Computational studies on Escherichia coli metabolism : from constitutive bricks to large-scale behaviour

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Publications associées (29)

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In the beginning was the metabolism. The biochemical processes that make life possible transformed the soup of chemicals into the life on Earth we know today. Since then, living organisms have evolved, and life on Earth has become more complex. Living orga ...

EPFL2023

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

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A new and efficient magnetisation transfer P-31 magnetic resonance fingerprinting (MT-P-31-MRF) approach is introduced to measure the creatine kinase metabolic rate kCK

{k}_{\mathrm{CK}}

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Molecular reconstruction and simulation of the Neuron-Glia-Vasculature system

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The outstanding information processing capacity of the brain relies on numerous molecular mechanisms. It is an extremely energy-expensive function, which involves specialization and collaboration of different cell types. A common framework to study the com ...

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We propose an image-based elastography method to measure the heterogeneous stiffness inside a cell and its nucleus. It uses a widely accessible setup consisting of plate compression imaged with fluorescence microscopy. Our framework recovers a spatial map ...

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Industrial Biotechnology Conservation Processes: Similarities with Natural Long-Term Preservation of Biological Organisms

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Cryopreservation and lyophilization processes are widely used for conservation purposes in the pharmaceutical, biotechnological, and food industries or in medical transplantation. Such processes deal with extremely low temperatures (e.g., -196 degrees C) a ...

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Hardware-Software co-design Methodologies for Edge AI Optimization

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ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

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Stochastic adaptive resampling for the estimation of discrete choice models

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In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

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On double-descent in uncertainty quantification in overparametrized models

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Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

PMLR Proceedings of Machine Learning Research2023

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