Nonlinear decision-making with enzymatic neural networks

Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks^1,2. Non-enzymatic networks could in principle support neuromorphic architectures, and seminal proofs-of-principle have been reported^3,4. However, leakages (that is, the unwanted release of species), as well as issues with sensitivity, speed, preparation and the lack of strong nonlinear responses, make the composition of layers delicate, and molecular classifications equivalent to a multilayer neural network remain elusive (for example, the partitioning of a concentration space into regions that cannot be linearly separated). Here we introduce DNA-encoded enzymatic neurons with tuneable weights and biases, and which are assembled in multilayer architectures to classify nonlinearly separable regions. We first leverage the sharp decision margin of a neuron to compute various majority functions on 10 bits. We then compose neurons into a two-layer network and synthetize a parametric family of rectangular functions on a microRNA input. Finally, we connect neural and logical computations into a hybrid circuit that recursively partitions a concentration plane according to a decision tree in cell-sized droplets. This computational power and extreme miniaturization open avenues to query and manage molecular systems with complex contents, such as liquid biopsies or DNA databases.

[Role of dietary fibers in the nutritional management of chronic renal failure]

During the past few decades, considerable attention has been given to the impact of nutrition on kidney disease. Although most dietary attempts to treat chronic renal failure (CRF) and to decrease uremia recommend a protein restriction, another dietetic approach, based on dietary fibers (DF), can lead to the same urea-lowering effect by increasing urea-nitrogen (N) excretion in stool with a concomitant decrease of the total N quantity excreted in urine. In fact, feeding DF results in a greater rate of urea N transfer from blood to large bowel, where it will be hydrolyzed by bacterial ureases before subsequent microflora metabolism and proliferation. Because elevated concentration of serum urea N have been associated with adverse clinical symptoms of CRF, these results suggested a possible usefulness of combining DF with a low protein diet to increase N excretion via the fecal route. These results have been shown in animal models of experimental renal failure and in CRF patients. Further investigations in this population of patients are currently in progress to study whether DF may be beneficial on CRF progression and on CRF terminal stage tolerance. A part of this work is financed by the French Society of Nephrology.