Ensemble methods for stochastic networks with special reference to the biological clock of Neurospora crassa.

ABSTRACT: A major challenge in systems biology is to infer the parameters of regulatory networks that operate in a noisy environment, such as in a single cell. In a stochastic regime it is hard to distinguish noise from the real signal and to infer the noise contribution to the dynamical behavior. When the genetic network displays oscillatory dynamics, it is even harder to infer the parameters that produce the oscillations. To address this issue we introduce a new estimation method built on a combination of stochastic simulations, mass action kinetics and ensemble network simulations in which we match the average periodogram and phase of the model to that of the data. The method is relatively fast (compared to Metropolis-Hastings Monte Carlo Methods), easy to parallelize, applicable to large oscilla

SUBMITTER: Caranica C 

PROVIDER: S-EPMC5955539 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature