VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
A. A Further Report of Common Principles
Sole, Ricard and Sergi Valverde. Macroevolution in Silico: Scales, Constraints and Universals. Santa Fe Institute Working Papers. 12-11-019, November, 2012. Barcelona systems biologists continue their project to gain quantified insights into life’s iterative, sequential advance into somatic and behavioral complexities.
Large-scale evolution involves several layers of complexity spanning multiple scales, from genes and organisms to whole ecosystems. In this paper we review several models involving the macroevolution of artificial organisms, communities or ecosystems, highlighting their importance and potential role in expanding the modern synthesis. Afterwards, we summarize the key results obtained from our model of artificially evolved ecosystems where individuals are defined as embodied entities within a physical, simulated world where they can evolve different traits and exploit multiple resources. Starting from an initial state where single cells with identical genotypes are present, the system evolves towards complex communities where the feedbacks between population expansion, evolved cell adhesion and the structure of the environment leads to a major innovation resulting from the emergence of ecosystem engineering. The tempo and mode of this process illustrates the relevance in considering a physical embedding as part of the model description, and the feedbacks between different scales within the evolutionary hierarchy. (Abstract)
Song, Chaoming, et al.
Self-Similarity of Complex Networks.
The power-law scaling which distinguishes small world networks is found to exhibit a self-repeating fractal geometry. This is achieved by a renormalization procedure that ‘coarse-grains’ the system into a nest of nodes and modules. This novel work is introduced by Steven Strogatz in the same issue (365) with the title Romanesque Networks since this property is ideally displayed by broccoli of this name.
Here we show that real complex networks, such as the world-wide web, social, protein-protein interaction networks and cellular networks are invariant or self-similar under a length-scale transformation. (392) These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics. (392)
Stanley, Eugene. Universality and Scale Invariance: Organizing Principles that Transcend Disciplines. www.societyforchaostheory.org/conf2003/abstracts.html. A keynote paper presented at the annual Society for Chaos Theory and Psychology Conference. The Boston University systems physicist describes how the same power-law behavior is being found in widely diverse realms from statistical physics to the “econophysics” of financial markets.
Stephens, Greg, et al. Searching for Simplicity: Approaches to the Analysis of Neurons and Behavior. arXiv:1012.3896. Posted in December 2010. By virtue of such many advances as this site tries to report, we altogether seem at the edge of some revelatory evidence, closer to history’s great secret answer. Geneticist Stephens and physicist William Bialek, Princeton University, and neurobiologist Leslie Osborne, University of Chicago, enter still another glimpse of constant analogs across widely diverse phenomena, ever suggestive of an implicate, iterative, genome-like source.
What fascinates us about animal behavior is its richness and complexity, but understanding behavior and its neural basis requires a simpler description. An alternative is to ask whether we can search through the dynamics of natural behaviors to and explicit evidence that these behaviors are simpler than they might have been. We review two mathematical approaches to simplification, dimensionality reduction and the maximum entropy method, and we draw on examples from different levels of biological organization, from the crawling behavior of C. elegans to the control of smooth pursuit eye movements in primates, and from the coding of natural scenes by networks of neurons in the retina to the rules of English spelling. In each case, we argue that the explicit search for simplicity uncovers new and unexpected features of the biological system, and that the evidence for simplification gives us a language with which to phrase new questions for the next generation of experiments. The fact that similar mathematical structures succeed in taming the complexity of very different biological systems hints that there is something more general to be discovered. (Abstract)
Strassmann, Joan, et al. In the Light of Evolution V: Cooperation and Conflict. Proceedings of the National Academy of Sciences. Supplement 2, 2011. With co-authors David Queller, John Avise, and Francisco Ayala, an Introduction to this edition in the title series in search and support of an evolutionary vision, namely in the light of Theodosius Dobzhansky. We note in this section to record its witness of a universal principle, in this case a creative tension or union between entity and group, autonomy and assembly, that spans life’s emergence. Papers cover selfish and cooperative genes, bacterial symbiosis, kinship in social insects, animal troops, hominid clans, and onto human adaptations via collective learning.
