Metabolic engineering involves the manipulation of genes which have the potential to increase the yield of metabolite production. This book presents a research of in silico metabolic engineering by improving a hybid of Genetic Ant Colony Optimization (GACO) and Flux Balance Analysis (FBA) algorithms. It starts with an extensive literature on metabolic engineering, and models and methods in metabolic engineering such as FBA, GACO and many more. This is followed by a complete methodology on the research carried out by the authors. Two public datasets of genome scale metabolic models are used for the research in this book, which are iAF1260 (E.coli) and Yeast 4.05 (S.cerevisiae). The book also contains a detailed technical framework of GACO and FBA including pseudo code, comprehensive experimental results, and analyses on both Yeast and E.coli datasets. The information from this book is helpful for understanding processes and techniques involved in in silico metabolic engineering.