Computational modeling of Clostridium acetobutylicum
Jonathan Smeton
Mentored by Dr. Margaret Hurley
Introduction
Materials and Methods
A hierarchy of genomic-metabolic models of C. acetobutylicum were
constructed in COmplex PAthway SImulation (COPASI) (version 4.6.
www.copasi.org), a freely available software for analysis and simulation of
biochemical networks (Hoops et al., 2006). Initial models centered on the
acid/solvent formation pathways in C. acetobutylicum based upon prior work
by Desai & Harris (1999). More comprehensive models were developed,
attempting to incorporate published research on transcriptomic data:
carbohydrate utilization as described in Servinksy, Keil, Dupuy, & Sund
(2010). The preliminary form of this model excluded oxidation-reduction
couples and coenzymes. Mass action kinetics was assumed, with kinetic
constants proportional to the appropriate tRNA concentration of genes
coding for each enzyme or transport protein. Basal values for kinetic
parameters were set to one, with a simple carbohydrate-based switch
deciding whether reaction rates would increase to relative rates described by
supplementary microarray data (Servinsky, Keil, Dupuy, & Sund, 2010). A
subsequent model was then developed utilizing all reactions involved in
standard Embden-Meyerhoff-Parnasas and Entner-Dodoroff glycolysis, and
the pentose phosphate pathway. Finally, more attention was paid to redox
Conclusion
pairs, and NADH/NAD+ and NADPH/NADP+ oxidation-reduction
couples and ATP/ADP were included in all applicable reactions. At this
point, if-then switches for protein concentrations were changed to ordinary
differential equations (ODE). The half-time for transcription was set to six
hours and the maximum kinetic constant was kept as described previously.
Time course analysis was performed on the model for each carbon source.
Results
Present models of the acid/solvent formation pathways do not correctly
model butanol-producing behavior of C. acetobutylicum. Time course analysis
was correctly performed on the carbohydrate utilization model, while grown
on thirteen different carbon sources: arabinogalactans, arabinose, cellobiose,
fructose, galactose, glucose, lactose, maltose, mannose, pectin, starch,
sucrose, and xylose. Qualitative analysis indicates the model’s capability of
degrading each carbohydrate to pyruvate correctly mirrors the behavior of
C. acetobutylicum grown on each carbon source. An example of a pathway
(Figure 1 ) and the corresponding time course simulation (Graph 1) is
shown below. As expected, peaks of each intermediate metabolite appear in
order of their appearance in the metabolic network (Figure 1). Resulting
steady state concentration of 9.525 mmol for pyruvate indicates most
galactose breakdown was distributed to pyruvate production and glycolytic
breakdown as expected with a small quantity unexpectedly forming
galactose-1-phosphate. Other models repeat similar behaviors, with most
carbohydrate forming pyruvate and minor concentrations of secondary
metabolites.
Extracellular Gal
Gal6P Gal1P
T6P
GlP
T16BP
Gal
F6P
F16BP
DHAP
G3P
3PG
2PG
[Galactose_ext]
[Galactose-6-P_int]
[Galactose-1-P_int]
[Tagatose-6-P_int]
[Tagatose-1,6-BP_int]
[Dihydroxyacetone-P_int]
[3-Phosphoglycerate_int]
[2-Phosphoglycerate_int]
[Pyruvate_int]
8
7
6
5
4
3
2
1
BPG
PEP
9
Pyr
0
0
4
8
12
16
Time (h)
20
Previous models of the acid/solvent formation pathways in C.
acetobutylicum have been capable of replicating the behavior of specific batch
fermentations (Desai & Harris, 1999). Unfortunately, these models, if
created with the intention of predicting the behavior of C. acetobutylicum in
all environments, often come short of the anticipated goal. In the previous
experiment, the inaccuracy in the current models relies on the lack of
experimental data published in regards to the kinetics of each individual
enzyme in the pathway and the transcriptomics of the genes that code for
them. Nonetheless, these models furnish a useful starting point for future
predictive work given the advent of more experimental detail.
Nevertheless, the results of this experiment indicate that the current
understanding of carbohydrate utilization pathways of C. acetobutylicum is
extensive enough to qualitatively model the transport and degradation of
thirteen carbon sources. The pool of pyruvate and the sequential buildup of
intermediate metabolites that results from time course analysis on each
carbon source verifies the accuracy of this model. However, the buildup of
secondary metabolites, such as galactose-1-phosphate in the case of
galactose utilization, is indicative of mathematical errors that result from the
same lack of experimental data as the acid/solvent formation pathways.
Fortunately, computational modeling allows for a large degree of
malleability and, given applicable future publications, these errors can be
resolved. In the future, models will include more carbon sources, such as
cellulose and mannitol, as well as carbon-catabolite response elements .
These models, as well as the current one, will help researchers better
understand and modify butanol-production in C. acetobutylicum.
References
Time Course Simulation - Galactose
10
Concentration (mmol)
Since the advent of high-throughput genomic sequencing, the integration
of genomic, proteomic, metabalomic, and transcriptomic data in the
computational analysis of microorganisms has become increasingly
common (de Lorenzo, 2008). As of March 2010, thirty-five metabolic
models of various bacteria were available to the public in online databases
with exponentially more models becoming obtainable each year (Orth,
Theile, & Palsson, 2010). Creation of these models is becoming increasingly
easy as new modeling techniques, software, and technologies are becoming
available to construct and analyze metabolic networks: steady-state,
stoichiometric, time course, metabolic control, and flux-balance (Hoops et
al., 2006). Due to these recent advancements and increasing interest in
eliminating national dependence on foreign oil, the butanol-producing
Clostridium acetobutylicum has been examined thoroughly (Desai & Harris,
1999). The pathway that accomplishes this feat is dependent upon the flux
of pyruvate, a product of the transport and breakdown of multiple carbon
sources. Understanding the utilization of differential carbon sources in C.
acetobutylicum is important in order to better understand butanol-producing
capabilities. Creation of mathematical and visual computation
representations of the in vivo metabolic pathways of C. acetobutylicum is
feasible, due to advances in computational modeling and quantitative
analysis of organic systems, and useful for commercial and environmental
applications.
Materials and Methods (cont.)
24
28
Figure 1 (left): Galactose utilization pathway in C. acetobutylicum (excluding ATP and redox
couples). 10 mmol of galactose is utilized to form 9.525 mmol of pyruvate.
Graph 1(right): Time course simulation of galactose utilization pathway.
de Lorenzo, V. (2008). Systems biology approach to bioremediation. Current Opinions in
Biotechnology, 19, (579-589).
Desai, R. P., Harris, L. M., (1999). Metabolic flux analysis elucidates the importance of the
acid-formation pathways in regulating solvent production by Clostridium acetobutylicum.
Metabolic Engineering, 1(3), 206-213.
Hoops. S., Sahle, S., Gauges, R., Lee, C., Jürgen, P., Simus, N., … Kummer, U. (2006).
COPASI—a COmplex PAthway SImulator. Systems biology. 22(24), 2067-3074.
Orth, J.D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis? Nature
Biotechnology , 28(3), 245-248.
Servinksy, M., D., Keil, J. T., Dupuy, N.F., & Sund, C. J. (2010). Transcriptional analysis of
differential carbohydrate utilization by Clostridium acetobutylicum. Microbiology, 156. doi
10.1099/mic.0.03785-0.
Acknowledgements
I would like to thank Dr. Margaret Hurley, Mrs. Linda McDonough, and
Mr. Jacob Rosenthal for all their assistance and guidance.