P.J. Gawthrop

This paper introduces a thermodynamic framework for reducing enzyme cycle models while preserving physical constraints, applicable to both hypothetical systems and real data like Na+/K+ ATPase.

This paper develops thermodynamically-consistent, reduced models of gene regulatory networks for scalable agent-based simulations of cell populations.

The paper combines bond graph modeling with feedback control theory to analyze biochemical oscillators, showing phase margin as a scalar indicator of oscillation presence and structure.

The paper extends bond graph methodology to model chemomechanical transduction in actin filament polymerisation, showing it matches the Brownian ratchet approach and can model flexibility and compliance.

The paper analyzes biochemical system sensitivity using bond graphs, showing parameter sensitivities as bond graph components and applying this to identify essential reactions in the Pentose Phosphate Pathway.

The paper extends an energy-based bond graph model to include advection, demonstrating its application in pharmacokinetic models and physiological systems.

This paper summarizes Crampin's group work on applying energy-based bond graph methods to biological systems, focusing on modeling photosynthesis with a new bond graph model.

This paper introduces BondGraphTools, a Python library for constructing and analysing energy-based models in biochemistry using bond graphs, ensuring consistency with physical laws.

The paper proposes using bond graphs to improve modularity and physical consistency in systems biology models, demonstrating their utility in modelling MAPK cascades and glycolysis.

The paper introduces a bond graph methodology to unify stoichiometry, thermodynamics, and kinetics in dynamic biomolecular modelling, demonstrating its application to E. coli.