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Identification of active pathways of Chlorella protothecoides by elementary mode analysis integrated with fluxomic data
Algal Research 45 (2020) 101767
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Identification of active pathways of Chlorella protothecoides by elementary mode analysis integrated with fluxomic data ⁎
Lujing Rena,b, Xiaoman Sunc, Lihui Zhangc, Quanyu Zhaod, , He Huangb,c,d,e,
T
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a
College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), People's Republic of China c School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Nanjing 210023, People's Republic of China d School of Pharmaceutical Science, Nanjing Tech University, 30 Puzhu South Road, Nanjing 211816, People's Republic of China e State Key Laboratory of Materials-oriented Chemical Engineering, Nanjing Tech University, No. 5 Xinmofan Road, Nanjing 210009, People's Republic of China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Chlorella protothecoides Elementary mode analysis Fluxomics Metabolic pathway Microalgae
Microalgae are bio-factories for CO2 fixation, biofuel production, and high-value added product biosynthesis and wastewater treatment. Deep understandings of the intracellular pathways, metabolic regulations and physiological responses of microalgae are critical for process optimization and strain modification. In this study, a metabolic network in small scale was reconstructed for the green microalgae, Chlorella protothecoides. The reliability of this metabolic network model was confirmed by metabolic phenotype prediction. The pathway length distribution of all the elementary modes was calculated. These elementary modes were divided into 5 groups based on their macroreactions of elementary modes. The group with a short pathway length was that of lactate production. The group with a long pathway length included biomass formation. In addition, the active pathways were identified by decomposition of the determined metabolic fluxes under three conditions from references onto elementary modes with six objective functions, maximum biomass formation, minimum norm, maximum entropy production principle, maximum activity of the shortest pathway length by linear programming, maximum activity of the shortest pathway length by quadratic programming and maximum activity of the longest pathway length by linear programming. The identified active pathways were consistent with the physiological states of microalgae. These results were helpful for exploring metabolic regulatory mechanism in microalgae.
1. Introduction Microalgae are novel cell factories for CO2 fixation, biofuel production, and high-value added product biosynthesis and wastewater treatment. Deep understandings of the intracellular pathways, metabolic regulations and physiological responses of microalgae are critical for process optimization and strain modification. The metabolic regulation of microalgae is more complex than that of bacteria and yeast [1]. Microalgae have high photosynthetic activity under autotrophic conditions thus allowing a CO2 fixation through photosynthesis [2–4]. Some microalgae can grow under heterotrophic or mixotrophic conditions [5]. This provides a possible solution for wastewater treatment by microalgae because the organic matters, residual nitrogen and phosphate in wastewater can be utilized by microalgae [6,7]. The optimal productivity of carbohydrate or lipid could be achieved under different culture modes and conditions [8–10]. In general, heterotrophic culture has higher cell density and lipid yield than autotrophic culture so that it ⁎
is adopted for the production of biofuel, lutein and other products [11,12]. With the development of high throughput omics technologies, the genome [13,14], transcriptome [2,4,15], proteome [16], metabolome [17,18] and fluxome [19,20] are used to explore the metabolic regulation mechanisms at the levels of DNA, RNA, protein, metabolite and flux. In the view of systems biology, omics technologies are wet method while metabolic network reconstruction and metabolic flux analysis are dry method [21]. Both wet and dry methods provide valuable information for investigation of genotype-phenotype relations, biological performance and development potential [22–24]. Metabolic systems analysis is considered to be essential for algal biotechnology development [25]. Few experimental metabolic fluxes of Chlorella sp. were reported [19,20]. The intracellular metabolic fluxes under nitrogen sufficient and nitrogen-limited conditions were determined [20]. In addition, the metabolic fluxes under autotrophic and heterotrophic conditions were
Correspondence to: Q. Zhao, Nanjing Tech University, 30 Puzhu South Road, 211816 Nanjing, People's Republic of China. Correspondence to: H. Huang, Nanjing Normal University, 2 Xuelin Road, Nanjing 210023, People's Republic of China. E-mail addresses: [email protected] (Q. Zhao), [email protected] (H. Huang).
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https://doi.org/10.1016/j.algal.2019.101767 Received 6 October 2019; Received in revised form 9 December 2019; Accepted 16 December 2019 2211-9264/ © 2019 Elsevier B.V. All rights reserved.
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presented for C. protothecoides [19]. They are available to identify the dominant or active pathway in the metabolic network. Approximately twenty years ago, a small metabolic network of green microalgae, Chlorella pyrenoidosa, was reconstructed under autotrophic, heterotrophic and mixotrophic conditions with 67 reactions [5]. To data, several metabolic networks in large scale have been reconstructed for Chlorella variabilis with 1455 reactions [26], C. protothecoides with 272 reactions [19] and Chlorella vulgaris with 347 reactions [27]. The effects of light quality onto algal growth could be estimated based on the reconstructed metabolic network [26]. The predicted growth rates and lipid productions under different nitrogen concentrations conditions were consistent with the experimental results [19,27]. Flux balance analysis proposes metabolic flux distribution using a selected objective function [28]. The predicted fluxes could be compared with the metabolic phenotype and used to investigate the intracellular behaviors. There are thousands of enzymes in microalgae that catalyze the related biochemical reactions. Not all of the reactions have important influence under specific conditions. The so-called dominant or active pathway reflects relevant physiological states [29–31]. The identification of these active pathways could be based on thermodynamic analysis [32–34] or decomposition of metabolic fluxes onto elementary modes [29]. The identification of the active pathway under specific conditions in microalgae is helpful to investigate the physiological state and candidate genes for modifications [35]. The elementary flux mode or the elementary mode is the minimal set of enzymes operating under steady state [36]. It is non-decomposable. In general, each elementary mode is composed of reactions from substrate uptake reaction, intracellular reactions and product transport reaction or intracellular cycles. The elementary mode is widely used for structure and robustness analysis of the metabolic network, metabolic regulation analysis, estimation of metabolic flux of mutants, rational design of microbial strain and dynamic model construction [37,38]. It was also used for the lipid yield analysis of C. protothecoides [39]. The identification of the active elementary mode could be achieved by the decomposition of metabolic flux onto the elementary mode [40–42]. The possible solution of elementary mode coefficients could be achieved based on equalities with the constrains of the determined metabolic fluxes. It was proved that there were multiple solutions for a specific metabolic phenotype so that the solutions varied with different objective functions [36]. A suitable objective function is important for both flux balance analysis and decomposition of metabolic flux onto the elementary mode [43,44]. In this study, a metabolic network in small scale was reconstructed. The determined metabolic fluxes under normal culture and nitrogen starvation conditions were decomposed onto elementary modes. The active pathways were identified based on several selected objective functions.
