Hoefnagel et al. (2002), Metabolic Control Analysis
May 2007, model of the month by Dominic P. Tolle
Original model: BIOMD0000000017
Biotechnology frequently aims at improving a micro-organisms production rate for certain chemicals. Genetic engineering is an important tool for achieving that goal, with overexpression or knockout of genes now common and relatively easily implementable methods. The metabolic network of most living organisms, however is a complex and convoluted system. An intuitive approach to the manipulation of the network does not always yield the most efficient or desired result. Many theoretical frameworks exist to analyse metabolism as a whole, such as metabolic control analysis (MCA) or biological system theory. (For an example of an alternative MCA model, see BIOMD000000076).
Figure 1: Reactions included in the model. From .
The model presented here deals with the flux through the acetolactate synthase branch in lactic acid bacteria  (Figure 1). Lactococcus lactis makes diacetyl, an important flavour component of many dairy products. The authors of this model use the kinetic model of the biochemical network to identify points of control of flux using MCA. MCA suggests enzyme targets for modification in order to influence the flux toward the desired result. The experimentally observed flux from wild-type and genetically modified organisms is then compared with the flux of the model, in silico, counter-parts.
In MCA the influence an enzyme has over a pathway flux is quantitated in terms of the flux-control coefficient. Analysis of the kinetic model using MCA pointed to two enzymes in particular, Lactate dehydrogenase (LDH) and NADH oxidase (NOX), that displayed the highest flux-control coefficients for the acetolactate branch (the LDH coefficient was negative, but of greater absolute value). Counter-intuitively, neither of these enzymes lie within the acetolactate branch itself. ldh deficient mutants (ldh-), mutants overexpressing NOX and mutants displaying both phenotypes were created, and flux of the distribution over the pyruvate branches measured experimentally. The experimental results were compared to results otained from equivalent in silico experiments using the kinetic model. The experimental and in silico results were in good agreement (see Figure 2).
Application of kinetic model builidng and analysis using a theoretical framework can be of great value, not just within the academic realm, but also within industry. The authors chose to model a biotechnologically important pathway. The kinetic model was used for the initial MCA, as well as comparatively with the experimental analysis. Overall, the authors show how an integrated approach to modelling, MCA and experiment can provide for a more effective metabolic engineering strategy.
For more information on MCA, see ref , 
Figure 2: Flux distribution over the pyruvate branches in L. Lactis. (a) wildtype (b) Ldh- mutant (c) NOX overexpression mutant (d) Ldh- and NOX overexpression mutant. Black letters on a white background show experimental results, white letters on a black background model predictions. From .
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- M.A. Savageau. Biochemical systems theory: operational differences among variant representations and their significance. J Theor Biol, 151:509-530, 1991. [SRS@EBI]
- M.H.N. Hoefnagel, M.J. Starrenburg, D.E. Martens, J. Hugenholtz, M. Kleerebezem, I.I. Van Swam, R. Bongers, H.V. Westerhoff, and J.L. Snoep. Metabolic engineering of lactic acid bacteria, the combined approach: kinetic modelling, metabolic control and experimental analysis. Microbiology, 148:1003-1013, 2002. [SRS@EBI]