From Top-Down to Bottom-Up: Computational Modeling Approaches for Cellular Redoxin Networks

Abstract

Thioredoxin, glutaredoxin, and peroxiredoxin systems play critical roles in a large number of redox-sensitive cellular processes. These systems are linked to each other by coupled redox cycles and common reaction intermediates into a larger network. Given the scale and connectivity of this network, computational approaches are required to analyze its dynamics and organization. Theoretical advances, as well as new redox proteomic methods, have led to the development of both top-down and bottom-up systems biology approaches to analyze the these systems and the network as a whole. Top-down approaches have been based on modifications to the Nernst equation or on graph theoretical approaches, while bottom-up approaches have been based on kinetic or stoichiometric modeling techniques. This review will consider the rationale behind these approaches and focus on their advantages and limitations. Further, the review will discuss modeling standards to ensure model accuracy and availability. Top-down and bottom-up approaches have distinct strengths and limitations in describing cellular redoxin networks. The availability of methods to overcome these limitations, together with the adoption of common modeling standards, is expected to increase the pace of model-led discovery within the redox biology field.