ArticleBulletin of mathematical biology2015
Laplacian Dynamics with Synthesis and Degradation.
Article in Bulletin of mathematical biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 9 citations in OpenAlex.
- Review
- Algebraic formulas for first-passage times of Markov processes in the linear framework.Bulletin of mathematical biology · 2025Article
- The linear framework: using graph theory to reveal the algebra and thermodynamics of biomolecular systems.Interface focus · 2022Review
- Allosteric conformational ensembles have unlimited capacity for integrating information.eLife · 2021Article
- Efficient manipulation and generation of Kirchhoff polynomials for the analysis of non-equilibrium biochemical reaction networks.Journal of the Royal Society, Interface · 2020Article
- Steady-State Differential Dose Response in Biological Systems.Biophysical journal · 2018Article
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2 authors at 1 institution in 1 country.
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Abstract
Analyzing qualitative behaviors of biochemical reactions using its associated network structure has proven useful in diverse branches of biology. As an extension of our previous work, we introduce a graph-based framework to calculate steady state solutions of biochemical reaction networks with synthesis and degradation. Our approach is based on a labeled directed graph G and the associated system of linear non-homogeneous differential equations with first-order degradation and zeroth-order synthesis. We also present a theorem which provides necessary and sufficient conditions for the dynamics to engender a unique stable steady state. Although the dynamics are linear, one can apply this framework to nonlinear systems by encoding nonlinearity into the edge labels. We answer an open question from our previous work concerning the non-positiveness of the elements in the inverse of a perturbed Laplacian matrix. Moreover, we provide a graph theoretical framework for the computation of the inverse of such a matrix. This also completes our previous framework and makes it purely graph theoretical. Lastly, we demonstrate the utility of this framework by applying it to a mathematical model of insulin secretion through ion channels in pancreatic β-cells.
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