Learning Objectives
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# Learning Objectives - Understand metabolic pathway representation - Apply Flux Balance Analysis (FBA) - Model metabolism for drug targeting ## 1. Representing Metabolism **Nodes:** Metabolites and Reactions.
Learning Objectives
- Understand metabolic pathway representation
- Apply Flux Balance Analysis (FBA)
- Model metabolism for drug targeting
1. Representing Metabolism
Nodes: Metabolites and Reactions. Edges: Substrates->reactions->products.
Genome-Scale Metabolic Models (GSMMs): Comprehensive reconstructions (Recon3D human, iJO1366 E. coli).
2. Flux Balance Analysis (FBA)
Constraint-based modeling. Key assumptions: steady-state (Sv=0), optimize objective (usually growth).
**Maximize cTv subject to:** S*v = 0, lb <= v <= ub
No kinetic parameters needed. Uses only stoichiometry and constraints.
3. Applications
Gene Essentiality: Knockout simulation predicts essential genes. Drug Targets: Identify pathogen-specific essential reactions. Metabolic Engineering: Predict modifications for overproduction.
Q1: What is the steady-state assumption in FBA?Internal metabolite concentrations constant. Production = consumption for each metabolite. Avoids needing kinetic parameters (usually unknown). Q2: How to predict gene essentiality?Simulate knockout (remove reaction, set flux=0). Run FBA. If biomass=0, gene essential. Matches experiments ~80-90%. Q3: What is a GSMM?Comprehensive reconstruction of all metabolic reactions. Includes: genes, enzymes, reactions, metabolites, compartments, biomass. Used for simulations and predictions. Q4: What is the stoichiometric matrix S?Rows = metabolites, columns = reactions. Entry = coefficient (negative for substrates, positive for products). Central to FBA. Q5: How to identify drug targets with FBA?Compare host and pathogen models. Find reactions essential in pathogen but not host (or with different enzymes). These are potential drug targets. Join Discord PreviousGene Regulatory NetworksNextNetwork Biology & Disease