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Signaling pathways capable of switching between two states are ubiquitous within living organisms. They provide the cells with the means to produce reversible or irreversible decisions. Switch-like behavior of biological systems is realized through biochemical reaction networks capable of having two or more distinct steady states, which are dependent on initial conditions. Investigation of whether a certain signaling pathway can confer bistability involves a substantial amount of hypothesis testing. The cost of direct experimental testing can be prohibitive. Therefore, constraining the hypothesis space is highly beneficial. One such methodology is based on chemical reaction network theory (CRNT), which uses computational techniques to rule out pathways that are not capable of bistability regardless of kinetic constant values and molecule concentrations. Although useful, these methods are complicated from both pure and computational mathematics perspectives. Thus, their adoption is very limited amongst biologists.

CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks