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第9行： − − − Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research. When it is crossing the field of systems theory and the applied mathematics methods, it develops into the sub-branch of complex systems biology.+ − − − + − Particularly from year 2000 onwards, the concept has been used widely in biology in a variety of contexts. The Human Genome Project is an example of applied systems thinking in biology which has led to new, collaborative ways of working on problems in the biological field of genetics. One of the aims of systems biology is to model and discover emergent properties, properties of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques of systems biology. − + − + − − Systems biology can be considered from a number of different aspects. − − Systems biology can be considered from a number of different aspects. 第40行：
第31行： − As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway or the heart beats).+ − − 系统生物学是这样一个研究领域，研究生物系统各组成部分之间的相互作用，以及这些相互作用如何产生该系统的功能和行为(例如，代谢通路或心跳中的酶和代谢物)。 − + − As a paradigm, systems biology is usually defined in antithesis to the so-called reductionist paradigm (biological organisation), although it's fully consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations: "The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." (Sauer et al.) "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in the full sense of the term." (Denis Noble) − + − + − + − + − + − As a series of operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models. − − 系统生物学是一系列用于进行研究的操作方案，即一个由理论、分析或计算模型组成的循环，首先提出关于生物系统的具体可检验的假设，接着进行实验验证，然后使用新获得的细胞或细胞过程的定量描述来优化计算模型或理论。既然目标是一个系统中相互作用的模型，那么最适合系统生物学的实验技术就是那些全系统范围的、尽可能完整的实验技术。因此，转录组学、代谢组学、蛋白质组学和高通量技术被用来收集定量数据，从而用于模型的建立和验证。 − --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“系统生物学是一系列用于进行研究的操作方案，即一个由理论、分析或计算模型组成的循环，首先提出”一句改为“作为一系列用于进行研究的操作方案，即一个由理论、分析或计算模型组成的循环，系统生物学提出”+ − − --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“既然目标是一个系统中相互作用的模型，那么最适合系统生物学的实验技术就是那些全系统范围的、尽可能完整的实验技术。”一句中的“既然、那么”改为“由于、所以” + + − As the application of dynamical systems theory to molecular biology. Indeed, the focus on the dynamics of the studied systems is the main conceptual difference between systems biology and bioinformatics.+ − + 第86行：
第71行： − 系统生物学也是一种社会科学现象，它利用跨学科的工具和人员，从不同的实验来源整合有关生物系统相互作用的复杂数据。+ − + − This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However, the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide. − 各种各样的观点说明了这样一个事实，即系统生物学指的是一系列周边重叠概念的集合，而不是一个单独界定清楚的领域。然而，随着系统生物学的教职和研究机构在全球范围内的激增，这个术语在2007年已经广泛流行和普及。+ − + 第112行：
第96行： − + − + − In 1960, Denis Noble developed the first computer model of the heart pacemaker. − 1960年，丹尼斯·诺布尔发明了第一个心脏起搏器的计算模型。+ − + − − The formal study of systems biology, as a distinct discipline, was launched by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland, Ohio, titled "Systems Theory and Biology". − − The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the metabolic control analysis and the biochemical systems theory. The successes of molecular biology throughout the 1980s, coupled with a skepticism toward theoretical biology, that then promised more than it achieved, caused the quantitative modeling of biological processes to become a somewhat minor field.+ − − 20世纪60年代和70年代发展了几种研究复杂分子系统的方法，如代谢控制分析和生化系统理论。整个20世纪80年代分子生物学的成功，以及人们对理论生物学的怀疑，并且收获小于预期，从而使得生物过程的定量模拟成为一个有点次要的领域。 − + − + − Shows trends in systems biology research by presenting the number of articles out of the top 30 cited systems biology papers during that time which include a specific topic+ − − 通过不同时间系统生物学论文中前30篇被引文章里各个主题的占比，显示系统生物学研究的趋势。 − + − − However, the birth of functional genomics in the 1990s meant that large quantities of high-quality data became available, while the computing power exploded, making more realistic models possible. In 1992, then 1994, serial articles <ref>B. J. Zeng, "On the holographic model of human body", 1st National Conference of Comparative − − Studies Traditional Chinese Medicine and West Medicine, Medicine and Philosophy, April 1992 ("systems medicine and pharmacology" termed).</ref><ref>B. J. Zeng, "Transgenic animal expression system – transgenic egg plan (goldegg plan)", 第165行：
第137行： − Communication on Transgenic Animal, Vol.1, No.11, 1994 (on the concept of system genetics and term coined).</ref><ref>B. J. Zeng, "The structure theory of self-organization systems", Communication on Transgenic Animals, 第171行：
第142行： − No.8-10, 1996. Etc.</ref> on systems medicine, systems genetics, and systems biological engineering by B. J. Zeng was published in China and was giving a lecture on biosystems theory and systems-approach research at the First International Conference on Transgenic Animals, Beijing, 1996. In 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a whole (hypothetical) cell. − − Around the year 2000, after Institutes of Systems Biology were established in Seattle and Tokyo, systems biology emerged as a movement in its own right, spurred on by the completion of various genome projects, the large increase in data from the omics (e.g., genomics and proteomics) and the accompanying advances in high-throughput experiments and bioinformatics. Shortly afterwards, the first departments wholly devoted to systems biology were founded (for example, the Department of Systems Biology at Harvard Medical School ). 第187行：
第155行： − In 2003, work at the Massachusetts Institute of Technology was begun on CytoSolve, a method to model the whole cell by dynamically integrating multiple molecular pathway models. Since then, various research institutes dedicated to systems biology have been developed. For example, the NIGMS of NIH established a project grant that is currently supporting over ten systems biology centers in the United States. As of summer 2006, due to a shortage of people in systems biology − + − + − − An important milestone in the development of systems biology has become the international project Physiome. 第210行：
第175行： − According to the interpretation of Systems Biology as the ability to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools, some typical technology platforms are phenomics, organismal variation in phenotype as it changes during its life span; genomics, organismal deoxyribonucleic acid (DNA) sequence, including intra-organismal cell specific variation. (i.e., telomere length variation); epigenomics/epigenetics, organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., DNA methylation, Histone acetylation and deacetylation, etc.); transcriptomics, organismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression; interferomics, organismal, tissue, or cell-level transcript correcting factors (i.e., RNA interference), proteomics, organismal, tissue, or cell level measurements of proteins and peptides via two-dimensional gel electrophoresis, mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry). Sub disciplines include phosphoproteomics, glycoproteomics and other methods to detect chemically modified proteins; metabolomics, measurements of small molecules known as metabolites in the system at the organismal, cell, or tissue level; glycomics, organismal, tissue, or cell-level measurements of carbohydrates; lipidomics, organismal, tissue, or cell level measurements of lipids. 第218行：
第182行： − In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell.The interactions studied include organismal, tissue, cell, and molecular interactions within the cell (interactomics). Currently, the authoritative molecular discipline in this field of study is protein-protein interactions (PPI), although the working definition does not preclude inclusion of other molecular disciplines. These molecular disciplines include; neuroelectrodynamics, an organismal network where the brain's computing function as a dynamic system includes underlying biophysical mechanisms and emerging computation by electrical interactions; fluxomics, measurements of molecular dynamic changes over time in a system such as a cell, tissue, or organism; − + − + − − In approaching a systems biology problem there are two main approaches. These are the top down and bottom up approach. The top down approach takes as much of the system into account as possible and relies largely on experimental results. The RNA-seq technique is an example of an experimental top down approach. Conversely, the bottom up approach is used to create detailed models while also incorporating experimental data. An example of the bottom up approach is the use of circuit models to describe a simple gene network. − − − Various technologies utilized to capture dynamic changes in mRNA, proteins, and post-translational modifications. Mechanobiology, forces and physical properties at all scales, their interplay with other regulatory mechanisms; biosemiotics, analysis of the system of sign relations of an organism or other biosystems; Physiomics, a systematic study of physiome in biology. − 有各种技术用于捕获mRNA、蛋白质的动态变化和翻译后修饰。<font color="#FF8000">生物力学 Mechanobiology</font>，跨尺度的力学和物理性质，以及它们与其他调节机制的相互作用；生物符号学，分析有机体或其他生物系统的符号关系系统；生理组学，生物学中生理的系统研究。+ − + − − Cancer systems biology is an example of the systems biology approach, which can be distinguished by the specific object of study (tumorigenesis and treatment of cancer). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing cancer genome in patient tumour samples) and tools (immortalized cancer cell lines, mouse models of tumorigenesis, xenograft models, high-throughput sequencing methods, siRNA-based gene knocking down high-throughput screenings, computational modeling of the consequences of somatic mutations and genome instability). The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for personalized cancer medicine and virtual cancer patient in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale in silico models of various tumours. 第251行：
第207行： − The investigations are frequently combined with large-scale perturbation methods, including gene-based (RNAi, mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries. Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality. A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.+ 第259行：
第215行： − The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., flux balance analysis).+ − 系统生物学方法经常涉及机制模型的发展，比如从动态系统的基本构件的定量特性重建动态系统。例如，一个细胞网络可以进行数学建模，使用的方法来自化学动力学和控制理论。由于细胞网络中参数、变量和约束的数量庞大，经常使用数值和计算技术(例如流平衡分析)。+ − + − 第269行：
第224行： − Other aspects of computer science, informatics, and statistics are also used in systems biology. These include new forms of computational models, such as the use of process calculi to model biological processes (notable approaches include stochastic π-calculus, BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and constraint-based modeling; integration of information from the literature, using techniques of information extraction and text mining; development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via loose coupling of software, websites and databases, or commercial suits; network-based approaches for analyzing high dimensional genomic data sets. For example, weighted correlation network analysis is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data analysis, e.g. approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members. Much of the analysis of genomic data sets also include identifying correlations. Additionally, as much of the information comes from different fields, the development of syntactically and semantically sound ways of representing biological models is needed. − + − + − − A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michealis Menten reaction. 第285行：
第237行： − Researchers begin by choosing a biological pathway and diagramming all of the protein interactions. After determining all of the interactions of the proteins, mass action kinetics is utilized to describe the speed of the reactions in the system. Mass action kinetics will provide differential equations to model the biological system as a mathematical model in which experiments can determine the parameter values to use in the differential equations. These parameter values will be the reaction rates of each proteins interaction in the system. This model determines the behavior of certain proteins in biological systems and bring new insight to the specific activities of individual proteins. Sometimes it is not possible to gather all reaction rates of a system. Unknown reaction rates are determined by simulating the model of known parameters and target behavior which provides possible parameter values. − 研究人员首先选择一条生物通路，绘制所有蛋白质相互作用的图表。确定了所有的蛋白质相互作用之后，使用符合质量作用定律的动力学来描述系统中反应的速率。质量作用定律动力学将提供微分方程，把生物系统模拟成一个数学模型，其中微分方程的参数可以由实验来确定。这些参数值是系统中每对蛋白质相互作用的反应速率。这个模型决定了生物系统中主要蛋白质的行为，并且为理解单个蛋白质的特殊行为提供了新的视角。有时候不可能收集一个系统的所有反应速率。可以通过模拟已知参数的模型并且提供可能参数值的目标行为，来确定未知的反应速率。+ − + − Plot of Concentrations vs time for the simple three protein negative feedback loop. All parameters are set to either 0 or 1 for initial conditions. The reaction is allowed to proceed until it hits equilibrium. This plot is of the change in each protein over time. 第393行：
第343行： − Zeng BJ., On the concept of systems biological engineering, Nov. 1994, Communication on Transgenic Animals, CAS, China. − − Zeng BJ., On the concept of systems biological engineering, Nov. 1994, Communication on Transgenic Animals, CAS, China.
