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6 thoughts on “Computing the worm: artificial intelligence approaches to planarian regeneration and beyond”

  1. Dear Dr. Levin,

    Since you published this paper, how many interesting NEW discoveries have you made? I’m afraid ‘inverse problems’ are not solvable unless you feed them new a priori knowledge. Moreover, your approach tries to bridge phenotypic morphologies with gene networks skipping the cell level which is considered homogeneous. To me, this is rather odd.

  2. Dear Dr. Baguñà,

    I see several different questions in your comment; let me try to address them:

    > Since you published this paper, how many interesting NEW discoveries have you made?

    since the publication of that paper, we have published about 16 new papers, and I hope at least some of them describe interesting new discoveries. But perhaps you are asking, how many new papers has this particular paper made possible. This is a good question, although perhaps it’s better to ask that question in a few years’ time: the system we describe, and the model it uncovered, have only existed since 2015 – less than a year. Surely not enough time to gauge its impact, if that is the point of the question. However, I am happy to tell you that the model makes a few unique, novel predictions which we have recently validated in planaria at the bench, and are writing the manuscript up now. We appreciate your enthusiasm in seeing the results come out and will publish it as soon as humanly possible. I’m sure you understand that testing models in planaria with good functional data can take a year; I believe we are not being unreasonably slow. We encourage others in the planarian community to do so as well – our system facilitates other labs to mine the data for predictive models and test them.
    I can also tell you that our other recent paper, showing a similar machine learning platform’s inference of melanoma dynamics in vertebrates (http://stke.sciencemag.org/cgi/content/full/sigtrans;8/397/ra99?ijkey=DNnSge7tn.tcE&keytype=ref&siteid=sigtrans ) also generated predictions that enabled us to produce a desired phenotype never before described; the AI-discovered model actually enabled a new capability and suggested the exact reagents that can be used to implement that desired outcome in Xenopus tadpoles. Our manuscript describing this is likewise in preparation and we look forward to sharing it with the community.
    In any case, we are in agreement that it is important to test models empirically, and we’re doing just that. I also welcome any practical advice on how to make the resulting papers come out more rapidly.

    > I’m afraid ‘inverse problems’ are not solvable unless you feed them new a priori knowledge

    well, we agree on the difficulty of inverse problems ( http://rsif.royalsocietypublishing.org/cgi/reprint/rsif.2013.0918?ijkey=r6H7rxetYCz9r9k&keytype=ref ), however it is not obvious to me what you mean by “new a priori knowledge”. Human scientists work on the assumption that by looking at existing data, they can infer a predictive model of what’s going on. I think it’s premature to assert that this process has some ineffable quality that is unreachable by machine learning and requires humans at every step. It may turn out to be the case, but it’s way too soon to assert that without trying to provide tools to augment human scientists’ efforts. Too many other problems have been thought of as human-only, and then shown to benefit from computerized tools. What scientists do is look at data, and try to come up with a model that explains that data. That is what our machine learning platform does. Testing those models is then done by humans at the bench, but given the dearth of comprehensive models in this field, I would think a computational tool to provide candidate models for us to test would be a welcome addition to the toolkit.

    > Moreover, your approach tries to bridge phenotypic morphologies with gene networks

    I certainly do not claim that gene networks are sufficient to explain morphogenesis. Indeed many of our papers address additional biophysical systems that interact with gene networks to regulate patterning (http://ase.tufts.edu/biology/labs/levin/publications/bioelectricity.htm ). However, one has to start somewhere – if one tries to model every detail simultaneously, no progress will be made. Since many people are indeed interested in inferring gene regulatory networks for pattern formation (as evidenced by numerous papers in which the last figure is some sort of GRN model), we produced a tool that facilitates this process. We started with gene networks, but I am in complete agreement with you that this is not the end, and we are in the process of augmenting our simulator to include several there signaling modalities. This is a significant undertaking, and requires a lot of work. But now that the GRN component works, we can turn our attention to the next step which will broaden the framework beyond gene networks.

    > skipping the cell level which is considered homogeneous

    you are absolutely correct in that our model did not model individual cells (just as many gradient models in developmental biology do not). We did this as a simplifying first step, and future versions of this system will explicitly represent cells as part of including bioelectric signaling. However, I point out that it is a reasonable strategy to see how far one can get with a minimum of complexity in a model. It remains to be seen whether a spatialized model does better than ours in explaining the published results on planarian regeneration; for now, our system seems to have found a fairly high-quality model (not claiming “the correct” model, as no model is), without using discretization of cells. We look forward to comparing it with any alternatives you or other labs may produce with different methods.

    I hope that addresses your thoughtful questions; thank you for your interest in our work,

    Mike Levin

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  3. Sorry for the delay. I read it through but I could not grasp it entirely. Three comments/questions. First, I do not understand hnf4- worms having a WT phenotype. If hnf4+ gene is a key gene for gut, RNAi against it should heavily distort the central body region which it is not. Why is it so? Second, I don’t see much difference between tail areas between hnf4- and hh- worms except that the first has a larger blastema than the second that likely giving the statistical significance in Fig 3. Besides hh- animals are axially shifted (distorted). Third, and most importantly, I would like to see the same set of experiments using heads and tails instead of trunk regions. Are you planning to do them?

    Many thanks.

  4. Dear Dr. Levin,

    On May 26th 2016 I posted a comment on your new paper in Bioinformatics 2016 with, so far, no answer. Whenever you feel like, I would appreciate a short (or long) answer. Thanks a lot.

    Jaume Baguñà

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