real world
      • pro: by taking into account real world life history features, might we be able to find out whether particular life history characteristics correlate with/allow us to predict transitions between unicellularity and multicellularity?
      • note: the concept of "optimality" of performance or features might be dangerous or misleading (e.g. maybe having a variety of possible actions/responses might be better than a single one), although it seems convenient and might be useful for generating fasifiable hypotheses
    • Experiments: in the lab
      • pro: relatively controlled, known/knowable conditions
      • con: might not have relevance to real world
    • Theory
      • pro: could generate predictions that could be tested experimentally
      • con: in absence of data might be operating far outside real world space
      • con: bias of experimental design choices/lens - same as theory biases or not?
      • con: can suffer from "availability bias" /canalization of ideas
      • note: maybe experiments can "winnow" theory in useful ways
  • simultaneous flow of communication and data between approaches during experimental/theoretical design and implementation might be more fruitful than independent development of approaches and post-hoc apposition/confrontation/forced merging
    • this might be desirable, but it is true? hopeless?
    • we hope to hear some examples of success stories using this approach
  • one possible cycle of approaches could be
    • observe natural world --> construct theoretical model --> make predictions --> design experiments to test predictions/decide what to measure --> observe/perform experiments in natural world --> REPEAT
    • in practice we probably don't do this very often
    • starting from observations OR theoretical models is possible - is one more helpful than the other?

2. Game theory - why not?
  • last week much discussion revolved around questioning its utility in approaching understanding evol. of coop - i.e we asked "why use it?"
  • maybe we can also ask the reverse question: why not use it? what's wrong with it?

3. Fitness - what is it?
  • last week we appeared to have at least two views on this question:
  • (a) a scalar quantity; how it is calculated is essentially semantic --> don't worry about it ('s definition) too much
  • (b) a deep property, must be derived case by case, not an absolute value or fixed parameter in any system --> we need to worry about it

4. Cosmology - will/should we deal with it?
  • e.g. do black holes "spawn" universes? maybe we should talk about "evolution" on that scale
  • an important popular trend, but is it scientific crackpottery?

5. "Physicists" view of "biological data"
  • experimental biologists judge each other's work based on the (perceived) quality of
    • hypothesis testing
    • interpretation of data
    • technical precision/experimental design
  • but do not always agree on how different studies hold up when scrutinized through these lenses
  • are physicists aware of this?
    • i.e. just because biological data are present in the literature does not mean they are considered valid by all/other expert biologists
    • if not dialogue/education is important


Third general discussion (22 Jan, Week 3)


Below are images of the blackboard following our discussion on topics and questions of interest to the group in the third week. Rough transcriptions of the board content follows underneath the images. Please add/edit as you see fit.
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Fourth general discussion (28 Jan, Week 4)


Below are images of the blackboard, white boards and sticky notes following our discussion on topics and questions of interest to the group in the fourth week. Note that we took a different approach this week, having people write down their answers to general questions posted on the board, and then try to organise these ideas into groups of interest through discussion in smaller groups of people. The ideas and images generated in previous weeks' discussions were gathered by people volunteering to speak their ideas aloud in front of the group, but the sticky note approach this week yielded higher participation and more engaged discussion than the original approach. Thanks to Joan Strassman for suggesting this alternative approach, which we will continue to adopt going forward in the program. Rough transcriptions of the board/sticky note content follows underneath the images. Please add/edit as you see fit.

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Question 1: What do you know about multicellularity and cooperation that others here might not realise?

