A new way to model metabolism
Metabolic networks are designed based on complex genetic analysis and research. These networks provide a mathematical model of chemical reactions in living organisms, and are used by researchers to design microorganisms, which are utilized in manufacturing processes or in the study of disease.
However, a new analytical tool that has been developed by a team at Massachusetts Institute of Technology (MA, USA) suggests that many existing metabolic models may be wrong. Fortunately, their new method of analysis proposes a fairly simple solution to this issue.
Bonnie Berger, a professor at Massachusetts Institute of Technology and one of the tool’s developers, and her colleagues investigated metabolic models in a database at the University of California at San Diego (CA, USA). “It turns out that many of them were computed with floating-point arithmetic,” Berger commented.
This is a tool utilized by computer systems to increase efficiency, but as the team discovered, it is not exact enough to use in metabolic model design. “When we computed them in exact arithmetic,” elaborated Berger, “we found that many of the models that were believed to be realistic don’t produce any growth under any circumstances.”
Floating-point arithmetic works by representing numbers as a decimal multiplied by a base, raised to a particular power. Although when compared with exact arithmetic it sacrifices some accuracy, it is generally useful because of the time saved. When they determined that this method was not useful, Berger and Leonid Chindelevitch (Harvard School of Public Health, MA, USA), who was the first author on the paper describing the new tool, were faced with the challenge of developing a system for performing a precise exact-arithmetic analysis on a huge and complex metabolic network.
Metabolic networks represent every sequence of chemical reactions catalyzed by an organism’s DNA-coded enzymes. Every node of the network is representative of an intermediary stage in a chain of reactions, leading from nutrients to chemical products.
In order to simplify these networks sufficiently to enable exact arithmetical analysis, Chindelevitch and Berger developed a novel algorithm. This algorithm functions by first deleting all sequences of reactions that cannot, for whatever reason, function within the context of the model. It then identifies groups of reactions that always occur together, effectively performing a single reaction, and treats them as a single event in the model.
Critically, the team was able to mathematically prove that these simplifications would not affect the outcome of the analysis. Chindelevitch explained: “What the exact-arithmetic approach allows you to do is respect the key assumption of the model, which is that at steady state every metabolite is neither produced in excess nor depleted in excess. The production balances the consumption for every substance.”
Using this new method it was discovered that of 89 metabolic-network models in the San Diego database, 44 contained errors or omissions, with the possibility that the organism modeled would be unable to grow.
Chindelevitch explained the team’s future plans: “We’re hoping that this work will provide an impetus to reanalyze a lot of the existing metabolic-network model reconstructions and hopefully spur some collaborations where we actually perform this analysis and suggest corrections to the model before it is published.”
Source: Getting metabolism right; Chindelevitch L, Trigg J, Regev A, Berger B. An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models. Nat. Commun., 5,4893, doi:10.1038/ncomms5893 (2014)
