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.     

Ecological boundaries in the context of hierarchy theory

Ecological boundaries have been described as being multiscalar or hierarchical entities. However, the concept of the ecological boundary has not been explicitly examined in the context of hierarchy theory. We explore how ecological boundaries might be envisioned as constituents of scalar hierarchical systems. Boundaries may be represented by the surfaces of constituents or as constituents themselves. Where surfaces would correspond to abrupt transition zones, boundary systems might be quite varied depending on hierarchical context. We conclude that hierarchy theory is compatible with a functional vision of ecological boundaries where functions can be largely represented as the processing or filtering of ecological signals. Furthermore, we postulate that emergent ecological boundaries that arise on a new hierarchical level may contribute to the overconnectedness of mature ecosystems. Nevertheless, a thermodynamic approach to the emergence and development of boundary systems does indicate that in many situations, ecological boundaries would persist in time by contributing to the energy production of higher hierarchical levels.     

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.     

The Ecological Component of Ecological Risk Assessment: Lessons from a Field Experiment

To improve ecological relevance, regulatory agencies are promoting assessments of effects at higher levels of organization, an objective that requires an understanding of current ecological theories. One such theory, hierarchy theory, contends that the effects of a disturbance acting at one level of organization (e.g., population) are not, as a rule, transmitted to higher levels of organization (e.g., community). Conversely, effects at higher levels of organization only occur if lower level variables have been affected. Further, responses to disturbance depend on disturbance history. In this study, I determined the effects of a disturbance treatment at the population, guild, and community levels of organization for vegetation in five wetlands with a disturbance history ranging from highly to rarely disturbed. The 2-year field experiment revealed that the effects of the disturbance treatment were most strongly felt at the population level of organization in wetlands without a history of disturbance. These observed impacts took place against a backdrop of constant change. Thus, the eventual disappearance of treatment effects was not due to a return to the pre-treatment state, but rather a return to a trajectory similar to that exhibited by the control plots. The implications of these results for ecological risk assessment are: (1) the observed effects of a stressor in a system cannot be extrapolated to other systems unless they have similar disturbance histories, (2) detecting effects before they become serious requires monitoring at lower levels of organization, (3) recovery to a naturally innate state is not a viable concept, and (4) the traditional approach of using one post-treatment measurement to determine if reference and impact sites differ is of very questionable value.     

Predator-prey interactions in natural communities.

The familiar concepts of harvest and yield are developed for the purpose of describing predator-prey interactions in a community context. In this regard the functional response (appropriate for one predator-one prey systems) is replaced by a community harvest function. Conditions for the stability of an ecological community are obtained. Exploring the dynamics of predator-prey interactions within this framework leads to new interpretations of other dynamical models such as the Lotka-Volterra model. The concept of a community moving attractor point is introduced in order to describe the changes in all populations over time.     

Living in the branches: population dynamics and ecological processes in dendritic networks

Spatial structure regulates and modifies processes at several levels of ecological organization (e.g. individual/genetic, population and community) and is thus a key component of complex systems, where knowledge at a small scale can be insufficient for understanding system behaviour at a larger scale. Recent syntheses outline potential applications of network theory to ecological systems, but do not address the implications of physical structure for network dynamics. There is a specific need to examine how dendritic habitat structure, such as that found in stream, hedgerow and cave networks, influences ecological processes. Although dendritic networks are one type of ecological network, they are distinguished by two fundamental characteristics: (1) both the branches and the nodes serve as habitat, and (2) the specific spatial arrangement and hierarchical organization of these elements interacts with a species' movement behaviour to alter patterns of population distribution and abundance, and community interactions. Here, we summarize existing theory relating to ecological dynamics in dendritic networks, review empirical studies examining the population- and community-level consequences of these networks, and suggest future research integrating spatial pattern and processes in dendritic systems.     

Dynamics of a neural system with a multiscale architecture

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales-neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are 'slaved' to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested.     

