What can we learn from global sensitivity analysis of biochemical systems?

Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable p...
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Autor*in:	Kent, Edward [verfasserIn] Neumann, Stefan [verfasserIn] Kummer, Ursula - 1967- [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	November 14, 2013

Anmerkung:	Gesehen am 31.05.2017
Umfang:	13

Übergeordnetes Werk:	Enthalten in: PLOS ONE - San Francisco, California, US : PLOS, 2006, 8(2013,11) Artikel-Nummer e79244, 13 Seiten
Übergeordnetes Werk:	volume:8 ; year:2013 ; number:11 ; extent:13

Links:	Volltext Volltext

DOI / URN:	10.1371/journal.pone.0079244

Katalog-ID:	1559177179

Internformat


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912			\|a GBV_ILN_235
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912			\|a GBV_ILN_602
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author_variant	e k ek s n sn u k uk
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oclc_num	1340975885
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allfields	10.1371/journal.pone.0079244 doi (DE-627)1559177179 (DE-576)489177174 (DE-599)BSZ489177174 (OCoLC)1340975885 DE-627 ger DE-627 rda eng Kent, Edward verfasserin aut What can we learn from global sensitivity analysis of biochemical systems? Edward Kent, Stefan Neumann, Ursula Kummer, Pedro Mendes November 14, 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 31.05.2017 Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Neumann, Stefan verfasserin (DE-588)1133214185 (DE-627)888946228 (DE-576)489176844 aut Kummer, Ursula 1967- verfasserin (DE-588)115411682 (DE-627)691297975 (DE-576)176480897 aut Enthalten in PLOS ONE San Francisco, California, US : PLOS, 2006 8(2013,11) Artikel-Nummer e79244, 13 Seiten Online-Ressource (DE-627)523574592 (DE-600)2267670-3 (DE-576)281331979 1932-6203 nnns volume:8 year:2013 number:11 extent:13 http://dx.doi.org/10.1371/journal.pone.0079244 Verlag Resolving-System kostenfrei Volltext http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079244 Verlag kostenfrei Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2013 11 13 8(2013,11) Artikel-Nummer e79244, 13 Seiten 2013 01 DE-16-250 2970856921 00 --%%-- --%%-- --%%-- --%%-- l01 01-06-17 2403 01 DE-LFER 2971472485 00 --%%-- --%%-- n --%%-- l01 08-06-17 2403 01 DE-LFER http://dx.doi.org/10.1371/journal.pone.0079244 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1559357363 Neumann, Stefan 2013 01 DE-16-250 04 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1497999472 Kummer, Ursula 2013 01 DE-16-250 05 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3
spelling	10.1371/journal.pone.0079244 doi (DE-627)1559177179 (DE-576)489177174 (DE-599)BSZ489177174 (OCoLC)1340975885 DE-627 ger DE-627 rda eng Kent, Edward verfasserin aut What can we learn from global sensitivity analysis of biochemical systems? Edward Kent, Stefan Neumann, Ursula Kummer, Pedro Mendes November 14, 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 31.05.2017 Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Neumann, Stefan verfasserin (DE-588)1133214185 (DE-627)888946228 (DE-576)489176844 aut Kummer, Ursula 1967- verfasserin (DE-588)115411682 (DE-627)691297975 (DE-576)176480897 aut Enthalten in PLOS ONE San Francisco, California, US : PLOS, 2006 8(2013,11) Artikel-Nummer e79244, 13 Seiten Online-Ressource (DE-627)523574592 (DE-600)2267670-3 (DE-576)281331979 1932-6203 nnns volume:8 year:2013 number:11 extent:13 http://dx.doi.org/10.1371/journal.pone.0079244 Verlag Resolving-System kostenfrei Volltext http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079244 Verlag kostenfrei Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2013 11 13 8(2013,11) Artikel-Nummer e79244, 13 Seiten 2013 01 DE-16-250 2970856921 00 --%%-- --%%-- --%%-- --%%-- l01 01-06-17 2403 01 DE-LFER 2971472485 00 --%%-- --%%-- n --%%-- l01 08-06-17 2403 01 DE-LFER http://dx.doi.org/10.1371/journal.pone.0079244 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1559357363 Neumann, Stefan 2013 01 DE-16-250 04 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1497999472 Kummer, Ursula 2013 01 DE-16-250 05 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3
allfields_unstemmed	10.1371/journal.pone.0079244 doi (DE-627)1559177179 (DE-576)489177174 (DE-599)BSZ489177174 (OCoLC)1340975885 DE-627 ger DE-627 rda eng Kent, Edward verfasserin aut What can we learn from global sensitivity analysis of biochemical systems? Edward Kent, Stefan Neumann, Ursula Kummer, Pedro Mendes November 14, 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 31.05.2017 Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Neumann, Stefan verfasserin (DE-588)1133214185 (DE-627)888946228 (DE-576)489176844 aut Kummer, Ursula 1967- verfasserin (DE-588)115411682 (DE-627)691297975 (DE-576)176480897 aut Enthalten in PLOS ONE San Francisco, California, US : PLOS, 2006 8(2013,11) Artikel-Nummer e79244, 13 Seiten Online-Ressource (DE-627)523574592 (DE-600)2267670-3 (DE-576)281331979 1932-6203 nnns volume:8 year:2013 number:11 extent:13 http://dx.doi.org/10.1371/journal.pone.0079244 Verlag Resolving-System kostenfrei Volltext http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079244 Verlag kostenfrei Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2013 11 13 8(2013,11) Artikel-Nummer e79244, 13 Seiten 2013 01 DE-16-250 2970856921 00 --%%-- --%%-- --%%-- --%%-- l01 01-06-17 2403 01 DE-LFER 2971472485 00 --%%-- --%%-- n --%%-- l01 08-06-17 2403 01 DE-LFER http://dx.doi.org/10.1371/journal.pone.0079244 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1559357363 Neumann, Stefan 2013 01 DE-16-250 04 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1497999472 Kummer, Ursula 2013 01 DE-16-250 05 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3
