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Using a System Identification based Framework to Elucidate How Scheffersomyces stipitis Shifts Redox in Response to Reduced Oxygen Supply
Proceedings, 7th IFAC Conference on Proceedings, 7th IFAC Conference Foundations of Systems Biology inon Engineering Proceedings, 7th IFAC August Conference on Foundations of Systems Biology Engineering Chicago, Illinois, USA, 5-8,in2018 Available online at www.sciencedirect.com Proceedings, 7th IFAC Conference on Foundations of Systems Biology Engineering Proceedings, 7th IFAC August Conference on Chicago, Illinois, USA, 5-8,in Foundations of Systems Biology in2018 Engineering Chicago, Illinois, USA, August 5-8,in2018 Foundations of Systems Biology Engineering Chicago, Illinois, USA, August 5-8, 2018 Chicago, Illinois, USA, August 5-8, 2018
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IFAC PapersOnLine 51-19 (2018) 110–111 Using a System Identification based Framework to Elucidate How Using a System Identification Framework to Elucidate How Using a System Identification based Framework to Elucidate How Scheffersomyces stipitis Shifts Redoxbased in Response to Reduced Oxygen Supply Using a System Identification based Framework to Elucidate How Scheffersomyces stipitis Shifts Redox in Response to Reduced Oxygen Supply Using a System Identification based Framework to Elucidate How Scheffersomyces stipitis Shifts Redox in Response to Reduced Oxygen Supply + Scheffersomyces stipitis Shifts Redox in Response to Reduced Oxygen Andrew in Damiani*, Q. Peter Jin Wang* Scheffersomyces Matthew stipitis Hilliard*, Shifts Redox Response toHe*, Reduced Oxygen Supply Supply
Matthew Hilliard*, Andrew Damiani*, Q. Peter He*, Jin Wang*++ Matthew Hilliard*, Andrew Damiani*, Q. Peter Peter He*, He*, Jin Jin Wang* Wang*+ Matthew Hilliard*, Andrew Damiani*, Q. Matthew Hilliard*, Andrew Damiani*, Q. Peter He*, Jin Wang*+ *Auburn University, Auburn, AL 36849 USA (e-mails:[email protected]; [email protected]; [email protected]; *Auburn University, Auburn, AL 36849 USA (e-mails:[email protected]; [email protected]; [email protected]; [email protected]) *Auburn [email protected]; *Auburn University, University, Auburn, Auburn, AL AL 36849 36849 USA USA (e-mails:[email protected]; (e-mails:[email protected]; [email protected]; [email protected]; [email protected]; [email protected]) *Auburn University, Auburn, AL 36849 USA (e-mails:[email protected]; [email protected]; [email protected]; [email protected]) [email protected]) [email protected]) Abstract: Scheffersomyces stipitis has been recognized as an important yeast species in the field of Abstract: Scheffersomyces stipitis has been as an important species in the of biorenewables due to its native capacity forrecognized utilizing xylose. It has beenyeast well-recognized thatfield redox Abstract: Scheffersomyces stipitis has been recognized as an important yeast species in the field of biorenewables due to its native capacity for utilizing xylose. It has been well-recognized that redox Abstract: Scheffersomyces stipitis has been recognized as an important yeast species in the field of (im)balance plays antoimportant role forbeen S. for stipitis underxylose. oxygen limited conditions, in terms of field ethanol Abstract: Scheffersomyces stipitis has recognized as an important yeast species in the of biorenewables due its native capacity utilizing It has been well-recognized that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, in terms of ethanol biorenewables due to its native capacity for utilizing xylose. It has been well-recognized that redox production and xylitol (anative by-product) production. However, there has not been in any systems level biorenewables due to its capacity for utilizing xylose. It has been well-recognized that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, terms of ethanol production (a shift by-product) However, there hasconditions, not been shift any level (im)balance and plays an important role for production. S. stipitiscontribute under oxygen limited in terms ethanol understanding onxylitol how the in redox balance to the overall metabolic insystems S.of stipitis to (im)balance and plays an important role for production. S. stipitiscontribute under oxygen limited conditions, in terms ofstipitis ethanol production xylitol (a by-product) However, there has not been any systems level understanding on how the shift in redox balance to the overall metabolic shift in S. to production and xylitol (a by-product) production. However, there has not been any systems level cope with reduced oxygen uptake. In this work, with our recently developed genome-scale metabolic model production andonxylitol (a shift