Fatty Acid Optimization Via Metabolic Engineering of Yeast Cells Dallas Bridges, Brian Harris, Kyle Hawkins, Matt Kapinos, Keeana Ross, Tamika Tran Advisors: Dr. Xueyang Feng, Katherine Bland, Weihua Guo Problem Statement The biofuel industry is struggling because of the high cost of renewable fuel. We strive to create an ideal metabolic pathway to optimize fatty acid production via yeast cells. In doing this, we hope to increase production of biofuels and in turn help promote a greener solution to fuel needs. By incorporating and modifying known metabolic pathways we aim to discover a pathway that will generate the maximum amount of biofuel precursors. We intend to design an efficient process that will contribute to the future of the biofuel industry. Relevant Fatty Acid Metabolism Flow Diagram * Citrate Cycle Malonyl–CoA (MalCoA) Malonyl–[acp] Acetyl-CoA In cytoplasm/plastid RM021 In mitochondria RM018/RM020 ACP Acetyl-[acp] (AcACP) Butanoyl-[acp] MalACP 2* 4* 6* 8* Hexanoyl-[acp] Octanoyl-[acp] AcCoA (AcCoA) Butanoyl-CoA Hexanoyl-CoA Octanoyl-CoA Figure 1: Fatty acid metabolic map with gene identification YMR207C COBRA Functions Used initCobraToolbox() readCbModel(model) optimizeCbModel(model) singleGeneDeletion(model) Project Goal Our goal is to develop an ideal metabolic pathway in order to identify optimal gene deletions, and to maximize fatty acid production from yeast cells. Project Objectives To utilize Flux Balance Analysis (FBA) in order to induce metabolite flow to create the maximum viable product. Incorporate COBRA software, and use constraint based metabolic models that will allow the prediction pathways to optimize metabolic flux of reactions without the need for experimentation *This equation provides an arbitrary number that takes the flux for the knockout of each of the 905 genes present, the optimized wild type, and the growth rate of the knockout to determine how effective one gene deletion is compared to another. Constraints The model must be a genome-scale model Flux Balance Analysis cannot: Reflect real microbial metabolism Go beyond mass balance Non-adjustable constraint: S*v = 0 Criteria Simulate flow of metabolites Increase fatty acid production by at least 5% per gene deletion Flux Balance Analysis: The flux must be in units of mmol/gDCW/h Must maintain a normal growth environment (>0.05/h) Figure 2: Flowchart of Matlab code to optimize fatty acids Results Table 2: Optimized gene deletion list (FAS objective coefficient = 0.07) Figure 3: Flux fold analysis with different FAS objective coefficients YKL182W Standards ISO Standards. (2016). ISO 18457: Biomimetics -- Biomimetic materials, structures and components. Geneva, Switzerland: International Organization of Standardization ISO Standards. (2015). ISO 18458: Biomimetics -- Terminology, concepts and methodology. Geneva, Switzerland: International Organization of Standardization ISO Standards. (2015). ISO 18459: Biomimetics -- Biomimetic structural optimization. Geneva, Switzerland: International Organization of Standardization NCES.  (2002). NCES 5-1: Statistical analysis, inference and comparison. Washington, D.C.: National Center for Education Statistics. Table 1: Sample of flux variations based on gene deletions and their effects (FAS objective coefficient = 0.05) Through single gene deletion, the genes listed in Table 2 were found to produce the highest increase in flux when removed from the wild type genome. After increasing the objective coefficient, it was determined that the flux was optimal when this value was set to 0.07 within the program. Future Work This model functions in conjunction with Synbio-Team Rank’s program. By determining the top genes to remove to optimize fatty acid production we provide candidates who’s transcription factors can be up or down regulated. This model is modular so with slight adjustments in the Matlab code it can be used to optimize the production of many products by various organisms. Acknowledgements We would like to thank Dr. Xueyang Feng and Katherine Bland for their assistance and advice that helped us complete this project. And a special thanks to Weihua Guo who spent time answering many questions and providing insight.