Fig. 1. Metabolic network of C. protothecoides under heterotrophic conditions.
2.2. Decomposition of metabolic fluxes onto elementary mode using several algorithms Elementary flux modes under different culture conditions were calculated by CellNetAnalyzer [45]. The elementary mode matrix, E, was obtained in which the row was the reaction and the column was the elementary mode. The metabolic fluxes could be calculated by Eq. (1). (1)
V = E⋅λ
where V is flux vector and λ is elementary mode coefficient (λi ≥ 0). The determined metabolic fluxes by 13C tracer experiments were from studies of Xiong et al. [20] and Wu et al. [19]. Then, these metabolic fluxes were decomposed on the elementary modes by the Eq. (2). (2)
Ed⋅λ = Vd
2. Materials and methods
In which, Ed is the submatrix of the elementary mode matrix and Vd is the selected determined fluxes. In general, it is necessary to solve an underdetermined problem by the optimization method [40–42,46]. The objective functions are shown in Table 1. Elementary mode probability (EMP), ρ, was defined by Eq. (3) [46,47].
2.1. Reconstruction of the metabolic network of C. protothecoides A metabolic network in small scale was reconstructed for C. protothecoides, as shown in Fig. 1. There were 44 reactions and 48 metabolites in this model. It was suitable for the metabolic network analysis of C. protothecoides under heterotrophic culture conditions. Most of the reactions were related to central carbon metabolism including the glycolysis pathway, the pentose phosphate pathway and the citric acid cycle. The other reactions belonged to energy metabolism, cofactor metabolism and biomass formation reaction. Under different culture conditions, the biomass components varied so that the coefficients in the biomass formation reaction were different for three culture conditions. The metabolic reactions were also shown in a supplementary file.
ρi =
eglucose
uktake, i⋅λ i
vglucose
uptake
(3)
In which eglucose uptake, i is the element in the elementary mode matrix, E, for the glucose uptake reaction and the i-th elementary mode, vglucoseuptake is the determined flux for glucose uptake. The sum of the total elementary mode probabilities is one shown in Eq. (4).
∑ ρi = 1 i
2
(4)
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Table 1 Objective functions for evaluation of elementary mode coefficients. No.
Objective function
Equation
Optimization method
Ref.
OBJ-I
Maximum biomass formation
max ∑ e biomass, i λ i
Linear programming
[46]
Quadratic programming
[42]
Nonlinear programing
[46,47]
Linear programming
[41]
Quadratic programming
[40]
Linear programming
This study
i
OBJ-II
Minimum norm
min
OBJ-III
Maximum entropy production principle
max − ∑ ρi⋅ln ρi
OBJ-IV
Maximum activity of the shortest pathways by LP
∑ λ i2 i i
min ∑ Li⋅λ i i
OBJ-V
Maximum activity of the shortest pathways by QP
min ∑ Li2⋅λ i2 i
OBJ-VI
Maximum activity of the longest pathways by LP
max ∑ Li⋅λ i i
LP, linear programming; QP, quadratic programming.
The active elementary mode could be investigated by elementary mode probability.
Table 2 Macro-reactions for groups of elementary modes. Group
Macro-reaction
I II III IV V
1 3 1 1 1
2.3. Topological analysis of the elementary mode A pathway length of one elementary mode is defined as the number of reactions with a nonzero element in this elementary mode. It was used to decompose metabolic flux onto the elementary mode [40,41]. In the reconstructed metabolic model of this study, glucose, lactate, O2, CO2 and NH4 were the external metabolites. Each elementary mode could be converted to a macroreaction with only these external metabolites. The stoichiometric matrix for external metabolites, Se, was obtained from the reconstructed metabolic network in which the rows are the external metabolites and the columns are the reactions. The macroreaction matrix, Mr, could be obtained by Eq. (5).
Mr = Se⋅E
Glucose Glucose Glucose Glucose Glucose
→ + + + +
2 lactate 3 O2 → 3 CO2 + 5 lactate 6 O2 → 6 CO2 c1 O2 + c2 NH4 → c3 CO2 + c4 biomass c1 O2 + c2 NH4 → c3 CO2 + c4 biomass + c5 lactate
c1, c2, c3, c4 and c5 are coefficients in macro-reactions.
