1. In silico gene targeting approach integrating signaling, metabolic, and regulatory networks Bin Song Jan 29, 2009

2. Motivation (1) Scheme for the systems-level engineering of strains

3. Motivation (2)-models In silico models with rapid progress –Basic model: FBA (flux balance analysis) Advantage: No kinetic parameter needed Disadvantage: Simple, coarse model can not describe the process but result

4. Motivation (2)-models –multiple FBA steps to simulate growth dynamic (Luo et al 2006; Mahadevan 2002) –Incorporation of transcriptional regulatory network models ( Covert 2001;2004; Shlomi 2005;2007) –Integrating a regulatory network increasing the performance (10800 correct predictions out of 13750 cases in E. coli)

5. Motivation (2)-models –Current progress: integrating metabolic, transcriptional regulatory and signal transduction models iFBA(2008): rFBA ( regulatory FBA) + ODEs(ordinary differential equations) on E. coli idFBA(2008): kinetic information + FBA on S. cerevisiae

6. Motivation (3) – gene targeting Gene targeting approach can not catch up the progress of models –Bilevel optimization (Mixed integer programming) OptKnock(2003), OptStrain(2004),OptReg(2006) Disadvantage: 1.can not apply to the non-linear models ( only for FBA) 2.iFBA, idFBA exists iterations

7. Motivation (3) – gene targeting Genetic algorithm Sequential approach (Alper et al 2005) Disadvantage: Have not applied to the current models

8. iFBA

9. idFBA

10. Problem Definition Gene targeting problem: Given a goal process with time for some compounds, the gene targeting problem is to identify the set of genes whose operations lead to the process of these compounds as close to the goal process.