Metabolic control analysis

Metabolic control analysis (MCA) is a mathematical framework for describing metabolic, signaling and genetic pathways. MCA quantifies how variables, such as fluxes and species concentrations, depend on network parameters. In particular it is able to describe how network dependent properties, called control coefficients, depend on local properties called elasticities.

MCA was originally developed to describe the control in metabolic pathways but was subsequently extended to describe signaling and genetic networks. MCA has sometimes also been referred to as Metabolic Control Theory but this terminology was rather strongly opposed by Henrik Kacser, one of the founders.

More recent work has shown that MCA can be mapped directly on to classical control theory and are as such equivalent.

Biochemical systems theory is a similar formalism, though with a rather different objectives. Both are evolutions of an earlier theoretical analysis by Joseph Higgins.

Control Coefficients
A control coefficient  measures the relative steady state change in a system variable (e.g. fluxes or concentrations) in response to a relative change in a parameter (eg enzyme activity). The two main control coefficients are the flux and concentration control coefficients. Flux control coefficients are defined by:

$$ C^J_{v_i} = \left( \frac{dJ}{dp} \frac{p}{J} \right) \bigg/ \left( \frac{dv_i}{dp}\frac{p}{v_i} \right) = \frac{d\ln J}{d\ln v_i} $$

and concentration control coefficients by:

$$ C^S_{v_i} = \left( \frac{dS}{dp} \frac{p}{S} \right) \bigg/ \left( \frac{dv_i}{dp}\frac{p}{v_i} \right) = \frac{d\ln S}{d\ln v_i} $$

Summation Theorems
The flux control summation theorem was stated and proven by Kacser and Burns and it is one of the most fundamental discoveries of metabolic control analysis. The flux control summation theorem implies that metabolic fluxes are systemic properties and that their control is shared by all reactions in the system. When a single reaction changes its control of the flux this is compensated by changes in the control of the same flux by all other reactions.

$$ \sum_i C^J_{v_i} = 1 $$

$$ \sum_i C^S_{v_i} = 0 $$

Elasticity Coefficients
The rate of a chemical reaction is influenced by many different factors, such as temperature, pH, reactant and product concentrations and other effectors. The degree to which these factors change the reaction rate is described by the elasticity coefficient.

$$ \varepsilon^v_S = \frac{\partial v}{\partial S} \frac{S}{v} = \frac{\partial \ln v}{\partial \ln S} $$

Connectivity Theorems
The connectivity theorems are specific relationships between elasticities and control coefficients. They are useful because they highlight the close relationship between the kinetic properties of individual reactions and the system properties of a pathway. Two basic sets of theorems exists, one for flux and another for concentrations. The concentration connectivity theorems are divided again depending on whether the system species $$ S_n $$ is different from the local species $$ S_m $$.

$$ \sum_i C^J_i \varepsilon^i_S = 0 $$

$$ \sum_i C^{S_n}_i \varepsilon^i_{S_m} = 0 \quad n \neq m $$

$$ \sum_i C^{S_n}_i \varepsilon^i_{S_m} = -1 \quad n = m $$

It is possible to combine the summation with the connectivity theorems to obtain closed expressions that relate the control coefficients to the elasticity coefficients. For example, consider the simplest non-trivial pathway:

$$ X_o \rightarrow S \rightarrow X_1 $$

We assume that $$ X_o $$ and $$ X_1 $$ are fixed boundary species so that the pathway can reach a steady state. Let the first step have a rate $$ v_1 $$ and the second step $$ v_2 $$. Focusing on the flux control coefficients, we can write one summation and one connectivity theorem for this simple pathway:

$$ C^J_{v_1} + C^J_{v_2} = 1 $$

$$ C^J_{v_1} \varepsilon^{v_1}_S + C^J_{v_2} \varepsilon^{v_2}_S = 0 $$

Using these two equations we can solve for the flux control coefficients to yield:

$$ C^J_{v_1} = \frac{\varepsilon^{2}_S}{\varepsilon^{2}_S - \varepsilon^{1}_S} $$

$$ C^J_{v_2} = \frac{-\varepsilon^{1}_S}{\varepsilon^{2}_S - \varepsilon^{1}_S} $$

Using these equations we can look at some simple extreme behaviors. For example, let us assume that the first step is completely insensitive to its product, S, then $$ \varepsilon^{v_1}_S = 0 $$. In this case, the control coefficients reduce to:

$$ C^J_{v_1} = 1 $$

$$ C^J_{v_2} = 0 $$

That is all the control (or sensitivity) is on the first step. This situation represents the classic rate-limiting step that is frequently mentioned in text books. The flux through the pathway is completely dependent on the first step. Under these conditions, no other step in the pathway can affect the flux. The effect is however dependent on the complete insensitivity of the first step to its product. Such a situation is likely to be rare in real pathways. In fact the classic rate limiting step has almost never been observed experimentally. Instead, a range of limitingness is observed, with some steps having higher control than others.