Predictive modeling and control of nutrients for the cost-effective production of cultivated meat

Thesis

Scaling cultivated meat from proof-of-concept to commercially viable production requires cells that grow efficiently in cost-effective media while limiting toxic by-products. Two persistent hurdles are the lack of predictive, species- and tissue-relevant metabolic models to guide design, and limited mechanistic insight into how lactate and ammonia inhibit mammalian cell growth and metabolism. This thesis addresses these challenges through three complementary projects that combine mechanistic modeling with data-driven analysis.

First, a draft genome-scale metabolic model (GEM) for pig was reconstructed using orthology-based workflows and rigorous quality assessment, establishing a foundation for deriving context-specific muscle and adipose models relevant to edible tissues. This resource supports in silico exploration of nutrient demands and by-product formation in porcine cells.

Second, a hybrid framework was developed that couples enzyme-constrained flux balance analysis (ecFBA) with regression to estimate hard-to-measure constraints and to predict growth and secretion phenotypes across culture conditions. Focusing on lactate and ammonia, the framework quantified how non-growth associated maintenance and glutamate dehydrogenase capacity shape feasible flux states, enabling hypothesis-driven media reformulation and process tuning to mitigate inhibitory accumulation.

Third, a multi-modal approach was applied to integrate transcriptomic and flux-level evidence, mapping pathway-level effects of ammonia and lactate across mammalian systems. A synthesis of responses from independent datasets identified shared stress pathways and candidate metabolic nodes for media additives, bioprocess control, and genetic modification.

Collectively, the thesis provides (i) a porcine GEM for cultivated meat model systems, (ii) a practical ecFBA-plus-regression pipeline for predicting growth and by-product fluxes from limited measurements, and (iii) a multi-modal approach linking gene-level responses to pathway function. These contributions inform media design and bioreactor strategy, advancing model-guided, scalable production of cultivated meat.

Authors: Wee, H

• DOI: 10.5287/ora-beb8mzdyn
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Subjects: systems biology; computational biology