Group 3 was the full oxidation of glucose to CO2. Water was not involved in these metabolic networks so that water was ignored in the macroreactions. Both group 4 and 5 were related to the biosynthesis of biomass. In addition, group 5 included lactate formation. The pathway length distribution of elementary modes under culture conditions [19] is shown in Fig. 2. There were 3 elementary modes in group 1 and their pathway lengths were 14 or 16. Five elementary modes were in group 2 and their pathway lengths were in the range of 27–29. There were two parts of group 3. One part had 25–27 reactions in their elementary modes and there were 32–34 reactions in their elementary modes of another part. The pathway lengths were from 31 to 38 in group 4. There were 116 elementary modes in group 5 and the pathway lengths of 56 elementary modes in them were 36. Biomass formation required many precursors so that the elementary modes including biomass formation (group 4 and 5), had a related longer
(5)
where the row of Mr is the external metabolites and its column is the elementary mode. Se is the stoichiometric matrix only including the external metabolites. The elementary modes could be decomposed into several groups. 3. Results 3.1. Metabolic network reconstruction The reliability of the reconstructed metabolic network was evaluated by the predicted results of the metabolic phenotype and they were compared with the experimental data [19,20]. The specific growth rate under heterotrophic conditions was estimated based on the reconstructed metabolic network when the glucose uptake rate was 0.3028 mmol dry cell wt−1 h−1. The estimated value of the specific growth rate and the CO2 excretion rate were 0.0283 h−1 and 0.5582 mmol dry cell wt−1 h−1, respectively. The experimental specific growth rate was 0.0257 h−1 and the determined CO2 excretion rate was 0.5841 mmol dry cell wt−1 h−1 [19]. The estimated specific growth rates under high and low nitrogen concentration conditions were 0.0822 mmol g−1 h−1 and 0.0586 mmol g−1 h−1, respectively. They were similar to the experimental data [19,20]. It was proved that the reconstructed models were reliable. 3.2. Elementary mode analysis and topological analysis The total numbers of elementary modes under the three conditions were 207, 208 and 207, respectively. The pathway length and macroreaction of each elementary mode in metabolic network model 1 were calculated by Eq. (5). The elementary modes were divided into 5 groups based on their macroreactions shown in Table 2. Group 1 was the full conversion of glucose to lactate under anaerobic conditions. Group 2 was the biosynthesis of lactate with glucose under aerobic conditions.
Fig. 2. Pathway length distribution of the elementary modes under heterotrophic conditions. 3
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shown in Fig. 8. The reactions encoded by pgi, ppc, aceA, aceB, ldh and lactate transport were not included in the four active pathways. It should be mentioned that elementary modes 7, 9, and 11 did not include NH4 uptake, mae and biomass formation reaction. These active pathways corresponded to low nitrogen conditions [20]. In general, low nitrogen concentration is a nutrient limitation stress leading to lipid accumulation and less biomass formation. Under the three culture conditions, elementary modes were calculated. The elementary mode 154 in the three conditions was different.
pathway length than those for the biosynthesis of lactate (groups 1 and 2). In the previous study for elementary mode analysis of Escherichia coli, some elementary modes with short pathway length (less than 38) were related to ATP production and fermentation product formation, and other with long pathway length (more than 120) were for biomass formation [47]. 3.3. Decomposition of metabolic flux onto the elementary mode The ranks of three elementary mode matrixes were 7 and the numbers of elementary modes were not less than 207 so that the optimizations in this study based on Eq. (2) were underdetermined ones. There were more than 200 elementary modes although only some of them were active under specific culture conditions. These active pathways were identified by Eq. (2) with six objective functions. Both the elementary mode coefficient (λ) and elementary mode probability (EMP, ρ) are non-negative but they are different. λ may be larger than 1 [46,47] while ρ is not more than 1 based on Eq. (4). Each elementary mode represents one possible microscopic state and the macroscopic metabolic phenotype is the collection of these microscopic states [47]. The contribution of each elementary mode is evaluated by elementary mode probability. The estimated elementary mode probabilities are shown in Fig. 3 with the experimental metabolic fluxes under heterotrophic conditions [19] (culture condition 1). The single active pathway was identified by maximum biomass formation (OBJ-I) and maximum activity of the longest pathways (OBJ-VI) because the EMPs were approximately 0.9 for these elementary modes. The most active pathways were elementary mode 159 by OBJ-I and elementary mode 157 by OBJ-VI, respectively. Multiple active pathways were found by minimum norm (OBJ-II), maximum entropy production principle (OBJ-III), maximum activity of the shortest pathway by LP (OBJ-IV) and maximum activity of the shortest pathway by QP (OBJ-V). The elementary mode 157 was also identified as the most active pathway by the maximum entropy production principle (OBJ-III). The other active pathways were elementary mode 129 and 207. These active pathways are shown in Fig. 4. The reactions encoded by aceA, aceB, pntA, ATP drain, lactate transport and ldhA were not involved in these active pathways. In particular, zwf, pgl, gnd and ppc were missing in elementary mode 157. These results are consistent with the experimental metabolic flux distribution where the metabolic fluxes through the pentose phosphate pathway were very low [19]. The estimated elementary mode probabilities are shown in Fig. 5 with the experimental metabolic fluxes under heterotrophic and high nitrogen concentration conditions [20] (culture condition 2). It is interesting that the estimated elementary mode probabilities by OBJ-I were almost same as those by OBJ-II and OBJ-V. The top four active elementary modes are elementary mode 138, 103, 154 and 194. It should be mentioned that these pathways were also active in the results by OBJ-III while elementary mode 103 was less active than the others. The most active pathway by OBJ-III is elementary mode 10. The EMP of elementary mode 159 was 0.496 and the other active pathways included elementary mode 141, 161, 8, 6 and 10 by OBJ-VI. This result was significantly different to those for OBJ-I, OBJ-II, OBJ-III, OBJ-IV and OBJ-V. These active pathways, elementary mode 138, 10, 154 and 194, are shown in Fig. 6. The whole pentose phosphate pathway is not involved in elementary mode 10 so that the contribution of the pentose phosphate pathway was proposed by other elementary modes. The estimated elementary mode probabilities are shown in Fig. 7 with the experimental metabolic fluxes under heterotrophic and low nitrogen concentration conditions [19] (culture condition 3). Elementary modes 7, 9 and 11 were identified as active pathways by OBJ-I, OBJ-II, OBJ-IV, OBJ-V and OBJ-VI. Elementary mode 7 was inactive by OBJ-III although the number of active pathways by OBJ-III was more than the others. Elementary mode 154 was also by OBJ-II, OBJ-III and OBJ-V. These active pathways, elementary mode 7, 9, 11 and 154, are
