Understanding flux re-routing in metabolic networks through an analysis of synthetic lethal pairs


Robustness in metabolic systems can be attributed to the presence of multiple alternate pathways that have identical metabolic functions. This introduces metabolic redundancies. These redundancies show surprising variance in their distribution – some are due to gene duplication, while the others span different metabolic submodules. Higher-order lethals offer a straightforward method to study alternate pathways. When a single reaction that comprises a higher-order lethal is deleted, a complex rerouting of fluxes occurs in the network. Very little is known about how these organisms reroute their fluxes.

Double lethals, in particular, have been classified based on the activity of individual reactions into Plastic Synthetic Lethals (PSL) and Redundant Synthetic Lethals (RSL) [1]. The presence of two distinct reaction pair classes calls for us to analyze the cause behind the selective activation of reactions. How do reactions that are present in different metabolic submodules, compensate for each other? Are the inactive reactions more metabolically inefficient than the active ones? What kind of reactions make up the RSL pairs, especially since they are both simultaneously active?

Studying flux rerouting helps us to understand the complex metabolic reroutings that occur in several diseases, especially in the case of cancer. It will enable us in finding better therapeutic cures for diseases.

In this study, we

  • Describe a constraint-based approach to unravel these alternate pathways.
  • Propose a novel optimization method that minimizes the extent of rerouting between reactions that comprise synthetic lethal pairs.
  • Analyze the robustness introduced by PSL/RSL pairs and the metabolic efficiency of the reactions that comprise them.
  • Perform a systematic analysis of synthetic lethals by identifying the reaction classes that make up these synthetic lethals

Jul 12, 2022 7:30 PM
Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2022
ISMB 2022