An energetic reformulation of kinetic rate laws enables scalable parameter estimation for biochemical networks.


Department of Bioengineering, Stanford University, 443 Via Ortega Road, Stanford, CA 94305, United States; Allen Discovery Center for Systems Modeling of Infection, 443 Via Ortega Road, Stanford, CA 94305, United States. Electronic address: [Email]


The technology for building functionally complete or 'whole-cell' biological simulations is rapidly developing. However, the predictive capabilities of these simulations are hindered by the availability of parameter values, which are often difficult or even impossible to obtain experimentally and must therefore be estimated. Using E. coli's glycolytic network as a model system, we describe and apply a new method which can estimate the values of all the system's 102 parameters - fit to observations from studies of proteomics, metabolomics, enzyme kinetics and chemical energetics - and find that the resulting metabolic models are not only well-fit, but also dynamically stable. An analysis of how well parameter values in the network were determined by the training data revealed that over 80% of the parameter values were not well-specified. Moreover, the distribution of well-determined values was biased to a specific part of the network and against certain types of experimental data. Our results also suggest that perturbing the functional, energetic space of parameters (rather than traditional metabolic parameters) is a superior strategy for exploring the space of biological dynamics. The estimated parameter values matched both training data and previously withheld validation data within an order of magnitude for over 85% of the data points; notably, the area of greatest frustration in the network was also the most fully determined. Finally, our estimation method showed that fidelity to physiological observations such as network response time is enforced at the cost of fit to molecular parameter values. In summary, our reformulation enables estimation of accurate, biologically relevant parameters, generates insight into the biology of the simulated network, and appears generalizable to any biochemical network - potentially including whole-cell models.


Dynamics,Metabolism,Parameter estimation,Whole-cell modeling,