Bridging Experimental Fermentation Data and Mechanistic Models Through Literature Synthesis

Project Details

• Student(s): Carolina Hakam
• Advisor(s): Dr. Elie Chalhoub, Dr. Joanne Belovich (Co-contributor)
• Department: Chemical
• Academic Year(s): 2025-2026

Abstract

Fermentation processes are governed by complex biochemical, genetic, and process-level interactions that vary widely across studies, organisms, and operating conditions. However, a comprehensive and quantitative understanding of these systems is often limited by fragmented experimental data spanning different genetic modifications, bioreactor configurations, and downstream separation strategies. This study aims to address this gap through a systematic survey of the literature to compile, curate, and harmonize experimental datasets encompassing diverse gene-level interventions, reactor designs, operating modes, and product recovery processes relevant to microbial fermentation.

The assembled datasets will be subjected to rigorous data analysis to identify trends, correlations, and sources of variability across genetic, upstream, and downstream process dimensions. Building on these insights, we will develop a structured mechanistic model that integrates key metabolic, kinetic, transport, and separation phenomena governing fermentation performance. Model parameters will be estimated using aggregated literature data, followed by local and global sensitivity analyses to identify influential parameters and critical process mechanisms. Confidence intervals and uncertainty quantification methods will be employed to assess parameter identifiability, robustness, and predictive capability of the model.

By unifying heterogeneous experimental evidence across genetic, bioreactor, and downstream processing domains, this work seeks to provide a validated and uncertainty-aware mechanistic framework to support improved interpretation, comparison, and optimization of fermentation-based bioprocesses.