无编辑摘要
系统方法研究生物学的一个例证
系统方法研究生物学的一个例证
'''Systems biology''' is the [[computational modeling|computational]] and [[mathematical]] analysis and modeling of complex [[biological system]]s. It is a [[biology]]-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach ([[holism]] instead of the more traditional [[reductionist|reductionism]]) to biological research.<ref name="Tavassoly 487–500">{{Cite journal|last=Tavassoly|first=Iman|last2=Goldfarb|first2=Joseph|last3=Iyengar|first3=Ravi|date=2018-10-04|title=Systems biology primer: the basic methods and approaches|journal=Essays in Biochemistry|volume=62|issue=4|pages=487–500|doi=10.1042/EBC20180003|issn=0071-1365|pmid=30287586}}</ref> When it is crossing the field of [[systems theory]] and the [[applied mathematics]] methods, it develops into the sub-branch of [[complex systems biology]].
'''Systems biology''' is the [[computational modeling|computational]] and [[mathematical]] analysis and modeling of complex [[biological system]]s. It is a [[biology]]-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach ([[holism]] instead of the more traditional [[reductionist|reductionism]]) to biological research.<ref name="Tavassoly 487–500">{{Cite journal|last=Tavassoly|first=Iman|last2=Goldfarb|first2=Joseph|last3=Iyengar|first3=Ravi|date=2018-10-04|title=Systems biology primer: the basic methods and approaches|journal=Essays in Biochemistry|volume=62|issue=4|pages=487–500|doi=10.1042/EBC20180003|issn=0071-1365|pmid=30287586}}</ref> When it is crossing the field of [[systems theory]] and the [[applied mathematics]] methods, it develops into the sub-branch of [[complex systems biology]].
<font color="#FF8000">系统生物学 Systems biology</font>是对复杂生物系统进行演算分析、数学分析和建模的学科。它是一个以生物学为基础的跨学科研究领域，侧重于生物系统内复杂的相互作用，采用整体的方法(<font color="#FF8000">整体论 holism</font>而不是更传统的<font color="#FF8000">还原论 reductionism</font>)进行生物学研究。它跨越了系统论和应用数学方法的领域，发展成为<font color="#FF8000">复杂系统生物学 complex systems biology</font>的一个分支。
<font color="#FF8000">系统生物学 Systems biology</font>是对复杂生物系统进行计算、数学分析和建模的学科。它是一个以生物学为基础的跨学科研究领域，侧重于生物系统内复杂的相互作用，采用整体的方法(<font color="#FF8000">整体论 holism</font>而不是更传统的<font color="#FF8000">还原论 reductionism</font>)进行生物学研究。它跨越了系统论和应用数学方法的领域，发展成为<font color="#FF8000">复杂系统生物学 complex systems biology</font>的一个分支。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“对复杂生物系统进行计算、数学分析和建模的学科。”一句中的“计算”改为“演算分析”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
Particularly from year 2000 onwards, the concept has been used widely in biology in a variety of contexts. The [[Human Genome Project]] is an example of applied [[systems thinking]] in biology which has led to new, collaborative ways of working on problems in the biological field of genetics.<ref>{{cite book|last1=Zewail|first1=Ahmed|title=Physical Biology: From Atoms to Medicine|date=2008|publisher=Imperial College Press|page=339}}</ref> One of the aims of systems biology is to model and discover [[emergent property|emergent properties]], properties of [[cell (biology)|cell]]s, [[tissue (biology)|tissue]]s and [[organism]]s functioning as a [[system]] whose theoretical description is only possible using techniques of systems biology.<ref>{{Cite book|title=Perspectives on Organisms - Springer|last=Longo|first=Giuseppe|last2=Montévil|first2=Maël|doi=10.1007/978-3-642-35938-5|series=Lecture Notes in Morphogenesis|year=2014|isbn=978-3-642-35937-8}}</ref><ref name="Tavassoly 487–500"/> These typically involve [[metabolic networks]] or [[cell signaling]] networks.<ref name="pmid21570668">{{cite book|author=Bu Z, Callaway DJ|title=Protein Structure and Diseases|volume=83|pages=163–221|year=2011|pmid=21570668|doi=10.1016/B978-0-12-381262-9.00005-7|series=Advances in Protein Chemistry and Structural Biology|isbn=978-0-123-81262-9|chapter=Proteins MOVE! Protein dynamics and long-range allostery in cell signaling}}</ref><ref name="Tavassoly 487–500"/>
Particularly from year 2000 onwards, the concept has been used widely in biology in a variety of contexts. The [[Human Genome Project]] is an example of applied [[systems thinking]] in biology which has led to new, collaborative ways of working on problems in the biological field of genetics.<ref>{{cite book|last1=Zewail|first1=Ahmed|title=Physical Biology: From Atoms to Medicine|date=2008|publisher=Imperial College Press|page=339}}</ref> One of the aims of systems biology is to model and discover [[emergent property|emergent properties]], properties of [[cell (biology)|cell]]s, [[tissue (biology)|tissue]]s and [[organism]]s functioning as a [[system]] whose theoretical description is only possible using techniques of systems biology.<ref>{{Cite book|title=Perspectives on Organisms - Springer|last=Longo|first=Giuseppe|last2=Montévil|first2=Maël|doi=10.1007/978-3-642-35938-5|series=Lecture Notes in Morphogenesis|year=2014|isbn=978-3-642-35937-8}}</ref><ref name="Tavassoly 487–500"/> These typically involve [[metabolic networks]] or [[cell signaling]] networks.<ref name="pmid21570668">{{cite book|author=Bu Z, Callaway DJ|title=Protein Structure and Diseases|volume=83|pages=163–221|year=2011|pmid=21570668|doi=10.1016/B978-0-12-381262-9.00005-7|series=Advances in Protein Chemistry and Structural Biology|isbn=978-0-123-81262-9|chapter=Proteins MOVE! Protein dynamics and long-range allostery in cell signaling}}</ref><ref name="Tavassoly 487–500"/>
特别是从2000年起，这个概念在生物学中被广泛应用于各种场合。<font color="#FF8000">人类基因组计划 Human Genome Project</font>是生物学中应用系统思维的一个例子，它导致了在遗传学这个生物学领域新的协作的工作方式。系统生物学的目标之一是模拟和发现<font color="#FF8000">细胞 cells</font>、<font color="#FF8000">组织 tissues</font>和<font color="#FF8000">有机体 organisms</font>作为一个系统运作的涌现特性，其理论描述只有使用系统生物学技术才有可能实现。
特别是从2000年起，这个概念在生物学中被广泛应用于各种场合。<font color="#FF8000">人类基因组计划 Human Genome Project</font>是生物学中应用系统思维的一个例子，它在遗传学这个生物学领域中引入了新的协作型工作方式。。系统生物学的目标之一是模拟和发现<font color="#FF8000">细胞 cells</font>、<font color="#FF8000">组织 tissues</font>和<font color="#FF8000">有机体 organisms</font>作为一个系统运作的涌现特性，其理论描述只有使用系统生物学技术才有可能实现。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“它导致了在遗传学这个生物学领域新的协作的工作方式。”一句中的“导致了在遗传学这个生物学领域新的协作的工作方式。”改为“在遗传学这个生物学领域中引入了新的协作型工作方式。”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
== Overview 概述==
== Overview 概述==
系统生物学可以从许多不同的方面来考虑。
系统生物学可以从许多不同的方面来考虑。
As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the [[enzymes]] and [[metabolites]] in a [[metabolic pathway]] or the heart beats).<ref name="snoep05" /><ref name="21stcentury" /><ref name="noble06" />
As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the [[enzymes]] and [[metabolites]] in a [[metabolic pathway]] or the heart beats).<ref name="snoep05" /><ref name="21stcentury" /><ref name="noble06" />
系统生物学作为一个研究领域，具体探讨关于生物系统的组成部分之间的互动，以及各系统要素的相互作用如何产生该系统的功能和行为（例如，代谢通路中或心跳时产生的酶和代谢物)。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“系统生物学是这样一个研究领域，研究生物系统各组成部分之间的相互作用，以及这些相互作用如何产生该系统的功能和行为(例如，代谢通路或心跳中的酶和代谢物)。”一整句改为“系统生物学作为一个研究领域，具体探讨关于生物系统的组成部分之间的互动，以及各系统要素的相互作用如何产生该系统的功能和行为（例如，代谢通路中或心跳时产生的酶和代谢物)。”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
As a [[paradigm]], systems biology is usually defined in antithesis to the so-called [[reductionist]] paradigm ([[biological organisation]]), although it's fully consistent with the [[scientific method]]. The distinction between the two paradigms is referred to in these quotations: "The [[Reductionism|reductionist]] approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." (Sauer ''et al.'')<ref name="sauer07" /> "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in the full sense of the term." ([[Denis Noble]])<ref name="noble06" />
As a [[paradigm]], systems biology is usually defined in antithesis to the so-called [[reductionist]] paradigm ([[biological organisation]]), although it's fully consistent with the [[scientific method]]. The distinction between the two paradigms is referred to in these quotations: "The [[Reductionism|reductionist]] approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." (Sauer ''et al.'')<ref name="sauer07" /> "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in the full sense of the term." ([[Denis Noble]])<ref name="noble06" />
作为一种研究范式，系统生物学通常被认为与所谓的还原论范式(生物组织)相对立，尽管它完全符合科学方法。这些语录中提到了两种范式之间的区别: “还原论方法成功地确定了大多数组成部分和许多相互作用，但不幸的是，没有提供令人信服的概念或方法来理解系统特性是如何出现的... 生物网络中因果关系的多元性最好通过多个组分同时进行定量测量，以及与数学模型进行严格的数据整合来解决。”(Sauer 等人)“系统生物学...是关于合并而不是分解，是关于整合而不是简化。它要求我们发展出与我们的还原论方法一样严谨但不同的整合思维方式...这意味着彻底改变我们的哲学。”(丹尼斯 · 诺贝尔)
