Parameter sensitivity analysis of stochastic models: Application to catalytic reaction networks |
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| Affiliation: | 1. COSBI The Microsoft Research – University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068 Rovereto (TN), Italy;2. C.I.R.I. – Energy and Environment, Alma Mater Studiorum, University of Bologna, via F.lli Rosselli 107, 42123 Reggio Emilia, Italy;3. Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano Bicocca U14, Viale Sarca 336, I-20126 Milan, Italy.;1. School of Hydraulic, Energy and Power Engineering, Yangzhou University, China;2. Department of Wind Energy, Technical University of Denmark, Denmark;1. Departments of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA;2. Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA;3. Lineberger Comprehensive Cancer Center, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA;1. Departamento de Química, Universidad de Concepción, Chile;2. Gerencia de Investigación y Aplicaciones, Centro Atómico Constituyentes, Comisión Nacional de Energía Atómica, Buenos Aires, Argentina;3. Universidad Nacional San Martín, Buenos Aires, Argentina;4. Universidad Andrés Bello, Facultad de Ciencias Exactas, Departamento de Química, Quillota 980, Viña del Mar, Chile;5. Universidad Andrés Bello, Facultad de Ciencias Exactas, Departamento de Química, Republica 275, Santiago, Chile;1. Department of Anesthesiology, 1st Affiliated Hospital, Nanjing Medical University, Nanjing, China;2. Department of Anesthesiology, Huai’an First People''s Hospital, Nanjing Medical University, Huai’an, China |
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| Abstract: | A general numerical methodology for parametric sensitivity analysis is proposed, which allows to determine the parameters exerting the greatest influence on the output of a stochastic computational model, especially when the knowledge about the actual value of a parameter is insufficient. An application of the procedure is performed on a model of protocell, in order to detect the kinetic rates mainly affecting the capability of a catalytic reaction network enclosed in a semi-permeable membrane to retain material from its environment and to generate a variety of molecular species within its boundaries. It is shown that the former capability is scarcely sensitive to variations in the model parameters, whereas a kinetic rate responsible for profound modifications of the latter can be identified and it depends on the specific reaction network. A faster uptaking of limited resources from the environment may have represented a significant advantage from an evolutionary point of view and this result is a first indication in order to decipher which kind of structures are more suitable to achieve a viable evolution. |
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