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Showing 1–9 of 9 results
Advanced filters: Author: Jean-Loup Faulon Clear advanced filters

  • Cell-free lysates are a major platform for in vitro protein production but batch-to-batch variation makes production difficult to predict. Here the authors develop an active learning approach to optimising buffer conditions to bring homemade lysates up to commercial production potential.

    • Olivier Borkowski
    • Mathilde Koch
    • Jean-Loup Faulon
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-8

  • Automated design and build processes can rapidly accelerate work in synthetic biology and metabolic engineering. Here the authors present Galaxy-SynBioCAD, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology that they use to build and execute Galaxy scientific workflows from pathway design to strain engineering through the automated generation of scripts driving robotic workstations.

    • Joan Hérisson
    • Thomas Duigou
    • Jean-Loup Faulon
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-12

  • So far, synthetic genetic circuits have relied on digital logic for information processing. Here the authors present metabolic perceptrons that use analog weighted adders to vary the contributions of multiple inputs, resulting in different classification functions.

    • Amir Pandi
    • Mathilde Koch
    • Jean-Loup Faulon
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-13

  • Mechanistic models estimate the phenotype of microorganisms in different environments but may have limited predictive capabilities. Here, authors develop trainable hybrid models with improved predictability using mechanistic insights and smaller training sets than conventional machine learning techniques.

    • Léon Faure
    • Bastien Mollet
    • Jean-Loup Faulon
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-14

  • The increasing availability of data related to genes, proteins and their modulation by small molecules has provided a vast amount of biological information leading to the emergence of systems biology and the broad use of simulation tools for data analysis. However, there is a critical need to develop cheminformatics tools that can integrate chemical knowledge with these biological databases and simulation approaches, with the goal of creating systems chemical biology.

    • Tudor I Oprea
    • Alexander Tropsha
    • Mark D Rintoul
    Comments & Opinion
    Nature Chemical Biology
    Volume: 3, P: 447-450

  • Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.

    • Amir Pandi
    • Christoph Diehl
    • Tobias J. Erb
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-15

  • Pablo Carbonell et al. present an automated pipeline for the discovery and optimization of biosynthetic pathways for microbial production of fine chemicals. They apply their pipeline to the production of the flavonoid (2S)-pinocembrin in Escherichia coli and show improvement of the pathway by 500-fold.

    • Pablo Carbonell
    • Adrian J. Jervis
    • Nigel S. Scrutton
    ResearchOpen Access
    Communications Biology
    Volume: 1, P: 1-10