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|This is the electronic version of an article published in the proceedings of the 8th BTK meeting held in Göteborg in July 1998. The full bibliographic reference to the hard-copy version of this article is:|
|Mendes, P. & Kell, D.B. (1998) Numerical optimisation and simulation for rational metabolic engineering. in BioThermoKinetics in the post genomic era (eds. C. Larsson, I.-L. Påhlman and L. Gustaffson), Chalmers Reproservice, Göteborg, pp. 345-349.|
[ Introduction ] [ Model and Methods ] [ Results and Discussion ] [ Acknowledgement ] [ References ]
Dating from the work of Chakrabarty , there is much current interest in what has become known as Metabolic Engineering (e.g. [2-5]), i.e. in increasing by a directed mechanism (usually by rDNA technology, ) the concentrations or activities of enzymes in a pathway of interest, with a view to increasing the flux towards desirable products (or eliminating undesirable ones). However, if this is to be done rationally, one must have a way of targeting those metabolic steps that are to have the greatest effect on the fluxes of interest. Metabolic control analysis (MCA) seems to be one good candidate for such a purpose [7-12]. Notwithstanding the power and utility of these approaches, however, their take-up amongst biotechnologists has been less than widespread, due arguably to three main factors: i) some of the mathematics underpinning MCA is rather recondite from a biochemical point of view, ii) MCA is exact only for small changes in parameters such as enzyme concentrations (although in favourable cases its approximations for "large" changes may be good, [13, 14]), and iii) it is normally necessary to perform an extensive series of experiments to find out the distribution of the control of flux in a metabolic pathway between different enzymes before knowing which one(s) might be best to clone (see e.g. [15-18]). Here we show that a practical way of overcoming these limitations is to analyse the properties of an appropriate model of the pathway of interest using computer simulation. We have developed [19, 20] and are currently extending a user-friendly metabolic simulator (Gepasi) that is suitable for performing "what if?" experiments in numero prior to undertaking a series of difficult and possibly misguided experiments. The full specification of this software and related material is available over the Internet at http://qbab.aber.ac.uk/softw/gepasi.html. There are numerous uses for a friendly simulator of this type, including those in education. Here we illustrate how numerical optimisation methods, recently introduced in Gepasi, can be used as a tool for rational metabolic engineering through the analysis of the steady-state behaviour of a hypothetical model of a branched pathway.
We analyse the branched pathway depicted in Fig. 1. Our interest is to find conditions for which the flux towards P1 is high. We note that the structure of this pathway and the kinetics of its enzymes are such that it is almost impossible to guess the behaviour of this system by intuition. Apart from the conservation of the cofactor moiety ([A]+[AH]=constant), the first enzyme on the branch of interest is substrate-inhibited and the AH form of the cofactor is a modifier of the first enzyme of the other branch (following the Botts-Morales mechanism , AH can be either an activator or inhibitor depending on the values of the kinetic constants).
Figure 1 - Scheme of pathway analysed in the text. Arrows represent the enzymatic steps and point to the forward direction of flux, all reactions are chemically reversible. Dotted arrows represent regulatory effects, the symbol +/- indicates that the effect can be either of activation or inhibition. E1, E5 and E6 follow reversible Michaelian kinetics; E2, E4 and E7 follow ordered Bi Bi kinetics in which the cofactor is always the first to bind and the last to be released (this mechanism has 10 kinetic parameters); E3 is inhibited by its own substrate (i.e. M2 binds both the active and an inhibitory sites) and E6 follows the general Botts-Morales modifier-type kinetics, in which the modifier can be an inhibitor or activator depending on the values of the kinetic parameters (7 in total). We took the reference state to have all kinetic constants, enzyme concentrations and total concentration of the cofactor ([A]+[AH]) equal to 1. The equilibrium constants of all steps are also 1, and are not changed in any simulation. All values are in arbitrary units.
For this study we assume that we possess the technology to change enzyme concentrations (by cloning) and to do site-directed mutagenesis on the enzymes so that we could manipulate their kinetic parameters. The problem is what parameters to manipulate to obtain i) high flux to P1 with low flux to P2 and ii) high flux to P1 without affecting the flux to P2. In both cases we limit the adjustable parameters by specifying upper and lower boundaries, in addition ii) is also constrained by a fixed value of the flux towards P2. We defined the pathway in Gepasi, attributed arbitrary numerical values to the various parameters, simulated its steady state and took this as the reference state (our wild type strain). Then we selected 35 parameters to be potential targets for manipulation: all the enzyme concentrations, the total concentration of the cofactor plus 28 kinetic parameters (all those for the ordered Bi Bi enzymes, and all those related to the action of M2 as inhibitor of E3 and AH as modifier of E6. We allow these parameters to be adjusted to values within 3 orders of magnitude on either side of the "wild type" value.
