III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

2. A Consilience Unity as Physics, Biology and People Become One

Saclioglu, Cihan, et al. Group Behavior in Physical, Chemical and Biological Systems. Journal of Biosciences. 39/2, 2014. In an issue on Individuals and Groups (search S. Newman), biophysicists Saclioglu, Sabanci Universitesi, Istanbul with Onder Pekcan, Kadir Has Universitesi, Istanbul and Vidyanand Nanjundiah, Indian Institute of Science, Bangalore scope out how to situate and root in substantial nature, as must be the case, life’s persistent evolutionary formation of social assembles at each and every stage and instance. Section 2, for example, is Physical principles underlying collective behavior: Elementary particles and emergent macroscopic manifestations. Maybe in this Indian journal and milieu, an Eastern mind can better perceive how obvious this holistic unity must be. It goes on to a similar Group Behavior in Chemistry, Groups in Biology segment and more, altogether akin to 2017 papers by van Gestel/Tarnita and Sebe-Pedros, et al of a universal, independent recurrence from universe to us.

Groups exhibit properties that either are not perceived to exist, or perhaps cannot exist, at the individual level. Such ‘emergent’ properties depend on how individuals interact, both among themselves and with their surroundings. The world of everyday objects consists of material entities. These are, ultimately, groups of elementary particles that organize themselves into atoms and molecules, occupy space, and so on. It turns out that an explanation of even the most commonplace features of this world requires relativistic quantum field theory and the fact that Planck’s constant is discrete, not zero. Groups of molecules in solution, in particular polymers (‘sols’), can form viscous clusters that behave like elastic solids (‘gels’). Group behaviour among cells or organisms is often heritable and therefore can evolve. This permits an additional, typically biological, explanation for it in terms of reproductive advantage, whether of the individual or of the group. (Abstract excerpt)

Selesnick, Stephen, et al. Quantum-like Behavior without Quantum Physics. Journal of Biological Physics. Online July, 2017. As the Abstract notes, mathematician Selesnick, and physicist Gualtiero Piccinini, University of Missouri, with philosopher J. P. Rawling, Florida State University, consider ways that quantum effects, as they become better understood into the 21st century, can be noticed and quantified across classical phenomena such as neural activities.

Recently there has been much interest in the possible quantum-like behavior of the human brain in such functions as cognition, the mental lexicon, memory, etc., producing a vast literature. These studies are both empirical and theoretical, the tenets of the theory in question being mainly, and apparently inevitably, those of quantum physics itself, for lack of other arenas in which quantum-like properties are presumed to obtain. However, attempts to explain this behavior on the basis of actual quantum physics going on at the atomic or molecular level within some element of brain or neuronal anatomy do not seem to survive much scrutiny. In this paper we lay the groundwork for a theory that might explain the provenance of quantum-like behavior in complex systems whose internal structure is essentially hidden or inaccessible. The results reveal certain effects in such systems which, though quantum-like, are not identical to the kinds of quantum effects found in physics. (Abstract)

Seoane, Luis. Fate of Duplicated Neural Structures. arXiv:2008.00531. The Barcelona systems theorist is presently a postdoc at MIT’s Center for Brains Minds + Machines (search LS and visit the CBMM site). After prior collaborations with Ricard Sole (see arXiv site) and others, he continues his project to give life’s evolutionary biology and cognition a deeper statistical and computational physics basis, as it necessarily has to have. His innovative 2020 entry is a good instance of this scientific frontier as it seeks to quantify and join human and universe into a unified organic genesis. Its sections course through bilateral hemispheres, reactive and/or predictive brains, cortical columns, active algorithms, linguistics and more as each form and move by an energetic flow. The 193 references over the 2000s and 2010s document a 21st century worldwise revolution from everything in pieces to altogether now. So this panicky year seems also a time of historic local, global and ecosmic synthesis which, if we could witness, read and avail might help mitigate and guide.

