The Structure of Ontogenies in a Model Protocell

Emergent individuals are often characterized with respect to their viability: their ability to maintain themselves and persist in variable environments. As such individuals interact with an environment, they undergo sequences of structural changes that correspond to their ontogenies. Ultimately, individuals that adapt to their environment, and increase their chances of survival, persist. This article provides an initial step towards a more formal treatment of these concepts. A network of possible ontogenies is uncovered by subjecting a model protocell to sequential perturbations and mapping the resulting structural configurations. The analysis of this network reveals trends in how the protocell can move between configurations, how its morphology changes, and how the role of the environment varies throughout. Viability is defined as expected life span given an initial configuration. This leads to two notions of adaptivity: a local adaptivity that addresses how viability changes in plastic transitions, and a global adaptivity that looks at longer-term tendencies for increased viability. To demonstrate how different protocell-environment pairings produce different patterns of ontogenic change, we generate and analyze a second ontogenic network for the same protocell in a different environment. Finally, the mechanisms of a minimal adaptive transition are analyzed, and it is shown that these rely on distributed spatial processes rather than an explicit regulatory mechanism. The combination of this model and analytical techniques provides a foundation for studying the emergence of viability, ontogeny, and adaptivity in more biologically realistic systems.

[1]  W. Ashby Design for a Brain , 1954 .

[2]  Kenneth L. Artis Design for a Brain , 1961 .

[3]  H. Maturana,et al.  Autopoiesis: the organization of living systems, its characterization and a model. , 1974, Currents in modern biology.

[4]  H. Maturana,et al.  Autopoiesis and Cognition : The Realization of the Living (Boston Studies in the Philosophy of Scie , 1980 .

[5]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[6]  W. Fontana,et al.  “The arrival of the fittest”: Toward a theory of biological organization , 1994 .

[7]  Charles M. Grinstead,et al.  Introduction to probability , 1986, Statistics for the Behavioural Sciences.

[8]  Takashi Ikegami,et al.  Model of Self-Replicating Cell Capable of Self-Maintenance , 1999, ECAL.

[9]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[10]  John N. Tsitsiklis,et al.  Introduction to Probability , 2002 .

[11]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[12]  E. D. Paolo,et al.  Autopoiesis, Adaptivity, Teleology, Agency , 2005 .

[13]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[14]  F. Mavelli,et al.  Stochastic simulations of minimal self-reproducing cellular systems , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Tim J. Hutton,et al.  Evolvable Self-Reproducing Cells in a Two-Dimensional Artificial Chemistry , 2007, Artificial Life.

[16]  Xabier E. Barandiaran,et al.  Adaptivity: From Metabolism to Behavior , 2008, Adapt. Behav..

[17]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[18]  Ezequiel A. Di Paolo,et al.  Integrating Autopoiesis and Behavior: An Exploration in Computational Chemo-ethology , 2009, Adapt. Behav..

[19]  N. Virgo Thermodynamics and the structure of living systems , 2011 .

[20]  J. Kruschke Bayesian estimation supersedes the t test. , 2013, Journal of experimental psychology. General.

[21]  Randall D. Beer,et al.  The Cognitive Domain of a Glider in the Game of Life , 2014, Artificial Life.

[22]  Eran Agmon,et al.  Quantifying Robustness in a Spatial Model of Metabolism-Boundary Co-Construction , 2014, ALIFE.

[23]  Xabier E. Barandiaran,et al.  Norm-Establishing and Norm-Following in Autonomous Agency , 2014, Artificial Life.

[24]  Randall D. Beer,et al.  Characterizing Autopoiesis in the Game of Life , 2015, Artificial Life.

[25]  K. Ruiz-Mirazo,et al.  Biological regulation: controlling the system from within , 2016 .

[26]  Randall D. Beer,et al.  Exploring the Space of Viable Configurations in a Model of Metabolism–Boundary Co-construction , 2016, Artificial Life.