生物网络研究进展述评

生物网络是一类典型的复杂适应性系统,包含了许多个体的多层次的各种相互作用和关系,在过去的十年里,利用复杂网络理论对生物网络进行研究引起了人们的注意并获得了快速发展.本文首先从从度分布、聚类系数及鲁棒性等角度对现阶段生物网络性质的研究进行了简要介绍,后进一步对生物网络的聚类算法及主要建模理论做出了概括.今后的研究趋势在于如何建立合理的生物网络模型,以深入研究生物网络的各种性质.     

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.     

A survey of some formal models in tumor immunology

Summary Computer technology has acquired an important role in structuring a variety of biological systems. The availability of modern powerful computers has stimulated the development of good and accurate models of biological systems. Biological systems, such as the immune response against cancer, are complex and it is difficult to experimentally control all the interacting elements constituting the immune response of a host to cancer. Complex biosystems do not always behave or act as expected during experimental investigation. In these cases computer models can be helpful in understanding the behavior of such complex systems.The purpose of this review is to consider the use of mathematical models to study the immune response against cancer. The logic and design of some operable models relevant for tumor immunology will be discussed. Special attention is given to the conceptualization of a model based upon a new hypothesis of tumor rejection presented by De Weger et al. [10].Technical details concerning the mathematical aspects, differential equations, details on harware and software package etc. are not included in this survey. These details are contained to in the original papers.     

Nonlinear systems in medicine

Many achievements in medicine have come from applying linear theory to problems. Most current methods of data analysis use linear models, which are based on proportionality between two variables and/or relationships described by linear differential equations. However, nonlinear behavior commonly occurs within human systems due to their complex dynamic nature; this cannot be described adequately by linear models. Nonlinear thinking has grown among physiologists and physicians over the past century, and non-linear system theories are beginning to be applied to assist in interpreting, explaining, and predicting biological phenomena. Chaos theory describes elements manifesting behavior that is extremely sensitive to initial conditions, does not repeat itself and yet is deterministic. Complexity theory goes one step beyond chaos and is attempting to explain complex behavior that emerges within dynamic nonlinear systems. Nonlinear modeling still has not been able to explain all of the complexity present in human systems, and further models still need to be refined and developed. However, nonlinear modeling is helping to explain some system behaviors that linear systems cannot and thus will augment our understanding of the nature of complex dynamic systems within the human body in health and in disease states.     

The macro-ethics of genomics to health: the physiome project

Advances in pharmacology and genomics, and their intervention in human biology are beyond our abilities to understand their consequences. Therapeutic intervention in highly complex, non-linear, adaptive biological systems results in some unforeseen and undesirable consequences. To do the most good with the least harm, the information on biological systems should be gathered into databases, and into comprehensive quantitative models that can help to predict the long-range effects of proposed interventions. This is a societal or professional macro-ethical imperative. The Physiome Project helps to meet this imperative via databasing and creating models and tools for large-scale integration.     

Modelling Primate Behavioral Ecology

Models play an important role in any mature science because they force us to make explicit our assumptions about how a phenomenon works and allow us to explore the way in which different variables influence a complex biological system. I review the principal kinds of models that could be used to study primate behavior and ecology: linear programming models, systems models, optimality models, stochastic dynamic programming models and agent-based simulation models. Although less use has been made of modelling in primatology than in some other areas of behavioral ecology, there is considerable scope for exploiting the predictive and explanatory power of models in the field.     

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.     

Systems biology in drug discovery

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.     

On parameter synthesis by parallel model checking

An important problem in current computational systems biology is to analyze models of biological systems dynamics under parameter uncertainty. This paper presents a novel algorithm for parameter synthesis based on parallel model checking. The algorithm is conceptually universal with respect to the modeling approach employed. We introduce the algorithm, show its scalability, and examine its applicability on several biological models.     

