SPSP 2013 Contributed Papers: Computation and Simulation
Tweeted from the 4th biennial conference of the Society for Philosophy of Science in Practice in Toronto, Ontario, Canada, on June 29, 2013, during Concurrent Sessions VII
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About to start: session on “Computation and Simulation” #SPSP2013 #SPSP2013Toronto
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First up, Catherine Stinson, “Computational models as experimental systems” #SPSP2013 #SPSP2013Toronto
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The problem: how can we learn (about brain & cognition) from (connectionist) computational models? #SPSP2013 #SPSP2013Toronto
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Scientists seem to think that they’re learning *something* from “computational experiments” #SPSP2013 #SPSP2013Toronto
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Maybe they’re wrong, or using the vocabulary differently. But hypothesis is that they know what they’re doing #SPSP2013 #SPSP2013Toronto
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What the scientists say they’re doing? Models closely tied to the physiology of the brain; #SPSP2013 #SPSP2013Toronto
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Models have a ” ‘physiological’ flavor” (why the scare-quotes) #SPSP2013 #SPSP2013Toronto
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Trying to get a handle on real causes; simplifying to identify the thing that’s more important to study #SPSP2013 #SPSP2013Toronto
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[As I’m late to the Storify on this session, there are some tweets that I must copy and paste. I’m not sure whether to blame Storify or Twitter for this, but the moral of the story is not to dawdle in Storifying one’s conference tweets.]
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Two goals: understand or *mimic* brain. RT @docfreeride: hypothesis..they know what they’re doing #SPSP2013 #SPSP2013Toronto
7:38 AM – 29 Jun 13 · Details -
Computer/Cog Sci criticized often by confusing the two goals. (Sometimes justifiably) RT @docfreeride: #SPSP2013 #SPSP2013Toronto
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@drugmonkeyblog @docfreeride There are problems with tuning complex models to produce recognizable patterns … “right” output wrong reasons
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Right…if trying to learn about human brain. If trying to improve information processing….can be fine. @N3OX @docfreeride
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@drugmonkeyblog @N3OX @docfreeride Useful distinction. We may find many interesting things trying to model the brain, even if models wrong.
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@drugmonkeyblog @docfreeride Yeah, and I think even if you ARE trying to understand the brain, it’s fine if skepticism levels stay high.
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@IdiotTracker @drugmonkeyblog @docfreeride Right. Lot of promise in new things that are “bio-inspired” even if they work differently.
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But of course the need to justify work for funding purposes leads to interesting claims… @N3OX @IdiotTracker @docfreeride
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#Modelorg RT @drugmonkeyblog: The need to justify work for funding purposes leads to interesting claims… @N3OX @IdiotTracker @docfreeride
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Generic mechanism (generic = a thing in the world, belongs to an abstract type) #SPSP2013 #SPSP2013Toronto
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models have explanatory power in virtue of being simplified; generalizations as a result (not an assumption) #SPSP2013 #SPSP2013Toronto
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Scale models as example: generic system of particular shapes, relative sizes. Do expt. to see how things fit #SPSP2013 #SPSP2013Toronto
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Another generic model: a sample from a target population. Sample based on generic of interest #SPSP2013 #SPSP2013Toronto
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(& work to mitigate effects of other generics that they are) #SPSP2013 #SPSP2013Toronto
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Model organisms also generic models #SPSP2013 #SPSP2013Toronto
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Connectionist models: interconnected nodes supposed to be like a brain in terms of connections of diff strengths #SPSP2013 #SPSP2013Toronto
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Experiments with the model (meant to show how past tenses of English verbs could be conjugated with one system) #SPSP2013 #SPSP2013Toronto
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… experiments with model also build in idealizations #SPSP2013 #SPSP2013Toronto
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(Am not tweeting the picture w/ the nodes, because I’m not that good, yo!) #SPSP2013 #SPSP2013Toronto
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Some of the idealizations might be actual problems (does training data help too much in getting the result?) #SPSP2013 #SPSP2013Toronto
