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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)。

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)。

   --[[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“尤其是作为一个研究领域，系统生物学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】“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】“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”

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 />

   --[[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”

   --[[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]].<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>

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。

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。

   --[[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“20世纪60年代和70年代见证了几种研究复杂分子系统的方法的发展，”

   --[[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”

 

[[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>]]

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.&nbsp;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.&nbsp;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.&nbsp;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.&nbsp;J. Zeng, "Transgenic animal expression system – transgenic egg plan (goldegg plan)",

No.8-10, 1996. Etc.</ref> on systems medicine, systems genetics, and systems biological engineering by B.&nbsp;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.

No.8-10, 1996. Etc.</ref> on systems medicine, systems genetics, and systems biological engineering by B.&nbsp;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.

1996年8-10号。系统医学、系统遗传学和系统生物工程学等。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。

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

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 computational 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>

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 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;

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[[用户:Jxzhou|Jxzhou]]（[[用户讨论:Jxzhou|讨论]]）authoritative molecular discipline这样翻译是否合适？[[用户:Jxzhou|Jxzhou]]（[[用户讨论:Jxzhou|讨论]]）是蛋白质-蛋白质相互作用(PPI) ，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，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;

   --[[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/发挥 并不排斥其他分子学科/并不仅仅局限于该领域or学科/也有其它分子学科的作用”

   --[[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”

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>

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。

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。

   --[[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>，利用跨尺度的力学和物理性质，”

   --[[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，”

[[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">

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)。

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)。

   --[[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计算技术(例如流平衡分析)。”

   --[[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==

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.

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.

   --[[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】“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，”

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 另外可见==

== See also 参见==

{{Portal|Systems science|Biology|Evolutionary biology}}

{{Portal|Systems science|Biology|Evolutionary biology}}