Mathematical modeling and optimization of cellulase protein production usingTrichoderma reesei RL-P37

Abstract

The enzyme cellulase, a multienzyme complex made up of several proteins, catalyzes the conversion of cellulose to glucose in an enzymatic hydrolysis-based biomass-to-ethanol process. Production of cellulase enzyme proteins in large quantities using the fungus Trichoderma reesei requires understanding the dynamics of growth and enzyme production. The method of neural network parameter function modeling, which combines the approximation capabilities of neural networks with fundamental process knowledge, is utilized to develop a mathematical model of this dynamic system. In addition, kinetic models are also developed. Laboratory data from bench-scale fermentations involving growth and protein production by T. reesei on lactose and xylose are used to estimate the parameters in these models. The relative performances of the various models and the results of optimizing these models on two different performance measures are presented. An approximately 33% lower root-mean-squared error (RMSE) in protein predictions and about 40% lower total RMSE is obtained with the neural network-based model as opposed to kinetic models. Using the neural network-based model, the RMSE in predicting optimal conditions for two performance indices, is about 67% and 40% lower, respectively, when compared with the kinetic models. Thus, both model predictions and optimization results from the neural network-based model are found to be closer to the experimental data than the kinetic models developed in this work. It is shown that the neural network parameter function modeling method can be useful as a "macromodeling" technique to rapidly develop dynamic models of a process.

Genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei (syn. Hypocrea jecorina)

“…Production of these enzymes at economically viable levels will require an increased understanding of the dynamics of cell growth and enzyme production. Mathematical and kinetic models are being developed to optimize these processes 35 , and the availability of a complete genome sequence will provide a blueprint to improve the models and to empower strain improvement strategies for creating superior enzyme mixtures from a single highly productive strain.…”

Wood production, wood technology, and biotechnological impacts

“…Evidences for cellulosomes are found in some anaerobic rumen fungi such as Neocallimastix, Piromyces, and Orpinomyces species (Shoham et al 1999, Ximenes et al 2005 whilst aerobic fungi express mixtures of free enzymes , Rabinovich et al 2002. Bacteria and fungi are both used in batch, fed- Table 2 Examples of cellulase production by micro-organisms in batch processes (based on Tholodur et al 1999 (Bhat & Bhat 1997, Cen & Xia 1999, Tholudur et al 1999; Table 2) for applications in food and beverage industries (baking, malting, brewing, extraction processes), in animal feed and pharmaceutical industry, in the wood and textile industry (Nevalainen 1994; see Chapter 18 of this book), and potentially in ethanol production from lignocellulose (Sun & Cheng 2002; see Chapter 6 of this book). According to Tolan & Foody (1999), the annual consumption of cellulases in the 1990s derived from submerged cultivation comprised an amount of 23,000 t valued $US 125 million.…”

Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling

“…Recently, neural networks have frequently been used within hybrid models, which provide a better performance for interpolation and extrapolation properties (van Can et al, 1998). Hybrid models have already been applied to bioreactors (Tholudur and Ramirez, 1996;Chen et al, 2000;James et al, 2002;Patnaik, 2003), ethanol production by Sacharomyces cerevisae (Harada et al, 2000), and enzymatic production (Tholudur et al, 1996;Gonçalves, et al, 2002) and other processes.…”