Computational systems biology for the biological clock of Neurospora crassa

Abstract

Genetic networks have been applied to describe biological systems, e.g., the biological clock, from a systems biology perspective. A model-driven discovery process, Computing Life, is developed and used to identify an ensemble of genetic networks to describe quantitatively the biological clock of the lowly bread mould Neurospora crassa for its light-responsive behavior through iterative cycles combining both experiments and computational simulations. Central to this discovery process is a new methodology for the rational design of a Maximally Informative Next Experiment (MINE) based on the genetic network ensemble. In each cycle, the MINE approach is used to design the most informative new experiment for the biological goal of discovering clock-controlled genes which is the outputs of the clock. The new experimental results are then added back to the data pool to provide more information to improve the estimates and predictions made by the genetic network ensemble. The identified ensemble of light-responsive genetic networks is expanded trying to describe the temperature response of the N. crassa and has been proved to be sufficient to explain the wild type data under different temperatures.