(441f) Dynamic Behavior of Knockout Strains Predicted From Limited Data On Wild Type

In silico prediction of cellular response to gene knockouts is a challenging task as mutated strains often exhibit the significant change in uptake rate as well as in (relative) internal flux distribution. While constraint-based approaches including Flux Balance Analysis (FBA) (Reed and Palsson, 2003), Minimization Of Metabolic Adjustment (MOMA) (Segre et al., 2002), and Regulatory On/Off Minimization (ROOM) (Shlomi et al., 2005) have been applied to the simulation of knockout strains, it is difficult, in general, for such stoichiometry-based models to predict the change of actual ?rate? on gene deletion.

We present here a powerful methodology to predict the ?dynamic? behaviors of knockout mutants from limited metabolic data of wild-type strains using the lumped hybrid cybernetic model (L-HCM) recently proposed by Song and Ramkrishna (2010). L-HCM portrays uptake flux to be split in a hierarchical way i.e., first among lumped elementary modes (L-EMs) and next among individual EMs (I-EMs). Uptake kinetics through L-EMs can be identified from a few extracellular measurements while uptake kinetics through I-EMs from the network structure alone assuming that throughput flux is proportional to structural return-on-investment of each EM. Uptake flux distribution among L-EMs and I- EMs, respectively, is determined according to the cybernetic control laws. In mutated strains, EMs associated with the deleted gene are removed and the change of uptake rate and redistribution of internal fluxes are estimated by applying the cybernetic control laws to the surviving set of EMs. As a result, L-HCM makes a satisfactory prediction on dynamic behaviors of knockout strains as shown in various case studies. This proves the universal validity of the cybernetic regulatory mechanisms which work not only for wild-type strains as having been shown so far in various cases in the literature, but also for mutated strains as shown in this work. Correct model prediction of dynamic shift in mutant metabolism is a prerequisite for a shift in metabolic engineering approaches from being yield-centered to being productivity-centered.

References:

Reed JL, Palsson BO. 2003. Thirteen years of building constraint-based in silico models of Escherichia coli. Journal of Bacteriology 185(9):2692-2699

Segre D, Vitkup D, Church GM. 2002. Analysis of optimality in natural and perturbed metabolic networks, Proc Natl Acad Sci USA 99(23): 15112-15117

Shlomi T, Berkman O, Ruppin E. 2005. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci USA 102(21): 7695-7700

Song HS, Ramkrishna D. 2010. Prediction of Metabolic Function from Limited Data: Lumped Hybrid Cybernetic Modeling (L-HCM). Biotechnology and Bioengineering 106(2): 271-284