作为一种研究范式，系统生物学通常被认为与所谓的还原论范式(生物组织)相对立，尽管它完全符合科学方法。以下几句话中提到了两种范式之间的区别: “还原论方法成功地确定了大多数组成部分和许多相互作用，但不幸的是，没有提供令人信服的概念或方法来理解系统特性是如何出现的... 通过多个组分同时进行定量测量”改为“通过观察多个分组同时进行的定量实验。”(Sauer 等人)“系统生物学...是合并而不是分解，是整合而不是简化。它要求我们建立起与我们的还原论方法一样严谨但不同的整合思维方式...这意味着彻底改变我们的哲学。”(丹尼斯 · 诺贝尔)
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“生物网络中因果关系的多元性最好通过多个组分同时进行定量测量，以及与数学模型进行严格的数据整合来解决。”一句中的“通过多个组分同时进行定量测量”改为“通过观察多个分组同时进行的定量实验”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“它要求我们发展出与我们的还原论方法一样严谨但不同的整合思维方式...”一句中的“发展出”改为“建立起”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
--[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】 “这些语录中提到了两种范式之间的区别”一句把“这些语录”改为“以下几句话”
--[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】
--[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】 "系统生物学...是关于合并而不是分解，是关于整合而不是简化。"一句把两个“关于”去掉
--[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】
As a series of operational [[protocol (natural sciences)|protocol]]s used for performing research, namely a cycle composed of theory, [[Mathematical model|analytic]] or [[computational model]]ling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory.<ref name="kholodenko05" /> Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, [[transcriptomics]], [[metabolomics]], [[proteomics]] and [[High-throughput screening|high-throughput techniques]] are used to collect quantitative data for the construction and validation of models.<ref name=Romualdi09 />
As a series of operational [[protocol (natural sciences)|protocol]]s used for performing research, namely a cycle composed of theory, [[Mathematical model|analytic]] or [[computational model]]ling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory.<ref name="kholodenko05" /> Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, [[transcriptomics]], [[metabolomics]], [[proteomics]] and [[High-throughput screening|high-throughput techniques]] are used to collect quantitative data for the construction and validation of models.<ref name=Romualdi09 />
作为一系列用于进行研究的操作方案，即一个由理论、分析或计算模型组成的循环，系统生物学提出关于生物系统的具体可检验的假设，接着进行实验验证，然后使用新获得的细胞或细胞过程的定量描述来优化计算模型或理论。由于目标是一个系统中相互作用的模型，所以最适合系统生物学的实验技术就是那些全系统范围的、尽可能完整的实验技术。因此，转录组学、代谢组学、蛋白质组学和高通量技术被用来收集定量数据，从而用于模型的建立和验证。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
As the application of [[dynamical systems theory]] to [[molecular biology]]. Indeed, the focus on the dynamics of the studied systems is the main conceptual difference between systems biology and [[bioinformatics]].<ref name=Voit01>{{cite book|last1=Voit|first1=Eberhard|title=A First Course in Systems Biology|date=2012|publisher=Garland Science|isbn=9780815344674}}</ref>
As the application of [[dynamical systems theory]] to [[molecular biology]]. Indeed, the focus on the dynamics of the studied systems is the main conceptual difference between systems biology and [[bioinformatics]].<ref name=Voit01>{{cite book|last1=Voit|first1=Eberhard|title=A First Course in Systems Biology|date=2012|publisher=Garland Science|isbn=9780815344674}}</ref>
作为动力系统理论在分子生物学领域的应用，系统生物学对所研究的系统在动力学上的关注正是它和生物信息学之间的主要概念差异。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】（补充翻译）作为动力系统理论在分子生物学领域的应用，系统生物学对所研究的系统在动力学上的关注正是它和生物信息学之间的主要概念差异。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
As a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.
As a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.
作为一种社会科学现象，系统生物学由利用多样的跨学科的工具和人员的实验资源，寻求整合有关生物系统相互作用的复杂数据的战略所定义。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“系统生物学也是一种社会科学现象，它利用跨学科的工具和人员，从不同的实验来源整合有关生物系统相互作用的复杂数据。”一句中改为“作为一种社会科学现象，系统生物学由利用多样的跨学科的工具和人员的实验资源，寻求整合有关生物系统相互作用的复杂数据的战略所定义。”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However, the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide.
This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However, the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide.
各种各样的观点说明了这样一个事实，即系统生物学指的是一系列周边重叠概念的集合，而不是一个独立的领域。然而，随着系统生物学的教职和研究机构在全球范围内的激增，这个术语在2007年已经广泛流行和普及。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“而不是一个单独界定清楚的领域。”一句中的“单独界定清楚的”改为“独立的” （整段As开头想要以排比形式点明功用）
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
== History 历史==
== History 历史==
One of the theorists who can be seen as one of the precursors of systems biology is Ludwig von Bertalanffy with his general systems theory.
One of the theorists who can be seen as one of the precursors of systems biology is Ludwig von Bertalanffy with his general systems theory.
理论家卡尔·路德维希·冯·贝塔郎非和他的<font color="#FF8000">一般系统论 general systems theory</font>可以被看作是系统生物学先驱之一。
理论家卡尔·路德维希·冯·贝塔郎非和他的<font color="#FF8000">一般系统论 general systems theory</font>可以被看作是系统生物学先驱之一。英国神经生理学家、诺贝尔奖获得者艾伦•劳埃德•霍奇金和安德鲁•费尔丁•赫克斯利在1952年发表了最早的细胞生物学的数理分析之一，他们也创建了一个数学模型，解释了沿神经元细胞轴突传播的动作电位。他们的模型描述了一种由钾和钠两种不同的分子成分之间的相互作用所产生的细胞功能，所以这可以被看作是演算系统生物学的开端。无独有偶，艾伦•图灵在1952年发表了形态发生的化学基础，描述了最初同质的生物系统中是如何产生不均匀性的。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“理论家卡尔•路德维希•冯•贝塔郎非和他的<font color="#FF8000">一般系统论 general systems theory</font>可以被看作是系统生物学先驱之一。”一句后面补充翻译：英国神经生理学家、诺贝尔奖获得者艾伦•劳埃德•霍奇金和安德鲁•费尔丁•赫克斯利在1952年发表了最早的细胞生物学的数理分析之一，他们也创建了一个数学模型，解释了沿神经元细胞轴突传播的动作电位。他们的模型描述了一种由钾和钠两种不同的分子成分之间的相互作用所产生的细胞功能，所以这可以被看作是演算系统生物学的开端。无独有偶，艾伦•图灵在1952年发表了形态发生的化学基础，描述了最初同质的生物系统中是如何产生不均匀性的。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
In 1960, [[Denis Noble]] developed the first computer model of the [[heart pacemaker]].<ref name="noble60" />
In 1960, [[Denis Noble]] developed the first computer model of the [[heart pacemaker]].<ref name="noble60" />
1960年，丹尼斯·诺布尔创建了第一个心脏起搏器的计算模型。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“1960年，丹尼斯·诺布尔发明了第一个心脏起搏器的计算模型。”一句中的“发明”改为“创建”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
The formal study of systems biology, as a distinct discipline, was launched by systems theorist [[Mihajlo Mesarovic]] in 1966 with an international symposium at the [[Case Western Reserve University|Case Institute of Technology]] in [[Cleveland]], [[Ohio]], titled "Systems Theory and Biology".<ref name="mesarovic68" /><ref name="science68" />
The formal study of systems biology, as a distinct discipline, was launched by systems theorist [[Mihajlo Mesarovic]] in 1966 with an international symposium at the [[Case Western Reserve University|Case Institute of Technology]] in [[Cleveland]], [[Ohio]], titled "Systems Theory and Biology".<ref name="mesarovic68" /><ref name="science68" />
系统理论家米哈伊洛 · 梅萨罗维奇于1966年在俄亥俄州克利夫兰市的凯斯理工学院召开了一次题为“系统理论与生物学”的国际研讨会，开启了系统生物学作为一个独特领域的正式研究。
系统理论家米哈伊洛 · 梅萨罗维奇于1966年在俄亥俄州克利夫兰市的凯斯理工学院召开了一次题为“系统理论与生物学”的国际研讨会，开启了系统生物学作为一个独特领域的正式研究。
The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the [[metabolic control analysis]] and the [[biochemical systems theory]]. The successes of [[molecular biology]] throughout the 1980s, coupled with a skepticism toward [[theoretical biology]], that then promised more than it achieved, caused the quantitative modeling of biological processes to become a somewhat minor field.<ref name="hunter12" />
The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the [[metabolic control analysis]] and the [[biochemical systems theory]]. The successes of [[molecular biology]] throughout the 1980s, coupled with a skepticism toward [[theoretical biology]], that then promised more than it achieved, caused the quantitative modeling of biological processes to become a somewhat minor field.<ref name="hunter12" />
人们在20世纪六七十年代研究出几种研究复杂分子系统的方法，如代谢控制分析和生化系统理论。整个20世纪80年代分子生物学的成功，以及人们对理论生物学的怀疑，加之收获小于预期，使得生物过程的定量模拟成为一个日渐被轻视的领域。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“20世纪60年代和70年代发展了几种研究复杂分子系统的方法”一句改为“人们在20世纪六七十年代研究出几种研究复杂分子系统的方法”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“并且收获小于预期，从而使得生物过程的定量模拟成为一个有点次要的领域。”一句中的“并且、从而使得、有点次要”改为“加之、使得、日渐被轻视”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
[[File:SystemsBiologyTrendsInMostCitedResearch.PNG|alt=Shows trends in systems biology research. From 1992 to 2013 Database development articles increased. Articles about algorithms have fluctuated but remained fairly steady. Network properties articles and software development articles have remained low but experienced an increased about halfway through the time period 1992-2013. The articles on Metabolic flux analysis decreased from 1992 to 2013. In 1992 algorithms, equations, modeling and simulation articles were most cited. In 2012 the most cited were database development articles.|thumb|Shows trends in systems biology research by presenting the number of articles out of the top 30 cited systems biology papers during that time which include a specific topic<ref>{{Cite journal|last=Zou|first=Yawen|last2=Laubichler|first2=Manfred D.|date=2018-07-25|title=From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013|journal=PLOS One|language=en|volume=13|issue=7|pages=e0200929|doi=10.1371/journal.pone.0200929|issn=1932-6203|pmc=6059489|pmid=30044828|bibcode=2018PLoSO..1300929Z}}</ref>]]
