Addressing biological circuit simulation accuracy: Reachability for parameter identification and initial conditions

Meeko Oishi; Elebeoba May

doi:10.1109/LSSA.2007.4400907

Addressing biological circuit simulation accuracy: Reachability for parameter identification and initial conditions

Meeko Oishi, Elebeoba May

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Accurate simulation of biological networks is difficult not only due to the computational cost associated with large-scale systems simulation, but also due to the inherent limitations of mathematical models. We address two components to improve biological circuit simulation accuracy: 1) feasible initial conditions, and 2) identification of critical yet unknown model parameters. For those parameters that may not be available from experimental data, we incorporate reachability analysis to enhance our optimization/simulation framework and estimate those parameters that are capable of creating behaviors consistent with known experimental data. We apply these techniques to a biological circuit model of tryptophan biosynthesis in E. coli, and quantify the improvement in simulation accuracy when reachability analysis is used.

Original language	English (US)
Title of host publication	2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	152-155
Number of pages	4
ISBN (Print)	9781424418138
DOIs	https://doi.org/10.1109/LSSA.2007.4400907
State	Published - Jan 1 2007
Event	2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA - Bethesda, MD, United States Duration: Nov 8 2007 → Nov 9 2007

Other

Other	2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA
Country/Territory	United States
City	Bethesda, MD
Period	11/8/07 → 11/9/07

Keywords

Biological circuits
Escherichia coli
Hybrid systems
Parameter identification
Reachability
Tryptophan

ASJC Scopus subject areas

Computer Science Applications
Information Systems

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10.1109/LSSA.2007.4400907

Cite this

Addressing biological circuit simulation accuracy : Reachability for parameter identification and initial conditions. / Oishi, Meeko; May, Elebeoba.

2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA. Institute of Electrical and Electronics Engineers Inc., 2007. p. 152-155 4400907.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Oishi, M & May, E 2007, Addressing biological circuit simulation accuracy: Reachability for parameter identification and initial conditions. in 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA., 4400907, Institute of Electrical and Electronics Engineers Inc., pp. 152-155, 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA, Bethesda, MD, United States, 11/8/07. https://doi.org/10.1109/LSSA.2007.4400907

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abstract = "Accurate simulation of biological networks is difficult not only due to the computational cost associated with large-scale systems simulation, but also due to the inherent limitations of mathematical models. We address two components to improve biological circuit simulation accuracy: 1) feasible initial conditions, and 2) identification of critical yet unknown model parameters. For those parameters that may not be available from experimental data, we incorporate reachability analysis to enhance our optimization/simulation framework and estimate those parameters that are capable of creating behaviors consistent with known experimental data. We apply these techniques to a biological circuit model of tryptophan biosynthesis in E. coli, and quantify the improvement in simulation accuracy when reachability analysis is used.",

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AB - Accurate simulation of biological networks is difficult not only due to the computational cost associated with large-scale systems simulation, but also due to the inherent limitations of mathematical models. We address two components to improve biological circuit simulation accuracy: 1) feasible initial conditions, and 2) identification of critical yet unknown model parameters. For those parameters that may not be available from experimental data, we incorporate reachability analysis to enhance our optimization/simulation framework and estimate those parameters that are capable of creating behaviors consistent with known experimental data. We apply these techniques to a biological circuit model of tryptophan biosynthesis in E. coli, and quantify the improvement in simulation accuracy when reachability analysis is used.

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KW - Hybrid systems

KW - Parameter identification

KW - Reachability

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Addressing biological circuit simulation accuracy: Reachability for parameter identification and initial conditions

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Keywords

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