Theory of cell fate
Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlin...
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Veröffentlicht in: | Wiley interdisciplinary reviews. Mechanisms of disease 2020-03, Vol.12 (2), p.e1471-n/a |
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description | Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein–protein and protein–DNA interactions. This inherent complexity and nonlinearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer, we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell “fate”, and how cells change fate. We will also outline how modern experiments, particularly high‐throughput single‐cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities.
This article is categorized under:
Models of Systems Properties and Processes > Mechanistic Models
Biological Mechanisms > Cell Fates
Models of Systems Properties and Processes > Cellular Models
The cell as a dynamical system: combining theory and experiment to understand cell fates. |
doi_str_mv | 10.1002/wsbm.1471 |
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This article is categorized under:
Models of Systems Properties and Processes > Mechanistic Models
Biological Mechanisms > Cell Fates
Models of Systems Properties and Processes > Cellular Models
The cell as a dynamical system: combining theory and experiment to understand cell fates.</description><identifier>ISSN: 1939-5094</identifier><identifier>EISSN: 1939-005X</identifier><identifier>EISSN: 2692-9368</identifier><identifier>DOI: 10.1002/wsbm.1471</identifier><identifier>PMID: 31828979</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Biological activity ; Biological models (mathematics) ; Cell culture ; Cell fate ; Cell Fates ; Cellular Models ; Complexity ; Control theory ; Decision theory ; Deoxyribonucleic acid ; DNA ; Dynamic systems theory ; Feedback loops ; Mathematical analysis ; mathematical model ; Mathematical models ; Mechanistic Models ; Negative feedback ; Nonlinear systems ; Nonlinearity ; Primer ; Proteins ; System theory ; systems biology</subject><ispartof>Wiley interdisciplinary reviews. Mechanisms of disease, 2020-03, Vol.12 (2), p.e1471-n/a</ispartof><rights>2019 The Authors. published by Wiley Periodicals, Inc.</rights><rights>2019 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc.</rights><rights>2019. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5091-e98ff1d0868935ff32c0c2fd1c9670f9b73780a722b0216a5f7223770aa9ab2b3</citedby><cites>FETCH-LOGICAL-c5091-e98ff1d0868935ff32c0c2fd1c9670f9b73780a722b0216a5f7223770aa9ab2b3</cites><orcidid>0000-0002-5396-9750</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fwsbm.1471$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fwsbm.1471$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31828979$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Casey, Michael J.</creatorcontrib><creatorcontrib>Stumpf, Patrick S.</creatorcontrib><creatorcontrib>MacArthur, Ben D.</creatorcontrib><title>Theory of cell fate</title><title>Wiley interdisciplinary reviews. Mechanisms of disease</title><addtitle>Wiley Interdiscip Rev Syst Biol Med</addtitle><description>Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein–protein and protein–DNA interactions. This inherent complexity and nonlinearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer, we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell “fate”, and how cells change fate. We will also outline how modern experiments, particularly high‐throughput single‐cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities.
