Abstract
Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.
Highlights
The recent explosion of omics data has generated an interest in developing dynamic whole-cell models that account for the function of every gene and biomolecule over time
The formulation of dynamic simulation models for largescale biological systems remains a key challenge in systems biology
Several authors have acknowledged the need for energetic considerations to be integrated into modelling approaches, both to ensure that models are consistent with basic thermodynamic principles, and to enable calculation of energy flows and related concepts such as efficiency [67]
Summary
The recent explosion of omics data has generated an interest in developing dynamic whole-cell models that account for the function of every gene and biomolecule over time. As in the TKM [27,28] approach, the bond graph approach uses an alternative parameterization which satisfies thermodynamic constraints as long as the parameters are positive; such inequality constraints are easier to handle than nonlinear constraints. We illustrate this approach by generating a dynamic bond graph model of E. coli metabolism, using a well-established stoichiometric model [51] as a template and show that the 2 use of thermodynamic parameters can significantly streamline the process of parameter estimation.
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