Self-organization, Natural Selection, and Evolution: Cellular Hardware and Genetic Software

Perspectives on polarity - exploring biological asymmetry across scales

In this Perspective, Journal of Cell Science invited researchers working on cell and tissue polarity to share their thoughts on unique, emerging or open questions relating to their field. The goal of this article is to feature 'voices' from scientists around the world and at various career stages, to bring attention to innovative and thought-provoking topics of interest to the cell biology community. These voices discuss intriguing questions that consider polarity across scales, evolution, development and disease. What can yeast and protists tell us about the evolution of cell and tissue polarity in animals? How are cell fate and development influenced by emerging dynamics in cell polarity? What can we learn from atypical and extreme polarity systems? How can we arrive at a more unified biophysical understanding of polarity? Taken together, these pieces demonstrate the broad relevance of the fascinating phenomenon of cell polarization to diverse fundamental biological questions.

Base Pairing Promoted the Self-Organization of Genetic Coding, Catalysis, and Free-Energy Transduction

How Nature discovered genetic coding is a largely ignored question, yet the answer is key to explaining the transition from biochemical building blocks to life. Other, related puzzles also fall inside the aegis enclosing the codes themselves. The peptide bond is unstable with respect to hydrolysis. So, it requires some form of chemical free energy to drive it. Amino acid activation and acyl transfer are also slow and must be catalyzed. All living things must thus also convert free energy and synchronize cellular chemistry. Most importantly, functional proteins occupy only small, isolated regions of sequence space. Nature evolved heritable symbolic data processing to seek out and use those sequences. That system has three parts: a memory of how amino acids behave in solution and inside proteins, a set of code keys to access that memory, and a scoring function. The code keys themselves are the genes for cognate pairs of tRNA and aminoacyl-tRNA synthetases, AARSs. The scoring function is the enzymatic specificity constant, kcat/kM, which measures both catalysis and specificity. The work described here deepens the evidence for and understanding of an unexpected consequence of ancestral bidirectional coding. Secondary structures occur in approximately the same places within antiparallel alignments of their gene products. However, the polar amino acids that define the molecular surface of one are reflected into core-defining non-polar side chains on the other. Proteins translated from base-paired coding strands fold up inside out. Bidirectional genes thus project an inverted structural duality into the proteome. I review how experimental data root the scoring functions responsible for the origins of coding and catalyzed activation of unfavorable chemical reactions in that duality.

Redundancy and the role of protein copy numbers in the cell polarization machinery of budding yeast

How can a self-organized cellular function evolve, adapt to perturbations, and acquire new sub-functions? To make progress in answering these basic questions of evolutionary cell biology, we analyze, as a concrete example, the cell polarity machinery of Saccharomyces cerevisiae. This cellular module exhibits an intriguing resilience: it remains operational under genetic perturbations and recovers quickly and reproducibly from the deletion of one of its key components. Using a combination of modeling, conceptual theory, and experiments, we propose that multiple, redundant self-organization mechanisms coexist within the protein network underlying cell polarization and are responsible for the module's resilience and adaptability. Based on our mechanistic understanding of polarity establishment, we hypothesize that scaffold proteins, by introducing new connections in the existing network, can increase the redundancy of mechanisms and thus increase the evolvability of other network components. Moreover, our work gives a perspective on how a complex, redundant cellular module might have evolved from a more rudimental ancestral form.

Integrating gene expression data into a genome-scale metabolic model to identify reprogramming during adaptive evolution

The development of a method for identifying latent reprogramming in gene expression data resulting from adaptive laboratory evolution (ALE) in response to genetic or environmental perturbations has been a challenge. In this study, a method called Metabolic Reprogramming Identifier (MRI), based on the integration of expression data to a genome-scale metabolic model has been developed. To identify key genes playing the main role in reprogramming, a MILP problem is presented and maximization of an adaptation score as a criterion indicating a pattern of using metabolism with maximum utilization of gene expression resources is defined as an objective function. Then, genes with complete expression usage and significant expression differences between wild-type and evolved strains were selected as key genes for reprogramming. This score is also applied to evaluate the compatibility of expression patterns with maximal use of key genes. The method was implemented to investigate the reprogramming of Escherichia coli during adaptive evolution caused by changing carbon sources. cyoC and cydB responsible for establishing proton gradient across the inner membrane were identified to be vital in the E. coli reprogramming when switching from glucose to lactate. These results indicate the importance of the inner membrane in reprogramming of E. coli to adapt to the new environment. The method predicts no reprogramming occurs during the evolution for growth on glycerol.

