Constraint-Based Modeling to Understand ROS-Mediated Effects in Cancer

An oncogenic transformation has been associated with different factors such as unhealthy lifestyle and diet, exposure to harmful environmental conditions and infectious agents. These extrinsic and intrinsic carcinogens can be detrimental to cellular, genetic, and epigenetic functions of the cell, leading to abnormal cell growth and division. However, a significant hallmark of cancer cell metabolism is elevated intracellular reactive oxygen species (ROS) level. The increased ROS levels are known to promote cell proliferation, cell-to-cell adhesion, and motility, as well as angiogenesis in tumors. On the other hand, many cancer therapeutics exert their cytotoxic effects on cancer cells by mediating excess intracellular ROS generation, thus implicating a dual role of ROS in tumorigenesis and cancer cell death. Constraint-based modeling (CBM) and genome-scale metabolic models (GSMMs) have been widely used to study diverse biological systems at the metabolic level as well as to understand human diseases better. For instance, reprogrammed metabolism is central to sustaining growth and proliferation in transformed cells and hence needs to be modeled. For this purpose, a constraint-based reconstruction and analysis (COBRA) modeling strategy has been used to analyze cancer-specific metabolism and phenotypes, using context-specific metabolic models. Context-specific GSMMs have also proven advantageous in uncovering potential therapeutic targets. However, unlike numerous experimental studies that have already established the relevance of ROS in cancer, a CBM approach has not considered the role of ROS generation in understanding complex cancer metabolism thus far. This work discusses the significance of CBM in studying the scope of ROS in cancer and in devising novel cancer therapeutics.