[[File:SystemsBiologyTrendsInMostCitedResearch.PNG|alt=Shows trends in systems biology research. From 1992 to 2013 Database development articles increased. Articles about algorithms have fluctuated but remained fairly steady. Network properties articles and software development articles have remained low but experienced an increased about halfway through the time period 1992-2013. The articles on Metabolic flux analysis decreased from 1992 to 2013. In 1992 algorithms, equations, modeling and simulation articles were most cited. In 2012 the most cited were database development articles.|thumb|Shows trends in systems biology research by presenting the number of articles out of the top 30 cited systems biology papers during that time which include a specific topic<ref>{{Cite journal|last=Zou|first=Yawen|last2=Laubichler|first2=Manfred D.|date=2018-07-25|title=From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013|journal=PLOS One|language=en|volume=13|issue=7|pages=e0200929|doi=10.1371/journal.pone.0200929|issn=1932-6203|pmc=6059489|pmid=30044828|bibcode=2018PLoSO..1300929Z}}</ref>]]
不同时间维度生物学论文中被引用频率前30的文章里各个主题的占比，显示了系统生物学研究的趋势。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“通过不同时间系统生物学论文中前30篇被引文章里各个主题的占比，显示系统生物学研究的趋势。”一句改为“不同时间维度生物学论文中被引用频率前30的文章里各个主题的占比，显示了系统生物学研究的趋势。”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
However, the birth of [[functional genomics]] in the 1990s meant that large quantities of high-quality data became available, while the computing power exploded, making more realistic models possible. In 1992, then 1994, serial articles <ref>B. J. Zeng, "On the holographic model of human body", 1st National Conference of Comparative
However, the birth of [[functional genomics]] in the 1990s meant that large quantities of high-quality data became available, while the computing power exploded, making more realistic models possible. In 1992, then 1994, serial articles <ref>B. J. Zeng, "On the holographic model of human body", 1st National Conference of Comparative
然而，20世纪90年代功能基因组学的诞生意味着人们可以得到大量高质量的数据，同时计算能力爆炸式增长，使得更真实的模型成为可能。1992年，1994年，曾斌斌撰写系列文章《论人体的全息模型》 ，第一届全国比较研究会议
然而，20世纪90年代功能基因组学的诞生意味着人们可以得到大量高质量的数据，同时计算能力爆炸式增长，使得更真实的模型成为可能。1992年，1994年，曾斌斌撰写系列文章《论人体的全息模型》 ，第一届全国比较研究会议
Studies Traditional Chinese Medicine and West Medicine, Medicine and Philosophy, April 1992 ("systems medicine and pharmacology" termed).</ref><ref>Zeng (B.) J., On the concept of system biological engineering, Communication on Transgenic Animals, No. 6, June, 1994.</ref><ref>B. J. Zeng, "Transgenic animal expression system – transgenic egg plan (goldegg plan)",
Studies Traditional Chinese Medicine and West Medicine, Medicine and Philosophy, April 1992 ("systems medicine and pharmacology" termed).</ref><ref>Zeng (B.) J., On the concept of system biological engineering, Communication on Transgenic Animals, No. 6, June, 1994.</ref><ref>B. J. Zeng, "Transgenic animal expression system – transgenic egg plan (goldegg plan)",
研究中医和西医，医学和哲学，1992年4月(“系统医学和药理学”称)。《转基因动物表达系统-转基因卵子计划》 ,
研究中医和西医，医学和哲学，1992年4月(“系统医学和药理学”称)。《转基因动物表达系统-转基因卵子计划》 ,
''Communication on Transgenic Animal'', Vol.1, No.11, 1994 (on the concept of system genetics and term coined).</ref><ref>B. J. Zeng, "From positive to synthetic science", ''Communication on Transgenic Animals'', No. 11, 1995 (on systems medicine).</ref><ref>B. J. Zeng, "The structure theory of self-organization systems", ''Communication on Transgenic Animals'',
''Communication on Transgenic Animal'', Vol.1, No.11, 1994 (on the concept of system genetics and term coined).</ref><ref>B. J. Zeng, "From positive to synthetic science", ''Communication on Transgenic Animals'', No. 11, 1995 (on systems medicine).</ref><ref>B. J. Zeng, "The structure theory of self-organization systems", ''Communication on Transgenic Animals'',
关于转基因动物的交流，第1卷，第11期，1994(关于系统遗传学和术语杜撰的概念)。曾斌，《自我组织结构理论》 ，《转基因动物的交流》 ,
关于转基因动物的交流，第1卷，第11期，1994(关于系统遗传学和术语杜撰的概念)。曾斌，《自我组织结构理论》 ，《转基因动物的交流》 ,
No.8-10, 1996. Etc.</ref> on systems medicine, systems genetics, and systems biological engineering by B. J. Zeng was published in China and was giving a lecture on biosystems theory and systems-approach research at the First International Conference on Transgenic Animals, Beijing, 1996. In 1997, the group of [[Masaru Tomita]] published the first quantitative model of the metabolism of a whole (hypothetical) cell.<ref name="tomita97" />
No.8-10, 1996. Etc.</ref> on systems medicine, systems genetics, and systems biological engineering by B. J. Zeng was published in China and was giving a lecture on biosystems theory and systems-approach research at the First International Conference on Transgenic Animals, Beijing, 1996. In 1997, the group of [[Masaru Tomita]] published the first quantitative model of the metabolism of a whole (hypothetical) cell.<ref name="tomita97" />
曾斌于1996年在中国出版了《系统医学、系统遗传学和系统生物工程》 ，并在北京举行的第一届国际转基因动物会议上作了关于生物系统理论和系统方法研究的演讲。1997年，富田正丸小组发表了第一个关于整个(假设的)细胞新陈代谢的定量模型。
曾斌于1996年在中国出版了《系统医学、系统遗传学和系统生物工程》 ，并在北京举行的第一届国际转基因动物会议上作了关于生物系统理论和系统方法研究的演讲。1997年，富田正丸小组发表了第一个关于整个(假设的)细胞新陈代谢的定量模型。
Around the year 2000, after Institutes of Systems Biology were established in [[Seattle]] and [[Tokyo]], systems biology emerged as a movement in its own right, spurred on by the completion of various [[genome projects]], the large increase in data from the [[omics]] (e.g., [[genomics]] and [[proteomics]]) and the accompanying advances in high-throughput experiments and [[bioinformatics]]. Shortly afterwards, the first departments wholly devoted to systems biology were founded (for example, the Department of Systems Biology at Harvard Medical School <ref>{{cite web|title=HMS launches new department to study systems biology|url=https://news.harvard.edu/gazette/story/2003/09/hms-launches-new-department-to-study-systems-biology/|publisher=Harvard Gazette|date=September 23, 2003}}</ref>).
Around the year 2000, after Institutes of Systems Biology were established in [[Seattle]] and [[Tokyo]], systems biology emerged as a movement in its own right, spurred on by the completion of various [[genome projects]], the large increase in data from the [[omics]] (e.g., [[genomics]] and [[proteomics]]) and the accompanying advances in high-throughput experiments and [[bioinformatics]]. Shortly afterwards, the first departments wholly devoted to systems biology were founded (for example, the Department of Systems Biology at Harvard Medical School <ref>{{cite web|title=HMS launches new department to study systems biology|url=https://news.harvard.edu/gazette/story/2003/09/hms-launches-new-department-to-study-systems-biology/|publisher=Harvard Gazette|date=September 23, 2003}}</ref>).
2000年左右，在西雅图和东京建立了系统生物学研究所之后，由于各种基因组项目的完成、组学(如基因组学和蛋白质组学)数据的大量增加以及随之而来的高通量实验和生物信息学的进展，系统生物学作为一项独立的运动而涌现出来。不久之后，第一个完全致力于系统生物学的院系成立了(例如，哈佛医学院的系统生物学系)。
2000年左右，在西雅图和东京建立了系统生物学研究所之后，由于各种基因组项目的完成、组学(如基因组学和蛋白质组学)数据的大量增加以及随之而来的高通量实验和生物信息学的进展，系统生物学作为一项独立的运动而涌现出来。不久之后，第一个完全致力于系统生物学的院系成立了(例如，哈佛医学院的系统生物学系)。
In 2003, work at the [[Massachusetts Institute of Technology]] was begun on CytoSolve, a method to model the whole cell by dynamically integrating multiple molecular pathway models.<ref>{{cite journal|pmc=3032229|pmid=21423324|doi=10.1007/s12195-010-0143-x|volume=4|issue=1|title=CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models|date=March 2011|journal=Cell Mol Bioeng|pages=28–45|last1=Ayyadurai|first1=VA|last2=Dewey|first2=CF}}</ref> Since then, various research institutes dedicated to systems biology have been developed. For example, the [[NIGMS]] of [[NIH]] established a project grant that is currently supporting over ten systems biology centers in the United States.<ref>{{cite web|title=Systems Biology - National Institute of General Medical Sciences|url=http://www.nigms.nih.gov/Research/FeaturedPrograms/SysBio/|publisher=|access-date=12 December 2012|url-status=dead|archive-url=https://web.archive.org/web/20131019100123/http://www.nigms.nih.gov/Research/FeaturedPrograms/SysBio/|archive-date=19 October 2013}}</ref> As of summer 2006, due to a shortage of people in systems biology<ref name="careers" /> several doctoral training programs in systems biology have been established in many parts of the world. In that same year, the [[National Science Foundation]] (NSF) put forward a grand challenge for systems biology in the 21st century to build a mathematical model of the whole cell.{{Citation needed|date=October 2019}} In 2012 the first whole-cell model of ''[[Mycoplasma genitalium]]'' was achieved by the Karr Laboratory at the Mount Sinai School of Medicine in New York. The whole-cell model is able to predict viability of ''M. genitalium'' cells in response to genetic mutations.<ref>{{cite journal|last1=Karr|first1=Jonathan R.|last2=Sanghvi|first2=Jayodita C.|last3=Macklin|first3=Derek N.|last4=Gutschow|first4=Miriam V.|last5=Jacobs|first5=Jared M.|last6=Bolival|first6=Benjamin|last7=Assad-Garcia|first7=Nacyra|last8=Glass|first8=John I.|last9=Covert|first9=Markus W.|title=A Whole-Cell Computational Model Predicts Phenotype from Genotype|journal=Cell|date=July 2012|volume=150|issue=2|pages=389–401|doi=10.1016/j.cell.2012.05.044|pmid=22817898|pmc=3413483}}</ref>
