MuSiHC

Winner of the 2024 call for projects.

The project MUlti-SIze Hybrid Cell Models (MuSiHC) aims at developing novel hybrid approaches to cell and bioreactor modeling for the production of added-value compounds. MuSiHC addresses gaps in current cell models used to simulate bioproduction:

• Dichotomy between linear genome-scale and small kinetic models with no realistic intermediate solutions that describe metabolism precisely.
• Division between interpretable mechanistic models with a massive numerical effort, vs efficient but black-box Artificial Intelligence (AI)/Machine Learning (ML) models.

As a proof of concept, the project will focus on Escherichia coli as a platform for the bioproduction of 1,3-propanediol (1,3-POD), a high-value compound with vast applications in the chemical industry, ranging from solvents to antifreeze.

Action plan:

The project will develop a toolkit of hybrid models, combining mechanistic description and AI/ML of different size, to obtain more reliable cell and bioreactor simulations for E. coli producing 1,3-POD.

Models at different levels of detail will be connected with the help of ML:

• To model metabolism at a genome scale, Artificial Metabolic Network (AMN) models will be adapted and trained on experimental data for simulating E. coli as a platform for bioproduction. The aim is a genome scale neural-mechanistic hybrid model, to accurately predict production rates for different media and gene deletion sets. Using the novel AMNs, it will be possible to predict both heterologous pathways and gene deletions, and validate the predictions with batch cultures.
• In parallel, medium-scale kinetic models will be analyzed with ML (white-box and black-box) to better understand the usage of different Elementary Flux Modes (EFMs) by cells, depending on external conditions (composition of the growth medium). We will obtain effective rules describing choices of metabolic strategies, including bioproduct production, depending on the environment, which can inform the construction of simple, yet realistic cell models. The latter will be used within bioreactor models, and experiments at the lab scale will validate, fit, and improve the predictions.

Outcomes and validation:

Addressing different levels of detail, the hybrid models will:

• Develop approaches to switch between models of different resolution, creating well-justified methods for model reduction based on the concept of EFMs.
• Predict metabolic behavior as a function of extracellular metabolite concentrations, taking into account discrete transitions between EFMs, and allowing the simulation of engineered cells, with enzymes or pathways added or removed.
• Use small-sized models obtained from model reduction to numerically simulate a lab-scale bioreactor environment and explore whether these simulations can be used to dynamically optimize bioproduction, using reinforcement learning techniques and model-based control approaches on digital twins of the system.

Experimental data will be collected for model development and validation, plus proof-of-concepts for bioproduction. The experimental plan is to:

• Grow E. coli strains in a computer-controlled mini-bioreactor system to calibrate the reduced, small-sized models, by means of measurements of growth rate and uptake/secretion rates of key metabolites.
• Carry out experiments pf 1,3-POD production in connected fermentors to study the effects of spatial heterogeneity under controlled conditions, using cell populations models with distributions over their parameters.

Perform validation runs in larger, pilot-scale fermentors, optimizing bioproduction. Once the proof of concept on E. coli and 1,3-POD will be validated, the established methodology will be formalized in a protocol easily adaptable to different organisms and bioproducts.
 

Project lifetime:
 

2024 - 2028

Scientific manager:
 

Alberto Tonda (INRAE)