Complexity and self-organization in the evolution of cell polarization

Cellular life exhibits order and complexity, which typically increase over the course of evolution. Cell polarization is a well-studied example of an ordering process that breaks the internal symmetry of a cell by establishing a preferential axis. Like many cellular processes, polarization is driven by self-organization, meaning that the macroscopic pattern emerges as a consequence of microscopic molecular interactions at the biophysical level. However, the role of self-organization in the evolution of complex protein networks remains obscure. In this Review, we provide an overview of the evolution of polarization as a self-organizing process, focusing on the model species Saccharomyces cerevisiae and its fungal relatives. Moreover, we use this model system to discuss how self-organization might relate to evolutionary change, offering a shift in perspective on evolution at the microscopic scale.

Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller

Modularity is a desirable property for embodied agents, as it could foster their suitability to different domains by disassembling them into transferable modules that can be reassembled differently. We focus on a class of embodied agents known as voxel-based soft robots (VSRs). They are aggregations of elastic blocks of soft material; as such, their morphologies are intrinsically modular. Nevertheless, controllers used until now for VSRs act as abstract, disembodied processing units: Disassembling such controllers for the purpose of module transferability is a challenging problem. Thus, the full potential of modularity for VSRs still remains untapped. In this work, we propose a novel self-organizing, embodied neural controller for VSRs. We optimize it for a given task and morphology by means of evolutionary computation: While evolving, the controller spreads across the VSR morphology in a way that permits emergence of modularity. We experimentally investigate whether such a controller (i) is effective and (ii) allows tuning of its degree of modularity, and with what kind of impact. To this end, we consider the task of locomotion on rugged terrains and evolve controllers for two morphologies. Our experiments confirm that our self-organizing, embodied controller is indeed effective. Moreover, by mimicking the structural modularity observed in biological neural networks, different levels of modularity can be achieved. Our findings suggest that the self-organization of modularity could be the basis for an automatic pipeline for assembling, disassembling, and reassembling embodied agents.

Pattern formation features might explain homoplasy: fertile surfaces in higher fungi as an example

Fungi show a high degree of morphological convergence. Regarded for a long time as an obstacle for phylogenetic studies, homoplasy has also been proposed as a source of information about underlying morphogenetic patterning mechanisms. The "local-activation and long-range inhibition principle" (LALIP), underlying the famous reaction-diffusion model proposed by Alan Turing in 1952, appears to be one of the universal phenomena that can explain the ontogenetic origin of seriate patterns in living organisms. Reproductive structures of fungi in the class Agaricomycetes show a highly periodic structure resulting in, for example, poroid, odontoid, lamellate or labyrinthic hymenophores. In this paper, we claim that self-organized patterns might underlie the basic ontogenetic processes of these structures. Simulations based on LALIP-driven models and covering a wide range of parameters show an absolute mutual correspondence with the morphospace explored by extant agaricomycetes. This could not only explain geometric particularities but could also account for the limited possibilities displayed by hymenial configurations, thus making homoplasy a direct consequence of the limited morphospace resulting from the proposed patterning dynamics.

Perspectives on Complexity, Chaos and Thermodynamics in Environmental Pathology

Though complexity science and chaos theory have become a common scientific divulgation theme, medical disciplines, and pathology in particular, still rely on a deterministic, reductionistic approach and still hesitate to fully appreciate the intrinsic complexity of living beings. Herein, complexity, chaos and thermodynamics are introduced with specific regard to biomedical sciences, then their interconnections and implications in environmental pathology are discussed, with particular regard to a morphopathological, image analysis-based approach to biological interfaces. Biomedical disciplines traditionally approach living organisms by dissecting them ideally down to the molecular level in order to gain information about possible molecule to molecule interactions, to derive their macroscopic behaviour. Given the complex and chaotic behaviour of living systems, this approach is extremely limited in terms of obtainable information and may lead to misinterpretation. Environmental pathology, as a multidisciplinary discipline, should grant privilege to an integrated, possibly systemic approach, prone to manage the complex and chaotic aspects characterizing living organisms. Ultimately, environmental pathology should be interested in improving the well-being of individuals and the population, and ideally the health of the entire ecosystem/biosphere and should not focus merely on single diseases, diseased organs/tissues, cells and/or molecules.

The Roots of Genetic Coding in Aminoacyl-tRNA Synthetase Duality

Codon-dependent translation underlies genetics and phylogenetic inferences, but its origins pose two challenges. Prevailing narratives cannot account for the fact that aminoacyl-tRNA synthetases (aaRSs), which translate the genetic code, must collectively enforce the rules used to assemble themselves. Nor can they explain how specific assignments arose from rudimentary differentiation between ancestral aaRSs and corresponding transfer RNAs (tRNAs). Experimental deconstruction of the two aaRS superfamilies created new experimental tools with which to analyze the emergence of the code. Amino acid and tRNA substrate recognition are linked to phase transfer free energies of amino acids and arise largely from aaRS class-specific differences in secondary structure. Sensitivity to protein folding rules endowed ancestral aaRS-tRNA pairs with the feedback necessary to rapidly compare alternative genetic codes and coding sequences. These and other experimental data suggest that the aaRS bidirectional genetic ancestry stabilized the differentiation and interdependence required to initiate and elaborate the genetic coding table.

