A drug hits a cell, but finding exactly which metabolic junction it targets is often guesswork. This project aims to turn that guesswork into a precise calculation. The problem is that most biological networks—such as those controlling gene activity—are poorly mapped, so observed changes can have many explanations. Metabolic networks, however, are well understood: we know the structure and the basic rules of how molecules are transformed inside a cell. The researchers will exploit this known architecture to pinpoint where a drug-like molecule interferes. Using baker’s yeast as a testbed, they will develop numerical methods to estimate the parameters of the network equations, then apply the same approach to *Corynebacterium jeikeium*, a bacterium of interest to their industrial partner Unilever. If successful, the work will produce a suite of computational tools that can infer the site of action of any drug-like compound in any reasonably mapped metabolic network. This is fundamental science with a clear practical horizon: it could accelerate the design of antimicrobials or metabolic engineering in bacteria, reducing the trial-and-error that currently slows drug development. For now, the immediate impact is a proof-of-concept that metabolic networks can serve as a target-finding scaffold.
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There are many occasions where one may wish to know the site of interaction of a drug or other substance with a complex biological system (i.e. network), typically by detecting changes something that can be measured. These are usually hard problems, since there are many ways of explaining the changes if one does not in fact know the network. However, in contrast to biological signalling and gene regulatory networks, we normally DO know the structure and outline properties of METABOLIC networks. This makes it MUCH easier to determine the parts of the network changes might have been focussed. Initially using baker's yeast as a model organism, we wish to demonstrate that this strategy does indeed work. It is necessary to make simple assumptions about the general form of the the equations describing the interactions within these networks, and we shall develop and exploit modern numerical methods for parameter estimation. As stated, we shall initially develop and test these strategies in baker's yeast, Saccharomyces cerevisiae, since this is a well understood organism. However, our collaborative partner Unilever are extremely interested in Corynebacterium jeikeium, for which a genome sequence and network model exist, and using resources made available by them for this project we shall also exploit these methods in the analysis of metabolic fluxes in this organism. The deliverable will be a suite of novel methods with which to infer the site of action of any drug-like molecule in a reasonably well understood metabolic network.
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