Biocomplexity: adaptive behavior in complex stochastic dynamical systems

Existing methods of complexity research are capable of describing certain specifics of bio systems over a given narrow range of parameters but often they cannot account for the initial emergence of complex biological systems, their evolution, state changes and sometimes-abrupt state transitions. Chaos tools have the potential of reaching to the essential driving mechanisms that organize matter into living substances. Our basic thesis is that while established chaos tools are useful in describing complexity in physical systems, they lack the power of grasping the essence of the complexity of life. This thesis illustrates sensory perception of vertebrates and the operation of the vertebrate brain. The study of complexity, at the level of biological systems, cannot be completed by the analytical tools, which have been developed for non-living systems. We propose a new approach to chaos research that has the potential of characterizing biological complexity. Our study is biologically motivated and solidly based in the biodynamics of higher brain function. Our biocomplexity model has the following features, (1) it is high-dimensional, but the dimensionality is not rigid, rather it changes dynamically; (2) it is not autonomous and continuously interacts and communicates with individual environments that are selected by the model from the infinitely complex world; (3) as a result, it is adaptive and modifies its internal organization in response to environmental factors by changing them to meet its own goals; (4) it is a distributed object that evolves both in space and time towards goals that is continually re-shaping in the light of cumulative experience stored in memory; (5) it is driven and stabilized by noise of internal origin through self-organizing dynamics. The resulting theory of stochastic dynamical systems is a mathematical field at the interface of dynamical system theory and stochastic differential equations. This paper outlines several possible avenues to analyze these systems. Of special interest are input-induced and noise-generated, or spontaneous state-transitions and related stability issues.     

Behavioural robustness: a link between distributed mechanisms and coupled transient dynamics

The emergence of a unified cognitive behaviour relies on the coordination of specialized components that distribute across a ‘brain’, body and environment. Although a general dynamical mechanism involved in agent-environment integration is still largely unknown for behavioural robustness, discussions here are focussed on one of the most plausible candidate: the formation of distributed mechanisms working in transient during agent-environment coupling. This article provides discussions on this sort of coordination based on a mobile object-tracking task with situated, embodied and minimal agents, and tests for robust yet adaptive behaviour. The proposed scenario provides examples of behavioural mechanisms that counterbalance the functional organization of internal control activity and agents’ situatedness to enable the evolution of a two-agent interaction task. Discussions in this article suggest that future studies of distributed cognition should take into account that there are at least two possible modes of interpreting distributed mechanisms and that these have a qualitatively different effect on behavioural robustness.     

The analysis of distributed control and information processing in adaptive systems: a biologically motivated approach.

Biological systems have evolved hierarchical, distributed control structures that greatly enhance their adaptability. Two important determinants of biological adaptability considered here are: (i) the pattern of distribution of self-control capabilities; (ii) the degree of programmability of information processing. In this paper we model organizations as goal-oriented, adaptive systems, possessing properties similar to those of biological systems. We use the notion of implicit control (defined as the capability of self-control that is embedded in a system's own dynamics) in the analysis of the impact of specific patterns of distribution of control and information processing on the adaptability of organizations. A principle of design of organizational information systems, that captures important aspects of adaptability-preserving strategies of information processing in biological systems, is stated in terms of the implicit control concept.     

Evolvability and robustness in a complex signalling circuit

Biological systems at various levels of organisation exhibit robustness, as well as phenotypic variability or evolvability, the ability to evolve novel phenotypes. We still know very little about the relationship between robustness and phenotypic variability at levels of organisation beyond individual macromolecules, and especially for signalling circuits. Here, we examine multiple alternate topologies of the Saccharomyces cerevisiae target-of-rapamycin (TOR) signalling circuit, in order to understand the circuit's robustness and phenotypic variability. We consider each of the topological variants a genotype, a set of alternative interactions between TOR circuit components. Two genotypes are neighbours in genotype space if they can be reached from each other by a single small genetic change. Each genotype (topology) has a signalling phenotype, which we define via the concentration trajectories of key signalling molecules. We find that the circuits we study can produce almost 300 different phenotypes. The number of genotypes with a given phenotype varies very widely among these phenotypes. Some phenotypes have few associated genotypes. Others have many genotypes that form genotype networks extending far through genotype space. A minority of phenotypes accounts for the vast majority of genotypes. Importantly, we find that these phenotypes tend to have large genotype networks, greater robustness and a greater ability to produce novel phenotypes. Thus, over a broad range of phenotypic robustness, robustness facilitates phenotypic variability in our study system. Our observations show parallels to studies on macromolecules, suggesting that similar principles might govern robustness and phenotypic variability in biological systems. Our approach points a way towards mapping genotype spaces in complex circuitry, and it exposes some challenges such mapping faces.     

External and internal complexity of complex adaptive systems

We introduce concepts of external and internal complexity to analyze the relation between an adaptive system and its environment. We apply this theoretical framework to the construction of models in a cognitive system and the selection between hypotheses through selective observations performed on a data set in a recurrent process and propose a corresponding neural network architecture.     

