IEEE Control Systems Society & American Automatic Control Council
PaperPlaza
Conference Manuscript Management and Registration System
  

2008 ACC Paper Abstract

Close

Paper ThB10.4

Raffard, Robin (Stanford University), Lipan, Ovidiu (University of Richmond), Wong, Wing (Stanford University), Tomlin, Claire J. (Stanford Univ.)

Optimal Discovery of a Stochastic Genetic Network

Scheduled for presentation during the Invited Session "Modeling and Identification of Genetic Regulatory Networks" (ThB10), Thursday, June 12, 2008, 14:40−15:00, Cascade I (A)

2008 American Control Conference, June 11-13, 2008, Westin Seattle Hotel, Seattle, Washington, USA

This information is tentative and subject to change. Compiled on April 20, 2014

Keywords Biological systems

Abstract

In this paper, we present a parameter identification algorithm for the discovery of a genetic regulatory network. The genetic network is modeled, via a mechanistic approach, as a nonlinear stochastic regulatory network, in which transcription, translation and degradation processes are described as discrete stochastic events which depend nonlinearly on the number of molecules inside the cell. The system depends on several unknowns, namely the rates of transcription, the rates of translation and the degradation rates. Furthermore, the system is observable through the measure, at regular time intervals, of the factorial cumulants of the molecule counts. The unknown parameters are uncovered by studying the system output response to an arbitrary command input. The parameter search is posed as an optimization program, in which the cost function is the deviation between the observed factorial cumulants and the model output, and in which the constraint is the parameterized ordinary differential equation (ODE) governing the time evolution of the factorial cumulants. The optimization problem is solved via an adjoint-based quasi-Newton algorithm. The command input is found to have an important impact on the parameter search: Oscillatory input signals yield better parameter discovery than flat input signals. Finally, numerical results are presented for two systems: a Hill feedback and a Michaelis Menten process.

 

 

Technical Content © IEEE Control Systems Society.


This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2014 PaperCept, Inc.
Page generated 2014-04-20  06:19:08 PST Terms of use