Reconstruction of Biological Networks Using Differential Equation Models

• Activity Number: 376
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): James Henderson*+
• Companies: University of Michigan
• Keywords: network reconstruction ; dynamic systems ; ODEs ; time-series

Abstract

High-throughput data technologies have led to an explosion of interest in reconstructing biological networks. Dynamic models based on systems of coupled ordinary differential equations (ODEs) not only allow for reconstruction from time-course data, but also provide additional insight into the underlying biological mechanisms relative to methods based on steady state data. This work formalizes the network reconstruction problem from a dynamic systems point of view and proposes a novel coupling metric for quantifying the relationship between coupled nonlinear ODEs. Combining existing techniques in a novel way, methodology is developed for non-parametric estimation of a dynamic system. The methodology is illustrated using data from in silico models of E. coli sub-networks. This study also demonstrates how to modify the model to accommodate varied experimental conditions.