System Biology Project
Biosynthesis of Ethanol Using E.coli System
Group 11
生資所碩士班 924287 呂博凱
資工系博士班 928309 陳炯勳
資工系碩士班 924360 唐宗麟
Jan 20, 2005
The usage of ethanol
Ethanol is a clear, colorless liquid with a characteristic, agreeable odor. In dilute
aqueous solution, it has a somewhat sweet flavor, but in more concentrated solutions
it has a burning taste. Ethanol, CH3CH2OH, is an alcohol, a group of chemical
compounds whose molecules contain a hydroxyl group, –OH, bonded to a carbon
atom.
Ethanol melts at –114.1°C, boils at 78.5°C, and has a density of 0.789 g.mL-1 at
20°C. Its low freezing point has made it useful as the fluid in thermometers for
temperatures below –40°C, the freezing point of mercury, and for other
low-temperature purposes, such as for antifreeze in automobile radiators.
Ethanol is used as an automotive fuel by itself and can be mixed with gasoline
to form what has been called “gasohol”; the most common blends contain 10%
ethanol and 85% ethanol mixed with gasoline. Over 1 billion gallons of ethanol are
blended with gasoline every year in the United States. Because the ethanol molecule
contains oxygen, it allows the engine to more completely combust the fuel, resulting
in fewer emissions. Since ethanol is produced from plants that harness the power of
the sun, ethanol is also considered a renewable fuel. Therefore, ethanol has many
advantages as an automotive fuel.
The metabolic pathways, the enzymes and the genes involved in the
synthesis of ethanol
During glycolysis, cells convert simple sugars, such as glucose, into pyruvic acid,
with a net production of ATP and NADH. In the absence of a functioning electron
transport system for oxidative phosphorylation, at least 95% of the pyruvic acid is
consumed in short pathways which regenerate NAD+, an obligate requirement for
continued glycolysis and ATP production. The waste products of these NAD+
regeneration systems are commonly referred to as fermentation products. Under
conditions of oxygen insufficiency, microorganisms are particularly diverse in the
array of fermentation products, which are produced by different genera. These
products include organic acids, such as lactate, acetate, succinate, and butyrate, as
well as neutral products, such as ethanol, butanol, acetone, and butanediol.
Figure 1 represents the metabolic pathways involved in the biosynthesis of
ethanol, generated by the KEGG website. The green background shows the enzymes
that can be found in E.coli K-12 MG1655 strain. The red line shows the possible
metabolic pathways involved in the biosynthesis of ethanol. The blue numbering
shows the enzymes that may involve the biosynthesis of ethanol. There are nine
enzymes in the glycolysis pathway: sugar phosphotransferas, glucose-6-phosphate
isomerase, phosphofructokinase-2, fructose-bisphosphate aldolase, glyceraldehydes
-3-phosphate dehydrogenase, acylphosphate phosphohydrolase, phosphoglycerate
mutase, enolase, pyruvate kinase, respectively. They are encoded by ptsG, pgi, pfkB,
dhnA, gapC_2, pgk, gpmA and pykF genes, respectively.
There are two possible pathways in E.coli system that produce ethanol using
pyruvate as the reactant. Figure 2 represents the two possible pathways. In the
pathway 1, E coli is produced ethanol using pyruvate formate lyase (PFL) that
converts pyruvate to acetyl-coA. But it is necessary to find some means that increase
the flux of reaction 12, converting acetyl-coA to acetate. Acetate can convert to
ethanol using aldehyde dehydrogenase and alcohol dehydrogenase class III. In the
pathway 2, E coli is produced ethanol using pyruvate decarboxylase that converts
pyruvate to acetaldehyde directly.
Is E. Coli suitable for biosynthesis of ethanol?(LO, T et al. 1987; BS, MA et al. 2003)
The shortcoming of using the pathway 1 to produce ethanol
This fermentation pathway is unbalanced because one NADH, H+ is generated
for each pyruvate made from sugars, and two NADH, H+ are required for converting
pyruvate into ethanol. E. coli needs to balance it fermentation by also producing
acetic and succinic acids. This is the major disadvantage associated with using E.
coli cultures, a narrow and neutral pH growth range (ca. pH 6.0–8.0). According
to the reason, we think the pathway 1 in the E. coli system isn’t suitable for
biosynthesis of ethanol.
