Despite the growing number of chemicals successfully engineered in host organisms, bioproduction R&D is slow and expensive, as the process is mostly based on trial-and-error. To overcome this critical hindrance, we propose to implement a generic automated design-build-test and learn cyclic pipeline for the production of targeted chemicals. As an illustration, we will apply the pipeline for the metabolic engineering of a library of new antimicrobials against Gram-positive bacteria. The pipeline comprises state-of-the-art bioproduction pathway design tools, robotized strain engineering, and high throughput product quantification via biosensors. The whole process is driven by an original computational machine learning component that determines the next set of constructions that needs to be processed by the pipeline with the goal of increasing product yield. In the specific approach we will be using, named active learning, a growing training set of experimental results is acquired on the fly in an iterative process between learning and measurements. The remarkable advantage of active learning is to yield performances comparable to classical machine learning with training sets sizes that can be several orders of magnitude smaller. Active learning can thus drastically reduce the cost of performing measurements, and in the present application significantly reduce the number of iterations for strain optimization. We propose to apply the pipeline for the production of nutritional and antimicrobial flavonoids. Precisely, the pipeline will be run for four research objectives that complement each other: (RO1) to learn enzyme sequences that maximize flavonoid titers, (RO2) to determine enzyme expression levels limiting intermediates accumulation and increasing final product yields, (RO3) to regulate the expression of the genes of the host strain to optimize both growth and flavonoid titers, and (RO4) to produce novel flavonoid structures with maximal toxicity against Gram-positive bacteria. While moving toward optimizing strains and producing novel flavonoids, our project will offer a technological rupture to industrial biotechnology where machine learning is driving experimental implementation and measurement. We anticipate this innovative solution will bring tremendous gains in throughput and speed. The project will be illustrated with the production of a library of flavonoids, but the design-build-test-learn pipeline is general enough to be applied to other molecules of interest to the health, food, chemistry and energy industrial sectors, including commodity chemicals, and fine and specialty chemicals. Our approach could for instance be extended to other pharmaceutical applications beyond the search for antimicrobial activity, as long as there exists a screening method relevant to the problem. Beyond small molecule bioproduction a similar pipeline could also be implemented to metabolize alternative but commercially attractive feedstock and to develop biosensors for environmental pollutants. The expertise gained in the project will drastically improve our SME partner strain development platform and in return the SME partner will bring the technology to the market seeking for industrial collaborations through a specific exploitation task. While we plan to release our computational methods to the academic community through web services, for specific applications, our know-how and software products will be packaged in an integrated pipeline and commercialized as a service. We foresee large industrial groups will want to customize development of the pipeline for their own application. The service we will provide to the industry will generate revenues and will also be a source for job creation.

EDMs, i.e. electric dipole moments of electrons, neutrons or nuclei are sensitive probes for new physics beyond the Standard Model of particle physics. In the present project, we propose to measure the EDM of those systems embedded in a cryogenic solid matrix of inert gas or hydrogen. Matrices offer unprecedented sample sizes while maintaining characteristics of an atomic physics experiment, such as the possibility of manipulation by lasers. An EDM experiment on molecules in inert gas matrices has the potential to reach a statistical sensitivity of the order of 1e–36 e cm; a value beyond that of any other proposed technique. With this project, in a strong collaboration between experimental (LAC, ISMO,LPL) and theoretical (CIMAP) groups, we first aim at performing a detailed investigation of all limiting effects (mainly the ones limiting the optical pumping performance and coherence time) using Cs atoms. This should provide a first proof of principle EDM measurement and set the ground for precise study of systematic effects which will allow EDMMA to reach unprecedented precision