SPSRG: a prediction approach for correlated failures in distributed computing systems

Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. In this work, we propose a correlated failure prediction approach (CFPA) to predict correlated failures of computing elements in DCSs. The approach models correlated-failure patterns using the concept of probabilistic shared risk groups and makes a prediction for correlated failures by exploiting an association rule mining approach in a parallel way. We conduct extensive experiments to evaluate the feasibility and effectiveness of CFPA using both failure traces from Los Alamos National Lab and simulated datasets. The experimental results show that the proposed approach outperforms other approaches in both the failure prediction performance and the execution time, and can potentially provide better prediction performance in a larger system.     

Variability and robustness in biomolecular systems

The need to perform sophisticated information processing in an environment that is variable and noisy restricts the functional design of biological networks. We discuss several of the strategies that cells and multicellular organisms have evolved to deal with this demand.     

Degeneracy: a link between evolvability,robustness and complexity in biological systems

A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology.     

The basic reproduction number for complex disease systems: defining R(0) for tick-borne infections

Characterizing the basic reproduction number, R(0), for many wildlife disease systems can seem a complex problem because several species are involved, because there are different epidemiological reactions to the infectious agent at different life-history stages, or because there are multiple transmission routes. Tick-borne diseases are an important example where all these complexities are brought together as a result of the peculiarities of the tick life cycle and the multiple transmission routes that occur. We show here that one can overcome these complexities by separating the host population into epidemiologically different types of individuals and constructing a matrix of reproduction numbers, the so-called next-generation matrix. Each matrix element is an expected number of infectious individuals of one type produced by a single infectious individual of a second type. The largest eigenvalue of the matrix characterizes the initial exponential growth or decline in numbers of infected individuals. Values below 1 therefore imply that the infection cannot establish. The biological interpretation closely matches that of R(0) for disease systems with only one type of individual and where infection is directly transmitted. The parameters defining each matrix element have a clear biological meaning. We illustrate the usefulness and power of the approach with a detailed examination of tick-borne diseases, and we use field and experimental data to parameterize the next-generation matrix for Lyme disease and tick-borne encephalitis. Sensitivity and elasticity analyses of the matrices, at the element and individual parameter levels, allow direct comparison of the two etiological agents. This provides further support that transmission between cofeeding ticks is critically important for the establishment of tick-borne encephalitis.     

Risk-return relationship in a complex adaptive system

For survival and development, autonomous agents in complex adaptive systems involving the human society must compete against or collaborate with others for sharing limited resources or wealth, by using different methods. One method is to invest, in order to obtain payoffs with risk. It is a common belief that investments with a positive risk-return relationship (namely, high risk high return and vice versa) are dominant over those with a negative risk-return relationship (i.e., high risk low return and vice versa) in the human society; the belief has a notable impact on daily investing activities of investors. Here we investigate the risk-return relationship in a model complex adaptive system, in order to study the effect of both market efficiency and closeness that exist in the human society and play an important role in helping to establish traditional finance/economics theories. We conduct a series of computer-aided human experiments, and also perform agent-based simulations and theoretical analysis to confirm the experimental observations and reveal the underlying mechanism. We report that investments with a negative risk-return relationship have dominance over those with a positive risk-return relationship instead in such a complex adaptive systems. We formulate the dynamical process for the system's evolution, which helps to discover the different role of identical and heterogeneous preferences. This work might be valuable not only to complexity science, but also to finance and economics, to management and social science, and to physics.     

Genetic and environment-induced pathways to innovation: on the possibility of a universal relationship between robustness and adaptation in complex biological systems

Recently, there has been considerable interest in the idea that mutational robustness enhances the propensity for future adaptations, i.e. evolvability, if evolution proceeds over a neutral network that extends far throughout a fitness landscape. While the genetic neutral network (NN-G) model may have important implications to our understanding of evolution, little has been done to integrate these theoretical developments with empirical evidence that heritable phenotypes can also originate and become fixated as a result of changes in the environment. In this brief commentary, I reconsider the role of environmental change in the adaptation of species and ask whether positive robustness-evolvability relationships might exist not only for genetic but also environmental buffering. In particular, I ask whether the insensitivity of species fitness towards variability in its environment can have a positive influence on the likelihood of future environment-induced adaptations (i.e. ecological opportunities) in a manner analogous to that proposed by the NN-G model. After outlining scenarios where such a counter-intuitive relationship appears plausible, I comment on the merits of evolutionary theories that can integrate complementary pathways to adaptation under static and time-variant environments. I also speculate on some of the features that such a theory might have.     

