Seth Levine.

Post-Doctorate Researcher
University of California, Berkeley

Ph.D. Chemical and Biological Engineering, 2008
Northwestern UniversityB.S. Chemical Engineering, 2003
Case Western Reserve University

sethlevine(AT)berkeley.edu
Office Location: 214 Calvin Hall


Developing a Detailed Kinetic Model for Enzymatic Hydrolysis of Cellulose

The widespread use of biofuels depends on being able to produce enough biofeedstocks to produce significant amounts of biofuels. Utilizing energy crops grown specifically for biofuel production and making use of waste products such as corn stover as feedstocks drastically increases the available feedstock for biofuel production as well as removes the need to use food crops for fuel production. In order to utilize these feedstocks effective processes for converting these lignocellulosic materials to fermentable sugars are required. Enzymatic hydrolysis is a promising method of accomplishing this conversion by using cellulase enzymes, often from fungal sources, to break down the cellulose to glucose. Currently, the rate of enzymatic hydrolysis is too slow and the amount of enzymes necessary to effectively convert the cellulose to glucose is too high to develop an effective industrial process for the production of biofuels. Developing a detailed mechanistic model for enzymatic hydrolysis of cellulose provides a powerful tool for understanding the kinetic behavior of the enzymatic hydrolysis. This information is essential for improving the hydrolysis process quickly so that is can be used effectively on an industrial scale.

Previous kinetic models have made use of simplifications such as treating the different cellulase enzymes as a single enzyme concentration or treating the substrate as being totally soluble and accessible. Cellulose is an insoluble substrate so the model needs to account for the heterogeneous nature of the reaction system. Our model has been developed to track individual enzyme species. The model includes separate adsorption and binding steps. The substrate is characterized by tracking key substrate variables including the degree of polymerization of surface cellulose chains and the accessible surface area. It is our intention to develop a model that is general enough to be predictive for a range of substrates simply by changing the key substrate variables as long as the same enzyme system is applied. By including this level of detail we can gain insight into cellulase synergy, the common kinetic slowdown observed in the hydrolysis system, and through the use of sensitivity analysis we will identify the most critical parameters to improve enzyme performance. This valuable knowledge will aid in the development of advanced cellulose hydrolysis processes.