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

2020 ◽  
Vol 36 (12) ◽  
pp. 3922-3924
Author(s):  
Brandon C Reyes ◽  
Irene Otero-Muras ◽  
Michael T Shuen ◽  
Alexandre M Tartakovsky ◽  
Vladislav A Petyuk

Abstract Motivation 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. Results We brought CRNT approaches closer to experimental biologists by automating all the necessary steps in CRNT4SMBL. The input is based on systems biology markup language (SBML) format, which is the community standard for biological pathway communication. The tool parses SBML and derives C-graph representations of the biological pathway with mass action kinetics. Next steps involve an efficient search for potential saddle-node bifurcation points using an optimization technique. This type of bifurcation is important as it has the potential of acting as a switching point between two steady states. Finally, if any bifurcation points are present, continuation analysis with respect to a user-defined parameter extends the steady state branches and generates a bifurcation diagram. Presence of an S-shaped bifurcation diagram indicates that the pathway acts as a bistable switch for the given optimization parameters. Availability and implementation CRNT4SBML is available via the Python Package Index. The documentation can be found at https://crnt4sbml.readthedocs.io. CRNT4SBML is licensed under the Apache Software License 2.0.

2021 ◽  
Author(s):  
Hyukpyo Hong ◽  
Jinsu Kim ◽  
M Ali Al-Radhawi ◽  
Eduardo D. Sontag ◽  
Jae Kyoung Kim

Long-term behaviors of biochemical reaction networks (BRNs) are described by steady states in deterministic models and stationary distributions in stochastic models. Unlike deterministic steady states, stationary distributions capturing inherent fluctuations of reactions are extremely difficult to derive analytically due to the curse of dimensionality. Here, we develop a method to derive analytic stationary distributions from deterministic steady states by transforming BRNs to have a special dynamic property, called complex balancing. Specifically, we merge nodes and edges of BRNs to match in- and out-flows of each node. This allows us to derive the stationary distributions of a large class of BRNs, including autophosphorylation networks of EGFR, PAK1, and Aurora B kinase and a genetic toggle switch. This reveals the unique properties of their stochastic dynamics such as robustness, sensitivity and multi-modality. Importantly, we provide a user-friendly computational package, CASTANET, that automatically derives sym- bolic expressions of the stationary distributions of BRNs to understand their long-term stochasticity.


2019 ◽  
Vol 52 (26) ◽  
pp. 32-37
Author(s):  
Glenn Terje Lines ◽  
Łukasz Paszkowski ◽  
Leonard Schmiester ◽  
Daniel Weindl ◽  
Paul Stapor ◽  
...  

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Hyukpyo Hong ◽  
Jinsu Kim ◽  
M. Ali Al-Radhawi ◽  
Eduardo D. Sontag ◽  
Jae Kyoung Kim

AbstractLong-term behaviors of biochemical reaction networks (BRNs) are described by steady states in deterministic models and stationary distributions in stochastic models. Unlike deterministic steady states, stationary distributions capturing inherent fluctuations of reactions are extremely difficult to derive analytically due to the curse of dimensionality. Here, we develop a method to derive analytic stationary distributions from deterministic steady states by transforming BRNs to have a special dynamic property, called complex balancing. Specifically, we merge nodes and edges of BRNs to match in- and out-flows of each node. This allows us to derive the stationary distributions of a large class of BRNs, including autophosphorylation networks of EGFR, PAK1, and Aurora B kinase and a genetic toggle switch. This reveals the unique properties of their stochastic dynamics such as robustness, sensitivity, and multi-modality. Importantly, we provide a user-friendly computational package, CASTANET, that automatically derives symbolic expressions of the stationary distributions of BRNs to understand their long-term stochasticity.


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