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<identifier>oai:HAL:hal-00920806v1</identifier>
<datestamp>2014-10-13</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Seven competing ways to recover the Michaelis-Menten equation reveal the alternative approaches to steady state modeling</title>
<creator>Michel, Denis</creator>
<creator>Ruelle, Philippe</creator>
<contributor>TREC : Transcription, Environment and Cancer ; Institut de recherche, santé, environnement et travail [Rennes] (Irset) ; INSERM - École Nationale de la Santé Publique - Université de Rennes 1 (UR1) - Université des Antilles et de la Guyane (UAG) - Structure Fédérative de Recherche en Biologie-Santé de Rennes (Biosit) ; Université de Rennes 1 (UR1) - INSERM - CNRS - INSERM - CNRS - INSERM - École Nationale de la Santé Publique - Université de Rennes 1 (UR1) - Université des Antilles et de la Guyane (UAG) - Structure Fédérative de Recherche en Biologie-Santé de Rennes (Biosit) ; Université de Rennes 1 (UR1) - INSERM - CNRS - INSERM - CNRS</contributor>
<contributor>Institut de Recherche en Mathématiques et Physique (UCL IRMP) ; Université Catholique de Louvain (UCL)</contributor>
<description>International audience</description>
<source>Journal of Mathematical Chemistry</source>
<publisher>Springer Verlag (Germany)</publisher>
<identifier>hal-00920806</identifier>
<identifier>https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806</identifier>
<identifier>https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806/document</identifier>
<source>https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806</source>
<source>Journal of Mathematical Chemistry, Springer Verlag (Germany), 2013, 51 (9), pp.2271-2284. <10.1007/s10910-013-0237-5></source>
<identifier>ARXIV : 1312.5468</identifier>
<identifier>DOI : 10.1007/s10910-013-0237-5</identifier>
<language>en</language>
<subject lang=en>Stochastic reaction chain</subject>
<subject lang=en>Enzyme kinetics</subject>
<subject lang=en>Steady-state</subject>
<subject lang=en>Random walk</subject>
<subject>[PHYS.PHYS.PHYS-CHEM-PH] Physics/Physics/Chemical Physics</subject>
<subject>[CHIM.THEO] Chemical Sciences/Theoretical and/or physical chemistry</subject>
<type>Journal articles</type>
<description lang=en>The Michaelis-Menten enzymatic reaction is sufficient to perceive many subtleties of network modeling, including the concentration and time scales separations, the formal equivalence between bulk phase and single-molecule approaches, or the relationships between single-cycle transient probabilities and steady state rates. Seven methods proposed by different authors and yielding the same famous Michaelis-Menten equation, are selected here to illustrate the kinetic and probabilistic use of rate constants and to review basic techniques for handling them. Finally, the general rate of an ordered multistep reaction, of which the Michaelis-Menten reaction is a particular case, is deduced from a Markovian approach.</description>
<date>2013-10</date>
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