Ecological hierarchies and self-organisation – Pattern analysis,modelling and process integration across scales

A continuing discussion in applied and theoretical ecology focuses on the relationship of different organisational levels and on how ecological systems interact across scales. We address principal approaches to cope with complex across-level issues in ecology by applying elements of hierarchy theory and the theory of complex adaptive systems. A top-down approach, often characterised by the use of statistical techniques, can be applied to analyse large-scale dynamics and identify constraints exerted on lower levels. Current developments are illustrated with examples from the analysis of within-community spatial patterns and large-scale vegetation patterns. A bottom-up approach allows one to elucidate how interactions of individuals shape dynamics at higher levels in a self-organisation process; e.g., population development and community composition. This may be facilitated by various modelling tools, which provide the distinction between focal levels and resulting properties. For instance, resilience in grassland communities has been analysed with a cellular automaton approach, and the driving forces in rodent population oscillations have been identified with an agent-based model. Both modelling tools illustrate the principles of analysing higher level processes by representing the interactions of basic components.The focus of most ecological investigations on either top-down or bottom-up approaches may not be appropriate, if strong cross-scale relationships predominate. Here, we propose an ‘across-scale-approach’, closely interweaving the inherent potentials of both approaches. This combination of analytical and synthesising approaches will enable ecologists to establish a more coherent access to cross-level interactions in ecological systems.     

Modeling and simulation of genetic regulatory systems: a literature review.

In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.     

Problems of biochemical organization]

Biological organization has been defined as a unity of structure, function and regulation. Biological organization of hierarchical multilevel biological systems is represented by a hierarchy of functioning controllable structures. The hierarchy of levels of material organization predetermines the existence of a hierarchy of regulatory mechanisms. Biochemical organization involves the levels of material organization corresponding to biomacromolecules, supramolecular complexes and cellular organelles. The levels of biomacromolecules and supramolecular structures effectuating elementary functions and controlled by basic regulatory mechanisms occupy key positions in biological systems. These levels play the role of standard functional blocks; their combination leads to hierarchically higher structural levels (cell, tissue, organ, systems of organs, organism) performing more complex functions and controlled by hierarchically more important regulatory mechanisms. The peculiarities of regulation of biological systems that are due to the existence of a hierarchy of regulatory mechanisms are discussed.     

Protein networking: insights into global functional organization of proteomes

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.     

Periodic orbits: a new language for neuronal dynamics.

A new nonlinear dynamical analysis is applied to complex behavior from neuronal systems. The conceptual foundation of this analysis is the abstraction of observed neuronal activities into a dynamical landscape characterized by a hierarchy of "unstable periodic orbits" (UPOs). UPOs are rigorously identified in data sets representative of three different levels of organization in mammalian brain. An analysis based on UPOs affords a novel alternative method of decoding, predicting, and controlling these neuronal systems.     

Levels of organization of living systems: Cooperons

All creatures living on Earth are traditionally discussed in the context of structuralmorphological approach, in frame of which there are considered various systems (for instance, organisms and ecosystems) that have different sizes and organization and use different resources for their existence. These characteristics are sometimes added by some particular functional and ecological characteristics, but usually with respect to the structural ones. We believe that such traditional approach, although illustrating, but distracts from the circumstance that any living systems is to be considered an integrated structural-functional complex, the maintenance of existence of this system being impossible without the processes occurring constantly in it and aimed at preserving this complex. This leads us to the concept of cooperons—the self-preserved dynamic structures existing only as a result of various specifically organized cooperative processes (their intensities can vary depending on circumstances). From our point of view, all living systems are cooperons of different hierarchy levels. Some other systems, specifically the symbiotic ones, also are cooperons. In frame of this concept, it is possible to discuss functioning of living systems of different types of organization in a new context closer to physiologists, both for the case of “norm” and for the situation when the cooperative interrelations of parts of the system are impaired (for instance, in systemic diseases).     