In 2003, work at the [[Massachusetts Institute of Technology]] was begun on CytoSolve, a method to model the whole cell by dynamically integrating multiple molecular pathway models.<ref>{{cite journal|pmc=3032229|pmid=21423324|doi=10.1007/s12195-010-0143-x|volume=4|issue=1|title=CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models|date=March 2011|journal=Cell Mol Bioeng|pages=28–45|last1=Ayyadurai|first1=VA|last2=Dewey|first2=CF}}</ref> Since then, various research institutes dedicated to systems biology have been developed. For example, the [[NIGMS]] of [[NIH]] established a project grant that is currently supporting over ten systems biology centers in the United States.<ref>{{cite web|title=Systems Biology - National Institute of General Medical Sciences|url=http://www.nigms.nih.gov/Research/FeaturedPrograms/SysBio/|publisher=|access-date=12 December 2012|url-status=dead|archive-url=https://web.archive.org/web/20131019100123/http://www.nigms.nih.gov/Research/FeaturedPrograms/SysBio/|archive-date=19 October 2013}}</ref> As of summer 2006, due to a shortage of people in systems biology<ref name="careers" /> several doctoral training programs in systems biology have been established in many parts of the world. In that same year, the [[National Science Foundation]] (NSF) put forward a grand challenge for systems biology in the 21st century to build a mathematical model of the whole cell.{{Citation needed|date=October 2019}} In 2012 the first whole-cell model of ''[[Mycoplasma genitalium]]'' was achieved by the Karr Laboratory at the Mount Sinai School of Medicine in New York. The whole-cell model is able to predict viability of ''M. genitalium'' cells in response to genetic mutations.<ref>{{cite journal|last1=Karr|first1=Jonathan R.|last2=Sanghvi|first2=Jayodita C.|last3=Macklin|first3=Derek N.|last4=Gutschow|first4=Miriam V.|last5=Jacobs|first5=Jared M.|last6=Bolival|first6=Benjamin|last7=Assad-Garcia|first7=Nacyra|last8=Glass|first8=John I.|last9=Covert|first9=Markus W.|title=A Whole-Cell Computational Model Predicts Phenotype from Genotype|journal=Cell|date=July 2012|volume=150|issue=2|pages=389–401|doi=10.1016/j.cell.2012.05.044|pmid=22817898|pmc=3413483}}</ref>
2003年，麻省理工学院的研究从CytoSolve开始，这是一种通过动态整合多个分子通路模型来建立整个细胞模型的方法。从那时起，各种致力于系统生物学的研究机构已经发展起来。例如，美国国立卫生研究院的 NIGMS 建立了一个项目补助金，目前正在支持美国的十多个系统生物学中心。截至2006年夏天，由于系统生物学人才短缺，
2003年，麻省理工学院的研究从CytoSolve开始，这是一种通过动态整合多个分子通路模型来建立整个细胞模型的方法。从那时起，各种致力于系统生物学的研究机构已经发展起来。例如，美国国立卫生研究院的 NIGMS 建立了一个项目补助金，目前正在支持美国的十多个系统生物学中心。截至2006年夏天，由于系统生物学人才短缺，全球多地建起了系统生物学博士培养计划。同年，美国国家科学基金会 National Science Foundation,NSF 提出了二十一世纪系统生物学的一个巨大挑战：为整个细胞建立数学模型。2012年，纽约西奈山伊坎医学院的卡尔实验室完成了第一个对整个生殖支原体细胞的建模。该细胞模型能够预测基因变异后的生殖支原体细胞的存活时间。
--[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】 补充翻译：全球多地建起了系统生物学博士培养计划。同年，美国国家科学基金会 National Science Foundation,NSF 提出了二十一世纪系统生物学的一个巨大挑战：为整个细胞建立数学模型。2012年，纽约西奈山伊坎医学院的卡尔实验室完成了第一个对整个生殖支原体细胞的建模。该细胞模型能够预测基因变异后的生殖支原体细胞的存活时间。
--[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】
An important milestone in the development of systems biology has become the international project [[Physiome]].
An important milestone in the development of systems biology has become the international project [[Physiome]].
系统生物学发展的一个重要里程碑是国际性课题 Physiome。
系统生物学发展的一个重要里程碑是国际性课题 Physiome。
According to the interpretation of Systems Biology as the ability to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools, some typical technology platforms are [[phenomics]], organismal variation in [[phenotype]] as it changes during its life span; [[genomics]], organismal [[deoxyribonucleic acid]] (DNA) sequence, including intra-organismal cell specific variation. (i.e., [[telomere]] length variation); [[epigenomics]]/[[epigenetics]], organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., [[DNA methylation]], [[Histone acetylation and deacetylation]], etc.); [[transcriptomics]], organismal, tissue or whole cell [[gene expression]] measurements by [[DNA microarray]]s or [[serial analysis of gene expression]]; [[interferomics]], organismal, tissue, or cell-level transcript correcting factors (i.e., [[RNA interference]]), [[proteomics]], organismal, tissue, or cell level measurements of proteins and peptides via [[two-dimensional gel electrophoresis]], [[mass spectrometry]] or multi-dimensional protein identification techniques (advanced [[High-performance liquid chromatography|HPLC]] systems coupled with [[mass spectrometry]]). Sub disciplines include [[phosphoproteomics]], [[glycoproteomics]] and other methods to detect chemically modified proteins; [[metabolomics]], measurements of small molecules known as [[metabolites]] in the system at the organismal, cell, or tissue level;<ref name=":1">{{Cite journal|last=Cascante|first=Marta|last2=Marin|first2=Silvia|date=2008-09-30|title=Metabolomics and fluxomics approaches|journal=Essays in Biochemistry|language=en|volume=45|pages=67–82|doi=10.1042/bse0450067|pmid=18793124|issn=0071-1365}}</ref> [[glycomics]], organismal, tissue, or cell-level measurements of [[carbohydrate]]s; [[lipidomics]], organismal, tissue, or cell level measurements of [[lipids]].
According to the interpretation of Systems Biology as the ability to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools, some typical technology platforms are [[phenomics]], organismal variation in [[phenotype]] as it changes during its life span; [[genomics]], organismal [[deoxyribonucleic acid]] (DNA) sequence, including intra-organismal cell specific variation. (i.e., [[telomere]] length variation); [[epigenomics]]/[[epigenetics]], organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., [[DNA methylation]], [[Histone acetylation and deacetylation]], etc.); [[transcriptomics]], organismal, tissue or whole cell [[gene expression]] measurements by [[DNA microarray]]s or [[serial analysis of gene expression]]; [[interferomics]], organismal, tissue, or cell-level transcript correcting factors (i.e., [[RNA interference]]), [[proteomics]], organismal, tissue, or cell level measurements of proteins and peptides via [[two-dimensional gel electrophoresis]], [[mass spectrometry]] or multi-dimensional protein identification techniques (advanced [[High-performance liquid chromatography|HPLC]] systems coupled with [[mass spectrometry]]). Sub disciplines include [[phosphoproteomics]], [[glycoproteomics]] and other methods to detect chemically modified proteins; [[metabolomics]], measurements of small molecules known as [[metabolites]] in the system at the organismal, cell, or tissue level;<ref name=":1">{{Cite journal|last=Cascante|first=Marta|last2=Marin|first2=Silvia|date=2008-09-30|title=Metabolomics and fluxomics approaches|journal=Essays in Biochemistry|language=en|volume=45|pages=67–82|doi=10.1042/bse0450067|pmid=18793124|issn=0071-1365}}</ref> [[glycomics]], organismal, tissue, or cell-level measurements of [[carbohydrate]]s; [[lipidomics]], organismal, tissue, or cell level measurements of [[lipids]].
系统生物学具有利用跨学科工具从多个实验来源获取、整合和分析复杂数据集的能力，一些典型的技术平台包括：表型组学，即生物表型在其生命周期内的变化；基因组学，即生物脱氧核糖核酸序列、包括生物内部细胞特异性变异（例如端粒长度变化）；表观基因组学或表观遗传学，生命体和相应的细胞特异性转录调控因子没有经验性地编码在基因组序列中（例如 DNA 甲基化、组蛋白乙酰化和脱乙酰化等)；转录组学，通过 DNA 微阵列或基因表达的系列分析来测量生物体、组织或整个细胞的基因表达; 干扰素组学，即生物体、组织或细胞水平的转录校正因子（例如RNA干扰) ； 蛋白质组学，通过二维凝胶电泳、质谱法或多维蛋白质识别技术(先进的高效液相色谱系统加上质谱法)，进行生物体、组织或细胞水平的蛋白质和多肽测量。子学科包括磷酸蛋白质组学、糖蛋白质组学和其他检测化学修饰蛋白质的方法; 代谢组学，测量有机体、细胞或组织水平系统中被称为代谢物的小分子; 糖组学，有机体、组织或细胞水平的碳水化合物测量; 脂质组学，有机体、组织或细胞水平的脂质测量。