4. Discussion In general, the active pathways identified by different objective functions were different. Maximum biomass formation is widely used in flux balance analysis [48]. Microorganisms trend to survive under limited nutrients conditions and a rapid growth is beneficial to overcome the environmental stress. It was considered that the minimum norm assigned the maximum elementary mode coefficient to the elementary modes close to the actual state of the whole biological system [29,42]. It was proved that the maximum entropy production principle was better for metabolic flux prediction by enzyme control flux algorithm [37] than maximum biomass formation and minimum norm [46]. It is presumed that microalgae display maximum biomass formation, maximum activity of metabolic pathway or other optimal states but it is also proved that microorganism reach a suboptimal state in a real system [43]. In addition, the suboptimal state varies under different culture conditions. Maximum entropy production principle does not use the hypothesis based on physiological state of microalgae so that it could propose a reliable estimation for decomposing metabolic flux onto elementary modes. It was shown in Section 3.2 that elementary modes with short pathway length corresponded to lactate production in this study. In some cases, the shortest elementary mode was an internal cycle that had no contribution to metabolic phenotype. The longest elementary modes in this study were related to biomass formation and lactate production. The application potential of these algorithms using pathway activity, OBJ-IV, OBJ-V and OBJ-VI, could be investigated in a future study. 5. Conclusions In this study, the active pathways were identified by decomposition of the determined metabolic fluxes onto elementary modes. Six objective functions, including a new one, were compared for these optimizations. The obtained results were consistent with cellular physiological states. The elementary modes were divided into 5 groups. They were characterized by pathway length distribution. These results were helpful to explore relationships between metabolic genotype and phenotype by integrating fluxomic data into metabolic pathways of C. protothecoides. Statement of informed consent, human/animal rights No conflicts, informed consent, or human or animal rights are applicable to this study. CRediT authorship contribution statement Lujing Ren: Formal analysis, Writing - original draft, Writing - review & editing. Xiaoman Sun: Formal analysis, Writing - original draft, Writing - review & editing. Lihui Zhang: Writing - review & editing. Quanyu Zhao: Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing, Project administration. He Huang: Supervision, Writing - review & editing. 4
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Fig. 3. Estimated elementary mode probabilities (EMP) by six objective functions. (A) Maximum biomass formation; (B) minimum norm; (C) maximum entropy production principle; (D) maximum activity of the shortest pathways by LP; (E) maximum activity of the shortest pathways by QP; (F) maximum activity of the longest pathways. The experimental data were from [19] and the microalgae were cultivated under heterotrophic conditions. 5
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Fig. 4. Selected active pathways by decomposition of metabolic fluxes onto elementary modes. (A) Elementary mode 157; (B) elementary mode 159; (C) elementary mode 129; (D) elementary mode 207. The experimental data were from [19] and the microalgae were cultivated under heterotrophic conditions.
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Fig. 5. Estimated elementary mode probabilities (EMP) by six objective functions. (A) Maximum biomass formation; (B) minimum norm; (C) maximum entropy production principle; (D) maximum activity of the shortest pathways by LP; (E) maximum activity of the shortest pathways by QP; (F) maximum activity of the longest pathways. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and high nitrogen conditions. 7
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Fig. 6. Selected active pathways by decomposition of metabolic fluxes onto elementary modes. (A) Elementary mode 10; (B) elementary mode 138; (C) elementary mode 154; (D) elementary mode 194. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and high nitrogen conditions.
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Fig. 7. Estimated elementary mode probabilities (EMP) by six objective functions. (A) Maximum biomass formation; (B) minimum norm; (C) maximum entropy production principle; (D) maximum activity of the shortest pathways by LP; (E) maximum activity of the shortest pathways by QP; (F) maximum activity of the longest pathways. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and low nitrogen conditions. 9
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Fig. 8. Selected active pathways by decomposition of metabolic fluxes onto elementary modes. (A) Elementary mode 7; (B) elementary mode 9; (C) elementary mode 11; (D) elementary mode 154. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and low nitrogen conditions.
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Declaration of competing interest The authors declare no competing financial interests. Acknowledgement This study is supported by National Natural Science Foundation of China (21576278). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.algal.2019.101767. References [1] M. Cecchin, S. Benfatto, F. Griggio, A. Mori, S. Cazzaniga, N. Vitulo, M. Delledonne, M. Ballottari, Molecular basis of autotrophic vs mixotrophic growth in Chlorella sorokiniana, Sci. Rep. 8 (2018) 6465. [2] D. Cheng, X. Li, Y. Yuan, C. Yang, T. Tang, Q. Zhao, Y. Sun, Adaptive evolution and carbon dioxide fixation of Chlorella sp. in simulated flue gas, Sci. Total Environ. 650 (2019) 2931–2938. [3] D. Li, L. Wang, Q. Zhao, W. Wei, Y. Sun, Improving high carbon dioxide tolerance and carbon dioxide fixation capability of Chlorella sp. by adaptive laboratory evolution, Bioresour. Technol. 185 (2015) 269–275. [4] X. Li, Y. Yuan, D. Cheng, J. Gao, L. Kong, Q. Zhao, W. Wei, Y. Sun, Exploring stress tolerance mechanism of evolved freshwater strain Chlorella sp. S30 under 30 g/L salt, Bioresour. Technol. 250 (2018) 495–504. [5] C. Yang, Q. Hua, K. Shimizu, Energetics and carbon metabolism during growth of microalgal cells under photoautotrophic, mixotrophic and cyclic light-autotrophic/ dark-heterotrophic conditions, Biochem. Eng. J. 6 (2000) 87–102. [6] L. Wang, C. Xue, L. Wang, Q. Zhao, W. Wei, Y. Sun, Strain improvement of Chlorella sp. for phenol biodegradation by adaptive laboratory evolution, Bioresour. Technol. 205 (2016) 264–268. [7] W. Zhou, Y. Li, M. Min, B. Hu, P. Chen, R. Ruan, Local bioprospecting for high-lipid producing microalgal strains to be grown on concentrated municipal wastewater for biofuel production, Bioresour. Technol. 