Flux Control Analysis and the systems biology of the eukaryotic cell

Stephen Oliver
Keynote Talk, Afternoon Session, 3 September (11th MGED Meeting, 1-4 September, 2008)

pi muson budding out from the photon – physics shamelessly borrowing from budding yeast 😉

How can we deal with the complexity of this "simple" eukaryotic cell? Work top-down, with a coarse-grained model, and bottom-up, by defining discrete subsystems. Wrt to the former, there is metabolic control analysis (MCA), which is a "shortcut" to modelling metabolism. The central device of MCA is the control coefficient, specifically the flux control coefficient (C), is a measure of the degree of control an enzyme has on a pathway flux.

Changing the flux: the big experiment (taken 9 years so far). The idea is that the experimentalists should control the flux == the growth rate of the organism. Hypothesize that can do this by controlling what goes in the chemostat. By changing relative concentrations of micronutrients, we can make it so that one of those micronutrients is the growth-limiting micronutrient. For the vast majority of genes, there is no significant difference in flux when reducing carbon from 2 to 1. Phosphorus-limited is slower (decreased fitness). Nitrogen created an increase in fitness. 196 genes showed haploproficiency in the three defined nutrient limitations, and 350 genes that showed a haploinsufficient uner the same limitations.

Is it a universal law, or is it context dependent? Use a turbidostat rather than chemostat, and have no nutrient limitation. top classes that show haploinsufficieny include cytosolic ribosome, ribosomal subunit, etc. These are completely different than those in the chemostat. The laws determining which genes control growth rate may change according to the selective conditions. The discover of haploproficiency in nutrient-unconstrained conditions suggest that yeast has sacrificed short-term gain in favor of long-term sufficiency.

Metabolic mode: list of all metabolic reactions know for S.cerevisiae, taken from databases and primary papers. Approx 1174 reactions and 584 metabolites. Then they chose a selection of knockouts.  Synthetic lethality can come from redundant gene duplicates or alternative cellular pathways – both need to be interrupted to see the phenotype. How to find synthetic lethal gene pairs? Global mapping: screen all possible double gene deletion strains of non-essential genes. Problems: huge number of gene combinations, with about 4% completed in the first 3 years; and interactions are rare, with only 0.6% of pairs show synthetic lethality in yeast.

Can they use computational tools to guide them? The tool: flux balance analysis (FBA): reconstruct the metabolic network, define the nutrient environment and constrain for optimal biomass, etc. The yeast model predicts essential genes with 68-80% success for single-gene deletions.

These are just my notes and are not guaranteed to be correct.
Please feel free to let me know about any errors, which are all my
fault and not the fault of the speaker. 🙂

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