- Open Access
Landscape of Target State Directed Processes in Living Systems
PRX Life 4, 013024 – Published 24 February, 2026
DOI: https://doi.org/10.1103/cx3h-q6d2
Abstract
Across the living world, from cell biology and morphogenesis to behavior and cognition, system dynamics is often directed towards functionally important goals one might call target states. Dynamics towards such target states is never in equilibrium, always nonstationary, and typically swiftly over, terminating once the goal is reached. Complicating data-driven inference of effective dynamical models, trajectories of living systems homing in on target states are not only brief but exhibit initial conditions that are typically intrinsically heterogeneous and experimentally uncontrollable. Here we present a reverse-time stochastic dynamics theory for ensembles of target state directed processes, and uncover a universal space-time core of such ensembles that is independent of initial conditions. Examining this core structure, we demonstrate that the landscape of conceivable target state directed dynamics decomposes into sectors that qualitatively differ in their accessibility to data-driven inference. Ensembles undergo a phase transition from an opaque phase, in which their space-time core is uninformative about the underlying dynamics, to a transparent phase, in which the dynamics is directly encoded in its low-order moment functions. We use the reverse-time theory of target state directed processes to construct core-based methods for data-driven dynamical systems inference. Applied to biophysical models of cytokinesis, a directed process for which different regimes of actomyosin turnover cause distinct types of terminal dynamics, our approach precisely identifies the underlying molecular mechanism. Reverse-time ensemble theory establishes a versatile toolset for understanding target state directed biological processes and reveals that the mere presence of target states in living systems induces a novel kind of phase transition in dynamical systems inference.
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References (93)
- W. Sha, J. Moore, K. Chen, A. D. Lassaletta, C.-S. Yi, J. J. Tyson, and J. C. Sible, Hysteresis drives cell-cycle transitions in Xenopus laevis egg extracts, Proc. Natl. Acad. Sci. USA 100, 975 (2003).
- J. R. Pomerening, E. D. Sontag, and J. E. Ferrell, Jr., Building a cell cycle oscillator: Hysteresis and bistability in the activation of , Nat. Cell Biol. 5, 346 (2003).
- D. Coudreuse and P. Nurse, Driving the cell cycle with a minimal CDK control network, Nature (London) 468, 1074 (2010).
- S. Rata, M. F. S. P. Rodriguez, S. Joseph, N. Peter, F. E. Iturra, F. Yang, A. Madzvamuse, J. G. Ruppert, K. Samejima, M. Platani, et al., Two interlinked bistable switches govern mitotic control in mammalian cells, Curr. Biol. 28, 3824 (2018).
- C. Schwarz, A. Johnson, M. Kõivomägi, E. Zatulovskiy, C. J. Kravitz, A. Doncic, and J. M. Skotheim, A precise CDK activity threshold determines passage through the restriction point, Mol. Cell 69, 253 (2018).
- M. R. Domingo-Sananes, O. Kapuy, T. Hunt, and B. Novak, Switches and latches: A biochemical tug-of-war between the kinases and phosphatases that control mitosis, Philos. Trans. R. Soc. B 366, 3584 (2011).
- I. Nachman, A. Regev, and S. Ramanathan, Dissecting timing variability in yeast meiosis, Cell 131, 544 (2007).
- A. B. Pardee, A restriction point for control of normal animal cell proliferation, Proc. Natl. Acad. Sci. USA 71, 1286 (1974).
- R. Ahrends, A. Ota, K. M. Kovary, T. Kudo, B. O. Park, and M. N. Teruel, Controlling low rates of cell differentiation through noise and ultrahigh feedback, Science 344, 1384 (2014).
- H. Maamar, A. Raj, and D. Dubnau, Noise in gene expression determines cell fate in Bacillus subtilis, Science 317, 526 (2007).
- W. Xiong and J. E. Ferrell Jr, A positive-feedback-based bistable ‘memory module' that governs a cell fate decision, Nature (London) 426, 460 (2003).
- R. Losick and C. Desplan, Stochasticity and cell fate, Science 320, 65 (2008).
- G. Balázsi, A. van Oudenaarden, and J. J. Collins, Cellular decision making and biological noise: From microbes to mammals, Cell 144, 910 (2011).
- D. P. Hanes and J. D. Schall, Neural control of voluntary movement initiation, Science 274, 427 (1996).
