Knowing one's place: a free-energy approach to pattern regulation

Understanding how organisms establish their form during embryogenesis and regeneration represents a major knowledge gap in biological pattern formation. It has been recently suggested that morphogenesis could be understood in terms of cellular information processing and the ability of cell groups to model shape. Here, we offer a proof of principle that self-assembly is an emergent property of cells that share a common (genetic and epigenetic) model of organismal form. This behaviour is formulated in terms of variational free-energy minimization—of the sort that has been used to explain action and perception in neuroscience. In brief, casting the minimization of thermodynamic free energy in terms of variational free energy allows one to interpret (the dynamics of) a system as inferring the causes of its inputs—and acting to resolve uncertainty about those causes. This novel perspective on the coordination of migration and differentiation of cells suggests an interpretation of genetic codes as parametrizing a generative model—predicting the signals sensed by cells in the target morphology—and epigenetic processes as the subsequent inversion of that model. This theoretical formulation may complement bottom-up strategies—that currently focus on molecular pathways—with (constructivist) top-down approaches that have proved themselves in neuroscience and cybernetics.

[1]  Michael Levin,et al.  Molecular bioelectricity: how endogenous voltage potentials control cell behavior and instruct pattern regulation in vivo , 2014, Molecular biology of the cell.

[2]  Matthew J. Beal Variational algorithms for approximate Bayesian inference , 2003 .

[3]  Michael Levin,et al.  A linear-encoding model explains the variability of the target morphology in regeneration , 2014, Journal of The Royal Society Interface.

[4]  G. Auletta Teleonomy: The Feedback Circuit Involving Information and Thermodynamic Processes , 2011 .

[5]  Kwang-Hyun Cho,et al.  Modeling and simulation of intracellular dynamics: choosing an appropriate framework , 2004, IEEE Transactions on NanoBioscience.

[6]  Robert Marsland,et al.  Statistical Physics of Adaptation , 2014, 1412.1875.

[7]  Geoffrey E. Hinton Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.

[8]  J. Davies,et al.  Synthetic morphology: prospects for engineered, self‐constructing anatomies , 2008, Journal of anatomy.

[9]  Perceptual-learning systems as conservative structures: is economy an attractor? , 1990, Psychological research.

[10]  E. Davidson Network design principles from the sea urchin embryo. , 2009, Current opinion in genetics & development.

[11]  Karl J. Friston,et al.  An In Vivo Assay of Synaptic Function Mediating Human Cognition , 2011, Current Biology.

[12]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[13]  R. Doursat,et al.  Growing Fine-Grained Multicellular Robots , 2014 .

[14]  Karl J. Friston,et al.  Comparing dynamic causal models , 2004, NeuroImage.

[15]  MustardJessica,et al.  Bioelectrical Mechanisms for Programming Growth and Form: Taming Physiological Networks for Soft Body Robotics , 2014 .

[16]  Geoffrey E. Hinton,et al.  Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.

[17]  H. Crauel,et al.  Attractors for random dynamical systems , 1994 .

[18]  Ludovico Cademartiri,et al.  Using shape for self-assembly , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  Raymond J. Dolan,et al.  The anatomy of choice: active inference and agency , 2013, Front. Hum. Neurosci..

[20]  Hans Crauel,et al.  Global random attractors are uniquely determined by attracting deterministic compact sets , 1999 .

[21]  George F. R. Ellis,et al.  Top-down causation: an integrating theme within and across the sciences? , 2012, Interface Focus.

[22]  Michael Levin,et al.  Normalized shape and location of perturbed craniofacial structures in the Xenopus tadpole reveal an innate ability to achieve correct morphology , 2012, Developmental dynamics : an official publication of the American Association of Anatomists.

[23]  M. S. Steinberg,et al.  The differential adhesion hypothesis: a direct evaluation. , 2005, Developmental biology.

[24]  Gennaro Auletta,et al.  Information and Metabolism in Bacterial Chemotaxis , 2013, Entropy.

[25]  L Jaeger,et al.  Top-down causation by information control: from a philosophical problem to a scientific research programme , 2007, Journal of The Royal Society Interface.

[26]  R. Gregory Perceptions as hypotheses. , 1980, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[27]  Jeremy L. England,et al.  Statistical physics of self-replication. , 2012, The Journal of chemical physics.

[28]  T. Newman,et al.  Emergent cell and tissue dynamics from subcellular modeling of active biomechanical processes , 2011, Physical biology.

[29]  M. Levin,et al.  Bioelectric Controls of Stem Cell Function , 2014 .

[30]  Takashi Gojobori,et al.  Long-range neural and gap junction protein-mediated cues control polarity during planarian regeneration. , 2010, Developmental biology.

[31]  Denis J. Evans,et al.  A non-equilibrium free energy theorem for deterministic systems , 2003 .

[32]  E. M.,et al.  Statistical Mechanics , 2021, Manual for Theoretical Chemistry.

[33]  Geoffrey E. Hinton,et al.  The Helmholtz Machine , 1995, Neural Computation.

[34]  Karl J. Friston,et al.  Free Energy and Dendritic Self-Organization , 2011, Front. Syst. Neurosci..

[35]  Georgi Georgiev,et al.  Self-organization in non-equilibrium systems , 2015 .

[36]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[37]  Menas C. Kafatos,et al.  Complementarity in biological systems: A complexity view , 2013, Complex..

[38]  Massimo Vergassola,et al.  ‘Infotaxis’ as a strategy for searching without gradients , 2007, Nature.

[39]  Karl J. Friston,et al.  Generalised Filtering , 2010 .