耗散结构,等级系统理论与生态系统

耗散结构理论与其他热力学概念一起,可以解释生态学中的许多现象。生态系统是耗散系统,用耗散结构理论来分析和讨论生态平衡等问题更为合理、准确。等级系统理论是为理解和研究高度复杂系统而发展起来的系统理论。等级系统理论为研究生态系统的行为和特征提供了客观的、适用的概念构架和实践指南,并为生态系统科学的统一性理论的形成开辟了广阔前景。本文拟就耗散结构理论和等级系统理论的主要内容及其在生态学中的应用作一介绍和讨论。     

耗散结构、等级系统理论与生态系统

耗散结构理论与其他热力学概念一起,可以解释生态学中的许多现象。生态系统是耗散系统,用耗散结构理论来分析和讨论生态平衡等问题更为合理、准确。等级系统理论是为理解和研究高度复杂系统而发展起来的系统理论。等级系统理论为研究生态系统的行为和特征提供了客观的、适用的概念构架和实践指南,并为生态系统科学的统一性理论的形成开辟了广阔前景。本文拟就耗散结构理论和等级系统理论的主要内容及其在生态学中的应用作一介绍和讨论。     

Principles of organization of polistinae (Hymenoptera,Vespidae) population

The paper describes some invariant relations of the Polistinae population structure, including resistance to abiotic and biotic factors that occurs against the background of the hierarchy of biological systems and increasing autonomy of their functioning. A decrease in the dependence on the hostile environment is shown to be due to the activity of foundresses and workers adjusting to external rhythms, developing specialized responses to predators and parasites (predictable external noise of biotic nature), and creating new information. The population organization of Polistinae wasps is considered in the framework of Anokhin’s theory of functional systems and systemogenesis. There are specific processes in the population that unite individual colonies and their reproduction; they are accompanied by the formation of an advanced feedback and functional systems. Systemic processes can be simultaneously regarded as “adaptation” (reflecting the organization of environmental elements) and as “adaptiveness” (reflecting the organization of the activity of intra-colony processes and the organization of reproduction). The organization of the colony activity and reproduction in functional systems reflects the future survival rather than the preceding phenomena and events. The behavior of individuals in a colony is determined not only by the effects of abiotic and biotic factors (via transformation of cues into behavioral programs), but also by previous adaptations (stored in the “memory” as images of still absent events). General progress, limited or partial progress, and narrow specialization in the organization of polistine colonies and populations are considered using the examples of morphofunctional, environmental, energy and information criteria. The emphasis on invariant relations makes it possible to more fully describe biological systems in terms of such general categories as isomorphism, homeostasis or self-organization, and also enables us to use more effectively the theory of general functional systems in studying social insects.     

Coevolution-driven predator-prey cycles: predicting the characteristics of eco-coevolutionary cycles using fast-slow dynamical systems theory

Eco-coevolutionary theory predicts that predator-prey coevolution occurring on the time scale of ecological dynamics (e.g., changes in population abundances) can drive novel kinds of predator-prey cycles, e.g., cryptic cycles where one species cycles while the other remains effectively constant and clockwise cycles where peaks in predator density precede peaks in prey density. However, because this body of theory has focused on particular models and studied the different cycle types in isolation, it is unclear what biological characteristics (e.g., costs for offense or defense) determine when a particular cycle type will arise. In this study, I explore the kinds of predator-prey cycles that arise in a general eco-coevolutionary model where there is disruptive selection and the coevolutionary dynamics are fast relative to the ecological dynamics of the system. With a graphical tool created using the theory of fast-slow dynamical systems, I predict what kinds of cycles can arise in the model and how cycle type depends on the costs for prey defense and predator offense. Fast-slow dynamical systems theory requires a separation of time scales between the ecological and evolutionary processes; however, numerical simulations show that this tool can help predict how coevolution drives populations cycles in systems where the speeds of ecological and evolutionary dynamics are comparable. Thus, this work is a step forward in building a general eco-coevolutionary theory.     

Generation and reshaping of sequences in neural systems

The generation of informational sequences and their reorganization or reshaping is one of the most intriguing subjects for both neuroscience and the theory of autonomous intelligent systems. In spite of the diversity of sequential activities of sensory, motor, and cognitive neural systems, they have many similarities from the dynamical point of view. In this review we discus the ideas, models, and mathematical image of sequence generation and reshaping on different levels of the neural hierarchy, i.e., the role of a sensory network dynamics in the generation of a motor program (hunting swimming of marine mollusk Clione), olfactory dynamical coding, and sequential learning and decision making. Analysis of these phenomena is based on the winnerless competition principle. The considered models can be a basis for the design of biologically inspired autonomous intelligent systems.     

Sensitivity analysis of Repast computational ecology models with R/Repast

Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual‐based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom‐up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in‐silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.