A model of autowave self-organization as a hierarchy of active media in the biological evolution

Within the framework of the active media concept, we develop a biophysical model of autowave self-organization which is treated as a hierarchy of active media in the evolution of the biosphere. We also propose a mathematical model of the autowave process of speciation in a flow of mutations for the three main taxonometric groups (prokaryotes, unicellular and multicellular eukaryotes) with a naturally determined lower boundary of living matter (the appearance of prokaryotes) and an open upper boundary for the formation of new species. It is shown that the fluctuation-bifurcation description of the evolution for the formation of new taxonometric groups as a trajectory of transformation of small fluctuations into giant ones adequately reflects the process of self-organization during the formation of taxa. The major concepts of biological evolution, conditions of hierarchy formation as a fundamental manifestation of self-organization and complexity in the evolution of biological systems are considered.

From a discrete model of chemotaxis with volume-filling to a generalized Patlak-Keller-Segel model

We present a discrete model of chemotaxis whereby cells responding to a chemoattractant are seen as individual agents whose movement is described through a set of rules that result in a biased random walk. In order to take into account possible alterations in cellular motility observed at high cell densities (i.e. volume-filling), we let the probabilities of cell movement be modulated by a decaying function of the cell density. We formally show that a general form of the celebrated Patlak-Keller-Segel (PKS) model of chemotaxis can be formally derived as the appropriate continuum limit of this discrete model. The family of steady-state solutions of such a generalized PKS model are characterized and the conditions for the emergence of spatial patterns are studied via linear stability analysis. Moreover, we carry out a systematic quantitative comparison between numerical simulations of the discrete model and numerical solutions of the corresponding PKS model, both in one and in two spatial dimensions. The results obtained indicate that there is excellent quantitative agreement between the spatial patterns produced by the two models. Finally, we numerically show that the outcomes of the two models faithfully replicate those of the classical PKS model in a suitable asymptotic regime.

A Peptide-Nucleic Acid Replicator Origin for Life

Evolution requires self-replication. But, what was the very first self-replicator directly ancestral to all life? The currently favoured RNA World theory assigns this role to RNA alone but suffers from a number of seemingly intractable problems. Instead, we suggest that the self-replicator consisted of both peptides and nucleic acid strands. Such a nucleopeptide replicator is more feasible both in the light of the replication machinery currently found in cells and the complexity of the evolutionary path required to reach them. Recent theoretical and mathematical work supports this idea and provide a blueprint for future investigations.

Regulatory mechanism predates the evolution of self-organizing capacity in simulated ant-like robots

The evolution of complexity is one of the prime features of life on Earth. Although well accepted as the product of adaptation, the dynamics underlying the evolutionary build-up of complex adaptive systems remains poorly resolved. Using simulated robot swarms that exhibit ant-like group foraging with trail pheromones, we show that their self-organizing capacity paradoxically involves regulatory behavior that arises in advance. We focus on a traffic rule on their foraging trail as a regulatory trait. We allow the simulated robot swarms to evolve pheromone responsiveness and traffic rules simultaneously. In most cases, the traffic rule, initially arising as selectively neutral component behaviors, assists the group foraging system to bypass a fitness valley caused by overcrowding on the trail. Our study reveals a hitherto underappreciated role of regulatory mechanisms in the origin of complex adaptive systems, as well as highlights the importance of embodiment in the study of their evolution.

Coding of Class I and II Aminoacyl-tRNA Synthetases

The aminoacyl-tRNA synthetases and their cognate transfer RNAs translate the universal genetic code. The twenty canonical amino acids are sufficiently diverse to create a selective advantage for dividing amino acid activation between two distinct, apparently unrelated superfamilies of synthetases, Class I amino acids being generally larger and less polar, Class II amino acids smaller and more polar. Biochemical, bioinformatic, and protein engineering experiments support the hypothesis that the two Classes descended from opposite strands of the same ancestral gene. Parallel experimental deconstructions of Class I and II synthetases reveal parallel losses in catalytic proficiency at two novel modular levels-protozymes and Urzymes-associated with the evolution of catalytic activity. Bi-directional coding supports an important unification of the proteome; affords a genetic relatedness metric-middle base-pairing frequencies in sense/antisense alignments-that probes more deeply into the evolutionary history of translation than do single multiple sequence alignments; and has facilitated the analysis of hitherto unknown coding relationships in tRNA sequences. Reconstruction of native synthetases by modular thermodynamic cycles facilitated by domain engineering emphasizes the subtlety associated with achieving high specificity, shedding new light on allosteric relationships in contemporary synthetases. Synthetase Urzyme structural biology suggests that they are catalytically-active molten globules, broadening the potential manifold of polypeptide catalysts accessible to primitive genetic coding and motivating revisions of the origins of catalysis. Finally, bi-directional genetic coding of some of the oldest genes in the proteome places major limitations on the likelihood that any RNA World preceded the origins of coded proteins.