Gaia as a complex adaptive system

We define the Gaia system of life and its environment on Earth, review the status of the Gaia theory, introduce potentially relevant concepts from complexity theory, then try to apply them to Gaia. We consider whether Gaia is a complex adaptive system (CAS) in terms of its behaviour and suggest that the system is self-organizing but does not reside in a critical state. Gaia has supported abundant life for most of the last 3.8 Gyr. Large perturbations have occasionally suppressed life but the system has always recovered without losing the capacity for large-scale free energy capture and recycling of essential elements. To illustrate how complexity theory can help us understand the emergence of planetary-scale order, we present a simple cellular automata (CA) model of the imaginary planet Daisyworld. This exhibits emergent self-regulation as a consequence of feedback coupling between life and its environment. Local spatial interaction, which was absent from the original model, can destabilize the system by generating bifurcation regimes. Variation and natural selection tend to remove this instability. With mutation in the model system, it exhibits self-organizing adaptive behaviour in its response to forcing. We close by suggesting how artificial life ('Alife') techniques may enable more comprehensive feasibility tests of Gaia.     

The parametric pump mechanism in separation of components in heterogeneous systems. I. Macroscopic distributed systems.

A dynamic method is proposed for the separation of the electrolyte components using a parametric pump with an ion exchange column. It was studied experimentally and described mathematically. The parametric separation of mixtures is based on interactions of two oscillating fields with a heterogeneous system containing two phases, a liquid and a solid one, the components of the mixture being able to redistribute between the phases. The field of mechanical force is responsible for cyclic relative displacement of the phases, and synchronously changing temperature causes redistribution of the components between them. This results in sodium and potassium fluxes opposite in direction which in turn leads to accumulation of sodium and potassium in opposite end cells.     

Autonomy and robustness of translocation through the nuclear pore complex: a single-molecule study

All molecular traffic between nucleus and cytoplasm occurs via the nuclear pore complex (NPC) within the nuclear envelope. In this study we analyzed the interactions of the nuclear transport receptors kapα2, kapβ1, kapβ1ΔN44, and kapβ2, and the model transport substrate, BSA-NLS, with NPCs to determine binding sites and kinetics using single-molecule microscopy in living cells. Recombinant transport receptors and BSA-NLS were fluorescently labeled by AlexaFluor 488, and microinjected into the cytoplasm of living HeLa cells expressing POM121-GFP as a nuclear pore marker. After bleaching the dominant GFP fluorescence the interactions of the microinjected molecules could be studied using video microscopy with a time resolution of 5 ms, achieving a colocalization precision of 30 nm. These measurements allowed defining the interaction sites with the NPCs with an unprecedented precision, and the comparison of the interaction kinetics with previous in vitro measurements revealed new insights into the translocation mechanism.     

Quasi-multiparameter sensitivity measure for robustness analysis of complex biochemical networks

Robustness is the ability to resume reliable operation in the face of different types of perturbations. Analysis of how network structure achieves robustness enables one to understand and design cellular systems. It is typically true that all parameters simultaneously differ from their nominal values in vivo, but there have been few intelligible measures to estimate the robustness of a system's function to the uncertainty of all parameters.We propose a numerical and fast measure of a robust property to the uncertainty of all kinetic parameters, named quasi-multiparameter sensitivity (QMPS), which is defined as the sum of the squared magnitudes of single-parameter sensitivities. Despite its plain idea, it has hardly been employed in analysis of biological models. While QMPS is theoretically derived as a linear model, QMPS can be consistent with the expected variance simulated by the widely used Monte Carlo method in nonlinear biological models, when relatively small perturbations are given. To demonstrate the feasibility of QMPS, it is employed for numerical comparison to analyze the mechanism of how specific regulations generate robustness in typical biological models.QMPS characterizes the robustness much faster than the Monte Carlo method, thereby enabling the extensive search of a large parameter space to perform the numerical comparison between alternative or competing models. It provides a theoretical or quantitative insight to an understanding of how specific network structures are related to robustness. In circadian oscillators, a negative feedback loop with multiple phosphorylations is demonstrated to play a critical role in generating robust cycles to the uncertainty of multiple parameters.     

In pursuit of a molecular mechanism for adaptive gene amplification

"Adaptive" or "stationary-phase" mutation is a collection of apparent stress responses in which cells exposed to a growth-limiting environment generate genetic changes, some of which can allow resumption of rapid growth. In the well-characterized Lac system of Escherichia coli, reversions of a lac frameshift allele give rise to adaptive point mutations. Also in this system, adaptive gene amplification has been documented as a separate and parallel response that allows growth on lactose medium without acquisition of a compensatory frameshift mutation. In amplification, the DNA region containing the weakly functional lac allele becomes amplified to multiple copies, which produce sufficient enzyme activity to allow growth on the otherwise growth-limiting lactose medium. The amplifications are "adaptive" in that they occur after cells encounter the growth-limiting environment. Adaptive amplification is a reversible genetic change that allows adaptation and growth. It may be similar to chromosomal instability observed in the origins and progression of many cancers. We explore possible molecular mechanisms of adaptive amplification in the bacterial system and note parallels to chromosomal instability in other systems.