102 (2011) 6909–6919. [8] D. Cheng, D. Li, Y. Yuan, L. Zhou, X. Li, T. Wu, L. Wang, Q. Zhao, W. Wei, Y. Sun, Improving carbohydrate and starch accumulation in Chlorella sp. AE10 by a novel two-stage process with cell dilution, Biotechnol. Biofuels 10 (2017) 75. [9] Q. Hu, M. Sommerfeld, E. Jarvis, M. Ghirardi, M. Posewitz, M. Seibert, A. Darzins, Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances, Plant J. 54 (2008) 621–639. [10] Y. Yuan, H. Liu, X. Li, W. Qi, D. Cheng, T. Tang, Q. Zhao, W. Wei, Y. Sun, Enhancing carbohydrate productivity of Chlorella sp. AE10 in semi-continuous cultivation and unraveling the mechanism by flow cytometry, Appl. Biochem. Biotechnol. 185 (2018) 419–433. [11] J. Hu, D. Nagarajan, Q. Zhang, J.S. Chang, D.J. Lee, Heterotrophic cultivation of microalgae for pigment production: a review, Biotechnol. Adv. 36 (2018) 54–67. [12] X. Miao, Q. Wu, Biodiesel production from heterotrophic microalgal oil, Bioresour. Technol. 97 (2006) 841–846. [13] M.B. Arriola, N. Velmurugan, Y. Zhang, M.H. Plunkett, H. Hondzo, B.M. Barney, Genome sequences of Chlorella sorokiniana UTEX 1602 and Micractinium conductrix SAG 241.80: implications to maltose excretion by a green alga, Plant J. 93 (2018) 566–586. [14] C.J. Joshi, C.A.M. Peebles, A. Prasad, Modeling and analysis of flux distribution and bioproduct formation in Synechocystis sp PCC 6803 using a new genome-scale metabolic reconstruction, Algal Res. 27 (2017) 295–310. [15] L. Zhou, D. Cheng, L. Wang, J. Gao, Q. Zhao, W. Wei, Y. Sun, Comparative transcriptomic analysis reveals phenol tolerance mechanism of evolved Chlorella strain, Bioresour. Technol. 227 (2017) 266–272. [16] J. Longworth, D. Wu, M. Huete-Ortega, P.C. Wright, S. Vaidyanathan, Proteome response of Phaeodactylum tricornutum, during lipid accumulation induced by nitrogen depletion, Algal Res. 18 (2016) 213–224. [17] X. Yu, X. Niu, X. Zhang, G. Pei, J. Liu, L. Chen, W. Zhang, Identification and mechanism analysis of chemical modulators enhancing astaxanthin accumulation in Haematococcus pluvialis, Algal Res. 11 (2015) 284–293. [18] C. Zhang, L. Zhang, J. Liu, The interrelation between photorespiration and astaxanthin accumulation in Haematococcus pluvialis using metabolomic analysis, Algal Res. 41 (2019) 101520. [19] C. Wu, W. Xiong, J. Dai, Q. Wu, Genome-based metabolic mapping and 13C flux analysis reveal systematic properties of an oleaginous microalga Chlorella protothecoides, Plant Physiol. 167 (2015) 586–599.
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Identification of active pathways of Chlorella protothecoides by elementary mode analysis integrated with fluxomic data ⁎
Lujing Rena,b, Xiaoman Sunc, Lihui Zhangc, Quanyu Zhaod, , He Huangb,c,d,e,
T
⁎⁎
a
College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), People's Republic of China c School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Nanjing 210023, People's Republic of China d School of Pharmaceutical Science, Nanjing Tech University, 30 Puzhu South Road, Nanjing 211816, People's Republic of China e State Key Laboratory of Materials-oriented Chemical Engineering, Nanjing Tech University, No. 5 Xinmofan Road, Nanjing 210009, People's Republic of China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Chlorella protothecoides Elementary mode analysis Fluxomics Metabolic pathway Microalgae
Microalgae are bio-factories for CO2 fixation, biofuel production, and high-value added product biosynthesis and wastewater treatment. Deep understandings of the intracellular pathways, metabolic regulations and physiological responses of microalgae are critical for process optimization and strain modification. In this study, a metabolic network in small scale was reconstructed for the green microalgae, Chlorella protothecoides. The reliability of this metabolic network model was confirmed by metabolic phenotype prediction. The pathway length distribution of all the elementary modes was calculated. These elementary modes were divided into 5 groups based on their macroreactions of elementary modes. The group with a short pathway length was that of lactate production. The group with a long pathway length included biomass formation. In addition, the active pathways were identified by decomposition of the determined metabolic fluxes under three conditions from references onto elementary modes with six objective functions, maximum biomass formation, minimum norm, maximum entropy production principle, maximum activity of the shortest pathway length by linear programming, maximum activity of the shortest pathway length by quadratic programming and maximum activity of the longest pathway length by linear programming. The identified active pathways were consistent with the physiological states of microalgae. These results were helpful for exploring metabolic regulatory mechanism in microalgae.
1. Introduction Microalgae are novel cell factories for CO2 fixation, biofuel production, and high-value added product biosynthesis and wastewater treatment. Deep understandings of the intracellular pathways, metabolic regulations and physiological responses of microalgae are critical for process optimization and strain modification. The metabolic regulation of microalgae is more complex than that of bacteria and yeast [1]. Microalgae have high photosynthetic activity under autotrophic conditions thus allowing a CO2 fixation through photosynthesis [2–4]. Some microalgae can grow under heterotrophic or mixotrophic conditions [5]. This provides a possible solution for wastewater treatment by microalgae because the organic matters, residual nitrogen and phosphate in wastewater can be utilized by microalgae [6,7]. The optimal productivity of carbohydrate or lipid could be achieved under different culture modes and conditions [8–10]. In general, heterotrophic culture has higher cell density and lipid yield than autotrophic culture so that it ⁎
is adopted for the production of biofuel, lutein and other products [11,12]. With the development of high throughput omics technologies, the genome [13,14], transcriptome [2,4,15], proteome [16], metabolome [17,18] and fluxome [19,20] are used to explore the metabolic regulation mechanisms at the levels of DNA, RNA, protein, metabolite and flux. In the view of systems biology, omics technologies are wet method while metabolic network reconstruction and metabolic flux analysis are dry method [21]. Both wet and dry methods provide valuable information for investigation of genotype-phenotype relations, biological performance and development potential [22–24]. Metabolic systems analysis is considered to be essential for algal biotechnology development [25]. Few experimental metabolic fluxes of Chlorella sp. were reported [19,20]. The intracellular metabolic fluxes under nitrogen sufficient and nitrogen-limited conditions were determined [20]. In addition, the metabolic fluxes under autotrophic and heterotrophic conditions were
Correspondence to: Q. Zhao, Nanjing Tech University, 30 Puzhu South Road, 211816 Nanjing, People's Republic of China. Correspondence to: H. Huang, Nanjing Normal University, 2 Xuelin Road, Nanjing 210023, People's Republic of China. E-mail addresses: [email protected] (Q. Zhao), [email protected] (H. Huang).
