(568d) Extending a Scalable Bayesian Metabolic Modeling Framework with Thermodynamic Constraints and Support for Transcriptional Regulation
AIChE Annual Meeting
Wednesday, October 31, 2018 - 4:24pm to 4:42pm
To overcome this limitation, Tran et al. have developed the Ensemble Modeling framework where instead of directly inferring parameter values, they generate an ensemble of parameter sets and keep those that, through the model, agree with multiple experimental observations (1). However, there are two relevant limitations: While this method works well for small and medium-scale models, it does not scale well to the genome-scale due to the need to integrate ordinary differential equations to attain the steady-state for every parameter set in the ensemble. Second, the approach employs rejection-sampling which does not give clear estimates of parameter uncertainties.
Here, we present an approximate kinetic framework where we use linear-logarithmic (lin-log) kinetics, allowing the steady-state fluxes to be solved for linearly, greatly reducing the computational time required (2). Because we can quickly solve for the steady-state, we are able to use advanced Bayesian inference methods such as Markov Chain Monte Carlo to estimate posterior distributions in model parameters. Importantly, these Bayesian techniques give us probability distributions for every parameter, which can be more informative than the single values typically generated by other non-Bayesian parameter estimation algorithms. We extend the modeling framework further based on ideas developed by Westerhoff and van Dam in order to incorporate thermodynamic constraints to improve model performance (3). Finally, we add effects of transcriptional regulation to the framework, which are typically difficult to include in kinetic models of metabolism due to the mechanistic complexity of the transcription and translation processes. We evaluate the performance of this framework by demonstrating the method on published small, medium, and genome-scale experimental datasets. We show that a genome-scale model with 25 experimental -omics data points can be run in 4 â 6 hours on a single modern CPU. Estimated posterior probability distributions were consistent with measured values. The framework we present here overcomes the computationally intensive and limited parameter estimation techniques put forth previously. We anticipate this modeling framework will be valuable to explicitly identify genetic modifications in metabolic networks that will improve desired phenotypes.
- M. Tran, M. L. Rizk, J. C. Liao, Biophysical Journal. 95, 5606â5617 (2008).
- Wu, W. Wang, W. A. van Winden, W. M. van Gulik, J. J. Heijnen, European Journal of Biochemistry. 271, 3348â3359 (2004).
- Westerhoff, K. van Dam, âThermodynamics and Control of Biological Free-Energy Transductionâ (1987).