Statistical mechanics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism percolates throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored which yields life, cognition, and Darwinian evolution. Neurons and neural circuits then reside between statistical mechanics, computation, and cognitively in natural selection. A statistical physics theory of neural circuits would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions.

With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at central nervous systems with a stress on computational thresholds that might prompt this redundancy and at duplicated circuits for complex phenotypes. From this we derive phase diagrams and transitions between single and duplicated circuits, which constrain evolutionary paths that lead to complex cognition. Similar phase diagrams and transitions might constrain internal connectivity patterns of neural circuits at large. Thus the formalism of statistical mechanics seems a natural framework for this promising line of research. (Abstract)

Simpson, Kevin, et al. Spatial biology of Ising-like synthetic genetic networks. BMC Biology. 21/185, 2003. This contribution by Pontificia Universidad Católica de Chile, Santiago geneticists could exemplify the 2023 cross-discipline integrations as it at once cites theoretic reasons for complex genome systems which can now be seen to be deeply grounded in active physical principles. By so doing the whole dynamic operation is realized to embody a Ising model (see below) as it forms self-organizing fractal patterns.

Understandings of how spatial patterns of gene expression emerge from the interaction of individual gene networks remains a challenge in biology. We propose an experimental system with a theoretical framework that captures the emergence of short- and long-range correlation from interacting gene networks. Our method combines synthetic biology, statistical mechanics, and computational simulations to study the spatial behavior of synthetic gene networks. By this approach, we describe the spatial behavior of bi-stable and chemically coupled synthetic gene networks that self-organize into long-range correlations with power-law scalings. The resultant patterns are then found to resemble ferromagnetic and anti-ferromagnetic configurations of the Ising model near critical points. (Excerpt)

Here, we apply a theoretical framework based on the Ising model to study how spatial correlations emerge from chemically coupled, bistable SGNs in Escherichia coli colonies. Ww construct synthetic toggle switches whose states are based on quorum sensing signaling. These SGNs self-organize in long-range spatial correlations and fractal patterns reminiscent of ferromagnetic systems of the Ising model. (2)

Our findings shed light on the spatial biology of coupled and bistable gene networks in growing cell populations. This emergent spatial behavior could provide insights into the study and engineering of self-organizing gene patterns in eukaryotic tissues and bacterial consortia. (12)

The Ising model, after the physicist Ernst Ising, is a mathematical model of ferromagnetism in statistical mechanics. It consists of discrete variables that represent magnetic dipole moments of atomic "spins" that can be in one of two states.

Siva, Karthnik, et al. Spin Glass Models of Syntax and Language Evolution. arXiv:1508.00504. We note this paper by Caltech mathematicians as a good example of the cosmic synthesis of physics and people, condensed matter and cultural discourse, a rooting of life and us in a fertile uniVerse. The Principles and Parameters model of Generative Linguistics due to Noam Chomsky is applied from Albanian to Zulu which leads to subject-verb language networks. If linguistics can be described by way of particles in relative motions, then this affinity would imply in turn that physical substance is literally textual in kind. The senior coauthor is Professor Matilde Marcolli. If there is any doubt that women can do STEM studies, check her website publications page where you will find work on Multifractals, Mumford Curves, Eternal Inflation and much more. For an April 2016 edition see her Syntactic Parameters and a Coding theory Perspective on Entropy and Complexity of Language Families paper in Entropy. And for even more see Semantic Spaces at 1605.0504238 and Syntactic Phylogenetic Trees at 1607.02791..

