Commercial feasibility of a cell-free reactor setup for optimisation of complex enzymatic pathways

Informations projet

OptiPlex

N° de convention de subvention: 101069237

DOI

10.3030/101069237

Projet clôturé

Date de signature de la CE 28 Avril 2022

Date de début 1 Juillet 2022

Date de fin 31 Decembre 2023

Financé au titre de

• European Research Council (ERC)

Coût total

Aucune donnée

Contribution de l’UE

€ 150 000,00

Coordonné par

STICHTING RADBOUD UNIVERSITEIT
Netherlands

Periodic Reporting for period 1 - OptiPlex (Commercial feasibility of a cell-free reactor setup for optimisation of complex enzymatic pathways)

Période du rapport: 2022-07-01 au 2023-12-31

Résumé du contexte et des objectifs généraux du projet

Living cells rely on enzymatic reaction networks (ERNs) to produce energy and building blocks to support cellular processes. Evolution has shaped these ERNs into interconnected sub-pathways to generate multiple outputs from multiple inputs, driving product formation across complex kinetic landscapes. Recently, significant progress has been made in reconstituting ERNs in vitro with the aim to produce value-added chemicals from sustainable substrates as an advanced biotechnology. However, most of these networks typically do not feature interconnected sub-pathways to simultaneously generate multiple outputs. Controlling such networks remains challenging due to the lack of sufficiently informative experimental datasets that can be utilized to train kinetic models which trace the dynamic properties of large ERNs and enable on-demand design.
In this project, we will develop an active learning-based pipeline in combination with microfluidic reactors, to obtain maximally informative datasets and fully map the kinetics of complex reaction networks

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

Kinetic modelling of in vitro enzymatic reaction networks is vital for the future development of enzymatic reaction networks for cell-free synthesis of valuable organic compounds. However, modelling is severely hampered by the lack of training data.We have developed a wokflow that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designed a sequence of out-of-equilibrium perturbations to maximise the information about the reaction kinetics, yielding a descriptive model that allowed control of the output of the network towards any cost function. We experimentally validated the model using a small enzymatic network based on the nucleotide salvage pathway and consisting of 6 enzymes, by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

The optimal experimental design approach has been validated using laboratory experiments and can be scaled to larger reaction networks.
The work has been accepted for publication in Nature Communications.
Further uptake and success is not determined by the technical or scientific quality of our results, but by a lack of a market that currently needs our method. We have discussed results with a number of companies working with enzymes in an industrial setting. We learned that immobilizing enzymes is not the limiting factor, as often only 1 or a few enzymes are immobilized. Kinetics are important, but only a single objective - maximum yield - is typically taken into account. For this, our OED approach is not so relevant, as this objective can be reached using more traditional methods