III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet Lifescape
1. A Consilience of Biology and Physics: Active Matter
Cheng, Zhao, et al. Pattern Phase Transitions of Self-Propelled Particles. New Journal of Physics. 18/10, 2016. Huazhong University of Science and Technology, China, University of Newcastle, Australia, and Eotvos University, Hungary (Tamas Viczek) systems physicists advance our understanding of how internally motive entities interact together with a dynamic, formative consistence.
To understand the collective behaviors of biological swarms, flocks, and colonies, we investigated the non-equilibrium dynamic patterns of self-propelled particle systems using statistical mechanics methods and H-stability analysis of Hamiltonian systems. By varying the individual vision range, we observed phase transitions between four phases, i.e., gas, crystal, liquid, and mill-liquid coexistence patterns. In addition, by varying the inter-particle force, we detected three distinct milling sub-phases, i.e., ring, annulus, and disk. Based on the coherent analysis for collective motions, one may predict the stability and adjust the morphology of the phases of self-propelled particles, which has promising potential applications in natural self-propelled particles and artificial multi-agent systems. (Abstract)
Choi, Jeong-Mo, et al. Physical Principles Underlying the Complex Biology of Intracellular Phase Transitions. Annual Review of Biophysics. 49/107, 2020. Washington University, St. Louis biomedical scientists describe and illustrate another way that life’s intrinsic genetic, metabolic vitality can be traced to, rooted in and manifestly exhibit this substantial phenomena.
Many biomolecular condensates appear to form via spontaneous or driven processes that have the hallmarks of intracellular phase transitions. This suggests that a common underlying physical framework might govern the formation of functionally and compositionally unrelated compositions. In this review, we summarize recent work that leverages a stickers-and-spacers framework adapted from the field of associative polymers for understanding how multivalent protein and RNA molecules drive phase transitions that give rise to biomolecular condensates. (Abstract excerpt)
Cichos, Frank, et al. Machine Learning for Active Matter. Nature Machine Intelligence. February, 2020. As many studies herein find that the movements of living systems from colloids and microbes to birds and people seem to be guided by and exhibit common self-organizing patterns, Leipzig University and University of Gothenburg add a further AI technique. An opening graphic cites molecular motors, turbulence, living crystals, growing tissues, chemotaxis, foraging, swimming, clustering and more, while a second shows convolutional neural nets, reservoir computing, genetic algorithms and other skill-sets. In regard, as brain-based deep AI gains wide, analytic utility, evinced by this application to an lively materiality, another window upon a natural genesis which avails and repeats the same iconic, bicameral, triality code everywhere is opened.
Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random forests to analyse time series. Yet machine learning can also help with the complexity of biological active matter, to establish a relation between the genetic code and bacterial behaviour, navigation strategies in complex environments, and to map physical cues onto animal behaviours. In this Review, we highlight the current state of the art and discuss opportunities and challenges. (Abstract excerpt)
Cocco, Simona, et al. Inverse Statistical Physics of Protein Sequences. arXiv:1703.01222. Sorbonne University, Paris, computational theorists post a “Key Issues Review” of humanity’s project, by way of translating and clarifying terms and concepts, to realize nature’s animate and physical systems as a singular, self-conceiving uniVerse.
In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. (Abstract)
Coveney, Peter, et al. Bridging the Gaps at the Physics-Chemistry-Biology Interface. Philosophical Transactions of the Royal Society A. Vol. 374/Iss. 2080, 2016. Senior theorists Coveney (search), University College London, with Jean-Pierre Boon, Free University of Brussels, and Sauro Succi, Harvard, introduce a subject issue of papers from an April 2016 Solvay Workshop in Belgium. An opener, Big Data Need Big Theory Too by Coveney, et al, argues that myriad pieces have little worth without a coherent model to make sense of them. Further technical entries are Kinetics and Thermodynamics of Living Copolymerization, Multiscale Simulation of Molecular Processes in Cellular Environments, and Chimera Simulation of Complex States of Flowing Matter. The edition appears concurrently with The Science of Complexity and the Role of Mathematics in the European Physical Journal Special Topics. See also Complex Systems: Physics Beyond Physics by Yurij Holovatch, Ralph Kenna, and Stefan Thurner at arXiv.1610.01002 for another synthesis.
