Calibration of dynamic models of biological systems with KInfer.
Methods for parameter estimation that are robust to experimental uncertainties and to stochastic and biological noise and that require a minimum of a priori input knowledge are of key importance in computational systems biology. The new method presented in this paper aims to ensure an inference model that deduces the rate constants of a system of biochemical reactions from experimentally measured time courses of reactants. This new method was applied to some challenging parameter estimation problems of nonlinear dynamic biological systems and was tested both on synthetic and real data. The synthetic case studies are the 12-state model of the SERCA pump and a model of a genetic network containing feedback loops of interaction between regulator and effector genes. The real case studies consist of a model of the reaction between the inhibitor kappaB kinase enzyme and its substrate in the signal transduction pathway of NF-kappaB, and a stiff model of the fermentation pathway of Lactococcus lactis.
Lecca
Paola
P
The Microsoft Research-University of Trento, Centre for Computational and Systems Biology, Piazza Manci 17, Povo, Trento, Italy. lecca@cosbi.eu
Palmisano
Alida
A
Ihekwaba
Adaoha
A
Priami
Corrado
C
D000465:Algorithms; D002138:Calibration; D019295:Computational Biology; D003198:Computer Simulation; D005285:Fermentation; D008433:Mathematics; D008956:Models, Chemical; D016328:NF-kappa B / Q000737:chemistry; D017711:Nonlinear Dynamics; D049490:Systems Biology / Q000379:methods*; D013598:Systems Theory
D016328:NF-kappa B