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Module 1: Flux Balance Analysis

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Flux Balance Analysis - Lesson Summary

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Metabolic Network Analysis Module 2 Summary

The key points from this module are as follows:

Basic features of the stoichiometric matrix:
1. Usually represented by S
2. Contains stoichiometric information from reactions and chemical network.
3. It is formed from the stoichiometric coefficient of the reactions that comprise a reaction network.
4. The entries (compound, reactions, atoms) are integers
5. It is a linear transformation of the flux vector.
Defining network boundaries - Each reaction has a flux, which has an absolute value that ranges from negative infinity to positive infinity (-∞ to +∞). This brings the assumption that, when the flux value is negative, then it is going in a reverse direction; if it is positive, then it is going in the forward direction.
Partitioning the flux vector – They are partitioned into internal and external fluxes. External fluxes are those that flow across the cellular boundary. They are denoted by bi. They can be estimated from experimental data.
Internal fluxes – are those that take place within the cell (within the cellular boundary).
Network maps and the stoichiometric matrix - There are two types of network maps:
1. Reaction maps – In this network, the nodes are actually the metabolites and the links are the reaction.
2. Compound maps – In this network, the nodes are the reaction and the links are the metabolites.
The five-step process of network reconstruction
1. Use the information from databases and match biotransformations using omics data to generate reaction list.
2. Balance the charge elements
3. Construct (compute) the biomass equation
4. Network connectivity analysis and restoration
5. Assemble genome-scale metabolic model
The constraints under which a cell operates are:
1. Physicochemical constraints
2. Spatial and topological constraints
3. Environmental constraints
4. Regulatory constraints
Thermodynamically infeasible cycles – how to fix thermodynamically infeasible cycles:
Case 1: Duplicate reactions - By removing the duplicate reactions and leaving just one (a single) reaction.
Case 2: Lumped reactions – By removing the lumped reaction
Case 3: Cofactor specificity – By using a highly specific cofactor
The objective functions of the metabolic network
1. To minimize ATP production.
2. To minimize nutrient uptake.
3. To maximize biomass and metabolite production.
4. It provides more detailed functions via thermodynamics and kinetics.
Flux balance analysis optimization problem statement:
Since equivalent optimal solutions exist, how can we find and characterize them?
Solution – The solution was provided via flux variability and analysis (FVA).
Flux coupling finder (FCF) – It is based on a linear programming technique to minimize and maximize the ratio between all pairwise combination of fluxes in a reaction network. The types of coupling could be:
• Directionally coupled
• Partially coupled
• Fully coupled
We also have uncoupled reactions (no coupling).
Merits of flux coupling:
1. Used to see how pairs of fluxes affect one another
2. Done by calculating the minimum and maximum ration between two fluxes
3. A coupled reaction needs to be transformed to make it a linear problem
Overview of cellular growth – in both aerobic and anaerobic batch culture:
- Biomass increases over time
- Carbons source e.g. glucose decreases over time
- Acetate is secreted in the absence of oxygen supply (plus other byproducts in anaerobic condition)