It is commonly agreed that the most challenging problems in modern science and engineering involve the concurrent and nonlinear interaction of multiple phenomena, acting on a broad and disparate spectrum of scales in space and time. It is also understood that such phenomena lie at the interface between different disciplines, such as physics, chemistry, material science and biology. The multiscale and multi-level nature of these problems commands a paradigm shift in the way they need to be handled, both conceptually and in terms of the corresponding problem-solving computational tools. The above phenomena take place far from equilibrium, where the organizing power of nonlinearity is fully exposed and macroscopic universality is compromised by the necessary degrees of microscopic (molecular) individualism. Indeed, the ability to integrate universality and molecular individualism is perhaps the most challenging task of modern multiscale modelling. At the same time, the recent decades have also witnessed substantial progress in the development of modelling methodologies at all scales, including, for example, ab initio molecular dynamics and quantum mechanics/molecular mechanics techniques for atomic and nano-scales, and dissipative particle dynamics for mesoscales, and various grid-based methods for the several macroscales. (Abstract)
Crosato, Emanuele, at al. Thermodynamics of Emergent Structure in Active Matter. Physical Review E. Online October, 2019. Nine years after this title phrase came about, University of Sydney, Complex Systems Research Group theorists EC, Mikhail Prokopenko, and Richard Spinney quantify energetic properties that further distinguish this spontaneously animate materiality. As the quotes say, as 2020 near, our website survey is well able to report a truly organic, fertile ecosmos from which phenomenal persons arise, awaken and discover.
Active matter is rapidly becoming a key paradigm of out-of-equilibrium soft matter exhibiting complex collective phenomena, yet the thermodynamics of such systems remain poorly understood. In this letter we study the nonequilbrium thermodynamics of large scale active systems capable of mobility-induced phase separation and polar alignment, using a fully under-damped model which exhibits hidden entropy productions not previously reported in the literature. We quantify steady state entropy production at each point in the phase diagram, revealing characteristic dissipation rates associated with the distinct phases and configurational structure. This reveals sharp discontinuities in the entropy production at phase transitions and facilitates identification of the thermodynamics of micro-features, such as defects in the emergent structure. (Abstract)
De Magistris, Giulio and Davide Marenduzzo. An Introduction to the Physics of Active Matter. Physica A. Online July, 2014. As the Abstract notes, University of Edinburgh physicists offer a tutorial upon the intrinsic dynamics of “active particles,” self-motile systems, hydrodynamics of active gels, and so on.
In these notes we provide an introductory description of the physics of active matter, focusing on theoretical aspects, and on some methods which are often used in the field. We discuss a selection of active systems, where activity comes from different microscopic sources (mainly self-replication, self-propulsion, non-thermal forces), and in all cases we focus on their statistical physics and emergent collective behaviour, which is often linked to underlying nonequilibrium phase transitions. We hope to convey the idea that this field is a fascinating growing area of research at the interphase between statistical, soft matter and biological physics, and that active matter systems can possess, in general, a much richer physics than their passive counterparts. (Abstract)
Deem, Michael. Statistical Mechanics of Modularity and Horizontal Gene Transfer. Annual Review of Condensed Matter Physics. 4/287, 2013. The Rice University biophysicist is a leading advocate of and researcher for the cross-fertilization of these seemingly disparate fields. As an increasing number realize and profess, a newly spontaneous physical matter can join with and serve as a fertile source for emergent biological vitality.
Biological structure organizes over evolutionary timescales. This review discusses the spontaneous emergence of hierarchical structure in biology as a result of environmental change. A body of theoretical and experimental work on evolutionary dynamics is reviewed, and a theory for these results based on a principle of least action is discussed. The structure that has emerged in biology is complementary to a type of evolutionary dynamics known as horizontal gene transfer. How horizontal gene transfer ameliorates the difficulty that finite populations would otherwise have to evolve on rugged fitness landscapes is also discussed. (Abstract)
Deng, Pan, et al. The Ecological Basis of Morphogenesis: Branching Patterns in Swarming Colonies of Bacteria.. New Journal of Physics. 16/015006, 2014. In a Focus on the Physics of Biofilms section, Memorial Sloan-Kettering Cancer Center researchers offer another contribution, by way of microbial assemblies, that joins life’s communal developments with physical substrates and mathematical principles. And it is worth noting that this Institute of Physics IOP periodical, as others, contains a good percentage of articles on biological and complexity phenomena, as scientific pursuits now reconverge.