  • more than one dozen independent origins of differentiated multicellularity
  • just as there are different types of multicellularity there are different types of cooperation, each with their own problems, vulnerabilities, advantages etc,
  • from a physicist's point of view, many details have to be included into the modeling process to explain cooperation. microscopic details matter.
  • Drosophila germ line specification mechanisms are an insect-specific innovation not shared with other animals (or even many insects)
  • a "germ line" (cell lineage with exclusive ability to transmit genes to next generation) has evolved repeatedly in independent instances of the evolution of multicellularity
  • germ cells and somatic cells may not have the same mutation rate
  • multiple species interacting that lead to coarsening like behaviour - coexistence of species
  • the role of flows
  • spatial dynamics can qualitatively change the evolution of cooperation in a population
  • Compucell 3D is a good tool for rapidly developing simulations with spatial dynamics
  • multilcellularity is interesting to non-scientists
  • the mechanisms that structure a population may not be easily disentangled from the organism "strategies"
  • "game theory" is a very crude instrument as currently employed...I suspect one can do better...
  • cooperation - molecular basis not limited to genomics
  • there is an RNA version of a cooperating cycle that has not been modeled exhaustively
  • probably nothing...


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Question 2: What is a result from your own research that you are most proud of?
  • a story I wrote helped get a wrongfully convicted person out of prison
  • first empirical fitness landscape of 4[24] sequences of RNA
  • "dynamical quorum sensing" : density-dependence of collective osc (as a means of cellular organization?)
  • I don't think Eigen's paradox exists
  • programmed death has species-specific effects
  • multiple, independent origins and reversions of developmental characters related to multicellularity
  • characterising linear versus radial range expansions
  • paper showing simple model (Fisher's equation) flow leads to images similar to observed in real satellite images of plankton
  • fact: evolution seems to optimise objective functions sometimes... question: when and why?
  • off topic: social movements exhibit a two-tiered geographical signature in social media communication. e.g. Occupy Wall Street, arab spring on Twitter
  • math modeling shows the differences in types of cooperation
  • networks of genes required for ancestral animal neural or stem cell function have been co-opted to germ line function in many lineages
  • that I am still doing it


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Question 3: What tool/data do you wish you had to solve some specific question about multicellularity or cooperation? What is that question?
  • how should one model tradeoffs e.g. antibiotic resistance vs growth rate in E. coli
  • "game theory" is a very crude instrument as currently employed...I suspect one can do better
  • ability to compare aggregation versus staying together in the same environment
    • what is the difference between "aggregation" and "staying together"? it might have to do with differences in
      • cell motility
        • active vs passive movement
      • cell lineage relationships
        • cytoplasmic sharing
      • autonomy/non-autonomy of behavior
  • theory on how somatic mutation effects the evolution of mutation rate
  • what is fitness of a collective, and how do you measure it?
  • cooperation? measure emergent properties
  • experimental facilities that can help in exploration of population dynamics in flows
  • satellite or field data to test theories
  • i would like to have experimental data on environmental conditions under which cooperation is established in the experimental system
  • first i would like to see more falsifiable questions formulated
  • the ability to synthesise and analyse 20[100] sequences
  • better ways to phenotype individual bacteria to explore interactions between them, switches between states that indicate differentiation, etc.
  • a method to perform successful inter-species transplantations of germ cells
  • complete genomes and/or transcriptomes of non-model species
  • my own documentary company and $2 million for expenses
  • what are the features of tumor growth that are most important for modeling evolution in a tumor?

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Question 4: What other questions are you here to ask?
  • is fitness a scalar?
  • "paradox" of evolvability
    • which paradox? there are many
  • can you have cooperation without self-replication?
  • Is there a unified framework for assessing all types of correlations, genetic, spatial, group, etc?
    • is "cooperation" just part of this?
    • perhaps this lies within (statistical) information theory e.g. ames Crutchfield
    • in terms of information theory, their "distinguished" role is due to their stability

Fifth general discussion (28 Jan, Week 6 (we did not have a discussion like this during the conference week, which was Week 5))


Below are images of the blackboard, white boards and sticky notes following our discussion on topics and questions of interest to the group in the sixth week. Rough transcriptions of the board/sticky note content follows underneath the images. Please add/edit as you see fit.

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Question 1: What do you know about multicellularity and cooperation that others here might not realise?

  • more than one dozen independent origins of differentiated multicellularity

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Question 2: What is a result from your own research that you are most proud of?

