Applications of mass spectrometry in metabolomic studies of animal model and invertebrate systems

Metabolomics provides rich datasets for systems biology. Massspectrometric (MS) techniques are rapidly gaining in importancefor untargeted metabolic profiling. In this review, we surveythe various techniques for sample preparation and analysis relatingto the various MS techniques and illustrate the potential ofthese techniques for both observing complete metabolomes anddetecting changes in the metabolism resulting from genetic mutationof other perturbations. The use of some of these techniquesin the study of model organisms including rodent and variousinvertebrate models is described. The invertebrate systems areof particular interest since such organisms have valuable mutantresources, such as RNAi panels directed against nearly all thegenes in the genome. The demonstration that they are readilycompatible with metabolomic approaches is particularly importantfor systems approaches to metabolic pathways.      

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。     

Zebrafish as a model for systems biology

Zebrafish offer a unique vertebrate model for research areas such as drug development, disease modeling and other biological exploration. There is significant conservation of genetics and other cellular networks among zebrafish and other vertebrate models, including humans. Here we discuss the recent work and efforts made in different fields of biology to explore the potential of zebrafish. Along with this, we also reviewed the concept of systems biology. A biological system is made up of a large number of components that interact in a huge variety of combinations. To understand completely the behavior of a system, it is important to know its components and interactions, and this can be achieved through a systems biology approach. At the end of the paper we present a concept of integrating zebrafish into the systems biology approach.     

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.     

The origin of correlations in metabolomics data

A phenomenon observed earlier in the development of metabolomics as a systems biology methodology, consists of a small but significant number of metabolites whose levels are highly correlated between biological replicates. Contrary to initial interpretations, these correlations are not necessarily only between neighboring metabolites in the metabolic network. Most metabolites that participate in common reactions are not correlated in this way, while some non-neighboring metabolites are highly correlated. Here we investigate the origin of such correlations using metabolic control analysis and computer simulation of biochemical networks. A series of cases is identified which lead to high correlation between metabolite pairs in replicate measurement. These are (1) chemical equilibrium, (2) mass conservation, (3) asymmetric control distribution, and (4) unusually high variance in the expression of a single gene. The importance of identifying metabolite correlations within a physiological state and changes of correlation between different states is discussed in the context of systems biology.     

从一般系统论到后基因组时代的系统生物学

论述了贝塔朗菲的一般系统论的思想起源、主要内容,基于一般系统论的系统生物学的产生、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,以及系统生物学未来的发展进行了评价。     

Improved understanding of gene expression regulation using systems biology

This article reviews the current state of systems biology approaches, including the experimental tools used to generate ‘omic’ data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.     

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.     

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。     

生理学与基因组学:走向系统生物学

近十年来,生理学与基因组学达到了空前的融合。尽管生理基因组学还是一个非常年轻的研究领域,系统生物学概念的引入必将推进生理基因组学达到全新的水平。本文概要地叙述了这个令人振奋的生理科学的新时代给生理学家带来的机遇和挑战,并以我们自己近十年来的经验为例,讨论了怎样通过扩展和延伸生理学与基因组学的结合,从而对生物学得到系统的理解。     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.