Biological aspects of reactive nitrogen species.

Nitric oxide (NO) plays an important role as a cell-signalling molecule, anti-infective agent and, as most recently recognised, an antioxidant. The metabolic fate of NO gives rise to a further series of compounds, collectively known as the reactive nitrogen species (RNS), which possess their own unique characteristics. In this review we discuss this emerging aspect of the NO field in the context of the formation of the RNS and what is known about their effects on biological systems. While much of the insight into the RNS has been gained from the extensive chemical characterisation of these species, to reveal biological consequences this approach must be complemented by direct measures of physiological function. Although we do not know the consequences of many of the dominant chemical reactions of RNS an intriguing aspect is now emerging. This review will illustrate how, when specificity and amplification through cell signalling mechanisms are taken into account, the less significant reactions, in terms of yield or rates, can explain many of the biological responses of exposure of cells or physiological systems to RNS.     

Epidemiological aspects and organization of ecological expertise of project data

The necessity of organizing the ecological evaluation of projects, the interrelation of measures for the protection of nature and ecological forecasting, the importance of introducing the section of ecological forecasting into every project are discussed. Changes in the ecological systems, occurring due to different kinds of human intervention, and their consequences for human health are shown. The organization of the ecological evaluation of project plans and specifications at different administrative levels of the sanitary and epidemiological service and at institutes is proposed , which must ensure the elimination-of consequences, unfavorable for human health.     

Network thinking in ecology and evolution

Although pairwise interactions have always had a key role in ecology and evolutionary biology, the recent increase in the amount and availability of biological data has placed a new focus on the complex networks embedded in biological systems. The increased availability of computational tools to store and retrieve biological data has facilitated wide access to these data, not just by biologists but also by specialists from the social sciences, computer science, physics and mathematics. This fusion of interests has led to a burst of research on the properties and consequences of network structure in biological systems. Although traditional measures of network structure and function have started us off on the right foot, an important next step is to create biologically realistic models of network formation, evolution, and function. Here, we review recent applications of network thinking to the evolution of networks at the gene and protein level and to the dynamics and stability of communities. These studies have provided new insights into the organization and function of biological systems by applying existing techniques of network analysis. The current challenge is to recognize the commonalities in evolutionary and ecological applications of network thinking to create a predictive science of biological networks.     

Executable cell biology

Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.     

Reconstructing Causal Biological Networks through Active Learning

Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.     

Simulating autonomous anticipation: the importance of Dubois' conjecture

Anticipation allows a system to adapt to conditions that have not yet come to be, either externally to the system or internally. Autonomous systems actively control their own conditions so as to increase their functionality (they self-regulate). Living systems self-regulate in order to increase their own viability. These increasingly stronger conditions, anticipation, autonomy and viability, can give an insight into progressively stronger classes of models of autonomy. I will argue that stronger forms are the relevant ones for Artificial Life. This has consequences for the design of and accurate simulation of living systems.     

Biological nitrogen and phosphorus removal in membrane bioreactors: model development and parameter estimation

Membrane bioreactors (MBR) are being increasingly used for wastewater treatment. Mathematical modeling of MBR systems plays a key role in order to better explain their characteristics. Several MBR models have been presented in the literature focusing on different aspects: biological models, models which include soluble microbial products (SMP), physical models able to describe the membrane fouling and integrated models which couple the SMP models with the physical models. However, only a few integrated models have been developed which take into account the relationships between membrane fouling and biological processes. With respect to biological phosphorus removal in MBR systems, due to the complexity of the process, practical use of the models is still limited. There is a vast knowledge (and consequently vast amount of data) on nutrient removal for conventional-activated sludge systems but only limited information on phosphorus removal for MBRs. Calibration of these complex integrated models still remains the main bottleneck to their employment. The paper presents an integrated mathematical model able to simultaneously describe biological phosphorus removal, SMP formation/degradation and physical processes which also include the removal of organic matter. The model has been calibrated with data collected in a UCT-MBR pilot plant, located at the Palermo wastewater treatment plant, applying a modified version of a recently developed calibration protocol. The calibrated model provides acceptable correspondence with experimental data and can be considered a useful tool for MBR design and operation.     

The immuno-dynamics of conflict intervention in social systems

We present statistical evidence and dynamical models for the management of conflict and a division of labor (task specialization) in a primate society. Two broad intervention strategy classes are observed--a dyadic strategy--pacifying interventions, and a triadic strategy--policing interventions. These strategies, their respective degrees of specialization, and their consequences for conflict dynamics can be captured through empirically-grounded mathematical models inspired by immuno-dynamics. The spread of aggression, analogous to the proliferation of pathogens, is an epidemiological problem. We show analytically and computationally that policing is an efficient strategy as it requires only a small proportion of a population to police to reduce conflict contagion. Policing, but not pacifying, is capable of effectively eliminating conflict. These results suggest that despite implementation differences there might be universal features of conflict management mechanisms for reducing contagion-like dynamics that apply across biological and social levels. Our analyses further suggest that it can be profitable to conceive of conflict management strategies at the behavioral level as mechanisms of social immunity.     

Biology by numbers: mathematical modelling in developmental biology

In recent years, mathematical modelling of developmental processes has earned new respect. Not only have mathematical models been used to validate hypotheses made from experimental data, but designing and testing these models has led to testable experimental predictions. There are now impressive cases in which mathematical models have provided fresh insight into biological systems, by suggesting, for example, how connections between local interactions among system components relate to their wider biological effects. By examining three developmental processes and corresponding mathematical models, this Review addresses the potential of mathematical modelling to help understand development.     

