Less is more, we often hear. But sometimes less is just less, as it is with too many genome-scale metabolic models. These models still have difficulty coping with tissue- and cell-specific differences. That is, they often fail to reflect how similar metabolic pathways may lead to different outcomes, depending on pathway context—whether a pathway is being followed in one tissue versus another, or whether it can take different turns in health or disease.
To help overcome this problem, researchers at Rice University suggest going beyond the usual approach, which they characterize as minimalistic. The usual approach, they say, amounts to stripping down a generalized model to fit a tissue- or cell-specific context, a process that can remove essential reactions from metabolic modeling.
To preserve detail and better reflect tissue- and cell-specific context in metabolic modeling, the Rice laboratory of bioengineer Amina Qutub, Ph.D., designed an algorithm, Cost Optimization Reaction Dependency Assessment (CORDA). In the new CORDA algorithm, metabolic reactions not supported by experimental data are assigned a high "cost" that gives them less importance. For accuracy, this cost is minimized in a method that depends on flux balance analysis, a standard method for simulating metabolism in a network.
CORDA is detailed in an article that appeared March 4 in PLOS Computational Biology, in an article entitled, “Reconstruction of Tissue-Specific Metabolic Networks Using CORDA.” The article argues that CORDA has many advantages over previous methods, including better agreement with experimental data and better model functionality. “CORDA,” the article states, “yields concise, but not minimalistic, tissue-specific models.”
“Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions,” the article’s authors reported. “These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.”
The researchers expect their algorithm will be a broad tool to model cell- and tissue-specific metabolism. In one example, the team looked at 271 metabolites known to be present in all of the models. The researchers found that two—both essential to fatty acid and glycerophospholipid pathways—stood out as essential selectively for cancer models. This comparison, as noted in the quoted passage above, also highlighted the up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation in cancerous tissues.
"Interestingly, although cancer metabolism has often been thought of collectively, CORDA was able to capture key differences between different cancer types," said Dr. Qutub. "In the future, the fast computational approach introduced by CORDA will allow for the high-throughput generation of patient-specific models of metabolism."