Revision as of 17:22, 21 September 2018

Model

Achievement Purpose CO₂ uptake CO₂ metabolism Analysis Reference

Achievement

Figure out how much CO₂ uptake into the E.coli to make our project more effective.

We will realize how much carbon we can fix in our project.

In order to achieve these two goals, we try to figure out three main questions：

Purpose

We aim to develop the biological model to make our experimental data more useful, even more effective to achieve the optimization parameters in the model. Therefore, we can save time to try and error on doing experiment. After that, we can analyze the rate of production and consumption. In this way, we can calculate the amount of carbon dioxide uptake into the Escherichia coli (E.coli) and calculate how many CO₂ can be used in our system. In addition, we also want to realize how much carbon we can fix. We have to understand what’s going on in E. coli after uptaking CO₂. Since we integrated non-native carbon fixation pathway into E. coli to let E. coli utilize CO₂, we can simplify the CO₂ utilization pathway in engineered E. coli into two parts, CO₂ uptake and CO₂ metabolism.

CO₂ uptake

CO₂ exists in the phase of gas. Therefore, E. coli uptake CO₂ by diffusion. However, the native diffusion rate is really low, not to mention the dissolved rate. We clone CA (carbonic anhydrase) gene to speed up the diffusion rate. The higher diffusion rate, the higher CO₂ concentration maintained in engineered E. coli. Higher CO₂ concentration in engineered E. coli provides more steady CO₂ condition for downstream pathway.

Table 1. parameters of CO₂ uptake pathway

CO₂ uptake		Kf	Kr
R1	$${[CO_2]_{air} \rightarrow [CO_2]_{uptake}}$$	9.23E-04	4.78E-04
R2	$${[CO_2]_{uptake} + [H_2O] \rightarrow [H_2CO_3]}$$	0.062	23.7
R3	$${[H_2CO_3] \rightarrow [H^+] + [HCO_3^-]}$$	8000000	4.7E10
R4	$${[HCO_3^-] \rightarrow [H^+] + [CO_3^{2-}]}$$	3	5E10
R5	$${[H^+] + [HCO_3^-] \longleftrightarrow [H_2O] + [CO_2]_{uptake}}$$	Vmax	Km
R5		0.47333	0.0062

Result

Fig. 2 CO₂ uptake amount change with time in engineered E. coli without CA gene.

Fig. 3 CO₂ uptake amount change with time in engineered E. coli with CA gene.

CO₂ Metabolism

Xylose is the main source of our engineered E. coli to uptake CO₂. However, there is another pathway for xylose metabolism, which means that there is only a part of xylose that E. coli consumed will react with CO₂. We build a model to predict the different carbon flux between original xylose metabolism, PP pathway (Pentose Phosphate pathway), and our CO₂ bypass pathway.

Both of two pathways will end up at pyruvate, which is a key intersection in the network of metabolic pathways. We built a model of TCA cycle to explain how pyruvate play an energy precursor role and then is converted into biomass of E. coli.

Pathway

Fig. 4 Metabolic pathway of CO₂-utilization E. coli. The introduced CO₂-utilization bypass pathway composed of PRK and Rubisco is drawn in green line and noted by ” A” reaction, while the central carbon metabolic pathway including PP pathway and TCA cycle is drawn in blue line and yellow line and noted by “B” reaction and “C” reaction, respectively. See more detail in Kinetic law.

Table 2. Kinetic parameters of CO₂-utilization bypass pathway

CO₂ bypass pathway		Vmax (1/S)		KM (mM)
A1	$${Xylose \rightarrow Xylulose}$$ $${Xylulose + ATP \rightarrow X5P}$$	0.02456		124.55879
A2	$${X5P \rightarrow Ru5P}$$	0.08		12.5
A3	$${Ru5P \xrightarrow{PRK} RuBP}$$	225.81858		0.27
A4	$${RuBP + CO_2 \xrightarrow{RuBisCO} 3PG}$$	Vmax	Krubp		KCO₂
A4	$${RuBP + CO_2 \xrightarrow{RuBisCO} 3PG}$$	333.33	53		2.7

Table 3. Kinetic parameters of PP pathway in E. coli

Pentose Phosphate Pathway		Vmax (1/S)			KM (mM)
B1	$${Ru5P \rightarrow R5P}$$	0.13672			12.5
B2	$${X5P + R5P \rightarrow S7P + GAP}$$
	$${X5P \rightarrow S7P}$$	58.27			4.96E-09
	$${X5P \rightarrow GAP}$$	58.27			48
	$${R5P \rightarrow S7P}$$	58.27			0.008
	$${R5P \rightarrow GAP}$$	58.27			3.05E-07
B3	$${S7P + GAP \rightarrow F6P + E4P}$$
	$${S7P \rightarrow E4P}$$	58.27			2.524
	$${S7P \rightarrow F6P}$$	58.27			6.10E-09
	$${GAP \rightarrow E4P}$$	58.27			1.73E-07
	$${GAP \rightarrow F6P}$$	58.27			8.23E-09
B4	$${E4P + X5P \rightarrow F6P + GAP}$$
	$${E4P \rightarrow F6P}$$	1			1
	$${E4P \rightarrow GAP}$$	5.827E-4			1.733E-7
	$${X5P \rightarrow F6P}$$	0.05827			1.733E-7
	$${X5P \rightarrow GAP}$$	0.005827			48.8
B5	$${FBP \rightarrow GAP}$$	135.425			4
B6	$${DHAP \longleftrightarrow GAP}$$	Vmf	Vmr		Km_DHAP		Km_GAP
B6	$${DHAP \longleftrightarrow GAP}$$	0.2394	0.24		0.812		0.245
B7	$${FBP \rightarrow DHAP}$$	0.0001247			0.04
B8	$${F6P + ATP \rightarrow FBP + ADP}$$	88.2533			0.017
B9	$${F6P \rightarrow G6P}$$	88.2353			0.017
B10	$${G6P + NADP^+ \rightarrow 6PG + NADPH}$$	Vmax		Km_G6P		Km_NADP
B10	$${G6P + NADP^+ \rightarrow 6PG + NADPH}$$	11.736		0.12		0.0123
B11	$${6PG + NADP^+ \rightarrow Ru5P + NADPH}$$	1.0			0.044
B12	$${GAP + NADP^+ + ADP \rightarrow 3PGA + NADPH + ATP}$$	0.4			0.013
B13	$${3PGA \rightarrow PEP}$$	0.43478			0.23
B14	$${PEP \rightarrow PYR}$$	0.23148			0.45

