Reaction Network Analysis of Metabolic Insulin Signaling

Lubenia, Patrick Vincent N.; Mendoza, Eduardo; Lao, Angelyn R.

doi:10.1007/s11538-022-01087-3

Cited by 7 publications

(9 citation statements)

References 22 publications

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“…We illustrate how COMPILES derives the analytic steady state with an example of a complex CRN that describes the signaling cascades activated by insulin [ 27 , 29 ] ( Fig 4a ). To use the package, one simply inputs the reactions in the system’s CRN.…”

Section: Resultsmentioning

confidence: 99%

A framework for deriving analytic steady states of biochemical reaction networks

Hernandez

Lubenia²,

Johnston

et al. 2023

PLoS Comput Biol

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The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.

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Section: Resultsmentioning

confidence: 99%

A framework for deriving analytic steady states of biochemical reaction networks

Hernandez

Lubenia²,

Johnston

et al. 2023

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

show abstract

“…We illustrate how COMPILES derives the analytic steady state with an example of a complex CRN that describes the signaling cascades activated by insulin [28, 31] (Fig. 4a).…”

Section: Resultsmentioning

confidence: 99%

A framework for deriving analytic long-term behavior of biochemical reaction networks

Hernandez

Lubenia²,

Johnston

et al. 2022

Preprint

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The long-term behaviors of biochemical systems are described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often challenging. Recent work has consequently focused on network-based approaches. Methods have been developed for transforming biochemical reaction networks into weakly reversible and deficiency zero networks, which allows the analytic steady states to be derived easily. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the network into smaller independent subnetworks and then deriving the analytic steady states of each subnetwork. We show that the analytic steady states of the original network can then be derived by stitching these solutions together. To facilitate this process, we develop a user-friendly and publicly-available package, COMPILES. With our approach, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous amount of numerical simulations. Furthermore, our approach can be used to identify absolute concentration robustness (ACR), the property of a system to maintain the concentration of particular species at steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.

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“…These relationships were illustrated through the data shown in [20] that Lubenia et al used for analyzing metabolic insulin signaling of healthy cells. It is important to note that ACR species of a subnetwork is always contained in the corresponding non-embedded subnetwork.…”

Section: Discussionmentioning

confidence: 99%

“…We then collect some relevant results from previous publications and add some new details. We conclude by using the results of the reaction network analysis of metabolic insulin signaling by Lubenia et al [20] to illustrate the different concepts and relationships. We in fact show that the various inequalities between the different equilibria variation values are sharp, i.e., equality is achieved in various subnetworks of the insulin system.…”

Section: Equilibria Variation Of Independent Subnetworkmentioning

confidence: 99%

“…Lubenia et al [20] constructed a mass-action kinetic realization of the widely used model of metabolic insulin signaling (in healthy cells) by Sedaghat et al [24]. They used the kinetic system's finest independent decomposition (FID) to conduct an ACR analysis and showed that m ACR ≥ 8 for all rate constants (such that the system has positive equilibria) and m ACR = 8 for some rate constants.…”

Section: The Example Of Metabolic Insulin Signaling In Healthy Cellsmentioning

confidence: 99%

See 1 more Smart Citation

Absolute Concentration Robustness in Rank-One Kinetic Systems

Mendoza,

Talabis,

Jose

et al. 2023

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A kinetic system has an absolute concentration robustness (ACR) for a molecular species if its concentration remains the same in every positive steady state of the system. Just recently, a condition that sufficiently guarantees the existence of an ACR in a rank-one mass-action kinetic system was found. In this paper, it will be shown that this ACR criterion does not extend in general to power-law kinetic systems. Moreover, we also discussed in this paper a necessary condition for ACR in multistationary rank-one kinetic system which can be used in ACR analysis. Finally, a concept of equilibria variation for kinetic systems which are based on the number of the system's ACR species will be introduced here.

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Reaction Network Analysis of Metabolic Insulin Signaling

Cited by 7 publications

References 22 publications

A framework for deriving analytic steady states of biochemical reaction networks

A framework for deriving analytic steady states of biochemical reaction networks

A framework for deriving analytic long-term behavior of biochemical reaction networks

Absolute Concentration Robustness in Rank-One Kinetic Systems

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