Preface

Mathematical modeling lies at the heart of modern engineering education, serving as a foundational tool for understanding and solving complex engineering problems. In a standard undergraduate math modeling course, lectures typically introduce theoretical concepts and analytical techniques using pen-and-paper methods, while laboratory sessions focus on applying these theories through computational exercises, often utilizing MATLAB or Python. Despite this balanced approach, a common student concern has been the limited exposure to cutting-edge computational tools that extend beyond traditional environments.

In alignment with the mission of CACHE (Computer Aids for Chemical Engineering), which focuses on bridging the gap between theory and practice through the integration of advanced computational tools and simulations, this supplementary course is designed to enhance students’ computational skill sets. It builds upon core concepts from thermodynamics, transport phenomena, kinetics, and differential equations, and moves toward practical, hands-on experiences with state-of-the-art modeling frameworks and optimization solvers.

This book presents a structured set of three modules centered around metabolic modeling. These modules guide students through topics ranging from flux balance analysis (FBA) to spatiotemporal FBA, culminating in the in silico modeling of a Petri dish environment. To achieve this, we will employ the professional-grade Gurobi optimization solver through MATLAB’s interface, as well as specialized dynamic flux balance analysis (DFBA) toolkits such as DFBALab and COMETS.

To further enrich the learning experience, we introduce advanced visualization tools, GUI interfaces, the Machine Learning Toolbox, and the actxserver function for integration with Aspen Plus. It is our hope that the content, which offers clear theoretical foundations, practical guidance, and illustrative sample codes, will serve as a valuable resource to support readers on their journey in mathematical modeling within chemical engineering.