- T. D. Hanks, C. D. Kopec, B. W. Brunton, C. A. Duan, J. C. Erlich, and C. D. Brody, Distinct relationships of parietal and prefrontal cortices to evidence accumulation, Nature (London) 520, 220 (2015).
- R. Ratcliff and G. McKoon, The diffusion decision model: Theory and data for two-choice decision tasks, Neural Comput. 20, 873 (2008).
- M. N. Shadlen and R. Kiani, Decision making as a window on cognition, Neuron 80, 791 (2013).
- B. W. Brunton, M. M. Botvinick, and C. D. Brody, Rats and humans can optimally accumulate evidence for decision-making, Science 340, 95 (2013).
- J. D. Roitman and M. N. Shadlen, Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task, J. Neurosci. 22, 9475 (2002).
- Q. Zhang, J. Brujić, and E. Vanden-Eijnden, Reconstructing free energy profiles from nonequilibrium relaxation trajectories, J. Stat. Phys. 144, 344 (2011).
- C. Dellago, P. G. Bolhuis, F. S. Csajka, and D. Chandler, Transition path sampling and the calculation of rate constants, J. Chem. Phys. 108, 1964 (1998).
- K. A. Dill, S. B. Ozkan, M. S. Shell, and T. R. Weikl, The protein folding problem, Annu. Rev. Biophys. 37, 289 (2008).
- K. A. Dill and J. L. MacCallum, The protein-folding problem, 50 years on, Science 338, 1042 (2012).
- K. Kappel, Y. Miao, and J. A. McCammon, Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor, Q. Rev. Biophys. 48, 479 (2015).
- J. D. Durrant and J. A. McCammon, Molecular dynamics simulations and drug discovery, BMC Biol. 9, 71 (2011).
- R. J. Allen, C. Valeriani, and P. R. Ten Wolde, Forward flux sampling for rare event simulations, J. Phys.: Condens. Matter 21, 463102 (2009).
- S. W. P. Turner, M. Cabodi, and H. G. Craighead, Confinement-induced entropic recoil of single DNA molecules in a nanofluidic structure, Phys. Rev. Lett. 88, 128103 (2002).
- L. Harker-Kirschneck, A. E. Hafner, T. Yao, C. Vanhille-Campos, X. Jiang, A. Pulschen, F. Hurtig, D. Hryniuk, S. Culley, R. Henriques, et al., Physical mechanisms of ESCRT-III–driven cell division, Proc. Natl. Acad. Sci. USA 119, e2107763119 (2022).
- F. Brauns, N. H. Claussen, M. F. Lefebvre, E. F. Wieschaus, and B. I. Shraiman, The geometric basis of epithelial convergent extension, eLife 13, RP95521 (2024).
- L. Deecke, B. Grözinger, and H. Kornhuber, Voluntary finger movement in man: Cerebral potentials and theory, Biol. Cybern. 23, 99 (1976).
- B. D. O. Anderson, Reverse-time diffusion equation models, Stoch. Proc. Appl. 12, 313 (1982).
- C. Jarzynski, Nonequilibrium equality for free energy differences, Phys. Rev. Lett. 78, 2690 (1997).
- G. E. Crooks, Path-ensemble averages in systems driven far from equilibrium, Phys. Rev. E 61, 2361 (2000).
- M. R. Frater, R. R. Bitmead, R. A. Kennedy, and B. D. Anderson, Rare events and reverse-time models, in Proceedings of the 28th IEEE Conference on Decision and Control (IEEE, 1989), pp. 1180–1183.
- J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, Deep unsupervised learning using nonequilibrium thermodynamics, in International Conference on Machine Learning (PMLR, 2015), pp. 2256–2265.
- Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, Score-based generative modeling through stochastic differential equations, in International Conference on Learning Representations (2021).
- C. W. Gardiner, Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences, Springer Series in Synergetics, Vol. 13 (Springer, Berlin, 1985).
- M. Zak, Terminal attractors in neural networks, Neural Netw. 2, 259 (1989).
- R. J. Pelham, Jr. and F. Chang, Actin dynamics in the contractile ring during cytokinesis in fission yeast, Nature (London) 419, 82 (2002).
- C. Bertet, L. Sulak, and T. Lecuit, Myosin-dependent junction remodelling controls planar cell intercalation and axis elongation, Nature (London) 429, 667 (2004).