Literary narrative as a cognitive structure in the brain

This paper proposes that literary narrative is the result of an unconscious computation in the brain, a computation that arises from the dynamical interaction of specific innate and representational lower-order neuronal circuits and mappings. It is also proposed that these specific circuits constitute the fundamental building blocks of literary narrative. The analysis further suggests that as literary narrative evolves in the brain, its development is influenced by an evolutionarily-biased broad class of attractors known as archetypes. In this context, a description of literary narrative is devised, and six phases leading to the production of literary narrative are then identified. The description presented here may have applications in the production of literary narrative by artificial systems.

On the thermodynamics of multilevel evolution

Biodiversity is hierarchically structured both phylogenetically and functionally. Phylogenetic hierarchy is understood as a product of branching organic evolution as described by Darwin. Ecosystem biologists understand some aspects of functional hierarchy, such as food web architecture, as a product of evolutionary ecology; but functional hierarchy extends to much lower scales of organization than those studied by ecologists. We argue that the more general use of the term "evolution" employed by physicists and applied to non-living systems connects directly to the narrow biological meaning. Physical evolution is best understood as a thermodynamic phenomenon, and this perspective comfortably includes all of biological evolution. We suggest four dynamical factors that build on each other in a hierarchical fashion and set the stage for the Darwinian evolution of biological systems: (1) the entropic erosion of structure; (2) the construction of dissipative systems; (3) the reproduction of growing systems and (4) the historical memory accrued to populations of reproductive agents by the acquisition of hereditary mechanisms. A particular level of evolution can underpin the emergence of higher levels, but evolutionary processes persist at each level in the hierarchy. We also argue that particular evolutionary processes can occur at any level of the hierarchy where they are not obstructed by material constraints. This theoretical framework provides an extensive basis for understanding natural selection as a multilevel process. The extensive literature on thermodynamics in turn provides an important advantage to this perspective on the evolution of higher levels of organization, such as the evolution of altruism that can accompany the emergence of social organization.

On the nature and shape of tubulin trails: implications on microtubule self-organization

Microtubules, major elements of the cell skeleton are, most of the time, well organized in vivo, but they can also show self-organizing behaviors in time and/or space in purified solutions in vitro. Theoretical studies and models based on the concepts of collective dynamics in complex systems, reaction-diffusion processes and emergent phenomena were proposed to explain some of these behaviors. In the particular case of microtubule spatial self-organization, it has been advanced that microtubules could behave like ants, self-organizing by 'talking to each other' by way of hypothetic (because never observed) concentrated chemical trails of tubulin that are expected to be released by their disassembling ends. Deterministic models based on this idea yielded indeed like-looking spatio-temporal self-organizing behaviors. Nevertheless the question remains of whether microscopic tubulin trails produced by individual or bundles of several microtubules are intense enough to allow microtubule self-organization at a macroscopic level. In the present work, by simulating the diffusion of tubulin in microtubule solutions at the microscopic scale, we measure the shape and intensity of tubulin trails and discuss about the assumption of microtubule self-organization due to the production of chemical trails by disassembling microtubules. We show that the tubulin trails produced by individual microtubules or small microtubule arrays are very weak and not elongated even at very high reactive rates. Although the variations of concentration due to such trails are not significant compared to natural fluctuations of the concentration of tubuline in the chemical environment, the study shows that heterogeneities of biochemical composition can form due to microtubule disassembly. They could become significant when produced by numerous microtubule ends located in the same place. Their possible formation could play a role in certain conditions of reaction. In particular, it gives a mesoscopic basis to explain the collective dynamics observed in excitable microtubule solutions showing the propagation of concentration waves of microtubules at the millimeter scale, although we doubt that individual microtubules or bundles can behave like molecular ants.

Topological singularities and symmetry breaking in development

The review presents a topological interpretation of some morphogenetic events through the use of well-known mathematical concepts and theorems. Spatial organization of the biological fields is analyzable in topological terms. Topological singularities inevitably emerging in biological morphogenesis are retained and transformed during pattern formation. It is the topological language that can provide strict and adequate description of various phenomena in developmental and evolutionary transformations. The relationship between local and global orders in metazoan development, i.e., between local morphogenetic processes and integral developmental patterns, is established. A topological inevitability of some developmental events through the use of classical topological concepts is discussed. This methodology reveals a topological imperative as a certain set of topological rules that constrains and directs embryogenesis. A breaking of spatial symmetry of preexisting pattern plays a critical role in biological morphogenesis in development and evolution.