系统生物学——生命科学的新领域

系统生物学是继基因组学、蛋白质组学之后一门新兴的生物学交叉学科,代表21世纪生物学的未来.最近,系统生物学研究机构纷纷成立.在研究上,了解一个复杂的生物系统需要整合实验和计算方法.基因组学和蛋白质组学中的高通量方法为系统生物学发展提供了大量的数据.计算生物学通过数据处理、模型构建和理论分析,成为系统生物学发展的一个必不可缺、强有力的工具.在应用上,系统生物学代表新一代医药开发和疾病防治的方向.     

Theses on Darwin.

The received view that teleology has been successfully eliminated from the modern scientific worldview is challenged. It is argued that both the theory of natural selection and molecular biology presuppose the existence of natural teleology, and so cannot explain it. A number of other issues in the foundations of biology are briefly examined, while stress is laid throughout on empirical evidence of the rational agency inherent in life. It is urged that teleology be rehabilitated and that the reigning functionalist philosophy be replaced by a realistic view of biological functions as emergent properties of living matter within a broad, selforganization framework.     

The Promise and Peril of Continuous In Vitro Evolution

Experimental evolution methods can be used to address and illuminate issues central to the understanding of evolutionary theory. One of the most powerful of these methods involves the in vitro evolution of nucleic acid enzymes, taking advantage of the direct relationship between the genotype of a nucleic acid sequence and the phenotype of its associated catalytic function. This review and commentary focuses on the past, present, and future potential of systems for the continuous in vitro evolution of nucleic acid enzymes as tools for modeling evolutionary processes in biology. It offers a candid appraisal of both the strengths and the limitations of these systems.     

The interface of protein structure, protein biophysics, and molecular evolution

Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction.     

系统生物学研究及其在医学中的应用

目前,系统生物学研究已初显端倪。生物学正从分子生物学走向系统生物学,由精细的分解研究转向系统的整体研究,由还原论的研究方法过渡到系统论的研究方法。简要论述了系统生物学的产生背景、结构和内容、研究思路和方法、与医学的关系等,重点介绍系统生物学的内容和研究方法,以及在疾病治疗和药物开发中的研究进展。     

细菌耐受噬菌体感染的分子机制研究进展

噬菌体是能感染细菌的病毒。为了抵抗噬菌体的感染,细菌进化出多种抵抗噬菌体感染的机制,这些机制的阐析极大地促进了基因编辑领域的发展,同时也为噬菌体治疗的开展奠定了基础。本文就细菌针对噬菌体感染的各个环节所进行的抵抗及其分子机制进行了简要综述,同时讨论了这些防御系统的存在对细菌自身的影响,分析了当前细菌耐受噬菌体机制研究存在的局限性,并对未来研究进行了展望。     

传统中国医药与现代系统生物学研究理论的殊途同归

系统生物学是研究一个生物系统中所有组成成分(基因、mRNA、蛋白质等)的构成与组分之间相互关系的学科,近年来,系统生物学作为后基因组学时代研究的一个重要内容,已广泛深入到生命科学和医药学的各个领域。而作为中国传统医学而言,似乎与之相去甚远,然而当我们对这两个新老学科基础理论进行比较时,我们发现:传统中国医药与现代系统生物学研究理论的殊途同归。有鉴于此,本文论述了系统生物学和中医学的思想起源、相互联系,基于系统生物学的发展、研究思路和方法,阐述了生物学由还原论的研究方法过渡到系统论的研究方法,强调对生命现象从系统和整体的层次进行研究和把握,对传统中医学研究方法的变革起到了推动作用,最后对系统生物学在中医药学未来发展进行了评价。     

Modeling and simulation of molecular biology systems using petri nets: modeling goals of various approaches

Petri nets are a discrete event simulation approach developed for system representation, in particular for their concurrency and synchronization properties. Various extensions to the original theory of Petri nets have been used for modeling molecular biology systems and metabolic networks. These extensions are stochastic, colored, hybrid and functional. This paper carries out an initial review of the various modeling approaches based on Petri net found in the literature, and of the biological systems that have been successfully modeled with these approaches. Moreover, the modeling goals and possibilities of qualitative analysis and system simulation of each approach are discussed.     

Biological applications of the theory of birth-and-death processes

In this review, we discuss applications of the theory of birth-and-death processes to problems in biology, primarily, those of evolutionary genomics. The mathematical principles of the theory of these processes are briefly described. Birth-and-death processes, with some straightforward additions such as innovation, are a simple, natural and formal framework for modeling a vast variety of biological processes such as population dynamics, speciation, genome evolution, including growth of paralogous gene families and horizontal gene transfer and somatic evolution of cancers. We further describe how empirical data, e.g. distributions of paralogous gene family size, can be used to choose the model that best reflects the actual course of evolution among different versions of birth-death-and-innovation models. We conclude that birth-and-death processes, thanks to their mathematical transparency, flexibility and relevance to fundamental biological processes, are going to be an indispensable mathematical tool for the burgeoning field of systems biology.     

Why Are Computational Neuroscience and Systems Biology So Separate?

Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both fields can learn quite a bit from each other. Several examples are given, covering sociological, software technical, and methodological aspects. Systems biology is a better organized community which is very effective at sharing resources, while computational neuroscience has more experience in multiscale modeling and the analysis of information processing by biological systems. Finally, I speculate about how the relationship between the two fields may evolve in the near future.     

Network genomics – A novel approach for the analysis of biological systems in the post-genomic era

Network Genomics studies genomics and proteomics foundations of cellular networks in biological systems. It complements systems biology in providing information on elements, their interaction and their functional interplay in cellular networks. The relationship between genomic and proteomic high-throughput technologies and computational methods are described, as well as several examples of specific network genomic application are presented.     

Systems biology as a foundation for genome-scale synthetic biology

As the ambitions of synthetic biology approach genome-scale engineering, comprehensive characterization of cellular systems is required, as well as a means to accurately model cell-scale molecular interactions. These requirements are coincident with the goals of systems biology and, thus, systems biology will become the foundation for genome-scale synthetic biology. Systems biology will form this foundation through its efforts to reconstruct and integrate cellular systems, develop the mathematics, theory and software tools for the accurate modeling of these integrated systems, and through evolutionary mechanisms. As genome-scale synthetic biology is so enabled, it will prove to be a positive feedback driver of systems biology by exposing and forcing researchers to confront those aspects of systems biology which are inadequately understood.     

Overview of Mathematical Approaches Used to Model Bacterial Chemotaxis I: The Single Cell

Mathematical modeling of bacterial chemotaxis systems has been influential and insightful in helping to understand experimental observations. We provide here a comprehensive overview of the range of mathematical approaches used for modeling, within a single bacterium, chemotactic processes caused by changes to external gradients in its environment. Specific areas of the bacterial system which have been studied and modeled are discussed in detail, including the modeling of adaptation in response to attractant gradients, the intracellular phosphorylation cascade, membrane receptor clustering, and spatial modeling of intracellular protein signal transduction. The importance of producing robust models that address adaptation, gain, and sensitivity are also discussed. This review highlights that while mathematical modeling has aided in understanding bacterial chemotaxis on the individual cell scale and guiding experimental design, no single model succeeds in robustly describing all of the basic elements of the cell. We conclude by discussing the importance of this and the future of modeling in this area.     

Flow cytometry and cell sorting: current state and prospects for use in molecular biology

A survey of the specific features of flow cytometry, principals of instrumentation and main parameters of the modern cell sorting cytometers is given. Analytical capacities of flow cytometry as well as the main directions of its applications in cell biology, clinical diagnostics, immunology, biotechnology and molecular biology are considered. Also a possible future development of flow cytometry instrumentation and applications in molecular biology are briefly discussed.     

生物进化研究的回顾与展望

生物进化是自然科学的永恒之迷。随着历史的发展和科学的进步,生物进化思想从早期的萌芽,到自然选择学说、新达尔文主义,从现代综合理论,到分子进化的中性学说。再到新灾变论和点断平衡论等。当前,由于生物学各分支学科的飞速发展．它们就各自的研究对象在宏观和微观上不断地拓展和深入,并在不同的层次上形成了广泛的交叉、渗透和融合,现代的进化生物学研究从宏观的表型到微观的分子,从群体遗传改变的微进化到成种事件以及地史上生物类群谱系演化的宏进化,从直接的化石证据到基于形态性状、分子证据和环境变迁的综合推理,从基于遗传基础的比较基因组学到演化机理的进化发育生物学等。可以预见,在新的世纪里,在哲学和具体方法论(如系统论、控制论和信息论)的指导下,在生命科学、其他自然科学乃至社会科学工作者的通力合作下,综合遗传、发育和进化等研究领域的各种理论成果,生物进化理论即将出现也一定会出现的一个新的大综合和新的大统一。     

New analysis technique for multistability detection

Systems with counter-clockwise input-output (I-O) dynamics were recently introduced in order to study the convergence of positive feedback loops (possibly to many different equilibrium states). The author shows how this notion can be used to perform bifurcation analysis and globally predict multistability of a closed-loop feedback interconnection just by using the knowledge of steady-state I-O responses of the systems. To illustrate the theory, this method is then applied to a recently published model of mitogen activated protein kinase (MAPK) cascade. Furthermore, some examples (mainly motivated by molecular biology) of systems that enjoy the property are presented and discussed.     

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.     

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.     

ZFIQ: a software package for zebrafish biology

Rapid development, transparency and small size are the outstanding features of zebrafish that make it as an increasingly important vertebrate system for developmental biology, functional genomics, disease modeling and drug discovery. Zebrafish has been regarded as ideal animal specie for studying the relationship between genotype and phenotype, for pathway analysis and systems biology. However, the tremendous amount of data generated from large numbers of embryos has led to the bottleneck of data analysis and modeling. The zebrafish image quantitator (ZFIQ) software provides streamlined data processing and analysis capability for developmental biology and disease modeling using zebrafish model. AVAILABILITY: ZFIQ is available for download at http://www.cbi-platform.net.