[40]  G. Pezzulo,et al.  Intentional action: from anticipation to goal-directed behavior , 2009, Psychological research.

[41]  Seth Lloyd,et al.  Information-theoretic approach to the study of control systems , 2001, physics/0104007.

[42]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[43]  Grégoire Nicolis,et al.  Self-Organization in nonequilibrium systems , 1977 .

[44]  Karl J. Friston,et al.  Active inference and epistemic value , 2015, Cognitive neuroscience.

[45]  A. Lander Pattern, Growth, and Control , 2011, Cell.

[46]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[47]  J. Tuszynski,et al.  Nanocarriers and Intracellular Transport: Moving Along the Cytoskeletal Matrix , 2009 .

[48]  Cees van Leeuwen,et al.  Perceptual-learning systems as conservative structures: Is economy an attractor? , 1990 .

[49]  Huaiyu Zhu On Information and Sufficiency , 1997 .

[50]  Hiroki Sayama,et al.  A review of morphogenetic engineering , 2013, Natural Computing.

[51]  L. L. Bologna,et al.  Self-organization and neuronal avalanches in networks of dissociated cortical neurons , 2008, Neuroscience.

[52]  Michael Levin,et al.  Endogenous bioelectrical networks store non‐genetic patterning information during development and regeneration , 2014, The Journal of physiology.

[53]  Karl J. Friston,et al.  A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[54]  Michael Levin,et al.  Reprogramming cells and tissue patterning via bioelectrical pathways: molecular mechanisms and biomedical opportunities , 2013, Wiley interdisciplinary reviews. Systems biology and medicine.

[55]  Hod Lipson,et al.  Resilient Machines Through Continuous Self-Modeling , 2006, Science.

[56]  Karl J. Friston Active inference and agency , 2014, Cognitive neuroscience.

[57]  T. Tomé Entropy production in nonequilibrium systems described by a Fokker-Planck equation , 2006 .

[58]  Fenguangzhai Song CD , 1992 .

[59]  R. Kass,et al.  Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models) , 1989 .

[60]  Michael Levin,et al.  Morphogenetic fields in embryogenesis, regeneration, and cancer: Non-local control of complex patterning , 2012, Biosyst..

[61]  Ville R. I. Kaila,et al.  Natural selection for least action , 2008, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[62]  Georgi Georgiev,et al.  The Least Action and the Metric of an Organized System , 2002, Open Syst. Inf. Dyn..

[63]  Alessandra Cucina,et al.  A systems biology approach to cancer: fractals, attractors, and nonlinear dynamics. , 2011, Omics : a journal of integrative biology.

[64]  W. Ashby,et al.  Principles of the self-organizing dynamic system. , 1947, The Journal of general psychology.

[65]  Karl J. Friston Life as we know it , 2013, Journal of The Royal Society Interface.

[66]  Christian Balkenius,et al.  The principles of goal-directed decision-making: from neural mechanisms to computation and robotics , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[67]  Denis Noble,et al.  A theory of biological relativity: no privileged level of causation , 2012, Interface Focus.

[68]  C. Pullar The physiology of bioelectricity in development, tissue regeneration, and cancer , 2016 .

[69]  J. Hubbell,et al.  Synthetic biomaterials as instructive extracellular microenvironments for morphogenesis in tissue engineering , 2005, Nature Biotechnology.

[70]  E. Rosch,et al.  The Embodied Mind: Cognitive Science and Human Experience , 1993 .

[71]  Michael Levin,et al.  Cracking the bioelectric code , 2013, Communicative & integrative biology.

[72]  R. Feynman The principle of least action in quantum mechanics , 1942 .

[73]  M. Levin Molecular bioelectricity in developmental biology: New tools and recent discoveries , 2012, BioEssays : news and reviews in molecular, cellular and developmental biology.

[74]  C. S. Thornton Regeneration in vertebrates , 1959 .

[75]  Giovanni Pezzulo,et al.  Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference , 2013, Cognitive, Affective, & Behavioral Neuroscience.

[76]  DoursatRené,et al.  Growing Fine-Grained Multicellular Robots , 2014 .

[77]  C. McCaig,et al.  Electrical dimensions in cell science , 2009, Journal of Cell Science.

[78]  J. Freyd The Facts of Perception. , 1991 .

[79]  A. M. Turing,et al.  The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[80]  Karl J. Friston,et al.  Free Energy, Value, and Attractors , 2011, Comput. Math. Methods Medicine.

[81]  W. Ashby,et al.  An Introduction to Cybernetics , 1957 .

[82]  Jeffrey K. Noel,et al.  The Dominant Folding Route Minimizes Backbone Distortion in SH3 , 2012, PLoS Comput. Biol..

[83]  W. Ashby,et al.  Every Good Regulator of a System Must Be a Model of That System , 1970 .

[84]  U. Seifert Stochastic thermodynamics, fluctuation theorems and molecular machines , 2012, Reports on progress in physics. Physical Society.

[85]  Michael P. Sheetz,et al.  Appreciating force and shape — the rise of mechanotransduction in cell biology , 2014, Nature Reviews Molecular Cell Biology.

[86]  H. Maturana,et al.  Autopoiesis and Cognition , 1980 .

[87]  Michael Levin,et al.  The wisdom of the body: future techniques and approaches to morphogenetic fields in regenerative medicine, developmental biology and cancer. , 2011, Regenerative medicine.

[88]  T. Morgan Growth and regeneration in Planaria lugubris , 2015, Archiv für Entwicklungsmechanik der Organismen.