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https://doi.org/10.1016/j.algal.2019.101767 Received 6 October 2019; Received in revised form 9 December 2019; Accepted 16 December 2019 2211-9264/ © 2019 Elsevier B.V. All rights reserved.
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presented for C. protothecoides [19]. They are available to identify the dominant or active pathway in the metabolic network. Approximately twenty years ago, a small metabolic network of green microalgae, Chlorella pyrenoidosa, was reconstructed under autotrophic, heterotrophic and mixotrophic conditions with 67 reactions [5]. To data, several metabolic networks in large scale have been reconstructed for Chlorella variabilis with 1455 reactions [26], C. protothecoides with 272 reactions [19] and Chlorella vulgaris with 347 reactions [27]. The effects of light quality onto algal growth could be estimated based on the reconstructed metabolic network [26]. The predicted growth rates and lipid productions under different nitrogen concentrations conditions were consistent with the experimental results [19,27]. Flux balance analysis proposes metabolic flux distribution using a selected objective function [28]. The predicted fluxes could be compared with the metabolic phenotype and used to investigate the intracellular behaviors. There are thousands of enzymes in microalgae that catalyze the related biochemical reactions. Not all of the reactions have important influence under specific conditions. The so-called dominant or active pathway reflects relevant physiological states [29–31]. The identification of these active pathways could be based on thermodynamic analysis [32–34] or decomposition of metabolic fluxes onto elementary modes [29]. The identification of the active pathway under specific conditions in microalgae is helpful to investigate the physiological state and candidate genes for modifications [35]. The elementary flux mode or the elementary mode is the minimal set of enzymes operating under steady state [36]. It is non-decomposable. In general, each elementary mode is composed of reactions from substrate uptake reaction, intracellular reactions and product transport reaction or intracellular cycles. The elementary mode is widely used for structure and robustness analysis of the metabolic network, metabolic regulation analysis, estimation of metabolic flux of mutants, rational design of microbial strain and dynamic model construction [37,38]. It was also used for the lipid yield analysis of C. protothecoides [39]. The identification of the active elementary mode could be achieved by the decomposition of metabolic flux onto the elementary mode [40–42]. The possible solution of elementary mode coefficients could be achieved based on equalities with the constrains of the determined metabolic fluxes. It was proved that there were multiple solutions for a specific metabolic phenotype so that the solutions varied with different objective functions [36]. A suitable objective function is important for both flux balance analysis and decomposition of metabolic flux onto the elementary mode [43,44]. In this study, a metabolic network in small scale was reconstructed. The determined metabolic fluxes under normal culture and nitrogen starvation conditions were decomposed onto elementary modes. The active pathways were identified based on several selected objective functions.
Fig. 1. Metabolic network of C. protothecoides under heterotrophic conditions.
2.2. Decomposition of metabolic fluxes onto elementary mode using several algorithms Elementary flux modes under different culture conditions were calculated by CellNetAnalyzer [45]. The elementary mode matrix, E, was obtained in which the row was the reaction and the column was the elementary mode. The metabolic fluxes could be calculated by Eq. (1). (1)
V = E⋅λ
where V is flux vector and λ is elementary mode coefficient (λi ≥ 0). The determined metabolic fluxes by 13C tracer experiments were from studies of Xiong et al. [20] and Wu et al. [19]. Then, these metabolic fluxes were decomposed on the elementary modes by the Eq. (2). (2)
Ed⋅λ = Vd
2. Materials and methods
In which, Ed is the submatrix of the elementary mode matrix and Vd is the selected determined fluxes. In general, it is necessary to solve an underdetermined problem by the optimization method [40–42,46]. The objective functions are shown in Table 1. Elementary mode probability (EMP), ρ, was defined by Eq. (3) [46,47].
2.1. Reconstruction of the metabolic network of C. protothecoides A metabolic network in small scale was reconstructed for C. protothecoides, as shown in Fig. 1. There were 44 reactions and 48 metabolites in this model. It was suitable for the metabolic network analysis of C. protothecoides under heterotrophic culture conditions. Most of the reactions were related to central carbon metabolism including the glycolysis pathway, the pentose phosphate pathway and the citric acid cycle. The other reactions belonged to energy metabolism, cofactor metabolism and biomass formation reaction. Under different culture conditions, the biomass components varied so that the coefficients in the biomass formation reaction were different for three culture conditions. The metabolic reactions were also shown in a supplementary file.
ρi =
eglucose
uktake, i⋅λ i
vglucose
uptake
(3)
In which eglucose uptake, i is the element in the elementary mode matrix, E, for the glucose uptake reaction and the i-th elementary mode, vglucoseuptake is the determined flux for glucose uptake. The sum of the total elementary mode probabilities is one shown in Eq. (4).
∑ ρi = 1 i
2
(4)
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Table 1 Objective functions for evaluation of elementary mode coefficients. No.
Objective function
Equation
Optimization method
Ref.
OBJ-I
Maximum biomass formation
max ∑ e biomass, i λ i
Linear programming
[46]
Quadratic programming
[42]
Nonlinear programing
[46,47]
Linear programming
[41]
Quadratic programming
[40]
Linear programming
This study
i
OBJ-II
Minimum norm
min
OBJ-III
Maximum entropy production principle
max − ∑ ρi⋅ln ρi
OBJ-IV
Maximum activity of the shortest pathways by LP
∑ λ i2 i i
min ∑ Li⋅λ i i
OBJ-V
Maximum activity of the shortest pathways by QP
min ∑ Li2⋅λ i2 i
OBJ-VI
Maximum activity of the longest pathways by LP
max ∑ Li⋅λ i i
LP, linear programming; QP, quadratic programming.
