Multi-criteria optimization based experimental design for parameter estimation of a double feedback gene switching model

Vaibhav Maheshwari, Manoj Kandpal, Lakshminarayanan Samavedham

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

Despite the rapid increase in quantity and quality of experimental data in many fields of engineering and science, quantitative measurements of many cellular components are still relatively scarce. This work deals with estimating the parameters of a double feedback gene-switching model. To achieve the goal, a model-based design of experiment (MBDOE) approach for parameter estimation is employed. To overcome the problem of convergence in parameter estimation step (due to correlation among the parameters), a non-dominated sorting genetic algorithm (NSGA-II) based, multi-objective optimization (MOO) based MBDOE has been used. The parameter estimates obtained through the MOO based DOE as well as a standard alphabetical DOE technique are then compared with the known true values from the literature to highlight the efficacy of the MOO-MBDOE technique.

Original languageEnglish (US)
Pages (from-to)333-337
Number of pages5
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume12
Issue numberPART 1
DOIs
StatePublished - Jan 1 2013
Event12th IFAC Symposium on Computer Applications in Biotechnology, CAB 2013 - Mumbai, India
Duration: Dec 16 2013Dec 18 2013

Fingerprint

Design of experiments
Parameter estimation
Genes
Multiobjective optimization
Feedback
Sorting
Genetic algorithms

Keywords

  • Design of Experiments
  • Feedback Loop
  • Gene Switching model
  • Multi-Criteria Optimization
  • Parameter Estimation

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

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abstract = "Despite the rapid increase in quantity and quality of experimental data in many fields of engineering and science, quantitative measurements of many cellular components are still relatively scarce. This work deals with estimating the parameters of a double feedback gene-switching model. To achieve the goal, a model-based design of experiment (MBDOE) approach for parameter estimation is employed. To overcome the problem of convergence in parameter estimation step (due to correlation among the parameters), a non-dominated sorting genetic algorithm (NSGA-II) based, multi-objective optimization (MOO) based MBDOE has been used. The parameter estimates obtained through the MOO based DOE as well as a standard alphabetical DOE technique are then compared with the known true values from the literature to highlight the efficacy of the MOO-MBDOE technique.",
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Multi-criteria optimization based experimental design for parameter estimation of a double feedback gene switching model. / Maheshwari, Vaibhav; Kandpal, Manoj; Samavedham, Lakshminarayanan.

In: IFAC Proceedings Volumes (IFAC-PapersOnline), Vol. 12, No. PART 1, 01.01.2013, p. 333-337.

Research output: Contribution to journalConference article

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