- R. Clément, B. Dehapiot, C. Collinet, T. Lecuit, and P.-F. Lenne, Viscoelastic dissipation stabilizes cell shape changes during tissue morphogenesis, Curr. Biol. 27, 3132 (2017).
- L. M. Ricciardi and S. Sato, First-passage-time density and moments of the Ornstein-Uhlenbeck process, J. Appl. Probab. 25, 43 (1988).
- S. Redner, A Guide to First-passage Processes (Cambridge University Press, New York, 2001).
- A. J. Bray, Random walks in logarithmic and power-law potentials, nonuniversal persistence, and vortex dynamics in the two-dimensional XY model, Phys. Rev. E 62, 103 (2000).
- E. Martin, U. Behn, and G. Germano, First-passage and first-exit times of a Bessel-like stochastic process, Phys. Rev. E 83, 051115 (2011).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/cx3h-q6d2 for details on calculations, which include Refs. [74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92].
- O. G. Berg, R. B. Winter, and P. H. Von Hippel, Diffusion-driven mechanisms of protein translocation on nucleic acids. 1. Models and theory, Biochemistry 20, 6929 (1981).
- J. Gorman, F. Wang, S. Redding, A. J. Plys, T. Fazio, S. Wind, E. E. Alani, and E. C. Greene, Single-molecule imaging reveals target-search mechanisms during DNA mismatch repair, Proc. Natl. Acad. Sci. USA 109, E3074 (2012).
- T. Schötz, R. A. Neher, and U. Gerland, Target search on a dynamic DNA molecule, Phys. Rev. E 84, 051911 (2011).
- J. Hettich and J. C. M. Gebhardt, Transcription factor target site search and gene regulation in a background of unspecific binding sites, J. Theor. Biol. 454, 91 (2018).
- S. N. Majumdar and H. Orland, Effective Langevin equations for constrained stochastic processes, J. Stat. Mech. (2015) P06039.
- G. Wilemski and M. Fixman, General theory of diffusion-controlled reactions, J. Chem. Phys. 58, 4009 (1973).
- D. Holcman, A. Marchewka, and Z. Schuss, Survival probability of diffusion with trapping in cellular neurobiology, Phys. Rev. E 72, 031910 (2005).
- Z. Schuss, Brownian Dynamics at Boundaries and Interfaces (Springer, New York, 2015).
- R. Erban and S. J. Chapman, Reactive boundary conditions for stochastic simulations of reaction–diffusion processes, Phys. Biol. 4, 16 (2007).
- A. J. Berglund, Statistics of camera-based single-particle tracking, Phys. Rev. E 82, 011917 (2010).
- A. Zumdieck, K. Kruse, H. Bringmann, A. A. Hyman, and F. Jülicher, Stress generation and filament turnover during actin ring constriction, PLoS ONE 2, e696 (2007).
- J. S. Speagle, dynesty: A dynamic nested sampling package for estimating Bayesian posteriors and evidences, Mon. Not. R. Astron. Soc. 493, 3132 (2020).
- R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, Neural ordinary differential equations, in Advances in Neural Information Processing Systems, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Vol. 31 (Curran Associates, Inc., 2018).
- A. Gu, K. Goel, and C. Ré, Efficiently modeling long sequences with structured state spaces, arXiv:2111.00396.
- V. Wollrab, R. Thiagarajan, A. Wald, K. Kruse, and D. Riveline, Still and rotating myosin clusters determine cytokinetic ring constriction, Nat. Commun. 7, 11860 (2016).
- H. Turlier, B. Audoly, J. Prost, and J.-F. Joanny, Furrow constriction in animal cell cytokinesis, Biophys. J. 106, 114 (2014).
- J. M. Sanger, J. W. Sanger, and F. S. Southwick, Host cell actin assembly is necessary and likely to provide the propulsive force for intracellular movement of Listeria monocytogenes, Infect. Immun. 60, 3609 (1992).
- L. A. Cameron, P. A. Giardini, F. S. Soo, and J. A. Theriot, Secrets of actin-based motility revealed by a bacterial pathogen, Nat. Rev. Mol. Cell Biol. 1, 110 (2000).
- M. Rauzi, P.-F. Lenne, and T. Lecuit, Planar polarized actomyosin contractile flows control epithelial junction remodelling, Nature (London) 468, 1110 (2010).
- R. Levayer and T. Lecuit, Biomechanical regulation of contractility: spatial control and dynamics, Trends Cell Biol. 22, 61 (2012).
