SciELO - Scientific Electronic Library Online

 
vol.21 issue2Identification of the Workspace of a Hexapod Mobile Robot Using Multobjective OptimizationComparative Study between Kleinberg Algorithm and Biased Selection Algorithm for Small World Networks Construction author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Abstract

MARTINEZ-GARCIA, Juan Carlos; AGUILAR-IBANEZ, Carlos  and  SORIA-LOPEZ, Alberto. Bridging the Gap Between Model-Based Design and Reliable Implementation of Feedback-Based Biocircuits: A Systems Inverse Problem Approach. Comp. y Sist. [online]. 2017, vol.21, n.2, pp.315-324. ISSN 1405-5546.  http://dx.doi.org/10.13053/cys-21-2-2740.

Our concern is the tuning of mathematical models describing rationally designed genetic biocir-cuits. Based on a deterministic lumped continuous-time approach, we propose a tuning methodology combining both exact algebraic parameter reconstruction and nonlinear parameter estimation of a given model supporting the design of a specific genetic biocircuit, i.e., we bridge the gap between model-based design and implementation as the solution of a systems inverse problem. As a proof of concept, our proposal is constrained to cyclic feedback systems characterizing synthesized transcriptional networks conditioned to display sustained oscillatory behavior. Our proposed methodology is illustrated via computer–based simu-lations involving the tuning of a state–based model describing a well–know cyclic feedback biocircuit: the celebrated repressilator. Tuning in our case is conceived as a procedure to adjust the parameter values of the mathematical model taking into account for this the actual behavior observed from the corresponding synthesized biocircuit.

Keywords : Systems biology; synthetic biology; tuning of mathematical models; algebraic parameter reconstruction; observer based system identification; synthetic transcriptional networks; cyclic feedback biocircuits.

        · text in English     · English ( pdf )