While advanced biofuels have potential to reduce dependence on fossil fuels and emissions of greenhouse gases, this has not been realized due to various challenges in production and conversion of feedstock into biofuels. This proposal addresses some of the challenges in the conversion of feedstock. Many researchers have demonstrated that the high price of cellulase enzymes up to $1/gal of ethanol (30-35% of ethanol production costs) is a stumbling block for commercialization of advanced biofuels. Any strategy that can reduce these costs could have significant and immediate practical impact on economics of cellulosic ethanol production. As cellulose is the most abundant biopolymer on earth that is metabolized by many organisms, a wide variety of cellulase enzymes exist in nature. Utilization of this wide array of cellulases for advanced biofuels production is challenging as many of these enzymes have different modes of action, temperature and pH optima. Currently the only way to determine the optimum enzyme ratio is to perform extensive experiments with different substrates. There is a critical need to develop a rational design framework that can optimize the mixture of different cellulase enzymes subject to various process, economic constraints; thus significantly reducing the total cost of enzymes used in ethanol production process.
The overall goal of this integrated proposal is to develop methods for rational design of cellulase enzymes using stochastic/kinetic models and control theoretic approaches. New methods of analysis for complex biochemical systems using tools from control theory will be developed. These analytical methods will have wide ranging application in bioprocessing and biofuels production. These techniques will be demonstrated on a laboratory scale system using pure enzymes and commercial enzyme mixtures.
This project will utilize stochastic modeling techniques and kinetic models with control theory approaches to develop a framework for rational design of cellulase enzymes. Since cellulose hydrolysis proceeds efficiently in the presence of multiple enzymes working in a synergistic manner, action of multiple enzymes will be modeled using two approaches: Kinetic models and stochastic molecular simulation models. Linear and nonlinear control theory methods will be applied in a novel way to identify and quantify the optimal enzyme composition for various design constraints. Control theory techniques will be used to evaluate the suitability of the optimal enzyme compositions for real world applications. This theoretical framework can also be used to aid model driven hypothesis formulation for design of cellulase mixtures that can be experimentally verified. Analysis techniques that will be developed in this project will have applications in the areas of bioprocessing and biofuels production.
A significant effort in this project will be devoted for conducting structured training and mentoring programs for graduate, undergraduate and high school students, curriculum development and outreach programs for wide dissemination of knowledge developed in this project. One new graduate level course will be developed and a laboratory section will be developed for an existing undergraduate course. A dedicated website will be developed for effective dissemination of the course materials developed in this project. Graduate and undergraduate students will be directly mentored and trained in research methods to create a knowledge base and serve as a catalyst for further innovation.