by-product) production. there has genome-scale not been shift anymetabolic systems level understanding the in balance contribute to overall metabolic in S. to cope with oxygen this work, with ourHowever, recently developed model understanding on how how theuptake. shiftwe inInredox redox contribute to the the overall metabolic shiftto inelucidate S. stipitis stipitis to (GEM) forreduced S. stipitis, iDH814, apply abalance system identification (SID) based framework how understanding on how the shift in redox balance contribute to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. In this work, with our recently developed genome-scale metabolic model (GEM) forreduced S. stipitis, iDH814, weInapply ainsystem (SID) based framework to elucidate how cope with oxygen uptake. this work, withidentification our recently oxygen developed genome-scale metabolic model the cellular metabolism of S. stipitis shifts response to reduced supply. The systems level analysis cope with reduced oxygen uptake. In this work, with our recently developed genome-scale metabolic model (GEM) for S. stipitis, iDH814, we apply a system identification (SID) based framework to elucidate how the cellular metabolism of S. stipitis shiftsapproach response reduced oxygen supply. The systems level analysis (GEM) forthat S. S. stipitis, we apply ainsystem identification (SID) based framework elucidate how indicates stipitisiDH814, uses a concerted to to cope with the stress associated withto reduced oxygen (GEM) forthat S. S. stipitis, iDH814, we apply ainsystem identification (SID) based framework toreduced elucidate how the cellular metabolism of S. stipitis shifts response to reduced oxygen supply. The systems level analysis indicates stipitis uses a concerted approach to cope with the stress associated with oxygen the cellular metabolism of S. stipitis shifts in response to reduced oxygen supply. The systems level analysis supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the cellular metabolism of S. stipitis shifts in response to reduced oxygen supply. The systems level analysis indicates that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and theS.shift of reducing power from NADPH to NADH seems toassociated be the center theme that oxygen directs indicates that stipitis uses astates. concerted approach to cope with the stress with reduced the overall shift in metabolic indicates that S. in stipitis uses astates. concerted approach to cope with the stress associated with reduced oxygen supply, and the of power the overall metabolic supply, andshift the shift shift of reducing reducing power from from NADPH NADPH to to NADH NADH seems seems to to be be the the center center theme theme that that directs directs supply, and the(International shift of reducing poweroffrom NADPH to NADH seems to be theLtd. center that directs the overall shift in states. © 2018, IFAC Federation Automatic Control) by Elsevier All theme rightsphase reserved. Keywords: System identification, modeling, fluxHosting balance analysis, phenotype plane the overall shift in metabolic metabolic states. metabolic the overall shift in component metabolic states. Keywords: System identification, metabolic modeling, flux balanceoptimal analysis, phenotype phase plane analysis, principal analysis, linear programing, alternative solution. Keywords: System identification, metabolic modeling, flux balance analysis, phenotype phase plane analysis, principal analysis, linear programing, alternative solution. Keywords: Systemcomponent identification, metabolic modeling, flux balanceoptimal analysis, phenotype phase plane Keywords: Systemcomponent identification, metabolic modeling, flux balanceoptimal analysis, phenotype phase plane analysis, principal analysis, linear programing, alternative solution. analysis, principal component analysis, linear programing, alternative optimal solution. analysis, principal component analysis, linear programing, alternative optimal solution. developed an improved GEM of S. stipitis, iDH814, based on 1. INTRODUCTION developed an improved GEM S. stipitis, iDH814, based on 1. INTRODUCTION iSS884 (Caspeta at al., 2012).of In this work, the SID-based developed an improved GEM of S. stipitis, iDH814, based on 1. INTRODUCTION developed an improved GEM of S. stipitis, iDH814, on iSS884 (Caspeta at al., 2012). In this work, the SID-based Scheffersomyces stipitis has been recognized as an important framework is adapted to analyze iDH814 to help based 1. INTRODUCTION us gain developed an improved GEM of S. stipitis, iDH814, based on iSS884 (Caspeta at al., 2012). In this work, the SID-based 1. INTRODUCTION Scheffersomyces stipitis has been recognizeddue as an important iSS884 (Caspeta at al., 