系统生物学具有利用跨学科工具从多个实验来源获取、整合和分析复杂数据集的能力，一些典型的技术平台包括：表型组学，即生物表型在其生命周期内的变化；基因组学，即生物脱氧核糖核酸序列、包括生物内部细胞特异性变异（例如端粒长度变化）；表观基因组学或表观遗传学，生命体和相应的细胞特异性转录调控因子没有经验性地编码在基因组序列中（例如 DNA 甲基化、组蛋白乙酰化和脱乙酰化等)；转录组学，通过 DNA 微阵列或基因表达的系列分析来测量生物体、组织或整个细胞的基因表达; 干扰素组学，即生物体、组织或细胞水平的转录校正因子（例如RNA干扰) ； 蛋白质组学，通过二维凝胶电泳、质谱法或多维蛋白质识别技术(先进的高效液相色谱系统加上质谱法)，进行生物体、组织或细胞水平的蛋白质和多肽测量。子学科包括磷酸蛋白质组学、糖蛋白质组学和其他检测化学修饰蛋白质的方法; 代谢组学，测量有机体、细胞或组织水平系统中被称为代谢物的小分子; 糖组学，有机体、组织或细胞水平的碳水化合物测量; 脂质组学，有机体、组织或细胞水平的脂质测量。
In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell.The interactions studied include organismal, tissue, cell, and molecular interactions within the cell ([[interactomics]]).<ref>{{Cite journal|last=Cusick|first=Michael E.|last2=Klitgord|first2=Niels|last3=Vidal|first3=Marc|last4=Hill|first4=David E.|date=2005-10-15|title=Interactome: gateway into systems biology|journal=Human Molecular Genetics|language=en|volume=14|issue=suppl_2|pages=R171–R181|doi=10.1093/hmg/ddi335|pmid=16162640|issn=0964-6906|doi-access=free}}</ref> Currently, the authoritative molecular discipline in this field of study is [[protein-protein interaction]]s (PPI), although the working definition does not preclude inclusion of other molecular disciplines. These molecular disciplines include; [[neuroelectrodynamics]], an organismal network where the brain's computing function as a dynamic system includes underlying biophysical mechanisms and emerging computation by electrical interactions;<ref>{{Cite journal|last=Aur|first=Dorian|date=2012|title=From Neuroelectrodynamics to Thinking Machines|journal=Cognitive Computation|language=en|volume=4|issue=1|pages=4–12|doi=10.1007/s12559-011-9106-3|issn=1866-9956}}</ref> [[fluxomics]], measurements of molecular dynamic changes over time in a system such as a cell, tissue, or organism;<ref name=":1" /> [[biomics]], systems analysis of the [[biome]]; and molecular biokinematics, the study of "biology in motion" focused on how cells transit between steady states such as in proteins molecular mechanism.<ref>{{Cite journal|last=Diez|first=Mikel|last2=Petuya|first2=Víctor|last3=Martínez-Cruz|first3=Luis Alfonso|last4=Hernández|first4=Alfonso|date=2011-12-01|title=A biokinematic approach for the co--mputational simulation of proteins molecular mechanism|journal=Mechanism and Machine Theory|volume=46|issue=12|pages=1854–1868|doi=10.1016/j.mechmachtheory.2011.07.013|issn=0094-114X}}</ref>
In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell.The interactions studied include organismal, tissue, cell, and molecular interactions within the cell ([[interactomics]]).<ref>{{Cite journal|last=Cusick|first=Michael E.|last2=Klitgord|first2=Niels|last3=Vidal|first3=Marc|last4=Hill|first4=David E.|date=2005-10-15|title=Interactome: gateway into systems biology|journal=Human Molecular Genetics|language=en|volume=14|issue=suppl_2|pages=R171–R181|doi=10.1093/hmg/ddi335|pmid=16162640|issn=0964-6906|doi-access=free}}</ref> Currently, the authoritative molecular discipline in this field of study is [[protein-protein interaction]]s (PPI), although the working definition does not preclude inclusion of other molecular disciplines. These molecular disciplines include; [[neuroelectrodynamics]], an organismal network where the brain's computing function as a dynamic system includes underlying biophysical mechanisms and emerging computation by electrical interactions;<ref>{{Cite journal|last=Aur|first=Dorian|date=2012|title=From Neuroelectrodynamics to Thinking Machines|journal=Cognitive Computation|language=en|volume=4|issue=1|pages=4–12|doi=10.1007/s12559-011-9106-3|issn=1866-9956}}</ref> [[fluxomics]], measurements of molecular dynamic changes over time in a system such as a cell, tissue, or organism;<ref name=":1" /> [[biomics]], systems analysis of the [[biome]]; and molecular biokinematics, the study of "biology in motion" focused on how cells transit between steady states such as in proteins molecular mechanism.<ref>{{Cite journal|last=Diez|first=Mikel|last2=Petuya|first2=Víctor|last3=Martínez-Cruz|first3=Luis Alfonso|last4=Hernández|first4=Alfonso|date=2011-12-01|title=A biokinematic approach for the co--mputational simulation of proteins molecular mechanism|journal=Mechanism and Machine Theory|volume=46|issue=12|pages=1854–1868|doi=10.1016/j.mechmachtheory.2011.07.013|issn=0094-114X}}</ref>
除了识别和定量化上述给定的分子之外，有进一步的技术来分析细胞内的动力学和相互作用。研究的相互作用包括生物、组织、细胞和细胞内分子的相互作用(相互作用组学)。目前，在这一领域的权威分子学科，虽然工作的定义并不排除包括其他分子学科。这些分子学科包括: 神经电动力学，这是一个有机体网络，其中大脑的计算功能作为一个动态系统，包括潜在的生物物理机制和新兴的电力相互作用的计算；流体学，测量一个系统里分子随着时间的动态变化，如细胞、组织或有机体;
除了识别和定量化上述给定的分子之外，有进一步的技术来分析细胞内的动力学和相互作用。研究的相互作用包括生物、组织、细胞和细胞内分子的相互作用(相互作用组学)。目前，在这一领域的权威分子学科，尽管这一效用的定义并不仅仅局限于该领域，也有其它分子学科的作用。这些分子学科包括: 神经电动力学，这是一个有机体网络，其中大脑的计算功能作为一个动态系统，包括潜在的生物物理机制和新兴的电力相互作用的计算；流体学，测量一个系统里分子随着时间的动态变化，如细胞、组织或有机体;
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“虽然工作的定义并不排除包括其他分子学科”一句中改为“尽管这一效用的定义并不仅仅局限于该领域，也有其它分子学科的作用”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
In approaching a systems biology problem there are two main approaches. These are the top down and bottom up approach. The top down approach takes as much of the system into account as possible and relies largely on experimental results. The [[RNA-Seq|RNA-seq]] technique is an example of an experimental top down approach. Conversely, the bottom up approach is used to create detailed models while also incorporating experimental data. An example of the bottom up approach is the use of circuit models to describe a simple gene network.<ref>{{Cite journal|title=Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism|last=Loor|first=Khuram Shahzad and Juan J.|date=2012-07-31|journal=Current Genomics|volume=13|issue=5|pages=379–394|language=en|doi=10.2174/138920212801619269|pmc=3401895|pmid=23372424}}</ref>
In approaching a systems biology problem there are two main approaches. These are the top down and bottom up approach. The top down approach takes as much of the system into account as possible and relies largely on experimental results. The [[RNA-Seq|RNA-seq]] technique is an example of an experimental top down approach. Conversely, the bottom up approach is used to create detailed models while also incorporating experimental data. An example of the bottom up approach is the use of circuit models to describe a simple gene network.<ref>{{Cite journal|title=Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism|last=Loor|first=Khuram Shahzad and Juan J.|date=2012-07-31|journal=Current Genomics|volume=13|issue=5|pages=379–394|language=en|doi=10.2174/138920212801619269|pmc=3401895|pmid=23372424}}</ref>
在处理系统生物学问题时，有两种主要的方法。它们分别是自上而下和自下而上的方法。自上而下的方法尽可能多把系统考虑在内，并且在很大程度上依赖于实验结果。RNA-seq 技术是自上而下实验方法的一个例子。相反，自下而上的方法用于创建详细的模型，同时也结合了实验数据。自下而上方法的一个例子是使用电路模型来描述一个简单的基因网络。
在处理系统生物学问题时，有两种主要的方法。它们分别是自上而下和自下而上的方法。自上而下的方法尽可能多把系统考虑在内，并且在很大程度上依赖于实验结果。RNA-seq 技术是自上而下实验方法的一个例子。相反，自下而上的方法用于创建详细的模型，同时也结合了实验数据。自下而上方法的一个例子是使用电路模型来描述一个简单的基因网络。
Various technologies utilized to capture dynamic changes in mRNA, proteins, and post-translational modifications. [[Mechanobiology]], forces and physical properties at all scales, their interplay with other regulatory mechanisms;<ref name="MechanicalSystemsBiology">{{cite journal|last1=Spill|first1=Fabian|last2=Bakal|first2=Chris|last3=Mak|first3=Michael|date=2018|title=Mechanical and Systems Biology of Cancer|journal=Computational and Structural Biotechnology Journal|volume=16|pages=237–245|doi=10.1016/j.csbj.2018.07.002 |pmid=30105089|pmc=6077126|bibcode=2018arXiv180708990S|arxiv=1807.08990}}</ref> [[biosemiotics]], analysis of the system of [[sign relation]]s of an organism or other biosystems; [[Physiomics]], a systematic study of [[physiome]] in biology.
Various technologies utilized to capture dynamic changes in mRNA, proteins, and post-translational modifications. [[Mechanobiology]], forces and physical properties at all scales, their interplay with other regulatory mechanisms;<ref name="MechanicalSystemsBiology">{{cite journal|last1=Spill|first1=Fabian|last2=Bakal|first2=Chris|last3=Mak|first3=Michael|date=2018|title=Mechanical and Systems Biology of Cancer|journal=Computational and Structural Biotechnology Journal|volume=16|pages=237–245|doi=10.1016/j.csbj.2018.07.002 |pmid=30105089|pmc=6077126|bibcode=2018arXiv180708990S|arxiv=1807.08990}}</ref> [[biosemiotics]], analysis of the system of [[sign relation]]s of an organism or other biosystems; [[Physiomics]], a systematic study of [[physiome]] in biology.