Swarup, Samarth and Les Gasser. Unifying Evolutionary and Network Dynamics. Physical Review E. 75/066114, 2007. University of Illinois computer scientists propose that the two realms noted in the article share common features and that a cross-fertilization between them would be beneficial.
Szabo, Gyorgy and Gabor Fath. Evolutionary Games on Graphs. Physics Reports. 446/4-6, 2007. An extensive tutorial on game theory as seen from a statistical physics viewpoint, which could be viewed as another encounter with, in translation, nature’s non-equilibrium, agent-based, self-organizing emergence.
Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games. (97)
Tao, Terence. E pluribus unum: From Complexity, Universality. Daedalus. 141/3, 2012. In a special issue on Science in the 21st Century, the UCLA polymathematician draws on the latest statistical physics to at last confirm this extant Nature is in fact grounded in and distinguished by an intelligible, recurrent, programmatic quality. Whatever the material realm or substrate the same phenomenal organization appears due to the constant interactions of many communicative entities. As a result, it becomes strongly evident that such an independent formative source or agency must be involved. And how could one not notice, quite fittingly, here anew is the very essence of our archetypal Taoist wisdom.
In this brief survey, I discuss some examples of the fascinating phenomenon of universality in complex systems, in which universal macroscopic laws of nature emerge from a variety of different microscopic dynamics. This phenomenon is widely observed empirically, but the rigorous mathematical foundation for universality is not yet satisfactory in all cases. (Abstract, 23)
Tauber, Uwe. Critical Dynamics: A Field Theory Approach to Equilibrium and Non-Equilibrium Scaling Behavior. Cambridge: Cambridge University Press, 2014. A Virginia Tech physicist contributes a technical text that well quantifies the latest and deepest theoretical basis for nature’s self-similar “scale invariance” from physical to planetary realms.
Introducing a unified framework for describing and understanding complex interacting systems common in physics, chemistry, biology, ecology, and the social sciences, this comprehensive overview of dynamic critical phenomena covers the description of systems at thermal equilibrium, quantum systems, and non-equilibrium systems. Powerful mathematical techniques for dealing with complex dynamic systems are carefully introduced, including field-theoretic tools and the perturbative dynamical renormalization group approach, rapidly building up a mathematical toolbox of relevant skills. Heuristic and qualitative arguments outlining the essential theory behind each type of system are introduced at the start of each chapter, alongside real-world numerical and experimental data, firmly linking new mathematical techniques to their practical applications.
Tero, Atsushi, et al. Rules for Biologically Inspired Adaptive Network Design. Science. 327/439, 2010. Along with a news note “Amoeba-Inspired Network Design” in the same issue, how such microbial colonies can be seen as exemplars of self-organizing systems, which are being found to recur throughout nature’s ascendant nest, especially for viable human societies. All of which it is said seems to spring from an independent, universal mathematical source.
Troisi, Alessandro, et al. An Agent-based Approach for Modeling Molecular Self-organization. Proceedings of the National Academy of Sciences. 102/255, 2010. In another example from the recent nonlinear dynamics shift in physics, University of Bologna and Northwestern University researchers search ways to independently articulate and define the universally prevalent, nested complexity that is being found everywhere to distinguish a genesis nature.
Self-organization is one of the most fascinating phenomena in nature. It appears in such apparently disparate arenas as crystal growth, the regulation of metabolism, and dynamics of animal and human behavior. One of the great challenges in the field of complexity is the definition of the common patterns that make possible the emergence of order from apparently disordered systems. One example is given by the scale invariant networks that appear to offer a good perspective for many complex systems. Another possibility is the study of emergent phenomena through agent-based (AB) modeling. In this paper, after defining briefly the principles of AB modeling, we explore the possibility that such a modeling paradigm could be useful for the study of self-organizing chemical systems, complementing the currently used stochastic (Monte Carlo) or deterministic (molecular dynamics) methods. (255)
Tsuchiya, Masa, et al. Local and Global Responses in Complex Gene Regulation Networks. Physica A. 388/1738, 2009. Keio University, Japan, and Istituto Superiore di Sanita, Italy, researchers, including Kumar Selvarajoo and Alessandro Giuliani, employ a statistical physics approach to reveal a network paradigm an intrinsic genome-wide dynamical essence.
One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. (Abstract)