We chose the pathway 2 to produce the ethanol in E. coli system. The pathway is
expression of pyruvate decarboxylase that exists in Yeasts and Z. mobilis, in E.
coli system. Pyruvate decarboxylase converts pyruvate to acetaldehyde, the reactant
of producing ethanol. It consumes only one NADH, H+ for each ethanol produced.
The shortcomings of using pathway 2 to produce ethanol and the methods to solve
Early attempts to introduce a yeast-like pathway for converting pyruvate to
ethanol were unsuccessful because the native alcohol dehydrogenase (ADH) activity
of E. coli was not sufficient to achieve high ethanol yields. Ingram et al. isolated adh
II from a genomic library of Z. mobilis using a novel indicator medium for ADH
activity. E. coli transformed with a plasmid expressing pdc and adh II produced
ethanol almost exclusively. The adhII and pdc genes were co-expressed under the
control of the native lac promoter, and the construct was named the PET (production
of ethanol).
While plasmids carrying the adhII and pdc genes were not readily lost in the
absence of antibiotics, these strains were still too genetically unstable for use in
industrial fermentations. Ingrams research group sought to stabilize ethanol
production by integrating the genes into the chromosome of strain ATCC11303.
The PET operon was inserted into the pyruvate formate lyase (pfl) gene, in an attempt
to eliminate an enzyme competing for pyruvate.
However, the resulting construct yielded much lower amounts of ethanol than the
plasmid bearing strains. This was attributed to reduced gene dosage. A strategy was
developed for selecting mutants with increased adhII and pdc expression. The
integrated DNA contained a chloramphenicol resistance marker. As a result, the final
strain (KO11) was able to convert glucose and xylose to ethanol at yields 103 to
106% of theoretical. The extra ethanol was thought to arise from fermentation of
carbohydrates present in the rich medium that were not accounted for in the sugar
balance.
The strain has not been widely used for SSF (simultaneous saccharification and
fermentation), probably because the optimal culture pH (6.5) is not compatible with T.
reesei cellulase complex, which has a pH optimum of 4.6.
Propose experiments to determine the metabolic fluxes of the
network associated with ethanol(F, CM et al. 1999; Torres and Voit. 2002)
The implementation of the optimization method is diagramatically represented in
Fig. 3. This may be divided into three main steps. Firstly, we need to design the
mathematical model. Secondarily, we need to optimize the solution. Finally, we can
assess the solutions.
Design of the mathematical model
Firstly, we need to define the relevant variables. Anaerobic fermentation pathway
of the E.coli containing the PDC and ADHII enzymes is represented in Fig. 4. The
metabolites and enzymes involved in the pathway is showed in the figure legends. In
the GMA representation each rate law is written as a product of power law functions.
For a system consisting of n dependent and m independent variables, these rate laws
are written:
The power law representation can be derived from first principles for arbitrary
laws and is obtained by specifying a rate constant and writing a power term for each
variable that directly influences the rate law in question. The exponent parameters in
each power term are referred to as kinetic orders and defined as:
Where the subscript 0 indicates evaluation at a given steady state. The rate constants
are defined as:
Accordingly the standard form of the GMA system of equations is:
The model is described by the following set of differential equations:
There are two means to estimate the kinetic orders and rate constants.
Direct estimation from flux data
1. If the effect of Xj on flux Vi+ is to be determined, it would be ideal to keep all variable but Xj at
their typical values, and to vary Xj about its normial value.The Flux Vi+ becomes a unvariate function
of Xj, and this function is linear when plotted as the logarithm of Vi+ against the logarithm of Xj.
2.The Slope of the function is equivalent to the kinetic order gij(hij) and can estimate the rate constants.