Replication in abstract and natural systems

Real reproducing systems are contrasted with cellular automata-based models of self-reproduction. It is argued that the reproduction described by the latter is trivial compared to its biological counterpart. The paper explores a few reasons and consequences of this situation, along with a discussion on different forms of the basic reproduction (and construction) process.     

Modeling Human Ecodynamics and Biocultural Interactions in the Late Pleistocene of Western Eurasia

Given the complex and multidimensional nature of human evolution, we need to develop theoretical and methodological frameworks to account for and model the dynamic feedbacks between co-operational biological and cultural evolutionary systems to better understand the processes that produced modern human behavior. Equally important is the generation of explicit theory-based models that can be tested against the empirical paleoanthropological record. We present a case study that examines evidence for culturally-driven behavioral change among Late Pleistocene hominins that altered the social niche occupied by hominins in western Eurasia, with consequences for subsequent biological and cultural evolution. We draw on a large sample of 167 Pleistocene assemblages across western Eurasia and employ mathematical and computational modeling to explore the feedbacks between cultural and biological inheritance. Shifts in land-use strategies changed the opportunities for social and biological interaction among Late Pleistocene hominins in western Eurasia with a cascade of consequences for cultural and biological evolution, including the disappearance of Neanderthals from the fossil and archaeological records, and the acceleration of cultural evolution among ancestors of modern humans.     

Genomic position mapping discrepancies of commercial SNP chips

The field of genetics has come to rely heavily on commercial genotyping arrays and accompanying annotations for insights into genotype-phenotype associations. However, in order to avoid errors and false leads, it is imperative that the annotation of SNP chromosomal positions is accurate and unambiguous. We report on genomic positional discrepancies of various SNP chips for human, cattle and mouse species, and discuss their causes and consequences.     

Invasion speeds in fluctuating environments

Biological invasions are increasingly frequent and have dramatic ecological and economic consequences. A key to coping with invasive species is our ability to predict their rates of spread. Traditional models of biological invasions assume that the environment is temporally constant. We examine the consequences for invasion speed of periodic and stochastic fluctuations in population growth rates and in dispersal distributions.     

Environment-dependent inbreeding depression: its ecological and evolutionary significance

Inbreeding depression is a major evolutionary and ecological force that influences population dynamics and the evolution of inbreeding-avoidance traits such as mating systems and dispersal. There is now compelling evidence that inbreeding depression is environment-dependent. Here, we discuss ecological and evolutionary consequences of environment-dependent inbreeding depression. The environmental dependence of inbreeding depression may be caused by environment-dependent phenotypic expression, environment-dependent dominance, and environment-dependent natural selection. The existence of environment-dependent inbreeding depression challenges classical models of inbreeding as caused by unconditionally deleterious alleles, and suggests that balancing selection may shape inbreeding depression in natural populations; loci associated with inbreeding depression in some environments may even contribute to adaptation to others. Environment-dependent inbreeding depression also has important, often neglected, ecological and evolutionary consequences: it can influence the demography of marginal or colonizing populations and alter adaptive optima of mating systems, dispersal, and their associated traits. Incorporating the environmental dependence of inbreeding depression into theoretical models and empirical studies is necessary for understanding the genetic and ecological basis of inbreeding depression and its consequences in natural populations.     

Hierarchical semantic composition of biosimulation models using bond graphs

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.     

By way of introduction: modelling living systems, their diversity and their complexity: some methodological and theoretical problems

Some principles for a current methodology for biological systems' modelling are presented. It seems possible to promote a model-centred approach of these complex systems. Among present questions, the role of mechanisms producing random or quasi-random issues is underlined, because they are implied in biological diversification and in resulting complexity of living systems. Now, biodiversity is one of our societies' and scientific research's main concerns. Basically, it can be interpreted as a manner, for Life, to resist environmental hazards. Thus, one may assume that biodiversity producing mechanisms could be selected during evolution to face to corresponding risks of disappearance: necessity of chance? Therefore, analysing and modelling these ‘biological and ecological roulettes’ would be important, and not only their outputs like nowadays by using the theory of probabilities. It is then suggested that chaotic behaviours generated by deterministic dynamical systems could mimic random processes, and that ‘biological and ecological roulettes’ would be represented by such models. Practical consequences can be envisaged in terms of biodiversity management, and more generally in terms of these ‘roulettes’ control to generate selected biological and ecological events' distribution. To cite this article: A. Pavé, C. R. Biologies 329 (2006).     

Impact of periodic nutrient input rate on trophic chain properties

Marine ecosystems are characterized by a strong influence of hydrodynamics on biological processes. The associated models involve the coupling of physical to biological models and therefore require a large number of state variables. The consequent high complexity limits our capacity to perform a complete and detailed study and even prevents any complete mathematical study of these models. It is also difficult to disentangle among all the processes involved, which ones actually drive the system at any moment. Hydrodynamics, among other consequences, affect the way under which the nutrients are supplied to marine ecosystems. The variability of nutrient input rate in marine systems generally results from runs-off in coastal systems and from physical processes (wind forcing and hydrodynamics) in open ocean. This paper is devoted to the study of the effects of the nutrient input rate variability on the dynamics and the functioning of trophic chains. In this context, we aim to provide an understandable study, based on simplified system models. We consider a periodic nutrient input rate and analyze how this variability modifies some system properties: its dynamics, its functioning and its structure. The dynamics is obtained by numerical simulations and when possible, enlighten by already published mathematical results. The functioning is measured by the time averaged state variables during the simulation period, and their variability. The structure concerns the number of surviving populations, a proxy of specific biodiversity. We show how these properties can be affected and provide some conditions under which the modifications can occur. We also highlight that, even if the physical process is the main driving force in the global dynamics, the choice of the biological model is important to understand the biological response of the system to physical forcing.