Using the SSWL database of syntactic parameters of world languages, and the MIT Media Lab data on language interactions, we construct a spin glass model of language evolution. We treat binary syntactic parameters as spin states, with languages as vertices of a graph, and assigned interaction energies along the edges. We study a rough model of syntax evolution, under the assumption that a strong interaction energy tends to cause parameters to align, as in the case of ferromagnetic materials. We also study how the spin glass model needs to be modified to account for entailment relations between syntactic parameters. This modification leads naturally to a generalization of Potts models with external magnetic field, which consists of a coupling at the vertices of an Ising model and a Potts model with q=3, that have the same edge interactions. We describe the results of simulations of the dynamics of these models, in different temperature and energy regimes. We discuss the linguistic interpretation of the parameters of the physical model. (Abstract)

Skjeltorp, Arne and Geir Helgesen. Editorial. European Physical Journal Special Topics. 225/4, 2016. An introduction to an issue on Cooperative Particles with papers such as Entangled Active Matter: From Cells to Ants and Patchy Colloidosomes – An Emerging Class of Structures, see abstract below. For another case, see Emergent Behavior in Active Colloids by Andreas Zotti and Holger Stark in the Journal of Physics: Condensed Matter (28/253001, 2016).

Both cells and ants belong to the broad field of active matter, a novel class of non-equilibrium materials composed of many interacting units that individually consume energy and collectively generate motion or mechanical stresses. However cells and ants differ from fish and birds in that they can support static loads. This is because cells and ants can be entangled, so that individual units are bound by transient links. Entanglement gives cells and ants a set of remarkable properties usually not found together, such as the ability to flow like a fluid, spring back like an elastic solid, and self-heal. In this review, we present the biology, mechanics and dynamics of both entangled cells and ants. We apply concepts from soft matter physics and wetting to characterize these systems as well as to point out their differences, which arise from their differences in size.

Smerlak, Matteo. Natural Selection as Coarsening. arXiv:1707.05317. A Perimeter Institute for Theoretical Physics senior postdoctoral researcher deftly considers how life’s seemingly vicarious evolution might actually be rooted in and exemplify fundamental physical phenomena. A concern is a somewhat arcane terminology, better definitions and translations would help, as they might apply to biological systems. But in the later 2010s, evinced by entries here and throughout, a salutary synthesis, long foreseen, is at last coming together. See also Limiting Fitness Distributions to Evolutionary Dynamics by Smerlak and Ahmed Youssef at 1511.00296.

Analogies between evolutionary dynamics and statistical mechanics, such as Fisher's second-law-like "fundamental theorem of natural selection" and Wright's "fitness landscapes", have had a deep and fruitful influence on the development of evolutionary theory. Here I discuss a new conceptual link between evolution and statistical physics. I argue that natural selection can be viewed as a coarsening phenomenon, similar to the growth of domain size in quenched magnets or to Ostwald ripening in alloys and emulsions. In particular, I show that the most remarkable features of coarsening---scaling and self-similarity---have strict equivalents in evolutionary dynamics. This analogy has three main virtues: it brings a set of well-developed mathematical tools to bear on evolutionary dynamics; it suggests new problems in theoretical evolution; and it provides coarsening physics with a new exactly soluble model. (Abstract)

Takatori, S. C. and J. F. Brady. Towards a Thermodynamics of Active Matter. Physical Review E. 91/032117, 2015. Caltech chemical engineers proceed to quantify the unique behaviors of cosmic nature’s animate media by way of nonequilibrium theories.

Self-propulsion allows living systems to display self-organization and unusual phase behavior. Unlike passive systems in thermal equilibrium, active matter systems are not constrained by conventional thermodynamic laws. A question arises, however, as to what extent, if any, can concepts from classical thermodynamics be applied to nonequilibrium systems like active matter. Here we use the new swim pressure perspective to develop a simple theory for predicting phase separation in active matter. Using purely mechanical arguments we generate a phase diagram with a spinodal and critical point, and define a nonequilibrium chemical potential to interpret the “binodal.” We provide a generalization of thermodynamic concepts like the free energy and temperature for nonequilibrium active systems. (Abstract)

Tsimring, Lev, et al. Focus on Swarming in Biological and Related Systems. New Journal of Physics. Circa 2013 -, 2014. Tsimring, Biocircuits Institute, University of California, San Diego, with Hugues Chate, Service Physics Solid State, CEA Saclay, France and Igor Aronson, Argonne National Laboratory, introduce an open collection on statistical physics as applied to organism assemblies for a broad range of articles. For example, “From Organized Internal Traffic to Collective Navigation of Bacterial Swarms” by Gil Ariel, et al, “Collective Motion Dynamics of Active Solids and Active Crystals,” Eliseo Ferrante, et al, and “Swarming, Schooling, Milling: A Data-Driven Fish School Model” by Daniel Calovi, et al. Search, for example, the February 2014 issue to locate.

