Resources, standards and tools for systems biology.

Modelling and simulation techniques are valuable tools for the understanding of complex biological systems. The design of a computer model necessarily has many diverse inputs, such as information on the model topology, reaction kinetics and experimental data, derived either from the literature, databases or direct experimental investigation. In this review, we describe different data resources, standards and modelling and simulation tools that are relevant to integrative systems biology.     

系统生物学中建模方法的研究现状及展望

系统生物学倡导利用系统论的思想和方法,从整体的高度分析、研究生命的复杂特性。这一点与实验生物学仅关注某一个或者某一些生物大分子是迥然不同的。系统生物学既要同时考虑多个层次、多种类型的生物信息,还要考虑时间因素。由于系统特性是由于不同组成部分、不同层次间相互作用而“涌现”出的新性质,因此,如果只是针对组成部分或单一层次的分析并不能真正准确地预测整体或高层次的行为。如何通过研究和整合去发现和理解“涌现”出的新的系统性质,是系统生物学面临的一个根本性的挑战。为了应对这一挑战,系统生物学,特别是计算系统生物学必须建立有效的方法,通过整合系统各个层次的信息,建立可反映该系统目前已知或已可测量的性质的物理、数学模型,并通过这样的模型来研究或预测目前还未知晓的系统性状。可以说：建模是系统生物学的最重要的研究手段之一。目前,生命科学的研究正逐步由对单一现象、单一过程的机械论式的描述型研究转向运用高通量实验技术获取海量生物信息,并在这些生物信息基础上建立物理、数学模型,最终通过建模与实验相接合的研究手段来定量阐述生命现象的本质规律。由于建模方法在系统生物学研究中的重要性,本文将对一些主要的建模类型,如定性建模方法;基于约束的建模方法;基于常微分/偏微分方程的定量建模和基于随机微分方程的定量建模方法等等分别予以简要介绍。     

Advanced computing for systems biology

Systems biology is based on computational modelling and simulation of large networks of interacting components. Models may be intended to capture processes, mechanisms, components and interactions at different levels of fidelity. Input data are often large and geographically disperse, and may require the computation to be moved to the data, not vice versa. In addition, complex system-level problems require collaboration across institutions and disciplines. Grid computing can offer robust, scaleable solutions for distributed data, compute and expertise. We illustrate some of the range of computational and data requirements in systems biology with three case studies: one requiring large computation but small data (orthologue mapping in comparative genomics), a second involving complex terabyte data (the Visible Cell project) and a third that is both computationally and data-intensive (simulations at multiple temporal and spatial scales). Authentication, authorisation and audit systems are currently not well scalable and may present bottlenecks for distributed collaboration particularly where outcomes may be commercialised. Challenges remain in providing lightweight standards to facilitate the penetration of robust, scalable grid-type computing into diverse user communities to meet the evolving demands of systems biology.     

Whither systems biology

Cell biologists are interested in how complexity arises from the interaction of different molecules. However, cells are many orders of magnitude larger than the protein-binding interfaces. To bridge these vast difference in scales, biologists construct hierarchies of organization of cellular structures. I describe how systems biology provides an approach to bridge these different scales.     

Agents in bioinformatics, computational and systems biology

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.     

Biochemical simulations: stochastic, approximate stochastic and hybrid approaches

Computer simulations have become an invaluable tool to studythe sometimes counterintuitive temporal dynamics of (bio-)chemicalsystems. In particular, stochastic simulation methods have attractedincreasing interest recently. In contrast to the well-knowndeterministic approach based on ordinary differential equations,they can capture effects that occur due to the underlying discretenessof the systems and random fluctuations in molecular numbers.Numerous stochastic, approximate stochastic and hybrid simulationmethods have been proposed in the literature. In this article,they are systematically reviewed in order to guide the researcherand help her find the appropriate method for a specific problem.      

Pharmacogenomics and systems biology of membrane transporters

Membrane transporters are essential for fundamental cellular functions and normal physiological processes. These molecules influence drug absorption and distribution, and play key roles in drug therapeutic effects. A primary goal of current research in drug discovery and development is to fully understand the interaction between transporters and drugs at both system level and individual level for personalized therapy. Pharmacogenomics studies the genetic basis of the individual variations in response to drug therapy, whereas systems biology provides the understanding of biological processes at the system level. The integration of pharmacogenomics with systems biology in membrane transporter study is necessary to solve complex problems in diseases and drug effects. Such integration provides insight to key issues of pharmacogenomics and systems biology of membrane transporters. These key issues include the correlations between structure and function, genotype and phenotype, and systematic interactions between different transporters, between transporters and other proteins, and between transporters and drugs. The exploration in these key issues may ultimately contribute to the personalized medicine with high efficacy but less toxicity, which is the overall goal of pharmacogenomics and systems biology.     

