Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation
Introduction
Escherichia coli is an important host organism for the production of many biopharmaceutical proteins. Although production of properly folded full-length antibodies has been achieved in E. coli (Simmons et al., 2002), humanized glycosylation remains the province of eukaryotic hosts. Even with this limitation, many therapeutic proteins can be produced in E. coli, with a good chance for higher volumetric productivity rates than mammalian hosts (mass protein/time/volume). It is therefore important to continue to develop process-scale computational models of E. coli to aid in the understanding, design, and troubleshooting of processes at conditions common in the industrial manufacture of recombinant proteins. This effort is further motivated by the FDA's process analytical technology (PAT) initiative, which foresees computer modeling as an integral part of demonstrating process understanding. In addition, a reasonable model that captures the majority of a process's salient features may be used as a technician training tool or for testing the effect of minor process deviations in silico.
A very robust and generalizable method of modeling cellular metabolism is one that relies solely on fundamental mass and energy balances. Following this approach, flux balance analysis (FBA) is especially well-developed and suitable for describing cellular metabolism (Feist and Palsson, 2008). FBA provides a general framework on which the modeler can hang a wide variety of cellular information and heuristic hypotheses over the system of cellular mass and energy balances. Examples of additional constraints include, but are not limited to, transcript levels (Covert and Palsson, 2002), thermodynamic information (Henry et al., 2007), and intracellular crowding (Beg et al., 2007). Despite the promise of this approach in aiding the solution to a variety of metabolic engineering problems (Bro et al., 2006; Pharkya and Maranas, 2006; Lee et al., 2007), few descriptions of applying FBA to industrial fermentations are available. Part of this dearth may be due to the perceived steady-state nature of FBA models, despite papers showing how they may be extended to describe dynamic systems (Mahadevan et al., 2002; Beg et al., 2007). Other challenges include the accurate description of simultaneous, multiple substrate consumption and the general complexity of industrial fermentation processes, which often span a wide range of metabolic states and nutrient concentrations. Ramakrishna and co-workers have long recognized that a maximal growth rate objective may be a useful way to understand multi-substrate conditions (e.g. Dhurjati et al., 1985; Ramakrishna et al., 1996; Young et al., 2008), although other possible cellular objectives have been discussed (Schuetz et al., 2007; Ow et al., 2009).
In this paper, we describe the utilization of the FBA modeling approach in validating and improving our understanding of industrial E. coli fermentations. We show that a maximum growth rate objective function is a useful metabolic objective and that FBA is a powerful way to interpret complex fermentation data sets, reveal unexpected behavior, and demonstrate process understanding.
Section snippets
Process description
The host and 10L analog of the process modeled here have previously been described by Andersen et al. (2001). The 1000L version of the process used component concentrations and conditions similar to the 10L fermentation. The base medium contained sugar as the primary carbon source, but also contained significant quantities of yeast extract and a protein hydrolysate (Andersen et al., 2001). The process was operated as a fed-batch 32 h in length, and glucose-limited to control the dissolved oxygen
General phases of metabolism
The process consisted of at least five distinct metabolic phases. Fig. 1 presents an overview of process variables, simulation results, and predicted metabolic fluxes:
- 1.
Rich media growth phase
- 2.
Transitional growth phase
- 3.
Acetate reconsumption growth phase
- 4.
Growth on glucose and methionine phase
- 5.
Phosphate depletion growth phase
Although the division into these phases is somewhat arbitrary, we feel it is a useful way to discretize the changes occurring in the process. A description of the phases can be
Acknowledgements
The authors would like to thank Stephen Meadows and Dr. Christina J. Lee for help with editing, Ramin Afrasiabi and Jane Gunson for assistance in obtaining samples, and Craig Azzolino, Dr. Michael Laird, and Dr. Robert Kiss for their helpful advice and insight.
References (30)
- et al.
In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production
Metab. Eng.
(2006) - et al.
Transcriptional regulation in constraints-based metabolic models of Escherichia coli
J. Biol. Chem.
(2002) - et al.
Genome-scale thermodynamic analysis of Escherichia coli metabolism
Biophys. J.
(2006) - et al.
Thermodynamics-based metabolic flux analysis
Biophys. J.
(2007) - et al.
A radical-chemical route to acetyl-CoA: the anaerobically induced pyruvate formate-lyase system of Escherichia coli
FEMS Microbiol. Rev.
(1990) - et al.
Dynamic flux balance analysis of diauxic growth in Escherichia coli
Biophys. J.
(2002) - et al.
An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems
Metab. Eng.
(2006) - et al.
Expression of full-length immunoglobulins in Escherichia coli: rapid and efficient production of aglycosylated antibodies
J. Immunol. Methods
(2002) - et al.
Metabolic capabilities of Escherichia coli. I. Synthesis of biosynthetic precursors and cofactors
J. Theor. Biol.
(1993) - et al.
Metabolic capabilities of Escherichia coli. II. Optimal growth patterns
J. Theor. Biol.
(1993)
Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis
Proc. Natl. Acad. Sci. USA
Metabolic oscillations in an E. coli fermentation
Biotechnol. Bioeng.
Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity
Proc. Natl. Acad. Sci. USA
A cybernetic view of microbial growth: modeling of cells as optimal strategists
Biotechnol. Bioeng.
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information
Mol. Syst. Biol.
Cited by (97)
Digital models in biotechnology: Towards multi-scale integration and implementation
2022, Biotechnology AdvancesA Constraint-based modeling approach to reach an improved chemically defined minimal medium for recombinant antiEpEX-scFv production by Escherichia coli
2022, Biochemical Engineering JournalCitation Excerpt :No need of significant amount of experimentations has made this method cost-effective and convenient for medium formulation. Constraint-based modeling has been used in different studies for overproduction of a wide range of products such as recombinant proteins [30–33]. For example, Swayambhu et al. used this method to overproduce siderophore compounds in E. coli.
Optimal control in chemical engineering: Past, present and future
2021, Computers and Chemical Engineering
- 1
Present address: Amyris Biotechnologies, Inc., 5885 Hollis St, Suite 100, Emeryville, CA 94608, United States.