  • theory of evolutionary transitions; most proud of how we combine theory & philosophy in study of multilcellularity in Volvox
  • germ cell genes can be conserved in expression across multiple animal phyla BUT play divergent roles at the molecular level
  • RB is a multilcellularity gene
  • I find certain organisms (bacteria) optimize certain objective functions - Question: when does this happen in general? can one predict this a priori?
  • one result from philosophy & modeling work is that an explicit model of how the group life cycle creates trait-fitness correlations is critical for recognizing group adaptation
  • a replicating vector I generated in now in clinical trials for cancer
  • how manipulation impacts the evolution of signaling
  • gene regulatory networks: mathematical models to construct Waddington diagram
  • drug screen program to convert cancer stem cells to non stem cells

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Question 3: What tool/data do you wish you had to solve some specific question about multicellularity or cooperation? What is that question?
  • I would like to have data about variance in allocation to cell types (germ and soma) for a variety of Volvox species & more info on how cell type allocation happens in different species. The question is: how does developmental canalization first evolve at the multicellular level
  • better genome annotations for comparative genomics
  • efficient single cell sequencing techniques "high throughput" to understand the role of genomic variation in the emergence of cooperation, e.g. in cancer, immune system etc.
  • I wish I had experiments carried out in controlled fashion; this would help to improve models towards increasing realism
  • quantitative chemical data on the "cost" of cellular processes. Question: how do differences in cell activities (any biological process) translate into "fitness" gains or losses for single cells? for multicellular groups?
  • quantitative data on the proportion of somatic cells in animal phyla. question: how long (time and phylogeny) did it take for animals to evolve life histories focusing on reproduction
  • I wish I had data on growth dynamics and cell behaviours involved in normal development and carcinogenesis. What are the relevant cell behaviours that one has to take into account to explain tissue homeostasis and transition towards malignancy?
  • how groups of individuals evolve into new individuals. tool most wish I had: time machine
  • knowledge of gene regulatory mechanisms that get more complex (recurrent) across an evolutionary tree. similarity for dynamical complexity of gene regulatory networks
  • I wish I had the "real ancestor" that gave rise to multicellular metazoans to see what their genes are doing
  • Question: emergence of cooperation. Tools: more modeling approaches. more examples.
  • would like to see connection between simulations and game theoretic dynamical models --> how to break space/time continuum
  • how is multilcellularity encoded of the level of gene regulation - accurate regulatory network data
  • (1) math model to combine cell differentiation and homeostasis (2) understand early multicellular program - cancer stem cell transition

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Question 4: What other questions are you here to ask?
  • (How) can we bridge gap(s) between scientists from disparate fields who claim to study the same problem(s)
    • My (Rick Michod) opinion is that the best way to integrate disparate fields it is by studying the same organism from the disparate perspectives--- after all the organism has "solved" the problems we are trying to define and understand...
  • Is fitness a tensor?
  • what genetic themes are shared or not shared to evolve multicellularity in different taxa?
  • is the apparent modular structure of biological networks the key to understanding their evolution?
  • is fitness at multiple leves thought of as a scalar or a vector?
    • This question and above. Not sure what issues the questions are wanting to raise. Very briefly, there are two general notions of fitness in evolutionary biology. One is operational fitness and the other is fitness in the sense of good design like in Darwin's phrase "survival of the fittest" . The second notion of fitness, design fitness, is sometimes called adaptedness. Operational fitness is a measurement made on populations like, expected reproductive success, intrinsic rate of increase, inclusive fitness and longer term measures like probability of extinction. Think of a key opening a lock. We can approach whether a particular key "fits" a particular lock by just trying the key in the lock. Does it open it? That is operational fitness. Alternatively, we can study the key and the tumbler in the lock from an mechanical engineering point of view. Does the cut of the key fit the tumbler in the lock? This is design fitness. We don't have to try the key and the lock, instead we can from engineering principles say if the key "fits" the lock. We can test our engineering understanding by trying the key in the lock. Darwin's infamous phrase "survival of the fittest" is not a tautology when understood in this way, which is how he and Huxley meant it, the phrase predicts that operational fitness (survival) depends on good design. It unites in a predictive way the two notions of fitness. (Rick Michod)