Tool use behaviors have evolved not only in primates but also in some birds (e.g. parrots and crows) and even in some teleost fish (e.g. wrasses and cichlids). As birds and teleosts do not have a six-layered neocortex, these similar cognitive functions must have evolved independently in the lineages of mammals, birds, and teleosts (convergent evolution). Our research project aims to identify the necessary conditions for the emergence of such cognitive abilities during vertebrate evolution. The ability for problem solving and sequential object manipulation is the prerequisite condition for evolving tool use ability. The EVONECTOME project investigates the connectivity (TASK 1) and functions (TASKs 2-4) of associative brain areas involved in a problem solving task and sequential object manipulation in parrots and cichlids. We propose to use Pyrrhura molinae as a parrot model and Amatitlania nigrofasciata as a cichlid model, which are optimal for combining anatomical and behavioral examinations. TASK 1: We will take a connectomic approach using ex-vivo diffusion MRI to visualize neuronal networks with regard to putative associative areas in the pallium. The main goal is to reveal the connectivity of the associative areas, notably with premotor/motor areas. The results will verify whether there is a shared network logic for cognitive-motor integration in primates, parrots, and cichlids. TASK 2: To examine problem solving and object manipulation abilities of parrots and cichlids, we will perform two different behavioral tasks: a puzzle box task and operant conditioning tasks that require object manipulation as responses. After the behavioral tests, the brain areas involved in these behaviors will be examined in TASK 3 and TASK 4. TASK 3: Neuronal activation during the behavioral tasks will be visualized by functional MRI, using Manganese-enhanced MRI (MEMRI). By detecting large-scale brain activity during the behavioral tasks (TASK 2), we expect to identify brain areas involved in problem solving and object manipulation. TASK 4: To assess the role of dopamine (DA) neurotransmission in the behavioral tests, we will destruct DA fibers in the associative areas by a local injection of 6-hydroxydopamine (6-OHDA). DA is known to play a critical role in various higher-order cognitive functions in mammals. If DAergic disruption in non-homologous brain areas leads to the same behavioral effects in mammals, birds, and teleosts, this will further support the importance of DA in the convergent evolution of higher-order cognitive functions. Altogether, our study will give an insight into how morphologically diversified nervous systems achieved similar cognitive functions during evolution. If the same network pattern emerged independently in primates, parrots, and cichlids, this would indicate the existence of a limited degree of biological freedom (high constraints) for the evolution of intelligence in vertebrate brains.

A human cell, aside from nuclear DNA, contains thousands of copies of mitochondrial DNA (mtDNA), a double-stranded, circular molecule of 16,569 bp. It has been proposed that mtDNA is a critical target of reactive oxygen species: by-products of oxidative phosphorylation that are generated in the organelle during aerobic respiration. Indeed, oxidative damage to mtDNA are more extensive and persistent as compared to that to nuclear DNA. Although transversions are the hallmark of mutations induced by reactive oxygen species, paradoxically, the majority of mtDNA mutations that occur during ageing and cancer are transitions. Furthermore, these mutations show a striking strand orientation bias: T?C/G?A transitions preferentially occur on the light (L) strand, whereas C?T/A?G on the heavy (H) strand of mtDNA. Before, to explain the unusual pattern of somatic mutations in mtDNA, we proposed that the majority of mtDNA progenies, created after multiple rounds of replication, are derived from the H-strand only, owing to asymmetric replication of only one DNA strand. Earlier, the classical Meselson–Stahl experiment (1958) employed stable isotope labelling of bacterial chromosome to address fundamental question on the mode of DNA replication in cellular organisms. Here we propose to use this classical approach to investigate mtDNA replication. First, DNA will be labeled with stable isotope nitrogen-15 (15N) by growing human cells in 15N rich-media. Then cells will be transferred to normal 14N media and the 15N/14N ratio in H- and L-strand of mtDNA will be measured at different time points. Comparison of the heavy-to-light isotope ratio in L- and H-strand over time of growth will allow us to verify if the isotope ratio in H-strand would decrease at slower rate as compared to that in L-strand. The data obtained in this project will help to discriminate between symmetric and asymmetric DNA strand inheritance and bring insight into uncommon pattern of somatic mutations in human mtDNA

Statistical physics, a century-old theory, is probably one of the most powerful constructions of physics. It predicts that the equilibrium properties of any system composed of a large number of particles depend only on a handful of macroscopic parameters, no matter what the particles are and how they exactly interact with each other. But the question of how many-body systems relax towards such equilibrium states remains largely unsolved. This problem is especially acute for quantum systems, which evolve in a much larger configuration space than classical systems and obey fundamentally non-local equations of motion. Despite the formidable complexity of quantum dynamics, recent theoretical advances have put forward a remarkably simple picture: the dynamics of closed quantum many-body systems would be essentially local, meaning that it would always take a finite time for correlations between two distant regions of space to reach their equilibrium value. This form of locality would be an emergent property of the whole system, similar to spontaneous symmetry breaking, and have its origin in the propagation of quasi-particle excitations. The fact is, however, that only few observations to date directly confirm this scenario. In particular, the role played by the dimensionality of the system and the range of the inter-particle interaction is largely unknown. The concept of this project is to take advantage of the great versatility of ultra-cold atom systems to investigate experimentally the relaxation dynamics in regimes well beyond the boundaries of our current knowledge. We will focus our attention on two-dimensional systems in which the particles interact through both short- and long-range potentials, when all previous experiments were bound to one-dimensional systems. The realisation of the project will hinge on the construction of a new-generation quantum gas microscope experiment for strontium gases. Amongst the innovative experimental techniques that we will develop for this project is the electronic state hybridisation with Rydberg states, called Rydberg dressing, which will enable us to shape the interaction potential between the atoms.