BondGraphs.jl: Composable energy-based modelling in systems biology

Forrest, Joshua; Rajagopal, Vijay; Stumpf, Michael; Pan, Michael

doi:10.1101/2023.04.23.537337

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Python code used to generate the figures in this paper is available as Jupyter notebooks at https://github.com/ gawthrop/Sensitivity23. Software tools for manipulating biochemical bond graphs are available in Python [57] at https:// pypi.org/project/BondGraphTools/ and in Julia [58] at https://github.com/jedforrest/BondGraphs.jl. Both sets of tools are designed for modularity and scaleability and can generate both symbolic ODEs and numerical ODEs for simulation.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of biochemical systems using bond graphs

Gawthrop

Pan

2023

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular, a sensitivity system is derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearized with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.

show abstract

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of biochemical systems using bond graphs

Gawthrop

Pan

2023

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

show abstract

Sensitivity Analysis of Biochemical Systems Using Bond Graphs

Gawthrop

Pan

2023

Preprint

View full text Add to dashboard Cite

The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. This approach naturally extends to finding sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.

show abstract

BondGraphs.jl: Composable energy-based modelling in systems biology

Cited by 2 publications

References 16 publications

Sensitivity analysis of biochemical systems using bond graphs

Sensitivity analysis of biochemical systems using bond graphs

Sensitivity Analysis of Biochemical Systems Using Bond Graphs

Contact Info

Product

Resources

About