On the Relationship of Steady States of Continuous and Discrete Models Arising from Biology

For many biological systems that have been modeled using continuous and discrete models, it has been shown that such models have similar dynamical properties. In this paper, we prove that this happens in more general cases. We show that under some conditions there is a bijection between the steady states of continuous and discrete models arising from biological systems. Our results also provide a novel method to analyze certain classes of nonlinear models using discrete mathematics.     

Computational models of cells and tissues: machines, agents and fungal infection

Computational models have been of interest in biology for many years and have represented a particular approach to trying to understand biological processes and phenomena from a systems point of view. Much of the early work was rather abstract and high level and probably seemed to many to be of more philosophical than practical value. There have, however, been some advances in the development of more realistic models and the current state of computer science research provides us with new opportunities through both the emergence of models that can model seriously complex systems and also the support that modern software can give to the modelling process. This paper describes a few of the early simple models and then goes on to look at some new ideas in the area with a particular application drawn from the world of mycology. Some general principles relating to how new and emerging computational techniques can help to represent and understand extremely complex models conclude the paper.     

Protonation free energy levels in complex molecular systems

All proteins, nucleic acids, and other biomolecules contain residues capable of exchanging protons with their environment. These proton transfer phenomena lead to pH sensitivity of many molecular processes underlying biological phenomena. In the course of biological evolution, Nature has invented some mechanisms to use pH gradients to regulate biomolecular processes inside cells or in interstitial fluids. Therefore, an ability to model protonation equilibria in molecular systems accurately would be of enormous value for our understanding of biological processes and for possible rational influence on them, like in developing pH dependent drugs to treat particular diseases. This work presents a derivation, by thermodynamic and statistical mechanical methods, of an expression for the free energy of a complex molecular system at arbitrary ionization state of its titratable residues. This constitutes one of the elements of modeling protonation equilibria. Starting from a consideration of a simple acid-base equilibrium of a model compound with a single tritratable group, we arrive at an expression which is of general validity for complex systems. The only approximation used in this derivation is the postulating that the interaction energy between any pair of titratable sites does not depend on the protonation states of all the remaining ionizable groups.     

Saltatory transitions are a naturally occurring property of evolving systems

On the basis of paleological evidence, it has been suggested that biological evolution need not necessarily be characterized by gradual change. Rather, evolutionary history may display saltatory periods of rapid speciation alternating with periods of relative quiescence, the whole dynamic being called punctuated equilibria. The empirical evidence that has been presented in support of this hypothesis has been the object of a vigorous dispute. Mathematical investigations of complex models of biological evolution that contain random elements have demonstrated that these systems can display saltatory behavior. In this paper we address a more abstract question: can saltations occur in the evolution of very simple, deterministic mathematical systems that function in a constant environment? The answer appears to be yes. Saltations appear as a natural dynamical behavior in the evolution of simplistic information processing networks. We stress that these networks do not constitute a model of biological evolution. However, the appearance of saltations in such simple systems suggests that their appearance in a process as complex as biological evolution is not surprising.     

Computer simulation of biological systems

The current status of mathematical models of biological systems is reviewed. Advances in supercomputer hardware allows more complex models to be constructed. The new generation of microcomputers are quite adequate for many computer simulations of biological systems. A theory of modeling is being developed to improve the relationship between the real biological system and the model. Deterministic models, stochastic models and applications of control theory and optimization methods are discussed. Examples given include models of molecular structure, of experimental techniques, and of biochemical reactions. It is recommended that experimental biologists consider the use of microcomputers to model the system under study as a part of their research program.     

Whole-genome expression analysis: challenges beyond clustering.