This article is categorized under:
Models of Systems Properties and Processes > Mechanistic Models
Biological Mechanisms > Cell Fates
Models of Systems Properties and Processes > Cellular Models
The cell as a dynamical system: combining theory and experiment to understand cell fates.</description><subject>Biological activity</subject><subject>Biological models (mathematics)</subject><subject>Cell culture</subject><subject>Cell fate</subject><subject>Cell Fates</subject><subject>Cellular Models</subject><subject>Complexity</subject><subject>Control theory</subject><subject>Decision theory</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>Dynamic systems theory</subject><subject>Feedback loops</subject><subject>Mathematical analysis</subject><subject>mathematical model</subject><subject>Mathematical models</subject><subject>Mechanistic Models</subject><subject>Negative feedback</subject><subject>Nonlinear systems</subject><subject>Nonlinearity</subject><subject>Primer</subject><subject>Proteins</subject><subject>System theory</subject><subject>systems biology</subject><issn>1939-5094</issn><issn>1939-005X</issn><issn>2692-9368</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp1kM9LwzAUgIMobk4P4l0GXvTQ7SVZmuYi6PAXTDw40VtIu8RV2mYmq2P_vambQwVPeZCPj_c-hA4x9DAA6S98WvbwgOMt1MaCigiAvWyvZwZi0EJ73r8BxGwgxC5qUZyQRHDRRkfjqbZu2bWmm-mi6Bo11_tox6jC64P120FP11fj4W00eri5G16Moiw4caRFYgyeQBIngjJjKMkgI2aCMxFzMCLllCegOCEpEBwrZsJIOQelhEpJSjvofOWd1WmpJ5mu5k4VcubyUrmltCqXv3-qfCpf7YfkQDgDHgSna4Gz77X2c1nmvjlDVdrWXhJKGAXAJAnoyR_0zdauCucFioVQMeY0UGcrKnPWe6fNZhkMskktm9SySR3Y45_bb8jvtgHor4BFXujl_yb5_Hh5_6X8BP0ehmQ</recordid><startdate>202003</startdate><enddate>202003</enddate><creator>Casey, Michael J.</creator><creator>Stumpf, Patrick S.</creator><creator>MacArthur, Ben D.</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7QP</scope><scope>7QR</scope><scope>7SS</scope><scope>7T5</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5396-9750</orcidid></search><sort><creationdate>202003</creationdate><title>Theory of cell fate</title><author>Casey, Michael J. ; Stumpf, Patrick S. ; MacArthur, Ben D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5091-e98ff1d0868935ff32c0c2fd1c9670f9b73780a722b0216a5f7223770aa9ab2b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biological activity</topic><topic>Biological models (mathematics)</topic><topic>Cell culture</topic><topic>Cell fate</topic><topic>Cell Fates</topic><topic>Cellular Models</topic><topic>Complexity</topic><topic>Control theory</topic><topic>Decision theory</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>Dynamic systems theory</topic><topic>Feedback loops</topic><topic>Mathematical analysis</topic><topic>mathematical model</topic><topic>Mathematical models</topic><topic>Mechanistic Models</topic><topic>Negative feedback</topic><topic>Nonlinear systems</topic><topic>Nonlinearity</topic><topic>Primer</topic><topic>Proteins</topic><topic>System theory</topic><topic>systems biology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Casey, Michael J.</creatorcontrib><creatorcontrib>Stumpf, Patrick S.</creatorcontrib><creatorcontrib>MacArthur, Ben D.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Wiley interdisciplinary reviews. Mechanisms of disease</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Casey, Michael J.</au><au>Stumpf, Patrick S.</au><au>MacArthur, Ben D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Theory of cell fate</atitle><jtitle>Wiley interdisciplinary reviews. Mechanisms of disease</jtitle><addtitle>Wiley Interdiscip Rev Syst Biol Med</addtitle><date>2020-03</date><risdate>2020</risdate><volume>12</volume><issue>2</issue><spage>e1471</spage><epage>n/a</epage><pages>e1471-n/a</pages><issn>1939-5094</issn><eissn>1939-005X</eissn><eissn>2692-9368</eissn><abstract>Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein–protein and protein–DNA interactions. This inherent complexity and nonlinearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer, we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell “fate”, and how cells change fate. We will also outline how modern experiments, particularly high‐throughput single‐cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities.
This article is categorized under:
Models of Systems Properties and Processes > Mechanistic Models
Biological Mechanisms > Cell Fates
Models of Systems Properties and Processes > Cellular Models
The cell as a dynamical system: combining theory and experiment to understand cell fates.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>31828979</pmid><doi>10.1002/wsbm.1471</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-5396-9750</orcidid><oa>free_for_read</oa></addata></record> |
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source | Wiley Online Library Journals Frontfile Complete |
subjects | Biological activity Biological models (mathematics) Cell culture Cell fate Cell Fates Cellular Models Complexity Control theory Decision theory Deoxyribonucleic acid DNA Dynamic systems theory Feedback loops Mathematical analysis mathematical model Mathematical models Mechanistic Models Negative feedback Nonlinear systems Nonlinearity Primer Proteins System theory systems biology |
title | Theory of cell fate |
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