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Advertised as showing patterns of language learning that young English speakers go through #SPSP2013 #SPSP2013Toronto
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You don’t use back-propagation in situations where that idealization would clearly interfere with answering the Q #SPSP2013 #SPSP2013Toronto
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Models as concrete objects we can manipulate to generate knowledge #SPSP2013 #SPSP2013Toronto
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Inferences from models needn’t involve the logic of representation (can use others kinds) #SPSP2013 #SPSP2013Toronto
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Q: how can we talk about idealization without still being in the neighborhood of representation? #SPSP2013 #SPSP2013Toronto
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Next: Miles MacLeod (& N Nersessian), “Integrating simulation & experiment: hybrid research in systems biology” #SPSP2013 #SPSP2013Toronto
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Actually, “Bimodal research in integrative systems biology” #SPSP2013 #SPSP2013Toronto
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DRawing on ethnographic study of model-building practice in 2 systems biology labs#SPSP2013 #SPSP2013Toronto
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Hypothesis: cellular redox environments affect drug efficacy #SPSP2013 #SPSP2013Toronto
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Lab G (computational lab): contains only modelers. Lab C (fully equipped wet-lab) w/ experimenters, modelers, …#SPSP2013 #SPSP2013Toronto
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… and researchers who do both expts & modeling (bimodal researchers)#SPSP2013 #SPSP2013Toronto
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Model-building in ISB characterized by complex problem-solving tasks #SPSP2013 #SPSP2013Toronto
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Complex nonlinear biological networks, model constrained by data, collaboration issues, and computation issues #SPSP2013 #SPSP2013Toronto
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Lack of theory for model-building that applies generally #SPSP2013 #SPSP2013Toronto
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Trying to understand insensitivity of diff cancer cell lines to chemo drugs #SPSP2013 #SPSP2013Toronto
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Hypothesis: cellular redox environments affect drug efficacy #SPSP2013 #SPSP2013Toronto
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Original model criticized as “very far from what occurs physiologically” #SPSP2013 #SPSP2013Toronto
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Refinement: not just one pathway, but whole redox pathway (ODE model built from lit.) #SPSP2013 #SPSP2013Toronto
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How does the drug alter the redox environment? Model didn’t match phenomena in cell lines #SPSP2013 #SPSP2013Toronto
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Isolated problem by experiment. The toxicity had to be from diff mech #SPSP2013 #SPSP2013Toronto
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Simulated hypothetical mechanisms, ran simulations to estimate unknown parameters, compared with experiments #SPSP2013 #SPSP2013Toronto
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Coupling experiment and simulation (bimodal work) as good modeling strategy #SPSP2013 #SPSP2013Toronto
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Simulation was main cognitive apparatus for interpreting data & dynamical relationships, structuring hypotheses #SPSP2013 #SPSP2013Toronto
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Experiments: main source of validation for model elements #SPSP2013 #SPSP2013Toronto
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Managing complexity through triangulation, limits search space by localizing errors #SPSP2013 #SPSP2013Toronto
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Pure modelers rely more on mathematical/algorithmic techniques to search #SPSP2013 #SPSP2013Toronto
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Bimodal researchers can only handle relatively small scale systems w/ manageable # of unknowns #SPSP2013 #SPSP2013Toronto
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But bimodal researchers can produce more reliable “biologically grounded” models (mechs) #SPSP2013 #SPSP2013Toronto
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Last paper, Annamaria Carusi, “Computational models and medical science: the honeymoon is over” #SPSP2013 #SPSP2013Toronto
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How are new encounters, particularly in clinic, challenging systems biology? #SPSP2013 #SPSP2013Toronto
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Lessons for epistemology of modeling & simulating in a biomedical context? #SPSP2013 #SPSP2013Toronto
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“Systems Biology” is very broad, could point to many different kinds of approaches… #SPSP2013 #SPSP2013Toronto
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Talk focused on a particular example in systems physiology (modeling w/ PDEs & ODEs) #SPSP2013 #SPSP2013Toronto