Table 4. Kinetic parameters of TCA cycle in E. coli

TCA cycle		Vmf (mM/min)	Vmr (mM/min)	Ks (mM)		Kp (mM)
C1	$${PEP + HCO_3^- + NAD^+ \rightarrow OAA + NADH}$$	1	1	1		1
C2	$${AcCoA \rightarrow CIT}$$	64.8	0.648	0.05		0.12
C3	$${CIT \rightarrow ICIT}$$	31.2	0.312	1.7		0.7
C4	$${ICIT + NADP^+ \rightarrow AKG + NADPH + CO_2}$$	10.2	0.102	0.03		0.3
C5	$${AKG + NAD^+ \rightarrow AKG + NADPH + CO_2}$$	57.344	0.57344	0.1		1.0
C6	$${SUCCoA + ADP \rightarrow SUCC + ATP}$$	6.54	0.0651	0.015		0.15
C7	$${SUCC + FAD \rightarrow FUM + FADH_2}$$	1.02	1.02	0.12		0.15
C8	$${FUM \rightarrow MAL}$$	887.7	87.7	0.25		2.38
C9	$${MAL + NAD^+ \rightarrow OAA + NADH}$$	184	184	0.833		0.0443
C10	$${OAA \rightarrow CIT}$$	64.8	0.648	0.012		0.12
C11	$${PYR \rightarrow NADH + AcCoA + CO_2}$$	Vmax (1/s)		KM (mM)
C11	$${PYR \rightarrow NADH + AcCoA + CO_2}$$	0.00518		1.5
C12	$${ICIT \rightarrow GOX}$$	1.172	0.01172	0.145		0.13
C13	$${GOX \rightarrow MAL}$$	20	0.2	0.057		1.0
C14	$${ICIT \rightarrow SUCC + GOX}$$	Vmf (mM/min)	Vmr (mM/min)	Kicit (mM)	Ksucc (mM)		Kgox (mM)
C14	$${ICIT \rightarrow SUCC + GOX}$$	1.172	0.012	0.145	0.59		0.13

Table 5. Species of Metabolic pathway of CO₂-utilization E. coli

E4P	erythrose-4-phosphate
F6P	fructose-6-phosphate
FBP	fructose-1,6-bisphosphate
G6P	glucose-6-phosphate
GAP	glyceraldehyde-3-phosphate
PEP	phosphoenol pyruvate
PYR	pyruvate
R5P	ribose-5-phosphate
Ru5P	ribulose-5-phosphate
S7P	sedoheptulose-7-phosphate
X5P	xylulose-5-phosphate
6PG	6-phosphogluconate
DHAP	Dihydroxyacetone phosphate
3PG	3-Phosphoglyceric acid
RuBP	ribulose-1,5-bisphosphate
PRK	Phosphoribulokinase
Rubisco	Ribulose-1,5-bisphosphate carboxylase/ oxygenase
AcCoA	Acetyl-CoA
CIT	Citrate
ICIT	Isocitrate
AKG	Alpha-ketoglutarate
SUCCoA	Succinyl-CoA
SUCC	Succinate
FUM	Fumarate
MAL	Malate
OAA	Oxaloacetic acid
GOX	Glyoxylate

Xylose v.s. CO₂ analysis in CO₂ utilization efficiency

Two main sources of CO₂ metabolism in engineered E. coli are xylose and CO₂. CO₂ utilization rate varies under different condition. We use 5% CO₂ (about 2.6 mM) and 0.4% (about 26 mM) xylose in experiment as an optimization CO₂ utilization rate. Constant CO₂ condition and limited CO₂ condition also effects metabolism performance. In other words, open system and close system showed different results. Therefore, adjust initial concentration of CO₂ and Xylose and choose open or close system to see the change of pyruvate production.

Concentration of xylose

Concentration of CO₂

Close system / Open system

Reference

Jacqueline E. G, Christopher P. L, Maciek R. A. Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis. Metab Eng. 2017 Jan; 39: 9–18.
Uwe Sauer, Bernhard J. E. The PEP—pyruvate—oxaloacetate node as the switch point for carbon flux distribution in bacteria. FEMS Microbiology Reviews, Volume 29, Issue 4, 1 September 2005, Pages 765–794.

@@ Line 44: / Line 44: @@
                                  <div id="purpose">
                                      <h3>Purpose</h3>
-                                     <p>We aim to develop the biological model to make our experimental data more useful,
+                                     <p class="pcontent">We aim to develop the biological model to make our experimental data more useful,
                                          even more effective to achieve the optimization parameters in the model.
                                          Therefore, we can save time to try and error on doing experiment.

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