Dual pathway for metabolic engineering of Escherichia coli to produce the highly valuable hydroxytyrosol.

One of the most abundant phenolic compounds traced in olive tissues is hydroxytyrosol (HT), a molecule that has been attributed with a pile of beneficial effects, well documented by many epidemiological studies and thus adding value to products containing it. Strong antioxidant capacity and protection from cancer are only some of its exceptional features making it ideal as a potential supplement or preservative to be employed in the nutraceutical, agrochemical, cosmeceutical, and food industry. The HT biosynthetic pathway in plants (e.g. olive fruit tissues) is not well apprehended yet. In this contribution we employed a metabolic engineering strategy by constructing a dual pathway introduced in Escherichia coli and proofing its significant functionality leading it to produce HT. Our primary target was to investigate whether such a metabolic engineering approach could benefit the metabolic flow of tyrosine introduced to the conceived dual pathway, leading to the maximalization of the HT productivity. Various gene combinations derived from plants or bacteria were used to form a newly inspired, artificial biosynthetic dual pathway managing to redirect the carbon flow towards the production of HT directly from glucose. Various biosynthetic bottlenecks faced due to feaB gene function, resolved through the overexpression of a functional aldehyde reductase. Currently, we have achieved equimolar concentration of HT to tyrosine as precursor when overproduced straight from glucose, reaching the level of 1.76 mM (270.8 mg/L) analyzed by LC-HRMS. This work realizes the existing bottlenecks of the metabolic engineering process that was dependent on the utilized host strain, growth medium as well as to other factors studied in this work

Spiking Neuron Hardware-Level Fault Modeling

International audienceThe deployment of Artificial Intelligence (AI) hardware accelerators in a variety of applications, including safety-critical ones, requires assessing their inherent reliability to hardware-level faults and developing cost-effective fault tolerance techniques. This entails performing large-scale fault simulation experiments. However, transistor-level fault simulation is prohibitive and fault simulation should be carried out at a higher abstraction level. In this work, we focus on spiking neural networks (SNNs), and we follow a bottom-up approach starting from transistor-level simulations for developing a neuron behavioral-level fault model that can be readily employed for performing behavioral-level fault simulation of deep SNNs