Project Summary:

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 project 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 research 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.


Stochastic Molecular Model of Cellulose Hydrolysis: Cellulose hydrolysis was modeled using a stochastic model. In this model, cellulose was modeled as a  group of microfribrils made of elementrary fibrils. The elementary fibrils were modeled as 3-dimensional matrix of glucose molecules. The enzymatic hydrolysis was simulated as a stochastic sequence of individual hydrolysis events (cleavage of the glycosidic bonds). The probability of a hydrolysis event was dependent on the structural properties of biomass and individual enzyme properties. The model was successful in capturing the dynamic behavior of cellulose hydrolysis during with single and multi-enzyme mixtures using literature data. A first version of this model has been published in Biotechnology for Biofuels. Further revisions of this model will incorporate inhibition due to xylanases and lignin.

Kinetic Models for Cellulose Hydrolysis: A kinetic model based on saturation kinetics has been developed to simulate the multienzyme cellulose hydrolysis. The model developed based on modifications to kinetic model reported by Kadam et al (2004) has been validated using literature data.

Optimization Frameworks for Rational Design of Cellulose Mixtures: Optimization frameworks using the SMM and Kinetic models of cellulose hydrolysis developed in this project were used to develop optimization framework using multiple strategies. The framework has the capability to predict static enzyme mixture composition that could be used to formulate optimal enzyme mixtures. Additionally, the optimal dynamic profiles for various component enzymes can also be determined using this framework.

We are currently testing and experimentally validating the frameworks. A protein purification system and protocols to purify individual enzymes from cellulases have been developed in our laboratory.

Research Opportunities:

  • Every year, we look for motivated and hard working undergraduate researchers to perform exciting research projects in this area.
  • We have opening for two openings every year for high school students through Apprenticeship in Science and Engineering (ASE), Saturday Academy program.

Acknowledgement: We are grateful to National Science Foundation (Energy for Sustainability Program) for supporting this project through Grant #1236349.


Peer Reviewed
  1. Kumar, D. and Murthy, G.S. 2012. Stochastic molecular model of enzymatic hydrolysis of cellulose for ethanol production. Biotechnol. Biofuels.6:63 (Highly Accessed)
Other Publications
  1. Kumar, D. and Murthy, G.S. 2013. Stochastic molecular modeling of multi enzyme cellulose hydrolysis. Proceedings of 47th ISAE meeting and international symposium on Bioenergy. Hyderabad, India.
  2. Kumar, D. and Murthy, G.S. 2013. Rational design of optimal enzyme mixture for deconstruction of cellulose for ethanol production. ASABE Abstract No.131595011. ASABE, St. Joseph, MI.
  3. Kumar, D. and Murthy, G.S. 2013. Synergistic action of multiple enzymes for cellulose hydrolysis: stochastic molecular modeling and experimental validation. ASABE Abstract No.131595023. ASABE, St. Joseph, MI.
  4. Kumar, D. and Murthy, G.S. 2012. Stochastic modeling of enzymatic hydrolysis of cellulose for ethanol production. Abstract No.121337921. ASABE, St. Joseph, MI.