Seoul National University · Engineering
이 교수의 연구실은 생물학적 네트워크의 정량적 분석을 위한 혁신적 모델링 기법을 개발하고 있으며, 특히 대사, 신호전달 및 유전자 조절 네트워크를 통합적으로 분석하는 Flux Balance Analysis(FBA) 기반의 동적 시뮬레이션 기법인 idFBA를 핵심으로 연구하고 있습니다. 메타볼로믹스와의 융합을 통해 생체 내 불확실성을 고려한 정량적 예측이 가능하도록 하며, 특히 유전자 조절, 대사 경로, 세포 신호 전달의 통합적 동역학을 기반으로 한 스케일업 가능한 생물계산 모델링을 추구합니다. 또한, 생물공정 최적화 및 생체세포 조절을 위한 마이크로유체 디바이스 개발 등 응용 분야로도 연구를 확장하고 있습니다.
Figures are computed from collected data and may differ slightly.
Flux 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
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