Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor
Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their...
Ausführliche Beschreibung
Autor*in: |
Affi, Mahmoud [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2016 |
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Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2016 |
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Übergeordnetes Werk: |
Enthalten in: Analytical and bioanalytical chemistry - Berlin : Springer, 2002, 408(2016), 30 vom: 04. Apr., Seite 8761-8770 |
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Übergeordnetes Werk: |
volume:408 ; year:2016 ; number:30 ; day:04 ; month:04 ; pages:8761-8770 |
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DOI / URN: |
10.1007/s00216-016-9490-3 |
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Katalog-ID: |
SPR002250152 |
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520 | |a Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. | ||
650 | 4 | |a Bioluminescent bacterial biosensor |7 (dpeaa)DE-He213 | |
650 | 4 | |a Numerical modeling |7 (dpeaa)DE-He213 | |
650 | 4 | |a Bioluminescence simulation |7 (dpeaa)DE-He213 | |
700 | 1 | |a Solliec, Camille |4 aut | |
700 | 1 | |a Legentilhomme, Patrick |4 aut | |
700 | 1 | |a Comiti, Jacques |4 aut | |
700 | 1 | |a Legrand, Jack |4 aut | |
700 | 1 | |a Jouanneau, Sulivan |4 aut | |
700 | 1 | |a Thouand, Gérald |4 aut | |
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10.1007/s00216-016-9490-3 doi (DE-627)SPR002250152 (SPR)s00216-016-9490-3-e DE-627 ger DE-627 rakwb eng Affi, Mahmoud verfasserin aut Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. Bioluminescent bacterial biosensor (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 Bioluminescence simulation (dpeaa)DE-He213 Solliec, Camille aut Legentilhomme, Patrick aut Comiti, Jacques aut Legrand, Jack aut Jouanneau, Sulivan aut Thouand, Gérald aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 408(2016), 30 vom: 04. Apr., Seite 8761-8770 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:408 year:2016 number:30 day:04 month:04 pages:8761-8770 https://dx.doi.org/10.1007/s00216-016-9490-3 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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_2360 GBV_ILN_2446 GBV_ILN_2470 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 408 2016 30 04 04 8761-8770 |
spelling |
10.1007/s00216-016-9490-3 doi (DE-627)SPR002250152 (SPR)s00216-016-9490-3-e DE-627 ger DE-627 rakwb eng Affi, Mahmoud verfasserin aut Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. Bioluminescent bacterial biosensor (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 Bioluminescence simulation (dpeaa)DE-He213 Solliec, Camille aut Legentilhomme, Patrick aut Comiti, Jacques aut Legrand, Jack aut Jouanneau, Sulivan aut Thouand, Gérald aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 408(2016), 30 vom: 04. Apr., Seite 8761-8770 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:408 year:2016 number:30 day:04 month:04 pages:8761-8770 https://dx.doi.org/10.1007/s00216-016-9490-3 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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_2360 GBV_ILN_2446 GBV_ILN_2470 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 408 2016 30 04 04 8761-8770 |
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10.1007/s00216-016-9490-3 doi (DE-627)SPR002250152 (SPR)s00216-016-9490-3-e DE-627 ger DE-627 rakwb eng Affi, Mahmoud verfasserin aut Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. Bioluminescent bacterial biosensor (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 Bioluminescence simulation (dpeaa)DE-He213 Solliec, Camille aut Legentilhomme, Patrick aut Comiti, Jacques aut Legrand, Jack aut Jouanneau, Sulivan aut Thouand, Gérald aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 408(2016), 30 vom: 04. Apr., Seite 8761-8770 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:408 year:2016 number:30 day:04 month:04 pages:8761-8770 https://dx.doi.org/10.1007/s00216-016-9490-3 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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_2360 GBV_ILN_2446 GBV_ILN_2470 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 408 2016 30 04 04 8761-8770 |
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10.1007/s00216-016-9490-3 doi (DE-627)SPR002250152 (SPR)s00216-016-9490-3-e DE-627 ger DE-627 rakwb eng Affi, Mahmoud verfasserin aut Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. Bioluminescent bacterial biosensor (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 Bioluminescence simulation (dpeaa)DE-He213 Solliec, Camille aut Legentilhomme, Patrick aut Comiti, Jacques aut Legrand, Jack aut Jouanneau, Sulivan aut Thouand, Gérald aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 408(2016), 30 vom: 04. Apr., Seite 8761-8770 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:408 year:2016 number:30 day:04 month:04 pages:8761-8770 https://dx.doi.org/10.1007/s00216-016-9490-3 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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_2360 GBV_ILN_2446 GBV_ILN_2470 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 408 2016 30 04 04 8761-8770 |
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10.1007/s00216-016-9490-3 doi (DE-627)SPR002250152 (SPR)s00216-016-9490-3-e DE-627 ger DE-627 rakwb eng Affi, Mahmoud verfasserin aut Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. Bioluminescent bacterial biosensor (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 Bioluminescence simulation (dpeaa)DE-He213 Solliec, Camille aut Legentilhomme, Patrick aut Comiti, Jacques aut Legrand, Jack aut Jouanneau, Sulivan aut Thouand, Gérald aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 408(2016), 30 vom: 04. Apr., Seite 8761-8770 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:408 year:2016 number:30 day:04 month:04 pages:8761-8770 https://dx.doi.org/10.1007/s00216-016-9490-3 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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_2360 GBV_ILN_2446 GBV_ILN_2470 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_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 408 2016 30 04 04 8761-8770 |