2012). In this work, the SID-based framework is adapted to analyze iDH814 to help us gain yeast species in the field of biorenewables to its native systems level understanding on how S. stipitis copes with the Scheffersomyces stipitis has been recognized as an important iSS884 (Caspeta at al., 2012). In this work, the SID-based framework is adapted to analyze iDH814 to help us gain Scheffersomyces stipitis has been recognized as an yeast species in the field of (Jeffries biorenewables due to important its second native framework is understanding adapted to analyze help with us gain systems level on redox howiDH814 S.shift. stipitistocopes the capacity for utilizing xylose et al., 2007), reduced oxygen supply through Scheffersomyces stipitis has been recognized as anthe important yeast species in the of biorenewables due its native is understanding adapted to analyze iDH814 tocopes help with us gain systems level on how S. stipitis the capacity for utilizing xylose (Jeffries et al.,biomass. 2007), second yeast species in sugar the field field of biorenewables due to tothe its nativea framework systems level understanding on how S. stipitis copes with the reduced oxygen supply through redox shift. most abundant in lignocellulosic Being yeast species in the field of (Jeffries biorenewables due tothe its second native systems capacity for utilizing xylose et al., 2007), level2.understanding on how S. stipitis copes with the reduced oxygen supply through redox shift. capacity for utilizing xylose (Jeffries et al., 2007), the second most abundant sugar in lignocellulosic biomass. Being a THE IMPROVED GEM iDH814 reduced oxygen supply through redox shift. Crabtree negative yeast strain, ethanol production by S. stipitis capacity for utilizing xylose (Jeffries et al.,biomass. 2007), theBeing seconda reduced oxygen most abundant sugar in lignocellulosic supply through redox shift. 2. THE IMPROVED GEM iDH814 most abundant sugar in lignocellulosic biomass. Being a Crabtree negative yeast strain, ethanol production by S. stipitis is triggered by oxygen limitation. It has been well-recognized 2. IMPROVED most abundant sugar in lignocellulosic biomass. Being a Crabtree negative yeast strain, ethanol production by S. stipitis of 972 metabolites,GEM 1380iDH814 reactions, 814 genes 2. THE THE IMPROVED GEM iDH814 Crabtree negative yeastlimitation. strain, ethanol production by S. S. stipitis stipitis iDH814 consists is triggered by oxygen It has beenrole well-recognized that redox (im)balance plays an important for 2. THE IMPROVED GEM consists of 972 metabolites, 1380iDH814 reactions, 814 genes Crabtree negative yeastlimitation. strain, ethanol production by S. stipitis iDH814 is triggered by oxygen It has been well-recognized and three compartments: cytosol, exchange and mitochondria. is triggered by oxygen It in has beenrole well-recognized that redox (im)balance plays an important S. stipitis of metabolites, 1380 reactions, 814 genes under oxygen limited limitation. conditions, of for ethanol and iDH814 and threeconsists compartments: cytosol, exchange and mitochondria. iDH814 consists of 972 972 metabolites, 1380 reactions, 814 is triggered by oxygen limitation. It hasterms beenrole well-recognized that redox (im)balance plays an important for S. stipitis In Damiani et al. (2018), we show that iDH814 notgenes only that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, in terms of ethanol and iDH814 consists of 972 metabolites, 1380 reactions, 814 genes and three compartments: cytosol, exchange and mitochondria. xylitol (a by-product) production. Itinhas been suggested that and threepredictions compartments: cytosol, exchange mitochondria. In Damiani et al. (2018), webetter show that and iDH814 not only that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, terms of ethanol and provides that show agreement with reported under oxygen limited conditions, terms ofsuggested ethanol and and xylitol (ainby-product) production. Itinhas that three compartments: cytosol, exchange and mitochondria. In Damiani et al. (2018), we show that iDH814 not only the shift redox balance is the result ofbeen oxygen limitation. Damiani et al. (2018), we show that iDH814 not only provides predictions that show better agreement with reported under oxygen limited conditions, inhas terms ofsuggested ethanol and In xylitol (a by-product) production. It been that experimental but also knowledge that align xylitol production. Itbeen hasofbeen suggested that In the shift(ainby-product) redox balance is thenot result oxygen limitation. Damiani etdata, al. (2018), wecaptures showagreement that iDH814 not only provides predictions that show better with reported However, to date, there has any systems level