The active elementary mode could be investigated by elementary mode probability.
Table 2 Macro-reactions for groups of elementary modes. Group
Macro-reaction
I II III IV V
1 3 1 1 1
2.3. Topological analysis of the elementary mode A pathway length of one elementary mode is defined as the number of reactions with a nonzero element in this elementary mode. It was used to decompose metabolic flux onto the elementary mode [40,41]. In the reconstructed metabolic model of this study, glucose, lactate, O2, CO2 and NH4 were the external metabolites. Each elementary mode could be converted to a macroreaction with only these external metabolites. The stoichiometric matrix for external metabolites, Se, was obtained from the reconstructed metabolic network in which the rows are the external metabolites and the columns are the reactions. The macroreaction matrix, Mr, could be obtained by Eq. (5).
Mr = Se⋅E
Glucose Glucose Glucose Glucose Glucose
→ + + + +
2 lactate 3 O2 → 3 CO2 + 5 lactate 6 O2 → 6 CO2 c1 O2 + c2 NH4 → c3 CO2 + c4 biomass c1 O2 + c2 NH4 → c3 CO2 + c4 biomass + c5 lactate
c1, c2, c3, c4 and c5 are coefficients in macro-reactions.
Group 3 was the full oxidation of glucose to CO2. Water was not involved in these metabolic networks so that water was ignored in the macroreactions. Both group 4 and 5 were related to the biosynthesis of biomass. In addition, group 5 included lactate formation. The pathway length distribution of elementary modes under culture conditions [19] is shown in Fig. 2. There were 3 elementary modes in group 1 and their pathway lengths were 14 or 16. Five elementary modes were in group 2 and their pathway lengths were in the range of 27–29. There were two parts of group 3. One part had 25–27 reactions in their elementary modes and there were 32–34 reactions in their elementary modes of another part. The pathway lengths were from 31 to 38 in group 4. There were 116 elementary modes in group 5 and the pathway lengths of 56 elementary modes in them were 36. Biomass formation required many precursors so that the elementary modes including biomass formation (group 4 and 5), had a related longer
(5)
where the row of Mr is the external metabolites and its column is the elementary mode. Se is the stoichiometric matrix only including the external metabolites. The elementary modes could be decomposed into several groups. 3. Results 3.1. Metabolic network reconstruction The reliability of the reconstructed metabolic network was evaluated by the predicted results of the metabolic phenotype and they were compared with the experimental data [19,20]. The specific growth rate under heterotrophic conditions was estimated based on the reconstructed metabolic network when the glucose uptake rate was 0.3028 mmol dry cell wt−1 h−1. The estimated value of the specific growth rate and the CO2 excretion rate were 0.0283 h−1 and 0.5582 mmol dry cell wt−1 h−1, respectively. The experimental specific growth rate was 0.0257 h−1 and the determined CO2 excretion rate was 0.5841 mmol dry cell wt−1 h−1 [19]. The estimated specific growth rates under high and low nitrogen concentration conditions were 0.0822 mmol g−1 h−1 and 0.0586 mmol g−1 h−1, respectively. They were similar to the experimental data [19,20]. It was proved that the reconstructed models were reliable. 3.2. Elementary mode analysis and topological analysis The total numbers of elementary modes under the three conditions were 207, 208 and 207, respectively. The pathway length and macroreaction of each elementary mode in metabolic network model 1 were calculated by Eq. (5). The elementary modes were divided into 5 groups based on their macroreactions shown in Table 2. Group 1 was the full conversion of glucose to lactate under anaerobic conditions. Group 2 was the biosynthesis of lactate with glucose under aerobic conditions.
Fig. 2. Pathway length distribution of the elementary modes under heterotrophic conditions. 3
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shown in Fig. 8. The reactions encoded by pgi, ppc, aceA, aceB, ldh and lactate transport were not included in the four active pathways. It should be mentioned that elementary modes 7, 9, and 11 did not include NH4 uptake, mae and biomass formation reaction. These active pathways corresponded to low nitrogen conditions [20]. In general, low nitrogen concentration is a nutrient limitation stress leading to lipid accumulation and less biomass formation. Under the three culture conditions, elementary modes were calculated. The elementary mode 154 in the three conditions was different.
