Jong Min Lee
Yonsei University · Engineering
About the Lab
Professor Jong Min Lee's research lab specializes in systems biology and process systems engineering, focusing on the integration of metabolic, signaling, and regulatory networks using computational modeling. The lab develops advanced quantitative methods such as flux balance analysis (FBA) and dynamic programming techniques to model and optimize cellular behavior, particularly in bioprocesses and disease-related networks. A key focus is on bridging systems biology with process control through hybrid data-driven and model-based approaches, including reinforcement learning and model predictive control for bioreactor optimization. The lab also investigates dynamic phenomena in complex systems, such as methane hydrate formation, using multimodal experimental and computational techniques.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Flux balance analysis (FBA) has emerged as an effective means to analyse biological networks in a quantitative manner. Much progress has been made on the extension of FBA to incorporate a priori biological knowledge, provide more practical descriptions of observed cell behaviours, and predict the outcome of network perturbations. Metabolomics is independently advancing as a set of high-throughput data acquisition tools providing dynamic profiles of metabolites in an unbiased manner. These data s
Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore,
Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore,
Abstract: This paper reviews dynamic programming (DP), surveys approximate solution methods for it, and considers their applicability to process control problems. Reinforcement Learning (RL) and Neuro-Dynamic Programming (NDP), which can be viewed as approximate DP techniques, are already established techniques for solving difficult multi-stage decision problems in the fields of operations research, computer science, and robotics. Owing to the significant disparity of problem formulations and ob
Research Areas
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