Towards Increasing the Productivity of Lignocellulosic Bioethanol: Rational Strategies Fueled by Modeling

Hyun-Seob Song, John A. Morgan and Doraiswami Ramkrishna

School of Chemical Engineering, Purdue University, West Lafayette, IN


1. Introduction

Bioethanol is not only currently the most widely used biofuel, but also potentially the most promising alternative to fossil fuels. The majority of bioethanol in today’s use is made from sucrose-containing (e. g., sugarcane, sugar beet, and sweet sorghum) or starch-based feedstocks (e. g., corn, wheat, rice, barley, and potatoes). The excessive production of such crop-based (first generation) bioethanol, however, imposes an adverse effect on global food supply. A sustainable alternative feedstock which can be used for non-crop (second generation) bioethanol is lignocellulosic biomass such as rice straw (Binod et al., 2010), wheat straw (Talebnia et al., 2010), corn stover (Kadam & McMillan, 2003), switchgrass (Keshwani & Cheng, 2009), sugarcane bagasse (Cardona et al., 2010), and various other agriculture and forest residues.

Lignocellulose primarily consists of cellulose, hemicellulose and lignin. Cellulose is a homopolymer of glucose, while hemicellulose is a heteropolymer of pentoses (i. e., xylose and arabinose) and hexoses (i. e., glucose, mannose, and galactose) sugars. Lignin is a rich source of aromatic carbon compounds but extremely recalcitrant. Lignocellulose is decomposed via pretreatment and hydrolysis into a spectrum of sugars in which glucose and xylose are the first and second most dominant. These cellulosic sugars are finally converted to bioethanol by fermentation. The lignocellulosic bioethanol has not yet been produced on a commercial scale due to lack of cost-effectiveness. For ensuring its economical viability, comprehensive efforts are required to reduce cost (and maximize the profit) throughout the entire process from biomass to bioethanol.

In the current discussion, we limit ourselves to the fermentation step only and examine various issues with increasing bioethanol productivity. Cost-benefit analysis of the fermentation process shows that the processing cost is more dominant (two-thirds of the total cost) than the feed cost (Lange, 2007; Wingren et al., 2003). It is thus important to improve the processing efficiency, not just the sugar conversion alone. In this regard, increasing the productivity should be a preferred target over increasing the yield, not only in the reactor optimization, but also in strain improvement.

The yeast Saccharomyces cerevisiae has typically been used for the production of crop-based bioethanol. This wild-type strain is, however, not suitable for converting cellulosic sugars as it can efficiently ferment glucose but hardly xylose. Considerable effort has been made to

endow S. cerevisiae with the ability to utilize xylose (Hahn-Hagerdal et al., 2007). Basic approaches to this end are to "push" and "pull" xylose into the central metabolism of S. cerevisiae. Push strategies introduce the transport and initial metabolic routes of xylose by expressing exogenous (i. e., foreign) genes. In pull strategies, reactions in the central metabolism are selectively overexpressed. Introduction of foreign plasmids imposes a "metabolic burden" or "metabolic load" on the host cell by consuming a significant amount of internal resources, hurting the normal metabolic functioning of the host cell (Glick, 1995). The most common observation is the decrease of cell growth rate (Bentley et al., 1990; Ricci & Hernandez, 2000). It is often (while not always) that as the product yield is increased, the production rate is reciprocally low (Chu & Lee, 2007).

Most of the recombinant yeast strains currently available show a sequential pattern in their consumption of mixed sugars (i. e., glucose and xylose). They preferably consume glucose with xylose on standby as denoted by the vertical line in Fig. 1.1(a). Then, simultaneous consumption take places along the tilted line only when the preferred substrate is depleted to a very low level (say, one tenth or one fifth of xylose level). Obviously, the productivity can be increased if simultaneous consumption occurs earlier (i. e. at higher concentrations of glucose). To achieve this, two different strategies can be considered. First, we may develop a more efficient fermenting organism through further pathway modifications of existing recombinant yeast. The goal of this attempt at the genetic level corresponds to making the slope of the tilted line steeper (Fig. 1.1(b)). Alternatively, we may design a more efficient fermentation process through optimization of operating conditions or reconfiguration of reactors. For example, if we change initial sugar composition in batch culture by increasing relative portion of xylose in the culture medium, this also leads to earlier start of the simultaneous consumption (Fig. 1.1(c)).


Fig. 1.1. (a) Sequential consumption of mixed sugars by existing recombinant yeast. Two possible ways to promote the simultaneous consumption: (b) metabolic pathway modification of fermenting organisms and (c) adjustment of sugar composition in the culture medium. Adapted from Song and Ramkrishna (2010) with minor modification.

In this chapter, we present model-based strategies for increasing the bioethanol productivity both at the genetic and reactor levels. Metabolic models help not only reduce trial and error, but also discover fresh strategies (Bailey, 1998). In view of the issues discussed above, there are two essential aspects of metabolic models required for the application to reactor and
metabolic engineering. First, the mathematical models should be able to address productivity as well as yield. Second, it should be possible to account for metabolic burden. While diverse modeling approaches have been suggested as a tool, the cybernetic framework (Ramkrishna, 1983) is unique in this regard (Maertens & Vanrolleghem, 2010). The cybernetic modeling approach describes cellular metabolism from the viewpoint that a microorganism is an optimal strategist making frugal use of limited internal resources to maximize its survival (Ramkrishna, 1983). Metabolic regulation of enzyme synthesis and their activities is made as the outcome of such optimal allocation of resources. This unique feature of accounting for metabolic regulation endows cybernetic models with the capability to accurately predict peculiar metabolic behaviors such as sequential or simultaneous consumption of multiple substrates. Further, in view of the constraint placed on resources, the cybernetic model provides a mechanism to account for metabolic burden imposed on the organism as a result of genetic changes.

After a brief sketch of the model structure (Section 2), we will see how metabolic models are used to establish rational strategies for increasing the productivity. In Section 3, basic guidelines for genetic modification of fermenting organisms are provided by identifying the potential target pathway and reactions. Diverse reactor-level strategies are also discussed in Section 4.