A team of researchers at Carnegie Mellon University’s (CMU) Machine Learning Department is working on a system to protect computer networks from security assaults using a yeast cell’s protection system against environmental threats as a basis.
Ziv Bar-Joseph, an associate professor in CMU’s Machine Learning Department and the Ray and Stephanie Lane Center for Computational Biology, points the role of “external noise” in biology as an insight of use in understanding how computational networks function.
A yeast cell has about 1200 “essential” genes, that means that if one or more of those particular genes are removed, the cell will die. However, this doesn’t frequently happen in nature, as they are protected by other genes lying nearer the cell surface that have evolved to withstand varying levels of stress, otherwise known as environmental “noise”. The research suggests that the topology of molecular sub networks or modules is “tightly linked” to the level of noise the module expects to encounter. Those less exposed are more vulnerable than those more exposed.
Bar-Joseph writes: “Over the last few years we have [begun] to witness a change in how biologically inspired computational methods are derived and studied. A number of recent bi-directional studies, by us and others, have demonstrated that thinking computationally about the settings, requirements and goals of information processing in biological networks can both improve our understanding of the underlying biology and lead to the development of novel computational methods providing solutions to decades-old problems.”
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