Maintaining optimal state probabilities in biological systems

Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological s...
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Gespeichert in:

Autor*in:	Ghosh, Madhumita [verfasserIn] Tiwary, Basant K. [verfasserIn] Datta, Dilip [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Systems biology Bayesian network State probability Joint probability Genetic algorithm

Übergeordnetes Werk:	Enthalten in: Systems and synthetic biology - Dordrecht : Springer Netherlands, 2007, 4(2010), 2 vom: Juni, Seite 117-124
Übergeordnetes Werk:	volume:4 ; year:2010 ; number:2 ; month:06 ; pages:117-124

Links:	Volltext

DOI / URN:	10.1007/s11693-010-9058-z

Katalog-ID:	SPR021763224

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allfields	10.1007/s11693-010-9058-z doi (DE-627)SPR021763224 (SPR)s11693-010-9058-z-e DE-627 ger DE-627 rakwb eng 570 ASE Ghosh, Madhumita verfasserin aut Maintaining optimal state probabilities in biological systems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels. Systems biology (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 State probability (dpeaa)DE-He213 Joint probability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Tiwary, Basant K. verfasserin aut Datta, Dilip verfasserin aut Enthalten in Systems and synthetic biology Dordrecht : Springer Netherlands, 2007 4(2010), 2 vom: Juni, Seite 117-124 (DE-627)54601142X (DE-600)2390101-9 1872-5333 nnns volume:4 year:2010 number:2 month:06 pages:117-124 https://dx.doi.org/10.1007/s11693-010-9058-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4328 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2010 2 06 117-124
spelling	10.1007/s11693-010-9058-z doi (DE-627)SPR021763224 (SPR)s11693-010-9058-z-e DE-627 ger DE-627 rakwb eng 570 ASE Ghosh, Madhumita verfasserin aut Maintaining optimal state probabilities in biological systems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels. Systems biology (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 State probability (dpeaa)DE-He213 Joint probability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Tiwary, Basant K. verfasserin aut Datta, Dilip verfasserin aut Enthalten in Systems and synthetic biology Dordrecht : Springer Netherlands, 2007 4(2010), 2 vom: Juni, Seite 117-124 (DE-627)54601142X (DE-600)2390101-9 1872-5333 nnns volume:4 year:2010 number:2 month:06 pages:117-124 https://dx.doi.org/10.1007/s11693-010-9058-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4328 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2010 2 06 117-124
allfields_unstemmed	10.1007/s11693-010-9058-z doi (DE-627)SPR021763224 (SPR)s11693-010-9058-z-e DE-627 ger DE-627 rakwb eng 570 ASE Ghosh, Madhumita verfasserin aut Maintaining optimal state probabilities in biological systems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels. Systems biology (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 State probability (dpeaa)DE-He213 Joint probability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Tiwary, Basant K. verfasserin aut Datta, Dilip verfasserin aut Enthalten in Systems and synthetic biology Dordrecht : Springer Netherlands, 2007 4(2010), 2 vom: Juni, Seite 117-124 (DE-627)54601142X (DE-600)2390101-9 1872-5333 nnns volume:4 year:2010 number:2 month:06 pages:117-124 https://dx.doi.org/10.1007/s11693-010-9058-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4328 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2010 2 06 117-124
allfieldsGer	10.1007/s11693-010-9058-z doi (DE-627)SPR021763224 (SPR)s11693-010-9058-z-e DE-627 ger DE-627 rakwb eng 570 ASE Ghosh, Madhumita verfasserin aut Maintaining optimal state probabilities in biological systems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels. Systems biology (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 State probability (dpeaa)DE-He213 Joint probability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Tiwary, Basant K. verfasserin aut Datta, Dilip verfasserin aut Enthalten in Systems and synthetic biology Dordrecht : Springer Netherlands, 2007 4(2010), 2 vom: Juni, Seite 117-124 (DE-627)54601142X (DE-600)2390101-9 1872-5333 nnns volume:4 year:2010 number:2 month:06 pages:117-124 https://dx.doi.org/10.1007/s11693-010-9058-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4328 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2010 2 06 117-124
allfieldsSound	10.1007/s11693-010-9058-z doi (DE-627)SPR021763224 (SPR)s11693-010-9058-z-e DE-627 ger DE-627 rakwb eng 570 ASE Ghosh, Madhumita verfasserin aut Maintaining optimal state probabilities in biological systems 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels. Systems biology (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 State probability (dpeaa)DE-He213 Joint probability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Tiwary, Basant K. verfasserin aut Datta, Dilip verfasserin aut Enthalten in Systems and synthetic biology Dordrecht : Springer Netherlands, 2007 4(2010), 2 vom: Juni, Seite 117-124 (DE-627)54601142X (DE-600)2390101-9 1872-5333 nnns volume:4 year:2010 number:2 month:06 pages:117-124 https://dx.doi.org/10.1007/s11693-010-9058-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4328 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2010 2 06 117-124
language	English
source	Enthalten in Systems and synthetic biology 4(2010), 2 vom: Juni, Seite 117-124 volume:4 year:2010 number:2 month:06 pages:117-124
sourceStr	Enthalten in Systems and synthetic biology 4(2010), 2 vom: Juni, Seite 117-124 volume:4 year:2010 number:2 month:06 pages:117-124
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Systems biology Bayesian network State probability Joint probability Genetic algorithm
dewey-raw	570
isfreeaccess_bool	false
container_title	Systems and synthetic biology
authorswithroles_txt_mv	Ghosh, Madhumita @@aut@@ Tiwary, Basant K. @@aut@@ Datta, Dilip @@aut@@
publishDateDaySort_date	2010-06-01T00:00:00Z
hierarchy_top_id	54601142X
dewey-sort	3570
id	SPR021763224
language_de	englisch
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author	Ghosh, Madhumita
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topic_title	570 ASE Maintaining optimal state probabilities in biological systems Systems biology (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 State probability (dpeaa)DE-He213 Joint probability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213
topic	ddc 570 misc Systems biology misc Bayesian network misc State probability misc Joint probability misc Genetic algorithm
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title	Maintaining optimal state probabilities in biological systems
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title_full	Maintaining optimal state probabilities in biological systems
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title_sort	maintaining optimal state probabilities in biological systems
title_auth	Maintaining optimal state probabilities in biological systems
abstract	Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels.
abstractGer	Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels.
abstract_unstemmed	Abstract A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels.
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Maintaining optimal state probabilities in biological systems

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