pathway length than those for the biosynthesis of lactate (groups 1 and 2). In the previous study for elementary mode analysis of Escherichia coli, some elementary modes with short pathway length (less than 38) were related to ATP production and fermentation product formation, and other with long pathway length (more than 120) were for biomass formation [47]. 3.3. Decomposition of metabolic flux onto the elementary mode The ranks of three elementary mode matrixes were 7 and the numbers of elementary modes were not less than 207 so that the optimizations in this study based on Eq. (2) were underdetermined ones. There were more than 200 elementary modes although only some of them were active under specific culture conditions. These active pathways were identified by Eq. (2) with six objective functions. Both the elementary mode coefficient (λ) and elementary mode probability (EMP, ρ) are non-negative but they are different. λ may be larger than 1 [46,47] while ρ is not more than 1 based on Eq. (4). Each elementary mode represents one possible microscopic state and the macroscopic metabolic phenotype is the collection of these microscopic states [47]. The contribution of each elementary mode is evaluated by elementary mode probability. The estimated elementary mode probabilities are shown in Fig. 3 with the experimental metabolic fluxes under heterotrophic conditions [19] (culture condition 1). The single active pathway was identified by maximum biomass formation (OBJ-I) and maximum activity of the longest pathways (OBJ-VI) because the EMPs were approximately 0.9 for these elementary modes. The most active pathways were elementary mode 159 by OBJ-I and elementary mode 157 by OBJ-VI, respectively. Multiple active pathways were found by minimum norm (OBJ-II), maximum entropy production principle (OBJ-III), maximum activity of the shortest pathway by LP (OBJ-IV) and maximum activity of the shortest pathway by QP (OBJ-V). The elementary mode 157 was also identified as the most active pathway by the maximum entropy production principle (OBJ-III). The other active pathways were elementary mode 129 and 207. These active pathways are shown in Fig. 4. The reactions encoded by aceA, aceB, pntA, ATP drain, lactate transport and ldhA were not involved in these active pathways. In particular, zwf, pgl, gnd and ppc were missing in elementary mode 157. These results are consistent with the experimental metabolic flux distribution where the metabolic fluxes through the pentose phosphate pathway were very low [19]. The estimated elementary mode probabilities are shown in Fig. 5 with the experimental metabolic fluxes under heterotrophic and high nitrogen concentration conditions [20] (culture condition 2). It is interesting that the estimated elementary mode probabilities by OBJ-I were almost same as those by OBJ-II and OBJ-V. The top four active elementary modes are elementary mode 138, 103, 154 and 194. It should be mentioned that these pathways were also active in the results by OBJ-III while elementary mode 103 was less active than the others. The most active pathway by OBJ-III is elementary mode 10. The EMP of elementary mode 159 was 0.496 and the other active pathways included elementary mode 141, 161, 8, 6 and 10 by OBJ-VI. This result was significantly different to those for OBJ-I, OBJ-II, OBJ-III, OBJ-IV and OBJ-V. These active pathways, elementary mode 138, 10, 154 and 194, are shown in Fig. 6. The whole pentose phosphate pathway is not involved in elementary mode 10 so that the contribution of the pentose phosphate pathway was proposed by other elementary modes. The estimated elementary mode probabilities are shown in Fig. 7 with the experimental metabolic fluxes under heterotrophic and low nitrogen concentration conditions [19] (culture condition 3). Elementary modes 7, 9 and 11 were identified as active pathways by OBJ-I, OBJ-II, OBJ-IV, OBJ-V and OBJ-VI. Elementary mode 7 was inactive by OBJ-III although the number of active pathways by OBJ-III was more than the others. Elementary mode 154 was also by OBJ-II, OBJ-III and OBJ-V. These active pathways, elementary mode 7, 9, 11 and 154, are
4. Discussion In general, the active pathways identified by different objective functions were different. Maximum biomass formation is widely used in flux balance analysis [48]. Microorganisms trend to survive under limited nutrients conditions and a rapid growth is beneficial to overcome the environmental stress. It was considered that the minimum norm assigned the maximum elementary mode coefficient to the elementary modes close to the actual state of the whole biological system [29,42]. It was proved that the maximum entropy production principle was better for metabolic flux prediction by enzyme control flux algorithm [37] than maximum biomass formation and minimum norm [46]. It is presumed that microalgae display maximum biomass formation, maximum activity of metabolic pathway or other optimal states but it is also proved that microorganism reach a suboptimal state in a real system [43]. In addition, the suboptimal state varies under different culture conditions. Maximum entropy production principle does not use the hypothesis based on physiological state of microalgae so that it could propose a reliable estimation for decomposing metabolic flux onto elementary modes. It was shown in Section 3.2 that elementary modes with short pathway length corresponded to lactate production in this study. In some cases, the shortest elementary mode was an internal cycle that had no contribution to metabolic phenotype. The longest elementary modes in this study were related to biomass formation and lactate production. The application potential of these algorithms using pathway activity, OBJ-IV, OBJ-V and OBJ-VI, could be investigated in a future study. 5. Conclusions In this study, the active pathways were identified by decomposition of the determined metabolic fluxes onto elementary modes. Six objective functions, including a new one, were compared for these optimizations. The obtained results were consistent with cellular physiological states. The elementary modes were divided into 5 groups. They were characterized by pathway length distribution. These results were helpful to explore relationships between metabolic genotype and phenotype by integrating fluxomic data into metabolic pathways of C. protothecoides. Statement of informed consent, human/animal rights No conflicts, informed consent, or human or animal rights are applicable to this study. CRediT authorship contribution statement Lujing Ren: Formal analysis, Writing - original draft, Writing - review & editing. Xiaoman Sun: Formal analysis, Writing - original draft, Writing - review & editing. Lihui Zhang: Writing - review & editing. Quanyu Zhao: Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing, Project administration. He Huang: Supervision, Writing - review & editing. 4
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Fig. 3. Estimated elementary mode probabilities (EMP) by six objective functions. (A) Maximum biomass formation; (B) minimum norm; (C) maximum entropy production principle; (D) maximum activity of the shortest pathways by LP; (E) maximum activity of the shortest pathways by QP; (F) maximum activity of the longest pathways. The experimental data were from [19] and the microalgae were cultivated under heterotrophic conditions. 5
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Fig. 4. Selected active pathways by decomposition of metabolic fluxes onto elementary modes. (A) Elementary mode 157; (B) elementary mode 159; (C) elementary mode 129; (D) elementary mode 207. The experimental data were from [19] and the microalgae were cultivated under heterotrophic conditions.
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Fig. 5. Estimated elementary mode probabilities (EMP) by six objective functions. (A) Maximum biomass formation; (B) minimum norm; (C) maximum entropy production principle; (D) maximum activity of the shortest pathways by LP; (E) maximum activity of the shortest pathways by QP; (F) maximum activity of the longest pathways. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and high nitrogen conditions. 7
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Fig. 6. Selected active pathways by decomposition of metabolic fluxes onto elementary modes. (A) Elementary mode 10; (B) elementary mode 138; (C) elementary mode 154; (D) elementary mode 194. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and high nitrogen conditions.
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Fig. 7. Estimated elementary mode probabilities (EMP) by six objective functions. (A) Maximum biomass formation; (B) minimum norm; (C) maximum entropy production principle; (D) maximum activity of the shortest pathways by LP; (E) maximum activity of the shortest pathways by QP; (F) maximum activity of the longest pathways. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and low nitrogen conditions. 9
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Fig. 8. Selected active pathways by decomposition of metabolic fluxes onto elementary modes. (A) Elementary mode 7; (B) elementary mode 9; (C) elementary mode 11; (D) elementary mode 154. The experimental data were from [20] and the microalgae were cultivated under heterotrophic and low nitrogen conditions.
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