Metabolic Perceptrons for Neural Computing in Biological Systems

Tremendous progress has recently been made in machine learning including deep learning such that machine learning is now used in many tasks that affect our daily life and is currently making strives regarding our health. Yet, little has been done to interface machine learning with biological systems at the molecular level, this is noteworthy considering that deep learning methods are inspired by the neural network of our brain. Using artificial neural network for diagnosis has been undertaken for many diseases, however, biomarker concentrations are needed to both train the network and use it for diagnosis. Taking raw clinical samples as network input and producing an output that can directly be measured is required to construct the network in a biological system. A research team led by Prof. Faulon (SYNBIOCHEM center) has just taken a first step in this direction by engineering a simple neural network in an E. coli cell-free extract1. The network classifies samples having different metabolic compositions, is trained in silico and actuated in a cell extract through the modulation of enzyme concentrations. In a second study2, Faulon’s team partnered with J. Bonnet’s group at INSERM (France) and performed validation tests on human urine for metabolites detection. Read more