SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework
Abstract
Lipid metabolism is probably the most annoying part in metabolism to model: Most organisms can produce thousands of different types of lipids, however only a handful of aggregated data is normally available for constraining simulations. It’s not a suprise then that lipid pathways exhibit high variability and therefore low confidence. In this study we developed SLIMEr, a method for including commonly available experimental lipid data in GEMs. It uses lipid profile data for constraining lipid classes, and FAME data for constraining the acyl chain distribution. We tested the approach in S. cerevisiae, and showed that with a more accurate description of lipid requirements we can better analyze how flexible lipid metabolism is, and how much does it cost to transition from one state to another. Fun fact: From conception of the idea to submission of the paper, this study took 6 months (which is pretty fast compared to my normal publication turnover).
