By IANS,
Washington : Cell metabolisms have evolved to handle potentially harmful changes like gene deletions and mutations, according to evidence unearthed by Northwestern University team.
The research was led by Julio M. Ottino, dean of Northwestern University McCormick School of Engineering and Applied Science and Walter P. Murphy, professor of chemical and biological engineering.
Cell metabolism is essentially a large network of reactions whose purpose is to convert nutrients into products and energy. Because the network is highly interconnected, it is possible for a single reaction failure (which may be precipitated by a gene deletion or mutation) to initiate a cascade that affects several other reactions in the system.
This event could be likened to disturbing a small area of snow that may trigger a large avalanche or the failure of a single transmission line in an electric power grid that may trigger a widespread blackout.
By measuring the size of these “cascade” events in simulated metabolic networks, Northwestern researchers were able to develop a quantitative measure of metabolic robustness: the more robust the network, the less the probability that small disturbances produce large cascades.
They found that the likelihood of large failure cascades in a metabolic network is unusually small, compared to what they would expect from comparable, randomly structured networks.
In other words, these metabolic networks have evolved to be exceptionally robust, adopting organisational structures that help minimise the potentially harmful impacts of gene deletions and mutations.
The results could be useful in areas like bioengineering and medicine, and in the design of robust synthetic networks for use in energy production and distribution networks and in critical infrastructures, such as transportation networks.
The Northwestern team concluded it is possible that nature, in this case, is the best teacher: improved understanding of how cell metabolisms have evolved to handle failure cascades may provide clues as to how one might design synthetic networks for similar robustness.
These findings were published online this week in PNAS.