In the most recent version of Gepasi we have devised an interface that allows the program to combine numerical optimisation algorithms with steady state or time course simulations. Apart from applications of the type described here, this also allows the program to carry out robust parameter estimation (fitting). This combination of optimisation and simulation has been implemented in such a way that many different optimisation algorithms can be used to attempt to solve a problem. This is an advantage as in numerical optimisation there is no single algorithm that is best for all kinds of problems . The ability to apply several of these is then almost a necessity for any moderately complex problem. In particular when the number of adjustable parameters is large (e.g. more than 10) or when the property to optimise is non-linear there is the high probability that there will be many local optima. For the pathway of Fig. 1 we have applied the following optimisation methods: random search, the Hooke and Jeeves direct search , steepest descent, a limited memory bounds-constrained quasi-Newton method (L-BFGS-B) , and a genetic algorithm [25-27].
We want to maximise the steady-state flux of step 5 (J5) subject only to bound constraints on the adjustable parameters. Using the new version of Gepasi we applied 5 different optimisation algorithms to this problem. Analysis of the results summarised in Table 1 reveals that the best estimate for the maximum (J5=294.24) is 4 orders of magnitude higher than the "wild type" and it was obtained with the direct search method of Hooke and Jeeves. This is a local optimisation method that does not require information from the gradient of the objective function; rather at each iteration it generates a new search direction using a heuristic based on the results of a few previous iterations. The second best solution was obtained with the genetic algorithm, with a value of J5 only 2% lower than the best estimate. This method required only 10% the number of simulations carried out by the Hooke and Jeeves method. The two gradient descent methods (steepest descent and L-BFGS-B) performed very badly (albeit still ten-fold higher than the "wild type").
Table 1 - Performance of several optimisation methods for the maximisation of the flux to P1 (J5) with simple bound constraints on the adjustable parameters. The wild type entry refers to the initial state before applying the optimisation. The last column lists the number of simulations that the optimisation method required to reach the solution.
|Hooke & Jeeves||294.24||1.3310 x10-5||0.60946||154195|
Comparing the parameter values of the "wild type" with our best solution we conclude that the optimal value of J5 was obtained by setting the concentration of all enzymes to their upper boundary value (1000) except E6 which was set to its lower boundary value (0.1). Interestingly this results in the finding that the only enzyme of the branch to P2 to have control over J8 is E6 (with a unity flux-control coefficient). However E1-E5 also control J8 but their flux-control coefficients cancel each other. If one were to engineer this pathway to increase the flux by 5 orders of magnitude for the same concentration of substrate one would have to: i) over-express all enzymes except E6 which must be under-expressed, ii) engineer all enzymes such that they become as sensitive as possible to their substrates and products (i.e. Km as low as possible), iii) engineer the three Bi Bi enzymes such that their inhibition constants of the second substrate and the first product are increased 1000-fold, but the one of the first substrate only 100-fold iv) reduce the strength of inhibition of E3 by its own substrate, v) increase the sensitivity of E6 towards its modifier (AH) and in such a way that it becomes an inhibitor.
Table 2 - Performance of several optimisation methods for the maximisation of the flux to P1 (J5) subject to the constraint of flux towards P2 to be within 5% of the wild type value.
More interesting, from a biological point of view, is the case where we want to maximise the flux towards P1 while keeping the competing flux to P2 essentially unchanged. In this case we have a constraint on one of the variables of the model and unfortunately only a couple of the optimisation methods can deal with this (random search and genetic algorithms). To implement this constraint we have instructed Gepasi only to allow solutions for which 0.032<J8<0.034 (roughly the "wild type" value ± 5%). Our best estimate for the maximum is 3 orders of magnitude higher (but this may actually be far from the true maximum) which is a considerable improvement. The genetic algorithm performed much better than the random search, as we anticipated. Interestingly, for this constrained solution the concentration of both E6 and E8 is reduced but not that of E7. The latter has a substrate and product common to the flux being maximised.
Optimisation of biochemical models is inherently a hard process due to the high dimensionality of the search space. If one were to examine the model above with a brute-force method (that examines all combinations of n values of each parameter) one would have to execute n35 simulations. It is obvious that such a solution is prohibitive, even on the fastest computers and for small n. Additionally, in such a high dimensional search space there are almost certainly numerous local optima, due to the high non-linearity of some of the kinetic functions. Even if multiple optima are not present, there will be large flat regions of parameter space where methods based on gradients will halt. We have reasons to believe that this is what happened with the gradient descent methods in our first example (see Table 1).
We hope that the by making available our software, biochemists who do not necessarily wish to develop this level of expertise in numerical analysis and computer programming can still benefit from the use of simulation-optimisation strategies.
We are indebted to the UK BBSRC for financial support.
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Copyright © 1998 Pedro Mendes & Douglas B. Kell