Measuring the expression of most or all of the genes in a biological system raises major analytic challenges. A wealth of recent reports uses microarray expression data to examine diverse biological phenomena - from basic processes in model organisms to complex aspects of human disease. After an initial flurry of methods for clustering the data on the basis of similarity, the field has recognized some longer-term challenges. Firstly, there are efforts to understand the sources of noise and variation in microarray experiments in order to increase the biological signal. Secondly, there are efforts to combine expression data with other sources of information to improve the range and quality of conclusions that can be drawn. Finally, techniques are now emerging to reconstruct networks of genetic interactions in order to create integrated and systematic models of biological systems.     

Reverse engineering systems models of regulation: discovery, prediction and mechanisms

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties.     

The effect of slow allosteric transitions in a coupled biochemical oscillator model.

The effect of slowed allosteric transitions in a coupled biochemical oscillator model showing complex dynamic behavior is investigated. When the allosteric transitions are sufficiently fast one can obtain a low-dimensional asymptotic approximation for the dynamics of the species that evolve on a slow time-scale. Such low-dimensional models are common in studies of biological control systems and little attention has, so far, been given to the dynamic effect of the large number of species usually eliminated from more biochemically detailed models. Here we investigate the dynamic effect of explicit inclusion of allosteric transitions having finite time-scales of equilibration. It is found that slowed allosteric transitions suppress complex dynamic modes such a bursting, quasi-periodicity and chaos. The effect arises as the enzyme of consideration becomes trapped in an active state where it is unable to respond to changes in effector concentration on the time-scale necessary to support the modes of complex dynamics. Slow allosteric transitions may be favourable in biological systems in which complex oscillations are not desirable but which, at the same time, may benefit from the presence of positive feedbacks. Our findings suggest that slow allosteric transitions and finite internal rates in general may contribute significantly to the dynamics of biological control mechanisms.     

The organization of time in biological systems

A notion of organization of time similar to the notion of organization of space in architecture has been introduced. The level and pattern of organization of time in biological systems differs from that in physical and chemical systems, which presents an independent problem. Analysis of the problem leads to a new definition of life as a process of renormalization of possibilities described by a Bayes formula. This definition leads to the notion of self-monitoring as a property of every biological system, and of complicated structure of the biological present, including the physical past and physical future. This is naturally followed by determination by far past, and, hence, memory, and determination by future, i.e. preadaptation, surpassing reflection, aim-setting etc. A direct dependence of a number of elements of a complex system on its stability has been demonstrated. The self-monitoring and organization of time can be traced at various levels of biological hierarchy from intracellular to biosphere level.     

Engineering microbes with synthetic biology frameworks

Typically, the outcome of biologically engineered unit operations cannot be controlled a priori due to the incorporation of ad hoc design into complex natural systems. To mitigate this problem, synthetic biology presents a systematic approach to standardizing biological components for the purpose of increasing their programmability and robustness when assembled with the aim to achieve novel biological functions. A complex engineered biological system using only standardized biological components is yet to exist. Nevertheless, current attempts to create and to implement modular, standardized biological components pave the way for the future creation of highly predictable artificial biological systems. Although synthetic biology frameworks can be applied to any biological engineering endeavor, this article will focus on providing a brief overview of advances in the field and its recent utilization for the engineering of microbes.     

Chemical biology of dynamic combinatorial libraries

Dynamic combinatorial chemistry (DCC) is a recently introduced supramolecular approach to generate libraries of chemical compounds based on reversible exchange processes. The building elements are spontaneously and reversibly assembled to virtually encompass all possible combinations, allowing for simple one-step generation of complex libraries. The method has been applied to a variety of combinatorial systems, ranging from synthetic models to materials science and drug discovery, and enables the establishment of adaptive processes due to the dynamic interchange of the library constituents and its evolution toward the best fit to the target. In particular, it has the potential to become a useful tool in the direct screening of ligands to a chosen receptor without extensive prior knowledge of the site structure, and several biological systems have been targeted. In the vast field of glycoscience, the concept may find special perspective in response to the highly complex nature of carbohydrate-protein interactions. This chapter summarises studies that have been performed using DCC in biological systems, with special emphasis on glycoscience.