- H. Mori, Transport, collective motion, and Brownian motion, Prog. Theor. Phys. 33, 423 (1965).
- R. Zwanzig, Memory effects in irreversible thermodynamics, Phys. Rev. 124, 983 (1961).
- N. Brunel, F. S. Chance, N. Fourcaud, and L. F. Abbott, Effects of synaptic noise and filtering on the frequency response of spiking neurons, Phys. Rev. Lett. 86, 2186 (2001).
- R. Moreno, J. de la Rocha, A. Renart, and N. Parga, Response of spiking neurons to correlated inputs, Phys. Rev. Lett. 89, 288101 (2002).
- N. Brunel and P. E. Latham, Firing rate of the noisy quadratic integrate-and-fire neuron, Neural Comput. 15, 2281 (2003).
- R. Moreno-Bote and N. Parga, Response of integrate-and-fire neurons to noisy inputs filtered by synapses with arbitrary timescales: Firing rate and correlations, Neural Comput. 22, 1528 (2010).
- A. Berezhkovskii and A. Szabo, Time scale separation leads to position-dependent diffusion along a slow coordinate, J. Chem. Phys. 135, 074108 (2011).
- I. M. Sokolov, Statistical mechanics of entropic forces: Disassembling a toy, Eur. J. Phys. 31, 1353 (2010).
- U. Seifert, Entropy production along a stochastic trajectory and an integral fluctuation theorem, Phys. Rev. Lett. 95, 040602 (2005).
- H. Föllmer, Time reversal on Wiener space, in Stochastic Processes in Mathematics and Physics (Springer, Berlin, Heidelberg, 1986), pp. 119–129.
- M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables (Courier Corporation, New York, 1964), Vol. 55.
- M. El Beheiry, M. Dahan, and J.-B. Masson, InferenceMAP: Mapping of single-molecule dynamics with Bayesian inference, Nat. Methods 12, 594 (2015).
- J.-B. Masson, P. Dionne, C. Salvatico, M. Renner, C. G. Specht, A. Triller, and M. Dahan, Mapping the energy and diffusion landscapes of membrane proteins at the cell surface using high-density single-molecule imaging and Bayesian inference: Application to the multiscale dynamics of glycine receptors in the neuronal membrane, Biophys. J. 106, 74 (2014).
- S. Tuerkcan, A. Alexandrou, and J.-B. Masson, A Bayesian inference scheme to extract diffusivity and potential fields from confined single-molecule trajectories, Biophys. J. 102, 2288 (2012).
- J.-B. Masson, M. Dahan, and A. Triller, Pitfalls in the quantitative analysis of random walks and the mapping of single-molecule dynamics at the cellular scale, arXiv:1502.07285.
- C. Dargatz, Bayesian inference for diffusion processes with applications in life sciences, Ph.D. thesis, lmu, 2010.
- G. B. Durham and A. R. Gallant, Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes, J. Bus. Econ. Stat. 20, 297 (2002).
- A. S. Hurn, J. Jeisman, and K. A. Lindsay, Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations, J. Financ. Econom. 5, 390 (2007).
- T. Ozaki, Statistical identification of storage models with application to stochastic hydrology 1, J. Am. Water Res. Assoc. 21, 663 (1985).
- M. Kessler, Estimation of an ergodic diffusion from discrete observations, Scand. J. Stat. 24, 211 (1997).
- O. Elerian et al., A note on the existence of a closed form conditional transition density for the Milstein scheme, Econ. Discuss. Pap. W18 (1998).
- Y. Aït-Sahalia, Maximum likelihood estimation of discretely sampled diffusions: A closed-form approximation approach, Econometrica 70, 223 (2002).
- Y. Ait-Sahalia et al., Closed-form likelihood expansions for multivariate diffusions, Ann. Stat. 36, 906 (2008).
- A. Beskos, O. Papaspiliopoulos, G. O. Roberts, and P. Fearnhead, Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion), J. R. Stat. Soc.: Ser. B. 68, 333 (2006).
- J. Skilling, Nested sampling, Bayesian Infer. Max. Entropy Meth. Sci. Eng. 735, 395 (2004).
- R. M. Ziff, S. N. Majumdar, and A. Comtet, Capture of particles undergoing discrete random walks, J. Chem. Phys. 130, 204104 (2009).
- https://github.com/MathHerring/ReverseTimeInference.