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| | 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 |
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| − | '''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。<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>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。 |
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| − | <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。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | 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。<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"/> |
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| − | 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。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
| + | ==概述== |
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| − | == Overview 概述== | |
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| | 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。 |
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| − | | + | 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)。<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" />
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| − | 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)。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | 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" />
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| − | 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)
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | --[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】
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| − | --[[用户:Thingamabob|Thingamabob]]([[用户讨论:Thingamabob|讨论]]) 【审校】
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| − | 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 />
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| − | 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。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | 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>
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| − | 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。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| | + | 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)<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" /> |
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| − | As a [[Socio-scientific issues|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.<ref>{{Cite book |last1=Baitaluk|first1=M. |chapter=System Biology of Gene Regulation |doi=10.1007/978-1-59745-524-4_4 |title=Biomedical Informatics |journal=<!--Bypass Citation bot --> |series=Methods in Molecular Biology |volume=569 |pages=55–87 |year=2009 |isbn=978-1-934115-63-3 |pmid=19623486 |pmc= }}</ref>
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| − | 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。<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 /> |
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| − | 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。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
| + | 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。<ref name=Voit01>{{cite book|last1=Voit|first1=Eberhard|title=A First Course in Systems Biology|date=2012|publisher=Garland Science|isbn=9780815344674}}</ref> |
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| − | 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.
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| | + | 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。<ref>{{Cite book |last1=Baitaluk|first1=M. |chapter=System Biology of Gene Regulation |doi=10.1007/978-1-59745-524-4_4 |title=Biomedical Informatics |journal=<!--Bypass Citation bot --> |series=Methods in Molecular Biology |volume=569 |pages=55–87 |year=2009 |isbn=978-1-934115-63-3 |pmid=19623486 |pmc= }}</ref> |
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| | 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。 | | 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。 |
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
| + | ==历史== |
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| − | == History 历史== | |
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| − | Systems biology finds its roots in{{Citation needed|date=May 2009}} the quantitative modeling of [[enzyme kinetics]], a discipline that flourished between 1900 and 1970, the mathematical modeling of [[population dynamics]], the simulations developed to study [[neurophysiology]], [[control theory]] and [[cybernetics]], and [[Synergetics (Haken)|synergetics]].
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| − | Systems biology finds its roots in the quantitative modeling of enzyme kinetics, a discipline that flourished between 1900 and 1970, the mathematical modeling of population dynamics, the simulations developed to study neurophysiology, control theory and cybernetics, and synergetics.
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| | 系统生物学根植于<font color="#FF8000">酶动力学 enzyme kinetics</font>的定量模型（酶动力学在1900年到1970年间蓬勃发展）、<font color="#FF8000">种群动力学 population dynamics</font>的数学模型、神经生理学模拟、控制理论和<font color="#FF8000">控制论 control theory</font>以及<font color="#FF8000">协同学 Synergetics</font>。 | | 系统生物学根植于<font color="#FF8000">酶动力学 enzyme kinetics</font>的定量模型（酶动力学在1900年到1970年间蓬勃发展）、<font color="#FF8000">种群动力学 population dynamics</font>的数学模型、神经生理学模拟、控制理论和<font color="#FF8000">控制论 control theory</font>以及<font color="#FF8000">协同学 Synergetics</font>。 |
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| | + | 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。<ref name="vonBertalanffy68" />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。<ref name="hodgkin52" /> 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。<ref name="lenovere07" /> 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。<ref>{{Cite journal|last1=Turing|first1=A. M.|authorlink=Alan Turing|title=The Chemical Basis of Morphogenesis|journal=Philosophical Transactions of the Royal Society B: Biological Sciences|volume=237|issue=641|pages=37–72|doi=10.1098/rstb.1952.0012|url=http://www.dna.caltech.edu/courses/cs191/paperscs191/turing.pdf|jstor=92463|year=1952|pmid=|pmc =|bibcode=1952RSPTB.237...37T}}</ref> |
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| | + | 1960年，丹尼斯·诺布尔创建了第一个心脏起搏器的计算模型。<ref name="noble60" /> |
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| − | 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]].<ref name="vonBertalanffy68" /> One of the first numerical simulations in [[cell biology]] was published in 1952 by the British neurophysiologists and Nobel prize winners [[Alan Lloyd Hodgkin]] and [[Andrew Fielding Huxley]], who constructed a mathematical model that explained the [[action potential]] propagating along the [[axon]] of a [[neuron]]al cell.<ref name="hodgkin52" /> Their model described a cellular function emerging from the interaction between two different molecular components, a [[Potassium channel|potassium]] and a [[sodium channel]], and can therefore be seen as the beginning of [[computational systems biology]].<ref name="lenovere07" /> Also in 1952, Alan Turing published [[The Chemical Basis of Morphogenesis]], describing how non-uniformity could arise in an initially homogeneous biological system.<ref>{{Cite journal|last1=Turing|first1=A. M.|authorlink=Alan Turing|title=The Chemical Basis of Morphogenesis|journal=Philosophical Transactions of the Royal Society B: Biological Sciences|volume=237|issue=641|pages=37–72|doi=10.1098/rstb.1952.0012|url=http://www.dna.caltech.edu/courses/cs191/paperscs191/turing.pdf|jstor=92463|year=1952|pmid=|pmc =|bibcode=1952RSPTB.237...37T}}</ref>
| + | 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。<ref name="science68" /> |
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| − | 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.
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| − | 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。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | In 1960, [[Denis Noble]] developed the first computer model of the [[heart pacemaker]].<ref name="noble60" />
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| − | 1960年，丹尼斯·诺布尔创建了第一个心脏起搏器的计算模型。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | 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" />
| + | 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。<ref name="hunter12" /> |
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| − | 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。
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| | + | 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。<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> |
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| − | 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" />
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| − | 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。
| + | 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》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》,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。<ref name="tomita97" /> |
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
| + | 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)<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>。 |
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| | + | 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。<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>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。<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>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。<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> |
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| − | [[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>]]
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| − | 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。
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| − | --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】
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| − | 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
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| − | 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
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| − | 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)",
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| − | 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》 ,
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| − | ''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'',
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| − | 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》 ,
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| − | 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" />
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| − | 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。
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| − | 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>).
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| − | 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)。
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| − | 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>
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| − | 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。
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| − | An important milestone in the development of systems biology has become the international project [[Physiome]].
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| | 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。 |
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| − | == Associated disciplines 相关领域== | + | ==相关领域== |
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| | [[File:Signal transduction pathways.svg|280px|thumb|right|Overview of [[signal transduction]] pathways]] | | [[File:Signal transduction pathways.svg|280px|thumb|right|Overview of [[signal transduction]] pathways]] |
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| | [信号转导通路]概览 | | [信号转导通路]概览 |
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| − | 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。<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> |
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| − | 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。
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| − | 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>
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| − | 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;
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| − | 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。<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> | + | 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)。<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>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<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>；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;<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>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。<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> |
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| − | 本中文词条由[[用户:Yillia Jing|Yillia Jing]]参与编译，[[用户:CecileLi|CecileLi]]审校，[[用户:许许|许许]]编辑，欢迎在讨论页面留言。 | + | 本中文词条由[[用户:Yillia Jing|Yillia Jing]]参与编译，[[用户:CecileLi|CecileLi]]、[[用户:Thingamabob|Thingamabob]]审校，[[用户:许许|许许]]编辑，欢迎在讨论页面留言。 |
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| | '''本词条内容源自wikipedia及公开资料，遵守 CC3.0协议。''' | | '''本词条内容源自wikipedia及公开资料，遵守 CC3.0协议。''' |