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第185行： − --[[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> − + − 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. − + − --[[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"> − − <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. + 第213行：
第201行： − 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】 − − == 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> + − + − --[[用户:CecileLi|CecileLi]]([[用户讨论:CecileLi|讨论]]) 【审校】 − + − − 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】 第247行：
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第323行： − + − − {{Wiktionary}} 第354行：
第329行： − {{Biology_nav}} − − {{Genomics}} − − {{Systems}} − − {{Authority control}} − − − − {{DEFAULTSORT:Systems Biology}} − − [[Category:Systems biology| ]] − − [[Category:Bioinformatics]] − − Category:Bioinformatics − − 类别: 生物信息学 − − [[Category:Computational fields of study]] − − Category:Computational fields of study − − 类别: 研究的计算领域 − <noinclude>+ − <small>This page was moved from [[wikipedia:en:Systems biology]]. Its edit history can be viewed at [[系统生物学/edithistory]]</small></noinclude>+ − + + + +
无编辑摘要
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;
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;
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。<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>
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。
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<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>；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。
<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)。<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>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。<ref name="byrne2010">
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<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> 。例如，一个细胞网络可以进行数学建模<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>，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。<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" />
== 生物信息学和数据分析==
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。
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；<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>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。<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> 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。<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>
== Creating biological models 建立生物学模型==
== 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。
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。<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>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。<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" />
[[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。
== See also 参见==
== 参见==
{{Portal|Systems science|Biology|Evolutionary biology}}
{{Portal|Systems science|Biology|Evolutionary biology}}
==External links 相关链接==
==相关链接==
* [http://www.bio-physics.at/wiki/index.php?title=Biological_Systems Biological Systems in bio-physics-wiki]
* [http://www.bio-physics.at/wiki/index.php?title=Biological_Systems Biological Systems in bio-physics-wiki]
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