Estimation from the literature presentations
Take the yeast system for example; the fluxes for the GMA take the following
power-law form:
Due to the inherent non-linearity of MM and GMA equations an assessment of
the model quality and robustness is not as straightforward as in the linear case. Once
the model has been established, those constraints necessary to guarantee cell viability
must be also defined. These are of two main types: the steady-state constraints and
the enzyme and metabolite concentration constraints. The former ensures that the
optimized solution is at steady state while the latter constrains variables to remain
within prescribed bounds. Additionally an appropriate objective function to be
optimized (e.g. yield or flux) must be selected and formulated in mathematical terms.
The optimization: a multi-start search algorithm
We use a multi-start search algorithm, an improved hillclimbing. As all
hillclimbing algorithms, the present one uses an iterative technique of improvement,
meaning that during a single iteration a new activity is selected from the
neighborhood of the current steady state. If the new activity set provides an improved
value of the objective function and the metabolite concentrations agree with the
imposed constraints, the new activity set becomes the current solution. Otherwise,
some other neighbor is selected randomly and tested against the current solution. The
search terminates if no further improvement is achieved. In the steady-state the model
reduces to a set of non-linear equations, where the independent variables (Yj) are the
enzyme activities and the dependent variables (Xi) are the metabolite concentrations.
The optimization routine was represented in Fig. 5.
Quality assessment of the solutions
Since the optimization itself does not address the question of stability, the
steady-state of the optimized solution must be checked with respect to its stability.
With this purpose the differential equations are integrated numerically after a
perturbation has been applied. The system is said to be stable (or metastable) if after a
perturbation recovers its initial (steady) state. Unstable steady-state solutions are
discarded. If this is the case the system is to be revisited after imposing more stringent
constraints.
Propose the ways to maximize yield
The maximization of the ethanol production rate requires modulation of up to six
enzymes.
There
are
sugar
transport
system,
VHK
(hexokinase),
VPFK
(phosphofructokinase), VGADP (glyceraldehyde 3-phosphate dehydrogenase), VPK
(pyruvate kinase), VATPase (ATPase), respectively.
The increase in the rate of ethanol production, VPK/(VPK) basal, is more than
50 times, and the yield of the optimized solution is around 100% of theoretical.
Figure 1. The metabolic pathways involved in the biosynthesis of ethanol. Green
background: Enzymes can be found in E.coli K-12 MG1655 strain. Red line: The
possible metabolic pathways involved in the biosynthesis of ethanol.
Figure 2.The two possible pathways involved in the biosynthesis of ethanol.
Figure 3. Flow diagram of the non-linear optimization
Figure 4. Anaerobic fermentation pathway of the E.coli containing the PDC and ADHII enzymes.
Metabolites: X1 (Glcin, intracellular glucose), X2 (G6P,glucose-6-phosphate), X3 (FDP, fructose
diphosphate), X4 (PEP, phosphoenol pyruvate), X5 (ATP). Enzymes: Y1 (Vin, sugar transport system)
Y2 (VHK, hexokinase), Y3 (VPFK,phosphofructokinase), Y4 (VGADP, glyceraldehyde 3-phosphate
dehydrogenase), Y5 (VPK, pyruvate kinase), Y6 (Vcarb, glycogen synthetase), Y7 (VATPase, ATPase)
Figure5. The optimization routine.
Reference
BS, D., C. MA, et al. (2003). "Bacteria engineered for fuel ethanol production: current
status." Appl Microbiol Biotechnol. 63: 258-266.
F, R.-A., R. CM, et al. (1999). "Non-linear optimization of biotechnological processes
by stochastic algorithms: application to the maximization of the production rate of
ethanol, glycerol and carbohydrates by Saccharomyces cerevisiae." Journal of
Biotechnology 68: 15-28.
LO, I., C. T, et al. (1987). "Genetic Engineering of Ethanol Production in Escherichia
coli." APPLIED AND ENVIRONMENTAL MICROBIOLOGY 53(10): 2420-2425.
Torres, N. V. and E. O. Voit. (2002). Pathway analysis and optimization in metabolic
engineering.