allfieldsGer	10.1371/journal.pone.0079244 doi (DE-627)1559177179 (DE-576)489177174 (DE-599)BSZ489177174 (OCoLC)1340975885 DE-627 ger DE-627 rda eng Kent, Edward verfasserin aut What can we learn from global sensitivity analysis of biochemical systems? Edward Kent, Stefan Neumann, Ursula Kummer, Pedro Mendes November 14, 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 31.05.2017 Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Neumann, Stefan verfasserin (DE-588)1133214185 (DE-627)888946228 (DE-576)489176844 aut Kummer, Ursula 1967- verfasserin (DE-588)115411682 (DE-627)691297975 (DE-576)176480897 aut Enthalten in PLOS ONE San Francisco, California, US : PLOS, 2006 8(2013,11) Artikel-Nummer e79244, 13 Seiten Online-Ressource (DE-627)523574592 (DE-600)2267670-3 (DE-576)281331979 1932-6203 nnns volume:8 year:2013 number:11 extent:13 http://dx.doi.org/10.1371/journal.pone.0079244 Verlag Resolving-System kostenfrei Volltext http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079244 Verlag kostenfrei Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2013 11 13 8(2013,11) Artikel-Nummer e79244, 13 Seiten 2013 01 DE-16-250 2970856921 00 --%%-- --%%-- --%%-- --%%-- l01 01-06-17 2403 01 DE-LFER 2971472485 00 --%%-- --%%-- n --%%-- l01 08-06-17 2403 01 DE-LFER http://dx.doi.org/10.1371/journal.pone.0079244 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1559357363 Neumann, Stefan 2013 01 DE-16-250 04 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1497999472 Kummer, Ursula 2013 01 DE-16-250 05 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3
allfieldsSound	10.1371/journal.pone.0079244 doi (DE-627)1559177179 (DE-576)489177174 (DE-599)BSZ489177174 (OCoLC)1340975885 DE-627 ger DE-627 rda eng Kent, Edward verfasserin aut What can we learn from global sensitivity analysis of biochemical systems? Edward Kent, Stefan Neumann, Ursula Kummer, Pedro Mendes November 14, 2013 13 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 31.05.2017 Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Neumann, Stefan verfasserin (DE-588)1133214185 (DE-627)888946228 (DE-576)489176844 aut Kummer, Ursula 1967- verfasserin (DE-588)115411682 (DE-627)691297975 (DE-576)176480897 aut Enthalten in PLOS ONE San Francisco, California, US : PLOS, 2006 8(2013,11) Artikel-Nummer e79244, 13 Seiten Online-Ressource (DE-627)523574592 (DE-600)2267670-3 (DE-576)281331979 1932-6203 nnns volume:8 year:2013 number:11 extent:13 http://dx.doi.org/10.1371/journal.pone.0079244 Verlag Resolving-System kostenfrei Volltext http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079244 Verlag kostenfrei Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2013 11 13 8(2013,11) Artikel-Nummer e79244, 13 Seiten 2013 01 DE-16-250 2970856921 00 --%%-- --%%-- --%%-- --%%-- l01 01-06-17 2403 01 DE-LFER 2971472485 00 --%%-- --%%-- n --%%-- l01 08-06-17 2403 01 DE-LFER http://dx.doi.org/10.1371/journal.pone.0079244 2013 01 DE-16-250 00 s hd2013 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_4 2013 01 DE-16-250 03 s s_13 2013 01 DE-16-250 04 p (DE-627)1559357363 Neumann, Stefan 2013 01 DE-16-250 04 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 2013 01 DE-16-250 05 p (DE-627)1497999472 Kummer, Ursula 2013 01 DE-16-250 05 k (DE-627)1416737987 Centre for Organismal Studies Heidelberg (COS) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_3
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title	What can we learn from global sensitivity analysis of biochemical systems?
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title_full	What can we learn from global sensitivity analysis of biochemical systems? Edward Kent, Stefan Neumann, Ursula Kummer, Pedro Mendes
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title_sort	what can we learn from global sensitivity analysis of biochemical systems?
title_auth	What can we learn from global sensitivity analysis of biochemical systems?
abstract	Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Gesehen am 31.05.2017
abstractGer	Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Gesehen am 31.05.2017
abstract_unstemmed	Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique. Gesehen am 31.05.2017
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title_short	What can we learn from global sensitivity analysis of biochemical systems?
url	http://dx.doi.org/10.1371/journal.pone.0079244 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079244
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up_date	2024-07-04T11:48:08.014Z
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In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. 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