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Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities
Biochemical systems consist of numerous elementary reactions governed by the law of mass action. However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simpl...
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description | Biochemical systems consist of numerous elementary reactions governed by the law of mass action. However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation. To facilitate the framework, we provide a user-friendly open-source computational package, ASSISTER, that automatically performs the present framework. |
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However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation. To facilitate the framework, we provide a user-friendly open-source computational package, ASSISTER, that automatically performs the present framework.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1008952</identifier><identifier>PMID: 34662330</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Approximation ; Binding ; Biochemistry ; Biological clocks ; Biology and Life Sciences ; Cell Communication ; Cell Cycle ; Cell signaling ; Chemical reactions ; Circadian Clocks ; Circadian rhythms ; Computational Biology - methods ; Computer applications ; Computer Simulation ; Deoxyribonucleic acid ; DNA ; Enzymes ; Gene regulation ; Heuristic methods ; Humans ; Kinetics ; Learning models (Stochastic processes) ; Models, Biological ; Physical Sciences ; Protein Binding ; Research and Analysis Methods ; Simulation ; Stochastic models ; Stochastic Processes ; Stochasticity ; Substrates ; Transcription factors ; Validity</subject><ispartof>PLoS computational biology, 2021-10, Vol.17 (10), p.e1008952-e1008952</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation. To facilitate the framework, we provide a user-friendly open-source computational package, ASSISTER, that automatically performs the present framework.</description><subject>Algorithms</subject><subject>Approximation</subject><subject>Binding</subject><subject>Biochemistry</subject><subject>Biological clocks</subject><subject>Biology and Life Sciences</subject><subject>Cell Communication</subject><subject>Cell Cycle</subject><subject>Cell signaling</subject><subject>Chemical reactions</subject><subject>Circadian Clocks</subject><subject>Circadian rhythms</subject><subject>Computational Biology - methods</subject><subject>Computer applications</subject><subject>Computer Simulation</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>Enzymes</subject><subject>Gene regulation</subject><subject>Heuristic methods</subject><subject>Humans</subject><subject>Kinetics</subject><subject>Learning models (Stochastic processes)</subject><subject>Models, Biological</subject><subject>Physical Sciences</subject><subject>Protein Binding</subject><subject>Research and Analysis Methods</subject><subject>Simulation</subject><subject>Stochastic models</subject><subject>Stochastic Processes</subject><subject>Stochasticity</subject><subject>Substrates</subject><subject>Transcription factors</subject><subject>Validity</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqVkktv1DAQxyMEoqXwDRBE4gKHXfyIHeeCVFU8VqpAgnK2HGe8deXYqe2s2G-Pl91WXcQF-eDR-Df_eXiq6iVGS0xb_P4mzNErt5x0b5cYIdEx8qg6xYzRRUuZePzAPqmepXSDUDE7_rQ6oQ3nhFJ0Wt3-9HYDMSnntvVGOTvUEYZZZxt8HUw9zi7bpJWDOuWgr1XKVte9LSaMtvjrtE0ZxlTPyfp1new4FdYHvwAHI_is4raeYpjAJ5stpOfVE6NcgheH-6y6-vTx6uLL4vLb59XF-eVCc0rzoldNowbKWtNpaHvUd4oBNz1TGoZe4VZTNZCW4wYhLpoBU4G5IBRRQwjS9Kx6vZedXEjyMKwkCesERoISVojVnhiCupFTtGMpVQZl5R9HiGupYunWgeS9AkFb3QBijWmV6AxpDBBNe4MFx0XrwyHb3I8w6NJ3VO5I9PjF22u5DhspGCeCoyLw9iAQw-0MKcuxjB2cUx7CvKtb0KYRpceCvvkL_Xd3yz21Ln8nrTeh5NXlDLt_Cx6MLf5zXngsSEdKwLujgMJk-JXXak5Jrn58_w_26zHb7FkdQ0oRzP1UMJK7Tb4rX-42WR42uYS9ejjR-6C71aW_AVja8vU</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Song, Yun Min</creator><creator>Hong, Hyukpyo</creator><creator>Kim, Jae Kyoung</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7419-8345</orcidid><orcidid>https://orcid.org/0000-0001-7562-5935</orcidid><orcidid>https://orcid.org/0000-0001-7842-2172</orcidid></search><sort><creationdate>20211001</creationdate><title>Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities</title><author>Song, Yun Min ; Hong, Hyukpyo ; Kim, Jae Kyoung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c633t-ba44ad357f9ce7b0b9a5e6fb5acedba17c3ad2761400684d1381682303f220c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Approximation</topic><topic>Binding</topic><topic>Biochemistry</topic><topic>Biological clocks</topic><topic>Biology and Life Sciences</topic><topic>Cell Communication</topic><topic>Cell Cycle</topic><topic>Cell signaling</topic><topic>Chemical reactions</topic><topic>Circadian Clocks</topic><topic>Circadian rhythms</topic><topic>Computational Biology - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Yun Min</au><au>Hong, Hyukpyo</au><au>Kim, Jae Kyoung</au><au>Igoshin, Oleg A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2021-10-01</date><risdate>2021</risdate><volume>17</volume><issue>10</issue><spage>e1008952</spage><epage>e1008952</epage><pages>e1008952-e1008952</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Biochemical systems consist of numerous elementary reactions governed by the law of mass action. However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation. To facilitate the framework, we provide a user-friendly open-source computational package, ASSISTER, that automatically performs the present framework.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34662330</pmid><doi>10.1371/journal.pcbi.1008952</doi><orcidid>https://orcid.org/0000-0001-7419-8345</orcidid><orcidid>https://orcid.org/0000-0001-7562-5935</orcidid><orcidid>https://orcid.org/0000-0001-7842-2172</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Approximation Binding Biochemistry Biological clocks Biology and Life Sciences Cell Communication Cell Cycle Cell signaling Chemical reactions Circadian Clocks Circadian rhythms Computational Biology - methods Computer applications Computer Simulation Deoxyribonucleic acid DNA Enzymes Gene regulation Heuristic methods Humans Kinetics Learning models (Stochastic processes) Models, Biological Physical Sciences Protein Binding Research and Analysis Methods Simulation Stochastic models Stochastic Processes Stochasticity Substrates Transcription factors Validity |
title | Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities |
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