A day of systems and synthetic biology for non‐experts

From understanding ageing to the creation of artificial membrane‐bounded ‘organisms’, systems biology and synthetic biology are seen as the latest revolutions in the life sciences. They certainly represent a major change of gear, but paradigm shifts? This is open to debate, to say the least. For scientists they open up exciting ways of studying living systems, of formulating the ‘laws of life’, and the relationship between the origin of life, evolution and artificial biological systems. However, the ethical and societal considerations are probably indistinguishable from those of human genetics and genetically modified organisms. There are some tangible developments just around the corner for society, and as ever, our ability to understand the consequences of, and manage, our own progress lags far behind our technological abilities. Furthermore our educational systems are doing a bad job of preparing the next generation of scientists and non‐scientists.     

Modeling and simulation of biological systems from image data

This essay provides an introduction to the terminology, concepts, methods, and challenges of image‐based modeling in biology. Image‐based modeling and simulation aims at using systematic, quantitative image data to build predictive models of biological systems that can be simulated with a computer. This allows one to disentangle molecular mechanisms from effects of shape and geometry. Questions like “what is the functional role of shape” or “how are biological shapes generated and regulated” can be addressed in the framework of image‐based systems biology. The combination of image quantification, model building, and computer simulation is illustrated here using the example of diffusion in the endoplasmic reticulum.     

Toward computational systems biology

The development and successful application of high-throughput technologies are transforming biological research. The large quantities of data being generated by these technologies have led to the emergence of systems biology, which emphasizes large-scale, parallel characterization of biological systems and integration of fragmentary information into a coherent whole. Complementing the reductionist approach that has dominated biology for the last century, mathematical modeling is becoming a powerful tool to achieve an integrated understanding of complex biological systems and to guide experimental efforts of engineering biological systems for practical applications. Here I give an overview of current mainstream approaches in modeling biological systems, highlight specific applications of modeling in various settings, and point out future research opportunities and challenges.     

Analyzing the biology on the system level

Although various genome projects have provided us enormous static sequence information, understanding of the sophisticated biology continues to require integrating the computational modeling, system analysis, technology development for experiments, and quantitative experiments all together to analyze the biology architecture on various levels, which is just the origin of systems biology subject. This review discusses the object, its characteristics, and research attentions in systems biology, and summarizes the analysis methods, experimental technologies, research developments, and so on in the four key fields of systems biology--systemic structures, dynamics, control methods, and design principles.     

系统生物学与细胞发生系统动力学

系统生物学是系统理论和实验生物技术、计算机数学模型等方法整合的生物系统研究,系统遗传学研究基因组的稳态与进化、功能基因组和生物性状等复杂系统的结构、动态与发生演变等。合成生物学是系统生物学的工程应用,采用工程学方法、基因工程和计算机辅助设计等研究人工生物系统的生物技术。系统与合成生物学的结构理论,序列标志片段显示分析与微流控生物芯片,广泛用于研究细胞代谢、繁殖和应激的自组织进化、生物体形态发生等细胞分子生物系统原理等。     

Web-based applications for building, managing and analysing kinetic models of biological systems

Mathematical modelling and computational analysis play an essentialrole in improving our capability to elucidate the functionsand characteristics of complex biological systems such as metabolic,regulatory and cell signalling pathways. The modelling and concomitantsimulation render it possible to predict the cellular behaviourof systems under various genetically and/or environmentallyperturbed conditions. This motivates systems biologists/bioengineers/bioinformaticiansto develop new tools and applications, allowing non-expertsto easily conduct such modelling and analysis. However, amonga multitude of systems biology tools developed to date, onlya handful of projects have adopted a web-based approach to kineticmodelling. In this report, we evaluate the capabilities andcharacteristics of current web-based tools in systems biologyand identify desirable features, limitations and bottlenecksfor further improvements in terms of usability and functionality.A short discussion on software architecture issues involvedin web-based applications and the approaches taken by existingtools is included for those interested in developing their ownsimulation applications.      

系统生物学和合成生物学研究在生物燃料生产菌株改造中的应用

基于生物质资源生产环境友好的生物燃料,对经济和社会的可持续发展具有重要意义,但其生产成本高的问题十分突出,而高效生产菌株的获得是解决这一问题的根本出路。以下综述了利用系统生物学研究所获得的信息进行菌种改造的过程,重点论述了生产菌株胁迫耐受性方面的研究进展,并讨论了系统生物学、合成生物学和代谢工程技术在改造生物燃料生产菌株中的应用,展望了合成生物学在构建高效生物能源生产菌株方面应用的前景。     

Metabolic engineering based on systems biology for chemicals production

Microorganisms have been the main sources for the production of chemicals. Production of chemicals requires the development of low-cost and higher-yield processes. Towards this goal, microbial strains with higher levels of production should be first considered. Metabolic engineering has been used extensively over the past two to three decades to increase production of these chemicals. Advances in omics technology and computational simulation are allowing us to perform metabolic engineering at the systems level. By combining the results of omics analyses and computational simulation, systems biology allows us to understand cellular physiology and characteristics, which can subsequently be used for designing strategies. Here, we review the current status of metabolic engineering based on systems biology for chemical production and discuss future prospects.