Transcript Genome-scale constraint-based metabolic model of Clostridium thermocellum School of Engineering Chris M. 1,3 Gowen , Seth B. Roberts , Stephen S. 1,2 Fong 1Department of Chemical and Life Science Engineering, Virginia Commonwealth.

Genome-scale constraint-based metabolic model of
Clostridium thermocellum
School of Engineering
Chris M.
1,3
Gowen ,
Seth B.
1
Roberts ,
Stephen S.
1,2
Fong
1Department of Chemical
and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA
2Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, USA
3Presenting author, contact: [email protected]
Cellulase
Enzymes
Typical process
Pretreatment
Enzymatic
Saccharification
Fermentation
Distillation
Cost prohibitive due to
supplemental enzymes
and additional process
steps
Cellulase
Enzymes
Simultaneous
Saccharification and
Fermentation
Consolidated
Bioprocessing[1]
Pretreatment
Pretreatment
Enzymatic Saccharification /
Fermentation
Biological Saccharification /
Fermentation
Distillation
Distillation
Current practice in
most pilot plants,
enzymes are costly
Obviates need for
additional enzymes
and maximizes
process efficiencies
Reactions
Metabolites (X)
Cellulose makes up roughly 60% of the dry weight of all plant biomass on earth and
therefore represents an extremely abundant and sustainable feedstock for the
production of liquid fuels. All current methods for the biochemical conversion of
cellulosic biomass to ethanol for fuel fall into three main categories:
2A + B → C + D
A
v1
0
B
.
0
.
0
.
0
D
.
0
…
.
0
·
C
1
S
Flux balance analysis [2]
max cellular objective
( fluxes)
 boundary constraints
Clostridium thermocellum is one of a number of organisms capable of direct
fermentation of cellulose to ethanol, and its cellulolytic system is one of the most
efficient known to researchers. This efficiency is achieved partly because C. thermocellum
assembles most of its cellulase enyzmes onto an extracellular, cell-associated scaffold. The
entire assembly is termed the “cellulosome” and maximizes synergies between the different
catalytic mechanisms of its cellulase arsenal and subsequent sugar uptake.
Ethanol
Acetate
Cellulose
Formate
Cellobiose
Lactate
Fructose
H2
CREDIT: DOE Joint Genome Institute
http://genome.jgi-psf.org/finished_microbes/cloth/cloth.home.html
CO2
The broad mixture of fermentation byproducts produced by C. thermocellum is reflective of
the fact that it has evolved towards its own ends, rather than for the production of ethanol.
We demonstrate here the creation of a genome-scale metabolic model of the metabolism of
C. thermocellum and discuss its use for model-guided metabolic engineering to optimize
production of ethanol from cellulosic substrates in C. thermocellum.
·
v
=
2
 networkstoichiometry
 thermodyn
amic constraints
FluxA
Any given flux state
can be defined as a
vector, and the
reaction matrix
combined with
boundary constraints
define the borders of a
solution space within which
the flux state must always
fall.
d[X] / dt
All reactions available to an organism
according to genome annotation and
biochemical evidence are compiled in a
stoichiometric matrix, S, which is part of a
genome-scale mass-balance problem.
Subject to :
Metabolic model of Clostridium thermocellum
Flux vector, v
Mass balance
statement
Metabolic solution space for growth of C. thermocellum on cellobiose
shows tradeoff between H2 and ethanol production
FluxB
Motivation
Reaction A
3
Boundary conditions are set based on observed substrate
uptake and byproduct secretion rates. Flux balance analysis
is then used to probe the resulting solution space by
maximizing a cellular objective such as growth rate within
the given constraints. The resulting vector describes the
predicted reaction fluxes throughout the model.
Results and Discussion
A constraint-based genome-scale metabolic model
has been constructed based on the published
annotation of Clostridium thermocellum’s genome
as well as the incorporation of biochemical
observation. The statistics of the resulting model are
shown in the table to the right. The model is unique
in its incorporation of proteomics data to account for
substrate-dependent production of the cellulosome.
Genome size
3.8 Mb
ORFs
3307
Included genes
432
Enzyme complexes
72
Isozyme cases
70
Reactions (excluding exchanges)
563
Transport
56
Gene associated
463
Non-gene associated intracellular
61
Non-gene associated transports
37
Distinct metabolites
Combined with flux balance analysis and suitable boundary constraints, the model is able
to closely match experimentally observed fermentation characteristics. For example,
researchers have long noted that C. thermocellum can be forced to increase ethanol
production by thermodynamically preventing H2 gas production [3].
The threedimensional depiction of the solution space (above, right) demonstrates that maximum
growth rate is achieved with no ethanol production, but as H2 secretion flux is restricted,
the maximum growth rate peak shifts towards higher ethanol production. Microorganisms
generally pursue (either through evolution or regulation) the maximum growth rate. This
heuristic also permits the use of this model as a tool for computational strain design. The
graph (below) shows single-gene deletions predicted to improve ethanol secretion at the
maximum growth rate. This technique can be used to inform genetic engineering decision
making.
529
Comparison of model predictions to experimental observations – C. thermocellum iSR432 was used to simulate growth in multiple conditions. Actual (□)
and predicted (▬) reaction flux rates are shown, and predicted fermentation product production rates are shown as ranges as determined by flux variability
analysis. For each simulation, the boundary fluxes for cellobiose, acetate, and formate were constrained to match the measured fluxes during (A) chemostat
growth on cellobiose and (B) fructose, and (C) batch growth on cellobiose.
Single gene deletions for which increased ethanol production is predicted.
References and Acknowledgements
The authors would like to thank J. Paul Brooks for his computational and operations expertise, David Hogsett
and Chris Herring for discussions regarding C. thermocellum physiology, Lee Lynd for generously providing C.
thermocellum cultures and Stephen Rogers and Evert Holwerda for providing valuable assistance.
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Microbial Genomics 2009