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Enthalten in Analytical and bioanalytical chemistry 408(2016), 30 vom: 04. Apr., Seite 8761-8770 volume:408 year:2016 number:30 day:04 month:04 pages:8761-8770 |
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Enthalten in Analytical and bioanalytical chemistry 408(2016), 30 vom: 04. Apr., Seite 8761-8770 volume:408 year:2016 number:30 day:04 month:04 pages:8761-8770 |
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Bioluminescent bacterial biosensor Numerical modeling Bioluminescence simulation |
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Analytical and bioanalytical chemistry |
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Affi, Mahmoud @@aut@@ Solliec, Camille @@aut@@ Legentilhomme, Patrick @@aut@@ Comiti, Jacques @@aut@@ Legrand, Jack @@aut@@ Jouanneau, Sulivan @@aut@@ Thouand, Gérald @@aut@@ |
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The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. 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Affi, Mahmoud |
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Affi, Mahmoud misc Bioluminescent bacterial biosensor misc Numerical modeling misc Bioluminescence simulation Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor |
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Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor Bioluminescent bacterial biosensor (dpeaa)DE-He213 Numerical modeling (dpeaa)DE-He213 Bioluminescence simulation (dpeaa)DE-He213 |
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misc Bioluminescent bacterial biosensor misc Numerical modeling misc Bioluminescence simulation |
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misc Bioluminescent bacterial biosensor misc Numerical modeling misc Bioluminescence simulation |
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Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor |
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Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor |
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Affi, Mahmoud |
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Analytical and bioanalytical chemistry |
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Affi, Mahmoud Solliec, Camille Legentilhomme, Patrick Comiti, Jacques Legrand, Jack Jouanneau, Sulivan Thouand, Gérald |
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numerical modeling of the dynamic response of a bioluminescent bacterial biosensor |
title_auth |
Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor |
abstract |
Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. © Springer-Verlag Berlin Heidelberg 2016 |
abstractGer |
Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. © Springer-Verlag Berlin Heidelberg 2016 |
abstract_unstemmed |
Abstract Water quality and water management are worldwide issues. The analysis of pollutants and in particular, heavy metals, is generally conducted by sensitive but expensive physicochemical methods. Other alternative methods of analysis, such as microbial biosensors, have been developed for their potential simplicity and expected moderate cost. Using a biosensor for a long time generates many changes in the growth of the immobilized bacteria and consequently alters the robustness of the detection. This work simulated the operation of a biosensor for the long-term detection of cadmium and improved our understanding of the bioluminescence reaction dynamics of bioreporter bacteria inside an agarose matrix. The choice of the numerical tools is justified by the difficulty to measure experimentally in every condition the biosensor functioning during a long time (several days). The numerical simulation of a biomass profile is made by coupling the diffusion equation and the consumption/reaction of the nutrients by the bacteria. The numerical results show very good agreement with the experimental profiles. The growth model verified that the bacterial growth is conditioned by both the diffusion and the consumption of the nutrients. Thus, there is a high bacterial density in the first millimeter of the immobilization matrix. The growth model has been very useful for the development of the bioluminescence model inside the gel and shows that a concentration of oxygen greater than or equal to 22 % of saturation is required to maintain a significant level of bioluminescence. A continuous feeding of nutrients during the process of detection of cadmium leads to a biofilm which reduces the diffusion of nutrients and restricts the presence of oxygen from the first layer of the agarose (1 mm) and affects the intensity of the bioluminescent reaction. The main advantage of this work is to link experimental works with numerical models of growth and bioluminescence in order to provide a general purpose model to understand, anticipate, or predict the dysfunction of a biosensor using immobilized bioluminescent bioreporter in a matrix. © Springer-Verlag Berlin Heidelberg 2016 |
collection_details |
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container_issue |
30 |
title_short |
Numerical modeling of the dynamic response of a bioluminescent bacterial biosensor |
url |
https://dx.doi.org/10.1007/s00216-016-9490-3 |
remote_bool |
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author2 |
Solliec, Camille Legentilhomme, Patrick Comiti, Jacques Legrand, Jack Jouanneau, Sulivan Thouand, Gérald |
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Solliec, Camille Legentilhomme, Patrick Comiti, Jacques Legrand, Jack Jouanneau, Sulivan Thouand, Gérald |
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doi_str |
10.1007/s00216-016-9490-3 |
up_date |
2024-07-04T02:21:44.909Z |
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score |
7.3992596 |