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Machine learning for integrated multi-parametric enzyme and bioprocess design

Project description

AI-driven multi-parametric enzyme and bioprocess design

Biocatalysis, the use of enzymes for chemical transformations, offers an alternative for the sustainable manufacturing of drugs, chemicals and materials. Thanks to directed evolution, enzymes can be tailored with improved functions. However, bioprocess scalability is limited by a poor understanding of enzyme function and by the so-called ‘valley of death’ or funding gaps between basic and applied research. With the support of the Marie Skłodowska-Curie Actions programme, the ELEGANCE project aims to establish an academic training programme based on experimental and computational methodologies, such as data science and AI, to engineer integrated multi-parametric enzymes and bioprocesses. Its key objective is to prepare the next-generation specialists with the necessary hard and soft skills to advance data-driven biocatalysis and accelerate bioprocess development.

Objective

Currently, our world is facing several global challenges like climate change, water pollution, land erosion, resource depletion and unsustainable manufacturing. Biotechnology can provide many solutions to these problems. One example is the development of sustainable bioprocesses, which is at the heart of the EU’s green deal. Enzymes are nature’s catalysts at the core of such transformation since they can catalyse complex biocatalytic reactions. Biocatalysis has been slowly but gradually displacing traditional organic chemistry in several processes by virtue of its better efficacy, with the added value of being less wasteful as evidenced by the cornucopia of EPA Green Chemistry Awards conferred to enzymatic processes. Thanks to protein directed evolution (Nobel Prize 2018), enzyme function can be tailored to new-to-nature industrial conditions within bioreactors. However, there are still many challenges limiting bioprocess scalability, owing to a lack of deep understanding of enzyme function and of efficient strategies to overcome the complex multi-factorial optimisation problem of both enzymes and bioprocesses. To address this gap, also known as the valley of death (i.e. a large investment gap between fundamental and applied research), ELEGANCE will offer a unique educational program combining state-of-the-art experimental and computational approaches including data science and artificial intelligence to design better enzymes and bioprocesses faster. The main goal is to train a new generation of specialists with essential hard and soft skills for transforming biocatalysis into a data-driven discipline to accelerate bioprocess development, thereby anticipating potential challenges arising in the valley of death. ELEGANCE will focus on enzymes catalysing highly selective oxyfunctionalisation reactions, unattainable through existing chemical technology, with ecological and economic attractiveness to enable the transition to a circular bioeconomy in many industries.

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HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-DN-01

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Coordinator

DANMARKS TEKNISKE UNIVERSITET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 671 780,16
Address
ANKER ENGELUNDS VEJ 101
2800 KONGENS LYNGBY
Denmark

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Region
Danmark Hovedstaden Københavns omegn
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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Participants (12)

Partners (15)

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