Understanding how large-scale shapes in tissues, organs and bacterial colonies emerge from local interactions among cells and how these shapes remain stable over time are two fundamental problems in biology. Here we investigate branching morphogenesis in an experimental model system, swarming colonies of the bacterium Pseudomonas aeruginosa. We combine experiments and computer simulation to show that a simple ecological model of population dispersal can describe the emergence of branching patterns. In our system, morphogenesis depends on two counteracting processes that act on different length-scales: (i) colony expansion, which increases the likelihood of colonizing a patch at a close distance and (ii) colony repulsion, which decreases the colonization likelihood over a longer distance. The two processes are included in a kernel-based mathematical model using an integro-differential approach borrowed from ecological theory. (Abstract)
Diaz, Jorge and Roberto Mulet. Statistical Mechanics of Interacting Metabolic Networks. Physical Review E. 101/042401, 2020. A University of Havana systems biologist and a physicist discern an array of affinities between cellular processes and condensed matter as life’s complexity and animate cosmos proceed to reunite and grow together. See also Characterizing Steady States of Genome Scale Metabolic Networks in PLoS Computational Biology (November 2017) and A Physical Model of Cell Metabolism in Nature Scientific Reports (8/8349, 2018) by the authors and colleagues.
We cast the metabolism of interacting cells within a statistical mechanics framework with regard to the phenotypic capacities of each cell and its interaction with its neighbors. Reaction fluxes will be the components of spin vectors, whose values are constrained by stochiometry and energy requirements of the metabolism. Within this picture, the phenotypic states of the population are equivalent to the equilibrium states of a disordered spin model. We apply this solution to a simplified model of metabolism and a complex metabolic network, the central core of Escherichia coli, to demonstrate that the combination of selective pressure and interactions defines a complex phenotypic space. Cells may specialize in producing or consuming metabolites, which is described by an equilibrium phase space akin to a spin-glass model. (Abstract excerpt)
Dorogovtsev, Sergei, et al. Critical Phenomena in Complex Networks. Reviews of Modern Physics. 80/4, 2008. Universidade de Aveiro, Portugal physicists provide a detailed tutorial for the evident ubiquity of scale-free nets to persist in a state of self-organized criticality.
Critical phenomena in networks include a wide range of issues: structural changes in networks, the emergence of critical—scale-free—network architectures, various percolation phenomena, epidemic thresholds, phase transitions in cooperative models defined on networks, critical points of diverse optimization problems, transitions in co-evolving couples—a cooperative model and its network substrate, transitions between different regimes in processes taking place on networks, and many others. We will show that many of these critical effects are closely related and universal for different models and may be described and explained in the framework of a unified approach. (1277)
Eckmann, Jean-Pierre, et al. Proteins: The Physics of Amorphous Evolving Matter. Reviews of Modern Physics. 91/031001, 2019. J-P E and Jacques Rougemont, University of Geneva and Tsvi Tlusty, Ulsan National Institute of Science and Technology, post a tutorial paper which traces a pathway by which to join and root life’s biochemical processes within fundamental condensed matter principles. In this computational view, proteins arise from collective many-body interactions in amino acid matter as the outcome of an evolutionary search in a high-dimensional space of gene sequences. In regard, an evolutionary learning process is seen to act as a combinatorial search within an optimization process. See also Physical Model of the Genotype to Phenotype Map of Proteins by the authors with Albert Libchaber in Physical Review X (7/021037, 2017). These and many other insightful efforts are presently revealing a unified, lively ovoGenesis uniVerse.
Proteins are a matter of dual nature. As a physical object, a protein molecule is a folded chain of amino acids with multifarious biochemistry. But it is also an instantiation along an evolutionary trajectory determined by the function performed by the protein within a hierarchy of interwoven interaction networks of the cell, the organism and the population. A physical theory of proteins therefore needs to unify both the biophysical and the evolutionary. We review physical approaches by way of a mechanical framework which treats proteins as evolvable condensed matter: Mutations introduce localized perturbations in the gene, which are similarly translated into the protein matter. A natural tool seems to be Green's functions (Wikipedia)as they map the evolutionary linkage among mutations in the gene to cooperative physical interactions among the amino acids. (Abstract excerpt)