有各种技术用于捕获mRNA、蛋白质的动态变化和翻译后修饰。如<font color="#FF8000">生物力学 Mechanobiology</font>，研究跨尺度的力学和物理性质，，以及它们与其他调节机制的相互作用；生物符号学，分析有机体或其他生物系统的符号关系系统；生理组学，生物学中生理的系统研究。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“<font color="#FF8000">生物力学 Mechanobiology</font>，跨尺度的力学和物理性质，”一句改为“如<font color="#FF8000">生物力学 Mechanobiology</font>，研究跨尺度的力学和物理性质，”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
[[Cancer systems biology]] is an example of the systems biology approach, which can be distinguished by the specific object of study ([[tumorigenesis]] and [[Cancer treatment|treatment of cancer]]). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing [[Cancer genome sequencing|cancer genome]] in patient tumour samples) and tools (immortalized cancer [[cell lines]], [[Animal testing on rodents|mouse models]] of tumorigenesis, [[xenograft]] models, [[high-throughput sequencing]] methods, siRNA-based gene knocking down [[high-throughput screening]]s, computational modeling of the consequences of somatic [[mutations]] and [[genome instability]]).<ref name="barillot13">{{cite book|last1=Barillot|first1 =Emmanuel |last2=Calzone|first2=Laurence|last3=Hupe|first3=Philippe|last4=Vert|first4 =Jean-Philippe|last5=Zinovyev|first5=Andrei|title=Computational Systems Biology of Cancer|year=2012|publisher=Chapman & Hall/CRCMathematical & Computational Biology|isbn=978-1439831441|page=461}}</ref> The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for [[Personalized medicine#Cancer management|personalized cancer medicine]] and [[Virtual Physiological Human|virtual cancer patient]] in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale ''[[in silico]]'' models of various tumours.<ref name="byrne2010">
[[Cancer systems biology]] is an example of the systems biology approach, which can be distinguished by the specific object of study ([[tumorigenesis]] and [[Cancer treatment|treatment of cancer]]). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing [[Cancer genome sequencing|cancer genome]] in patient tumour samples) and tools (immortalized cancer [[cell lines]], [[Animal testing on rodents|mouse models]] of tumorigenesis, [[xenograft]] models, [[high-throughput sequencing]] methods, siRNA-based gene knocking down [[high-throughput screening]]s, computational modeling of the consequences of somatic [[mutations]] and [[genome instability]]).<ref name="barillot13">{{cite book|last1=Barillot|first1 =Emmanuel |last2=Calzone|first2=Laurence|last3=Hupe|first3=Philippe|last4=Vert|first4 =Jean-Philippe|last5=Zinovyev|first5=Andrei|title=Computational Systems Biology of Cancer|year=2012|publisher=Chapman & Hall/CRCMathematical & Computational Biology|isbn=978-1439831441|page=461}}</ref> The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for [[Personalized medicine#Cancer management|personalized cancer medicine]] and [[Virtual Physiological Human|virtual cancer patient]] in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale ''[[in silico]]'' models of various tumours.<ref name="byrne2010">
<font color="#FF8000">癌症系统生物学 Cancer systems biology</font>是系统生物学研究的一个例子，它可以通过特定的研究对象(肿瘤发生和癌症治疗)来区分。它使用特定的数据(患者样本、高通量数据，特别注意在患者肿瘤样本中描述癌症基因组)和工具(永生化癌细胞系、肿瘤发生的小鼠模型、异种移植模型、高通量测序方法、基于siRNA的基因敲除高通量筛选、体细胞突变后果的计算模型和基因不稳定性)。癌症系统生物学的长期目标是能够更好地诊断癌症，对癌症进行分类，并更好地预测建议的治疗结果，这是个性化癌症医学和虚拟癌症患者在更远的前景的基础。在癌症的计算系统生物学方面已经做出了重大的努力，在各种肿瘤的计算机模型中创造了真实的多尺度。
<font color="#FF8000">癌症系统生物学 Cancer systems biology</font>是系统生物学研究的一个例子，它可以通过特定的研究对象(肿瘤发生和癌症治疗)来区分。它使用特定的数据(患者样本、高通量数据，特别注意在患者肿瘤样本中描述癌症基因组)和工具(永生化癌细胞系、肿瘤发生的小鼠模型、异种移植模型、高通量测序方法、基于siRNA的基因敲除高通量筛选、体细胞突变后果的计算模型和基因不稳定性)。癌症系统生物学的长期目标是能够更好地诊断癌症，对癌症进行分类，并更好地预测建议的治疗结果，这是个性化癌症医学和虚拟癌症患者在更远的前景的基础。在癌症的计算系统生物学方面已经做出了重大的努力，在各种肿瘤的计算机模型中创造了真实的多尺度。
The investigations are frequently combined with large-scale perturbation methods, including gene-based ([[RNAi]], mis-expression of [[wild type]] and mutant genes) and chemical approaches using small molecule libraries.{{Citation needed|date=May 2009}} [[Robot]]s and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality.{{Citation needed|date=May 2009}} A wide variety of quantitative scientists ([[computational biology|computational biologists]], [[statistician]]s, [[mathematician]]s, [[computer scientist]]s and [[physicist]]s) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.
The investigations are frequently combined with large-scale perturbation methods, including gene-based ([[RNAi]], mis-expression of [[wild type]] and mutant genes) and chemical approaches using small molecule libraries.{{Citation needed|date=May 2009}} [[Robot]]s and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality.{{Citation needed|date=May 2009}} A wide variety of quantitative scientists ([[computational biology|computational biologists]], [[statistician]]s, [[mathematician]]s, [[computer scientist]]s and [[physicist]]s) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.
这些研究经常与大规模的微扰方法结合，包括基于基因的(RNA干扰，野生型和突变型基因的错误表达)和使用小分子库的化学方法。机器人和自动化传感器使这种大规模的实验和数据采集成为可能。这些技术仍在出现，并且很多面临产生的数据量越大，质量就越低的问题。各种各样的定量科学家(计算生物学家、统计学家、数学家、计算机科学家和物理学家)正在努力提高这些方法的质量，并创建、完善和重新测试模型，以准确地反映观测结果。
这些研究经常与大规模的微扰方法结合，包括基于基因的(RNA干扰，野生型和突变型基因的错误表达)和使用小分子库的化学方法。机器人和自动化传感器使这种大规模的实验和数据采集成为可能。这些技术仍在出现，并且很多面临产生的数据量越大，质量就越低的问题。各种各样的定量科学家(计算生物学家、统计学家、数学家、计算机科学家和物理学家)正在努力提高这些方法的质量，并创建、完善和重新测试模型，以准确地反映观测结果。
The systems biology approach often involves the development of [[Mechanism (biology)|mechanistic]] models, such as the reconstruction of [[dynamic system]]s from the quantitative properties of their elementary building blocks.<ref name="dibernardo03" /><ref name="dibernardo05" /><ref name="dynamicmodel" /><ref>{{cite journal|last1=Korkut|first1=A|last2=Wang|first2=W|last3=Demir|first3=E|last4=Aksoy|first4=BA|last5=Jing|first5=X|last6=Molinelli|first6=EJ|last7=Babur|first7=Ö|last8=Bemis|first8=DL|last9=Onur Sumer|first9=S|last10=Solit|first10=DB|last11=Pratilas|first11=CA|last12=Sander|first12=C|title=Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.|journal=eLife|date=18 August 2015|volume=4|pmid=26284497|doi=10.7554/eLife.04640|pmc=4539601}}</ref> For instance, a cellular network can be modelled mathematically using methods coming from [[chemical kinetics]]<ref name=":0">{{Cite journal|last=Gupta|first=Ankur|last2=Rawlings|first2=James B.|date=April 2014|title=Comparison of Parameter Estimation Methods in Stochastic Chemical Kinetic Models: Examples in Systems Biology|journal=AIChE Journal|volume=60|issue=4|pages=1253–1268|doi=10.1002/aic.14409|issn=0001-1541|pmc=4946376|pmid=27429455}}</ref> and [[control theory]]. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., [[flux balance analysis]]).<ref name=dynamicmodel>{{cite book|last1=Tavassoly|first1=Iman|title=Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells|date=2015|publisher=Springer International Publishing|isbn=978-3-319-14961-5|doi=10.1007/978-3-319-14962-2|series=Springer Theses}}</ref><ref name=":0" />
The systems biology approach often involves the development of [[Mechanism (biology)|mechanistic]] models, such as the reconstruction of [[dynamic system]]s from the quantitative properties of their elementary building blocks.<ref name="dibernardo03" /><ref name="dibernardo05" /><ref name="dynamicmodel" /><ref>{{cite journal|last1=Korkut|first1=A|last2=Wang|first2=W|last3=Demir|first3=E|last4=Aksoy|first4=BA|last5=Jing|first5=X|last6=Molinelli|first6=EJ|last7=Babur|first7=Ö|last8=Bemis|first8=DL|last9=Onur Sumer|first9=S|last10=Solit|first10=DB|last11=Pratilas|first11=CA|last12=Sander|first12=C|title=Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.|journal=eLife|date=18 August 2015|volume=4|pmid=26284497|doi=10.7554/eLife.04640|pmc=4539601}}</ref> For instance, a cellular network can be modelled mathematically using methods coming from [[chemical kinetics]]<ref name=":0">{{Cite journal|last=Gupta|first=Ankur|last2=Rawlings|first2=James B.|date=April 2014|title=Comparison of Parameter Estimation Methods in Stochastic Chemical Kinetic Models: Examples in Systems Biology|journal=AIChE Journal|volume=60|issue=4|pages=1253–1268|doi=10.1002/aic.14409|issn=0001-1541|pmc=4946376|pmid=27429455}}</ref> and [[control theory]]. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., [[flux balance analysis]]).<ref name=dynamicmodel>{{cite book|last1=Tavassoly|first1=Iman|title=Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells|date=2015|publisher=Springer International Publishing|isbn=978-3-319-14961-5|doi=10.1007/978-3-319-14962-2|series=Springer Theses}}</ref><ref name=":0" />
系统生物学方法经常涉及机制模型的发展，比如从动态系统的基本构件的定量特性重建动态系统。例如，一个细胞网络可以进行数学建模，使用的方法来自化学动力学和控制理论。由于细胞网络中参数、变量和约束的数量庞大，系统生物学经常使用数值和如流平衡分析的计算技术。。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】前一段有部分翻译被不对称的标记语言掩盖，需要编辑后期处理
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“经常使用数值和计算技术(例如流平衡分析)。”一句改为“系统生物学经常使用数值和如流平衡分析的计算技术。”
== Bioinformatics and data analysis 生物信息学和数据分析==
== Bioinformatics and data analysis 生物信息学和数据分析==
Other aspects of computer science, [[informatics]], and statistics are also used in systems biology. These include new forms of computational models, such as the use of [[process calculi]] to model biological processes (notable approaches include stochastic [[π-calculus]], BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and [[constraint programming|constraint]]-based modeling; integration of information from the literature, using techniques of [[information extraction]] and [[text mining]];<ref>{{cite journal|last1=Ananadou|first1=Sophia|author1-link=Sophia Ananiadou|last2=Kell|first2=Douglas|last3=Tsujii|first3=Jun-ichi|title=Text mining and its potential applications in systems biology|journal=Trends in Biotechnology|volume=24|issue=12|pages=571–579|date=December 2006|doi=10.1016/j.tibtech.2006.10.002|pmid=17045684 }}</ref> development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via [[loose coupling]] of software, websites and databases, or commercial suits; network-based approaches for analyzing high dimensional genomic data sets. For example, [[weighted correlation network analysis]] is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data analysis, e.g. approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members.<ref name="pathvar2012">{{cite journal|last1=Glaab|first1=Enrico|last2=Schneider|first2=Reinhard|title=PathVar: analysis of gene and protein expression variance in cellular pathways using microarray data|volume=28|issue=3|pages=446–447|journal=Bioinformatics|doi=10.1093/bioinformatics/btr656|pmid=22123829|pmc=3268235|year=2012 }}</ref> Much of the analysis of genomic data sets also include identifying correlations. Additionally, as much of the information comes from different fields, the development of syntactically and semantically sound ways of representing biological models is needed.<ref>{{Cite journal|last=Bardini|first=R.|last2=Politano|first2=G.|last3=Benso|first3=A.|last4=Di Carlo|first4=S.|date=2017-01-01|title=Multi-level and hybrid modelling approaches for systems biology|journal=Computational and Structural Biotechnology Journal|volume=15|pages=396–402|doi=10.1016/j.csbj.2017.07.005|issn=2001-0370|pmc=5565741|pmid=28855977}}</ref>