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Disenchantment. Much funding on promise of building models that would be useful to medicine. #SPSP2013 #SPSP2013Toronto
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New collaboration in face of skepticism; new approach to validation #SPSP2013 #SPSP2013Toronto
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Current accepted practice in heart modeling #SPSP2013 #SPSP2013Toronto
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Major effort put into model construction, simulation techniques, for multi-scale integration #SPSP2013 #SPSP2013Toronto
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Data from existing resources (lit, data repositories). #SPSP2013 #SPSP2013Toronto
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Not so many new experiments in response to/in conversation w/ models #SPSP2013 #SPSP2013Toronto
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Role of simulations defn’d mathematically: solving equations. Simulations are evaluated on those grounds #SPSP2013 #SPSP2013Toronto
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Not into experimental hypothesis/test/discovery #SPSP2013 #SPSP2013Toronto
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Philosophy of modeling/simulation? Simulations as downward, motley, and autonomous #SPSP2013 #SPSP2013Toronto
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Validity, legitimacy, justification, evaluation of simulations can be internal or external #SPSP2013 #SPSP2013Toronto
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Internal: relationship between the equations and the simulations #SPSP2013 #SPSP2013Toronto
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In clinic, pervasive variability of biological processes #SPSP2013 #SPSP2013Toronto
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External: what makes this simulation a “model” of the target domain #SPSP2013 #SPSP2013Toronto
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Legitimacy grounded in certain features of model building practice. Trust in reliability of those practices #SPSP2013 #SPSP2013Toronto
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Modeler working too much in isolation from experimentalist, not perceptive to tinkering/tweaking relationship #SPSP2013 #SPSP2013Toronto
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Models are not single entities but hybrid systems, model-simulation-experiment (MSE) system #SPSP2013 #SPSP2013Toronto
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In clinic, pervasive variability of biological processes #SPSP2013 #SPSP2013Toronto
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Makes personalized medicine a pipe dream for many kinds of systems biology #SPSP2013 #SPSP2013Toronto
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Challenge of variability: don’t know if simulation & experiment are in the same state when try to compare them #SPSP2013 #SPSP2013Toronto
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Modeling & simulating can (should?) be brought in closer contact w/ expts #SPSP2013 #SPSP2013Toronto
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Trying to see what counts as a comparison, set up conditions where it’s possible to make them #SPSP2013 #SPSP2013Toronto
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Coupling experiment and simulation (bimodal work) as good modeling strategy #SPSP2013 #SPSP2013Toronto
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wtf…..a computational bashing session @SPSP????…as one of these trying to be a computational biologist peoples, i wonder if anyone at the symposium actually had any insight as to how one is to make and test models of neural systems without futzing around with a connectivity matrix and trying to do closed-loop neurophysiologic experiments???….a single neuron’s dynamics are simple to describe quantitatively, but the emergent properties of the network lie in the details of the connectivity matrix that ties any model together…..
it seems really obvious (to me at least) that those who have existed in this space thus far haven’t figured it out yet because of the enormous gap between knowing how to do the ‘magic’ of biology and the theory and practice of being a computationalist……so most of the computational work out there thus far seems to fall into the extremes of being overly-parameterized or overly-generalized…..both being of little use until we can really measure these properties precisely and directly from the biologic preparations…..
but imo simply having a bash session doesn’t seem like it would lead to any meaningful solutions……i hope this is just a consequence of the twitt-world and the symposium had more substance than im aware of….
I love your blog, but please consider putting VERY long tweet streams after a jump, for those of us who read the blog on the website (not via RSS) and otherwise have to scroll endlessly to the next post. =)
As a “bimodal” neuroscientist, I want to point out that just as with wet experiments, there is good computational practice and there is bad computational practice. Testing the model for robustness to parameter variation is good practice and limits over-parameterization. Validating the model against experimental data is also good practice and limits over-generalization.