Chemistry at the Nanoscale

Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variab...
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Autor*in:	Kumar, Ashwin B. R. [verfasserIn] Ramaswamy, Ram [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Chemical kinetics stochastic chemical kinetics Gillespie’s stochastic simulation algorithm synthetic gene oscillators

Übergeordnetes Werk:	Enthalten in: Resonance - Bangalore : Indian Academy of Sciences, 1996, 23(2018), 1 vom: Jan., Seite 23-40
Übergeordnetes Werk:	volume:23 ; year:2018 ; number:1 ; month:01 ; pages:23-40

Links:	Volltext

DOI / URN:	10.1007/s12045-018-0592-4

Katalog-ID:	SPR024112747

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520			\|a Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks.
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912			\|a GBV_ILN_213
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912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
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912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
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allfields	10.1007/s12045-018-0592-4 doi (DE-627)SPR024112747 (SPR)s12045-018-0592-4-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Kumar, Ashwin B. R. verfasserin aut Chemistry at the Nanoscale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks. Chemical kinetics (dpeaa)DE-He213 stochastic chemical kinetics (dpeaa)DE-He213 Gillespie’s stochastic simulation algorithm (dpeaa)DE-He213 synthetic gene oscillators (dpeaa)DE-He213 Ramaswamy, Ram verfasserin aut Enthalten in Resonance Bangalore : Indian Academy of Sciences, 1996 23(2018), 1 vom: Jan., Seite 23-40 (DE-627)352260963 (DE-600)2085223-X 0973-712X nnns volume:23 year:2018 number:1 month:01 pages:23-40 https://dx.doi.org/10.1007/s12045-018-0592-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 30.00 ASE AR 23 2018 1 01 23-40
spelling	10.1007/s12045-018-0592-4 doi (DE-627)SPR024112747 (SPR)s12045-018-0592-4-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Kumar, Ashwin B. R. verfasserin aut Chemistry at the Nanoscale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks. Chemical kinetics (dpeaa)DE-He213 stochastic chemical kinetics (dpeaa)DE-He213 Gillespie’s stochastic simulation algorithm (dpeaa)DE-He213 synthetic gene oscillators (dpeaa)DE-He213 Ramaswamy, Ram verfasserin aut Enthalten in Resonance Bangalore : Indian Academy of Sciences, 1996 23(2018), 1 vom: Jan., Seite 23-40 (DE-627)352260963 (DE-600)2085223-X 0973-712X nnns volume:23 year:2018 number:1 month:01 pages:23-40 https://dx.doi.org/10.1007/s12045-018-0592-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 30.00 ASE AR 23 2018 1 01 23-40
allfields_unstemmed	10.1007/s12045-018-0592-4 doi (DE-627)SPR024112747 (SPR)s12045-018-0592-4-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Kumar, Ashwin B. R. verfasserin aut Chemistry at the Nanoscale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks. Chemical kinetics (dpeaa)DE-He213 stochastic chemical kinetics (dpeaa)DE-He213 Gillespie’s stochastic simulation algorithm (dpeaa)DE-He213 synthetic gene oscillators (dpeaa)DE-He213 Ramaswamy, Ram verfasserin aut Enthalten in Resonance Bangalore : Indian Academy of Sciences, 1996 23(2018), 1 vom: Jan., Seite 23-40 (DE-627)352260963 (DE-600)2085223-X 0973-712X nnns volume:23 year:2018 number:1 month:01 pages:23-40 https://dx.doi.org/10.1007/s12045-018-0592-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 30.00 ASE AR 23 2018 1 01 23-40
allfieldsGer	10.1007/s12045-018-0592-4 doi (DE-627)SPR024112747 (SPR)s12045-018-0592-4-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Kumar, Ashwin B. R. verfasserin aut Chemistry at the Nanoscale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks. Chemical kinetics (dpeaa)DE-He213 stochastic chemical kinetics (dpeaa)DE-He213 Gillespie’s stochastic simulation algorithm (dpeaa)DE-He213 synthetic gene oscillators (dpeaa)DE-He213 Ramaswamy, Ram verfasserin aut Enthalten in Resonance Bangalore : Indian Academy of Sciences, 1996 23(2018), 1 vom: Jan., Seite 23-40 (DE-627)352260963 (DE-600)2085223-X 0973-712X nnns volume:23 year:2018 number:1 month:01 pages:23-40 https://dx.doi.org/10.1007/s12045-018-0592-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 30.00 ASE AR 23 2018 1 01 23-40
allfieldsSound	10.1007/s12045-018-0592-4 doi (DE-627)SPR024112747 (SPR)s12045-018-0592-4-e DE-627 ger DE-627 rakwb eng 500 ASE 30.00 bkl Kumar, Ashwin B. R. verfasserin aut Chemistry at the Nanoscale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks. Chemical kinetics (dpeaa)DE-He213 stochastic chemical kinetics (dpeaa)DE-He213 Gillespie’s stochastic simulation algorithm (dpeaa)DE-He213 synthetic gene oscillators (dpeaa)DE-He213 Ramaswamy, Ram verfasserin aut Enthalten in Resonance Bangalore : Indian Academy of Sciences, 1996 23(2018), 1 vom: Jan., Seite 23-40 (DE-627)352260963 (DE-600)2085223-X 0973-712X nnns volume:23 year:2018 number:1 month:01 pages:23-40 https://dx.doi.org/10.1007/s12045-018-0592-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 30.00 ASE AR 23 2018 1 01 23-40
language	English
source	Enthalten in Resonance 23(2018), 1 vom: Jan., Seite 23-40 volume:23 year:2018 number:1 month:01 pages:23-40
sourceStr	Enthalten in Resonance 23(2018), 1 vom: Jan., Seite 23-40 volume:23 year:2018 number:1 month:01 pages:23-40
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Chemical kinetics stochastic chemical kinetics Gillespie’s stochastic simulation algorithm synthetic gene oscillators
dewey-raw	500
isfreeaccess_bool	false
container_title	Resonance
authorswithroles_txt_mv	Kumar, Ashwin B. R. @@aut@@ Ramaswamy, Ram @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	352260963
dewey-sort	3500
id	SPR024112747
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author	Kumar, Ashwin B. R.
spellingShingle	Kumar, Ashwin B. R. ddc 500 bkl 30.00 misc Chemical kinetics misc stochastic chemical kinetics misc Gillespie’s stochastic simulation algorithm misc synthetic gene oscillators Chemistry at the Nanoscale
authorStr	Kumar, Ashwin B. R.
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title_sort	chemistry at the nanoscale
title_auth	Chemistry at the Nanoscale
abstract	Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks.
abstractGer	Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks.
abstract_unstemmed	Abstract Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks.
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title_short	Chemistry at the Nanoscale
url	https://dx.doi.org/10.1007/s12045-018-0592-4
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author2	Ramaswamy, Ram
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doi_str	10.1007/s12045-018-0592-4
up_date	2024-07-03T23:29:57.059Z
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