In the last 15 years, the collective motion of large numbers of self-propelled objects has become an increasingly active area of research. The examples of such collective motion abound: flocks of birds, schools of fish, swarms of insects, herds of animals etc. Swarming of living creatures is believed to be critical for the population survival under harsh conditions. The ability of motile microorganisms to communicate and coordinate their motion leads to the remarkably complex self-organized structures found in bacterial biofilms. Active intracellular transport of biological molecules within the cytoskeleton has a profound effect on the cell cycle, signaling and motility. Collective motion and self-propulsion leads to new and non-trivial material properties of the 'active' medium.

There has been a rapidly growing number of computational and theoretical works on the generic dynamic and statistical properties of collective behavior exhibited by self-propelled particles with simplified interactions, from point-like particles to rigid self-propelled rods. A variety of dynamic phases were predicted, from moving clusters, bands, to swarming states. However, to date, the connection between these simulations and experimentally observed dynamics of self-propelled particles remains unsatisfactory. (Excerpts)

Viswanathan, Gandhimohan, et al, eds. The Physics of Foraging: An Introduction to Random Searches and Biological Encounters. Cambridge: Cambridge University Press, 2009. The Federal University of Brazil editors Viswanathan, Marcos Da Luz, and Ernesto Raposo, are joined by Boston University’s Eugene Stanley, a pioneer since the 1970s of this presently extensive synthesis of statistical physics with organic phenomena, animal behaviors, human activities, in their environmental settings. Typical chapters are Random walks and Levy (Paul) flights, Wandering albatross, Human dispersal, and Superdiffusive searches. Once more, a scientific sense of an iterative mathematical domain underlying and guiding the movements of microbes and mice and peoples becomes evident. For later examples, see the work of Frederic Bartumeus (search) and colleagues, such as Experimental Evidence for Inherent Levy Search Behavior for Foraging Animals in Proceedings of the Royal Society B (Vol. 282/No. 1807, 2015).

Do the movements of animals, including humans, follow patterns that can be described quantitatively by simple laws of motion? If so, then why? These questions have attracted the attention of scientists in many disciplines, and stimulated debates ranging from ecological matters to queries such as 'how can there be free will if one follows a law of motion?' This is the first book on this rapidly evolving subject, introducing random searches and foraging in a way that can be understood by readers without a previous background on the subject. It reviews theory as well as experiment, addresses open problems and perspectives, and discusses applications ranging from the colonization of Madagascar by Austronesians to the diffusion of genetically modified crops. The book will interest physicists working in the field of anomalous diffusion and movement ecology as well as ecologists already familiar with the concepts and methods of statistical physics. (Synopsis)

Walker, Sara, et al. The Informational Architecture of the Cell. arXiv:1507.03877. With Hyunju Kim and Paul Davies, in this posting and New Scaling Relation for Information Transfer in Biological Networks, 1508.04174, Arizona State University astrobiologists continue their efforts for a better scientific explanation of living, self-replicating systems from their origins to our human inquiry. With reference to physicist Erwin Schrodinger’s 1940s statement and in line with Nigel Goldenfeld’s current project (search) a novel cross-fertilization, per the quotes, is proposed to inform a new physics by way of biological principles. In this regard, and for this section, the work is a major contribution to this imperative re-unification of cosmos and children.