Other aspects of computer science, [[informatics]], and statistics are also used in systems biology. These include new forms of computational models, such as the use of [[process calculi]] to model biological processes (notable approaches include stochastic [[π-calculus]], BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and [[constraint programming|constraint]]-based modeling; integration of information from the literature, using techniques of [[information extraction]] and [[text mining]];<ref>{{cite journal|last1=Ananadou|first1=Sophia|author1-link=Sophia Ananiadou|last2=Kell|first2=Douglas|last3=Tsujii|first3=Jun-ichi|title=Text mining and its potential applications in systems biology|journal=Trends in Biotechnology|volume=24|issue=12|pages=571–579|date=December 2006|doi=10.1016/j.tibtech.2006.10.002|pmid=17045684 }}</ref> development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via [[loose coupling]] of software, websites and databases, or commercial suits; network-based approaches for analyzing high dimensional genomic data sets. For example, [[weighted correlation network analysis]] is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data analysis, e.g. approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members.<ref name="pathvar2012">{{cite journal|last1=Glaab|first1=Enrico|last2=Schneider|first2=Reinhard|title=PathVar: analysis of gene and protein expression variance in cellular pathways using microarray data|volume=28|issue=3|pages=446–447|journal=Bioinformatics|doi=10.1093/bioinformatics/btr656|pmid=22123829|pmc=3268235|year=2012 }}</ref> Much of the analysis of genomic data sets also include identifying correlations. Additionally, as much of the information comes from different fields, the development of syntactically and semantically sound ways of representing biological models is needed.<ref>{{Cite journal|last=Bardini|first=R.|last2=Politano|first2=G.|last3=Benso|first3=A.|last4=Di Carlo|first4=S.|date=2017-01-01|title=Multi-level and hybrid modelling approaches for systems biology|journal=Computational and Structural Biotechnology Journal|volume=15|pages=396–402|doi=10.1016/j.csbj.2017.07.005|issn=2001-0370|pmc=5565741|pmid=28855977}}</ref>
计算机科学、信息学和统计学的其他方面也用于系统生物学。包括新形式的计算模型，如使用过程计算模拟生物过程(著名的方法包括随机演算，BioAmbients，Beta Binders，BioPEPA 和 Brane 演算)和基于约束的建模; 使用信息提取和文本挖掘技术，综合来自文献的信息；开发在线数据库和存储库共享数据和模型，数据库集成方法和软件互操作性，通过松散耦合的软件，网站和数据库，或商业诉讼; 基于网络的方法分析高维基因组数据集。例如，加权相关网络分析常常用于识别集群(称为模块)、建立集群之间的关系模型、计算集群(模块)成员的模糊度量、识别模块内中心成员，以及利用其他数据集研究集群保存; 基于通路的组学数据分析方法，例如识别和评价不同活性的基因、蛋白质或代谢物通路的方法。许多基因组数据集的分析也包括确定相关性。此外，由于大量的信息来自不同的领域，发展生物模型的语法和语义健全的表示方法是必要的。
计算机科学、信息学和统计学的其他方面也用于系统生物学。包括新形式的计算模型，如使用过程计算模拟生物过程(著名的方法包括随机演算，BioAmbients，Beta Binders，BioPEPA 和 Brane 演算)和基于约束的建模; 使用信息提取和文本挖掘技术，综合来自文献的信息；开发在线数据库和存储库共享数据和模型，以及通过软件，网站和数据库或商业诉讼的松散耦合实现数据库集成和软件互操作性的方法；; 基于网络的方法分析高维基因组数据集。例如，加权相关网络分析常常用于识别集群(称为模块)、建立集群之间的关系模型、计算集群(模块)成员的模糊度量、识别模块内中心成员，以及利用其他数据集研究集群保存; 基于通路的组学数据分析方法，例如识别和评价不同活性的基因、蛋白质或代谢物通路的方法。许多基因组数据集的分析也包括确定相关性。此外，由于大量的信息来自不同的领域，发展生物模型的语法和语义健全的表示方法是必要的。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“开发在线数据库和存储库共享数据和模型，数据库集成方法和软件互操作性，通过松散耦合的软件，网站和数据库，或商业诉讼; ”一句改为“开发在线数据库和存储库共享数据和模型，以及通过软件，网站和数据库或商业诉讼的松散耦合实现数据库集成和软件互操作性的方法；”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
== Creating biological models 建立生物学模型==
== Creating biological models 建立生物学模型==
[[File:Toy_Biological_Model.jpg|thumb|326x326px|A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michealis Menten reaction.<ref name=":03">{{Cite journal|last=Transtrum|first=Mark K.|last2=Qiu|first2=Peng|date=2016-05-17|title=Bridging Mechanistic and Phenomenological Models of Complex Biological Systems|journal=PLOS Computational Biology|volume=12|issue=5|pages=e1004915|doi=10.1371/journal.pcbi.1004915|pmid=27187545|pmc=4871498|arxiv=1509.06278|bibcode=2016PLSCB..12E4915T|issn=1553-7358}}</ref>]]
[[File:Toy_Biological_Model.jpg|thumb|326x326px|A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michealis Menten reaction.<ref name=":03">{{Cite journal|last=Transtrum|first=Mark K.|last2=Qiu|first2=Peng|date=2016-05-17|title=Bridging Mechanistic and Phenomenological Models of Complex Biological Systems|journal=PLOS Computational Biology|volume=12|issue=5|pages=e1004915|doi=10.1371/journal.pcbi.1004915|pmid=27187545|pmc=4871498|arxiv=1509.06278|bibcode=2016PLSCB..12E4915T|issn=1553-7358}}</ref>]]
用质量作用定律动力学微分方程建立简单的三蛋白质负反馈回路。每个蛋白质相互作用都是通过米氏反应来描述的。
用质量作用定律动力学微分方程建立简单的三蛋白质负反馈回路。每个蛋白质相互作用都是通过米氏反应来描述的。
Researchers begin by choosing a biological pathway and diagramming all of the protein interactions. After determining all of the interactions of the proteins, [[mass action kinetics]] is utilized to describe the speed of the reactions in the system. Mass action kinetics will provide differential equations to model the biological system as a mathematical model in which experiments can determine the parameter values to use in the [[differential equation]]s.<ref>{{Cite journal|last=Chellaboina|first=V.|last2=Bhat|first2=S. P.|last3=Haddad|first3=W. M.|last4=Bernstein|first4=D. S.|date=August 2009|title=Modeling and analysis of mass-action kinetics|journal=IEEE Control Systems Magazine|volume=29|issue=4|pages=60–78|doi=10.1109/MCS.2009.932926|issn=1941-000X}}</ref> These parameter values will be the reaction rates of each proteins interaction in the system. This model determines the behavior of certain proteins in biological systems and bring new insight to the specific activities of individual proteins. Sometimes it is not possible to gather all reaction rates of a system. Unknown reaction rates are determined by simulating the model of known parameters and target behavior which provides possible parameter values.<ref>{{Cite journal|last=Brown|first=Kevin S.|last2=Sethna|first2=James P.|date=2003-08-12|title=Statistical mechanical approaches to models with many poorly known parameters|journal=Physical Review E|volume=68|issue=2|pages=021904|doi=10.1103/physreve.68.021904|pmid=14525003|bibcode=2003PhRvE..68b1904B|issn=1063-651X}}</ref><ref name=":03" />
Researchers begin by choosing a biological pathway and diagramming all of the protein interactions. After determining all of the interactions of the proteins, [[mass action kinetics]] is utilized to describe the speed of the reactions in the system. Mass action kinetics will provide differential equations to model the biological system as a mathematical model in which experiments can determine the parameter values to use in the [[differential equation]]s.<ref>{{Cite journal|last=Chellaboina|first=V.|last2=Bhat|first2=S. P.|last3=Haddad|first3=W. M.|last4=Bernstein|first4=D. S.|date=August 2009|title=Modeling and analysis of mass-action kinetics|journal=IEEE Control Systems Magazine|volume=29|issue=4|pages=60–78|doi=10.1109/MCS.2009.932926|issn=1941-000X}}</ref> These parameter values will be the reaction rates of each proteins interaction in the system. This model determines the behavior of certain proteins in biological systems and bring new insight to the specific activities of individual proteins. Sometimes it is not possible to gather all reaction rates of a system. Unknown reaction rates are determined by simulating the model of known parameters and target behavior which provides possible parameter values.<ref>{{Cite journal|last=Brown|first=Kevin S.|last2=Sethna|first2=James P.|date=2003-08-12|title=Statistical mechanical approaches to models with many poorly known parameters|journal=Physical Review E|volume=68|issue=2|pages=021904|doi=10.1103/physreve.68.021904|pmid=14525003|bibcode=2003PhRvE..68b1904B|issn=1063-651X}}</ref><ref name=":03" />
研究人员首先选择一条生物通路，绘制所有蛋白质相互作用的图表。确定了所有的蛋白质相互作用之后，使用符合质量作用定律的动力学来描述系统中反应的速率。质量作用定律动力学将提供微分方程，把生物系统模拟成一个数学模型，其中微分方程的参数可以由实验来确定。这些参数值是系统中每对蛋白质相互作用的反应速率。这个模型决定了生物系统中主要蛋白质的行为，并且为理解单个蛋白质的特殊行为提供了新的视角。当不能够收集一整个系统的所有反应速率时。可以通过模拟已知参数的模型并且提供可能参数值的目标行为，来确定未知的反应速率。
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】“有时候不可能收集一个系统的所有反应速率。”一句改为“当不能够收集一整个系统的所有反应速率时，”
--[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
[[File:Toy_Model_Plot.jpg|thumb|325x325px|Plot of Concentrations vs time for the simple three protein negative feedback loop. All parameters are set to either 0 or 1 for initial conditions. The reaction is allowed to proceed until it hits equilibrium. This plot is of the change in each protein over time.]]
[[File:Toy_Model_Plot.jpg|thumb|325x325px|Plot of Concentrations vs time for the simple three protein negative feedback loop. All parameters are set to either 0 or 1 for initial conditions. The reaction is allowed to proceed until it hits equilibrium. This plot is of the change in each protein over time.]]
简单的三种蛋白质负反馈回路的浓度-时间图。对于初始条件，所有参数设置为0或1。反应持续进行，直到达到平衡。这张图是每种蛋白质随时间的变化。
简单的三种蛋白质负反馈回路的浓度-时间图。对于初始条件，所有参数设置为0或1。反应持续进行，直到达到平衡。这张图是每种蛋白质随时间的变化。
<ref name="21stcentury">{{cite web|url=http://www.systemsbiology.org/Intro_to_Systems_Biology/Systems_Biology_--_the_21st_Century_Science|title=Systems Biology: the 21st Century Science|publisher=Institute for Systems Biology|accessdate=15 June 2011}}</ref>
<ref name="21stcentury">{{cite web|url=http://www.systemsbiology.org/Intro_to_Systems_Biology/Systems_Biology_--_the_21st_Century_Science|title=Systems Biology: the 21st Century Science|publisher=Institute for Systems Biology|accessdate=15 June 2011}}</ref>
曾斌，《系统生物工程的概念》 ，1994年11月，《转基因动物交流》 ，中国科学院。
曾斌，《系统生物工程的概念》 ，1994年11月，《转基因动物交流》 ，中国科学院。