A Model-Based Reverse System Engineering Methodology for Analyzing Complex Biological Systems With a Case Study in Glycolysis

We propose a model-based reverse systems engineering (MBRSE) methodology for biological systems that relies on requirements analysis in conjunction with model-based systems engineering (MBSE). The goal of this methodology is to better understand complex multiscale biological systems, discover knowledge gaps, and make testable predictions. The similarities between human-engineered and biological systems motivate this approach. Furthermore, traditional reductionist paradigms in biology have proven insufficient for understanding and accurately predicting complex biological systems, as opposed to systems engineering approaches that have proven effective in supporting the design and analysis of complex engineered systems spanning multiple spatiotemporal scales. We employ our MBRSE methodology to analyze glycolysis in a biological case study using object process methodology as the primary MBSE language for conceptual qualitative modeling, in conjunction with SysML use case modeling. Using the MBRSE methodology, we derive twenty-two requirements, uncover five gaps in knowledge, and generate six predictions for the core metabolic pathway of glycolysis. One significant prediction is that the Warburg effect associated with cancer is the result of a natural response to tissue injury that has become unstable due to a failure in the feedback mechanism of the tissue injury control system.