While we have made significant advances in understanding biology over the last several decades, we have not made comparable advances in physics to unite our new understanding of biology with the foundations of physics. Schrodinger used physical principles to constrain unknown properties of biology associated with genetic heredity. One might argue that this could be done again, but now at the level of the epigenome, or interactome, and so on for any level of biological organization. But this kind of approach only serves to predict structures consistent with the laws of physics; it does not explain why they should exist in the first place. An alternative approach is that we might instead use insights from biology to constrain unknown physics. That is, we suggest a track working in the opposite direction from that proposed by Schrodinger; rather than using physics to inform biology, we propose to start by thinking about biology as a means to inform potentially new physics. (2, 1507.03877)

We avoid introducing new information-theoretic measure, since there already exist a plethora in the literature. Instead we choose a few known measures relevant to biological organization – including those of integrated information theory and information dynamics. Although we focus here on gene regulatory networks, we note that our analysis is not level specific. The formalism introduced in intended to be universal and may apply to any level of biological organization from the first self-organized living chemistries to technological societies. We do not focus on DNA per se, but consider distributed information processing as it operates within the cell, as we believe such analyses have great potential to explain, for example, why physical structures capable of storage and heredity, such as DNA, should exist in the first place. (3, 1507.03877)

Living systems are often described in terms of logic modules, information flows and computation. Such informational language is utilized in fields as diverse as evolutionary biology, neuroscience, pattern formation, colonial decision making in eusocial insects, and protein-protein interaction networks, to name just a few. Although informational analogies are widely applied, an important open question is whether information is intrinsic to the operation of biological systems or merely a useful conceptual metaphor. The debate over the ontological status of information in biology has far-reaching consequences, including implications for our understanding of whether biological organization is fully reducible to the known laws of physics and chemistry or new “informational laws" necessitating a foundational status for information in physical theory are necessary to account for living matter. (1, 1508.04174)

We hypothesize that the features reported herein may be common to biological networks of different function, and in particular, that scaling relations in information transfer may be a hallmark of biological organization. Our results are suggestive of previously unidentified information-based organizational principles that go beyond topological considerations such as scale-free structure, and may be critical to biological function. They thus open a new framework for addressing the debate over the status of information in biology, by demonstrating quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not exhibit the same informational properties as reported here. (13, 1508.04174)

Wang, Zhen, et al. Statistical Physics of Vaccination. arXiv:1608.09010. A global team from Japan, Canada, India, France, Italy, Slovenia, England, Switzerland and China, including Matjaz Perc, post a 150 page study with over 750 references for this nascent 2016 joining and synthesis of universe and human. As the long abstract conveys, complex network systems, as akin to many-body, condensed matter theories, can well quantify infectious disease epidemics as they spread through human populations. From our vantage, these advances imply an independent, universal mathematics which underlies, guides and constrains personal and social lives. By turns, it infers a revolutionary biological cosmos, as if graced by an anatomy, physiology, and indeed a stirring intelligence. The second quote is the closing paragraph – where do you ever find good literature in a technical paper? A further surmise would be a self-healing genesis uniVerse by virtue of an emergent worldwise knowledge which can be fed back to palliate, cure, and prevent the bodily and psychic maladies of a prior stochastic evolution.

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure.

Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. (Abstract)

Shakespeare famously ended his play “A Midsummer Night’s Dream" with an apology from the spirit Puck to the audience: “If we shadows have offended, Think but this, and all is mended — That you have but slumbered here While these visions did appear." We also wish to apologize to the readers in advance, for having inadvertently missed some excellent papers in the literature, having unintentionally misrepresented other papers that we did include, or being completely wrong about where the field should go next. However, there is no need for the reader to pretend our review was something they dreamed while slumbering. Instead, please write us if you feel our review was lacking in some respect, or better yet, publish some research that shows us the value of other perspectives on the field. If nothing else, we hope that our review has convinced the readers that coupled behavior-disease modeling is a rapidly growing area that promises exciting theoretical developments and empirical applications in the coming decade, especially if we draw on the accumulated knowledge of many decades of statistical physics. (125)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10  Next