Download In silico gene targeting approach integrating signaling

In silico gene targeting approach
integrating signaling, metabolic,
and regulatory networks
Bin Song
Jan 29, 2009
Motivation (1)
• Scheme for the systems-level engineering
of strains
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
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)
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
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
Motivation (3) – gene targeting
•
•
•
•
Genetic algorithm
Sequential approach (Alper et al 2005)
Disadvantage:
Have not applied to the current models
iFBA
idFBA
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.