Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Home 
 People 
 Current 
 Affiliates 
 Alumni 
 Visitors 
 Students 
 Research 
 ICAM-LANL 
 Quantum 
 Publications 
 Publications 
 2007 
 2006 
 2005 
 2004 
 2003 
 2002 
 2001 
 2000 
 <1999 
 Conferences 
 Workshops 
 Sponsorship 
 Talks 
 Colloquia 
 Colloquia Archive 
 Seminars 
 Postdoc Seminars Archive 
 Quantum Lunch 
 CMS Colloquia 
 Q-Mat Seminars 
 Q-Mat Seminars Archive 
 Archive 
 Kac Lectures 
 Dist. Quant. Lecture 
 Ulam Scholar 
 Colloquia 
 
 Jobs 
 Students 
 Summer Research 
 Student Application 
 Visitors 
 Description 
 Past Visitors 
 Services 
 General 
 PD Travel Request 
 
 History of CNLS 
 
 Maps, Directions 
 CNLS Office 
 T-Division 
 LANL 
 
Thursday, October 13, 2011
2:00 PM - 3:00 PM
CNLS Conference Room (TA-3, Bldg 1690)

Postdoc Seminar

Systematic Identification of Signal-Activated, Stochastic Gene Regulation

Brian Munsky
CCS-3: INFORMATION SCIENCES

Despite vast amounts of biochemical information, it remains difficult to understand or predict the quantitative responses of signal transduction and gene regulation pathways. In this presentation, I discuss new approaches to integrate dynamic single-cell and single-molecule experiments with discrete stochastic analyses. I use these methods to identify models capable of making quantitative predictions for transcriptional dynamics on the level of single cells. I illustrate the power of this approach in a combined experimental/computational investigation of the osmotic stress response pathway in Saccharomyces cerevisiae. After generating several thousand different model structures, we use simple parameter estimation and cross-validation analyses to exclude models that are either too simple or too complex to be supported with the available data. Through a process of iterative experiment design, we eventually select a single quantitative model with the greatest predictive capability. This model yields insight into several dynamical features, including multi-step regulation and low-pass filtering. Furthermore, the model predicts the transcriptional dynamics of cells in response to new environmental and genetic perturbations. Since our approach is general, it can facilitate a predictive understanding for signal-activated transcription in any gene, pathway or organism.

Host: Peter Loxley, loxley@lanl.gov