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{{short description|Computational and mathematical modeling of complex biological systems}}

{{short description|Computational and mathematical modeling of complex biological systems}}

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.

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.

系统生物学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(整体论而不是更传统的还原论)进行生物学研究。它跨越了系统论和应用数学方法的领域，发展成为复杂系统生物学的一个分支。

<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</front>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。

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.

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.

== Overview ==

== Overview 概述==

Systems biology can be considered from a number of different aspects.

Systems biology can be considered from a number of different aspects.

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.

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.

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。

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。

== History ==

== History 历史==

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]].

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]].

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.

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.

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.

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。

== Associated disciplines ==

== Associated disciplines 相关领域==

[[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]]

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.

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.

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.<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 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.<ref name="byrne2010">

癌症系统生物学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。< ref name ="byrne 2010">  

<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。< ref name ="byrne 2010">  

{{cite journal|last1=Byrne|first1=Helen M. |authorlink1=Helen Byrne

{{cite journal|last1=Byrne|first1=Helen M. |authorlink1=Helen Byrne

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)。

== Bioinformatics and data analysis ==

== 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，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。

== Creating biological models ==

== 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>]]

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 ==

== See also 另外可见==

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

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

* [[Systems immunology]]

* [[Systems immunology]]

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