Improved Parameter Estimation for Completely Observed Ordinary Differential Equations with Application to Biological Systems

We consider parameter estimation in ordinary differential equations (ODEs) from completely observed systems, and describe an improved version of our previously reported heuristic algorithm (IET Syst. Biol., 2007). Basically, in that method, estimation based on decomposing the problem to simulation of one ODE, is followed by estimation based on simulation of all ODEs of the system. The main algorithmic improvement compared to the original version, is that we decompose not only to single ODEs, but also to arbitrary subsets of ODEs, as a complementary intermediate step. The subsets are selected based on an analysis of the interaction between the variables and possible common parameters. We evaluate our algorithm on a number of well-known hard test problems from the biological literature. The results show that our approach is more accurate and considerably faster compared to other reported methods on these problems. Additionally, we find that the algorithm scales favourably with problem size

Computational Methods in Systems Biology, 7th International Conference, CMSB 2009

none2siThe proceedings contain 21 papers. The topics discussed include: modelling biological clocks with bio-PEPA: stochasticity and robustness for the neurospora crassa circadian network; quantitative pathway logic for computational biology; a prize-collecting steiner tree approach for transduction network inference; formal analysis of the genetic toggle; control strategies for the regulation of the eukaryotic heat shock response; computing reachable states for nonlinear biological models; on coupling models using model-checking: effects of irinotecan injections on the mammalian cell cycle; approximation of event probabilities in noisy cellular processes; equivalence and discretisation in bio-PEPA; improved parameter estimation for completely observed ordinary differential equations with application to biological systems; a Bayesian approach to model checking biological systems; probabilistic approximations of signaling pathway dynamics; and a reduction of logical regulatory graphs preserving essential dynamical properties.noneP. Degano; R. GorrieriP. Degano; R. Gorrier