Enabling the discovery of computational characteristics of enzyme dynamics

Biology demonstrates powerful information processing capabilities. Of particular interest are enzymes, which process information in highly complex dynamic environments. Exploring the information processing characteristics of an enzyme by selectively altering its environment may lead to the discovery of new modes of computation. The physical experiments required to perform such exploration are combinatorial in nature. Thus resource consumption, both time and money, poses major limiting factors on any exploratory work. New tools are required to mitigate these factors. One such tool is lab-on-chip based autonomous experimentation system, where a microfluidic experimentation platform is driven by machine learning algorithms. The lab-on-chip approach provides an automated platform that can perform complex protocols, which is also capable of reducing the resource cost of experimentation. The machine learning algorithms provide intelligent experiment selection that reduces the number of experiments required for discovery. Here we discuss development of the experimentation platform and machine learning software that will lead to fully autonomous characterisation of enzymes.

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