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广东快乐十分手机投:Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics
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The cost of an empirical bit in biophysics has fallen dramatically, and high-precision data are now abundant. However, biological systems are notoriously complex, multiscale, and inhomogeneous, so that we often lack intuition for transforming such measurements into theoretical frameworks. Modern machine learning can be used as an aid. Here we apply our Sir Isaac platform for automatic inference of a model of the escape response behavior in a roundworm directly from time series data. The automatically constructed model is more accurate than that curated manually, is biophysically interpretable, and makes nontrivial predictions about the system.
The roundworm Caenorhabditis elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.
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Author contributions: I.N. coordinated the project; B.C.D., W.S.R., and I.N. designed research; B.C.D. wrote the software; B.C.D., W.S.R., and I.N. performed research; W.S.R. collected experimental data; B.C.D., W.S.R., and I.N. analyzed data; and B.C.D., W.S.R., and I.N. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: Data related to this work have been deposited on figshare (https://figshare.com/articles/Data_and_Code_Archive_for_Automated_predictive_and_interpretable_inference_of_C_elegans_escape_dynamics_/7806602). The developed software is available on GitHub (https://github.com/EmoryUniversityTheoreticalBiophysics/SirIsaac).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1816531116/-/DCSupplemental.
Published under the PNAS license.