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Jianpeng Ma(02-25-05)
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(03-29-05)Markus Covert
(03-29-05)Markus Covert
(03-29-05)Markus Covert
CaltechTitle: Model-driven biological discovery
Abstract: At the heart of systems biology is the integration of experimental data and computational model predictions to derive insight into complex behaviors. I will discuss how this integrative approach has been applied to metabolic and transcriptional regulatory networks (Escherichia coli), as well as signal transduction networks in higher organisms (activation of NF-kappaB in mice). In each case, using computational model predictions to direct an experimental study greatly facilitated characterization of the network. I will compare these cases to address the following questions: (1) What is the value added by incorporating computational modeling into biological network characterization? (2) Which cellular processes have shown the most promise for such integrated studies? (3) What is required, in terms of both experimental data and computational methods, for such studies to be applied to other biological systems of interest?
Comment: He's using linear optimization/convex analysis to find the flux-space of a network model. His case study is feedback and feedforward network motif. Feedback displays oscillating characteristic. Several phenomena:
- TF's activity not correlated with its expr level.
- KnockOut of one gene can hardly have effect.
- Complex knockout, 2-genes, 3-genes, has effect.
Yu Huang 2006-03-25
Gene Regulatory Networks Special Feature (PNAS)