Metabolic networks need to meet more requirements than single enzymes, in order to be functional. Aware of the heterogeneity of (tumor) cell populations, we went after this principle and engaged in network based drug design. We thought that by identifying the metabolic potential of individual cells, we could identify which targets could be used to most effectively incapacitate most individuals of a tumor cell population.
We projected mRNA sequence counts obtained for >3000 cells out of a growing tumor-cell population onto the genome-wide metabolic map after converting the numbers to Vmax’s. We used Flux Balance Analysis to predict the pathways the individual cells could be using and thereby their vulnerabilities to potential metabolic drugs. That is at least what we thought we would do.
Much to our surprise however, none of the cells was predicted to be able to grow.
We then considered whether this could be due to the cells being social metabolically, i.e. massively exchanging metabolites, with some cells taking care of the upper part of glycolysis, others the TCA cycle, yet others the lower part of glycolysis. This was a nice and social idea, but apparently not realistic: subdividing the cells into subpopulations and offering metabolites synthesized by one subpopulation as substrates to the others, did not lead to growth of either subpopulation.
In this presentation we shall discuss what explanation of the growth in the absence of mRNA for the metabolic pathways, we did come up with.
And, we discuss how the actual resulting model did identify cholesterol and asparagine synthesis pathways as relevant, though complex, drug targets.