provides predictions that show better agreement with reported experimental data, but also captures knowledge that align xylitol (a by-product) production. It has been suggested that the shift in redox balance is the result of oxygen limitation. better with available understanding S.knowledge stipitis.with Specifically, However, date, there has not beenbalance systems level the shift intoredox balance is the result of any oxygen limitation. provides predictions that showcaptures betterofagreement reported experimental data, but also that align understanding on how the shift in redox contributes to experimental data, but also predicted captures knowledge that align better withproduction available understanding of S. stipitis. the shift intoredox balance is thenot result of any oxygen limitation. However, date, there has been systems level xylitol pattern by iDH814Specifically, agrees with However, date, there has not beenbalance level understanding on how the shift redox contributes to the experimental data, but also captures knowledge that align better with available understanding of S. stipitis. Specifically, the overall to metabolic shift in S.in stipitis to any copesystems with reduced better with available understanding of S. stipitis. Specifically, the xylitol production pattern predicted by iDH814 agrees with However, to date, there has not beenbalance any systems level understanding on how the shift in redox contributes to experimental observations. As iDH814 is able to predict understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced better withproduction available understanding of S. stipitis. Specifically, the xylitol pattern predicted by iDH814 agrees with oxygen uptake of ethanol. Genome-scale xylitol production pattern predicted by iDH814 agrees with experimental observations. As iDH814 is we ablecompared to predict understanding onand how production the shift instipitis redox balance contributes to the the overall metabolic shift in S. to cope with reduced xylitol production in a more reasonable way, its the overall metabolic shift have in S. been stipitis to cope with reduced the oxygen uptake and production of ethanol. Genome-scale xylitol production pattern As predicted by iDH814 agrees with experimental observations. iDH814 is able to predict metabolic models (GEMs) shown to be a powerful observations. Asdata iDH814 is we able to predict xylitol production in a more reasonable way, compared its the overall metabolic shift in S. stipitis to copeGenome-scale with reduced experimental oxygen uptake and production of ethanol. prediction with experimental available in literature on oxygento uptake production of ethanol. metabolic modelsand (GEMs) have been shown to Genome-scale beona powerful Asdata iDH814 is we able to predict xylitol production in reasonable way, its tool gain genome-wide understanding cellular experimental prediction withobservations. experimental available in compared literature on xylitol production in aa more more reasonable way, we compared its oxygen uptake and production of ethanol. Genome-scale metabolic models (GEMs) have been shown to be a powerful xylitol production (Farias et al., 2014). In S. stipitis, due to the metabolic models (GEMs) have been shown to be a powerful tool to gain genome-wide understanding on cellular xylitol production in a more reasonable way, we compared its prediction with experimental data available in literature on metabolism. However, due to been the shown scale to andbeon complexity prediction with experimental data available in literature on xylitol production (Farias et al.,(NADH 2014). In S. stipitis, due to the metabolic models (GEMs) have a powerful tool to gain genome-wide understanding cellular different cofactors preference vs NADPH) of xylose tool to ingain genome-wide understanding on cellular metabolism. However, due to the scale and complexity prediction with experimental data available in literature on xylitol production (Farias et al., 2014). In S. stipitis, due to the involved GEMs, validation and refinement of GEMs have production (Farias et al., 2014). In stipitis, to the different cofactors preference (NADH vs S. NADPH) ofa xylose tool to gainHowever, genome-wide understanding on cellular xylitol metabolism. due the scale and complexity reductase (XR) and xylitol dehydrogenase (XDH),due redox metabolism. However, due to to scale and complexity involved GEMs, validation andthe of GEMs have production (Farias et al., 2014). In S. stipitis, due to the different cofactors preference (NADH vs NADPH) of xylose been veryin challenging (Österlund etrefinement al., 2012; McCloskey et xylitol different cofactors preference (NADH vs NADPH) of xylose reductase (XR) and xylitol dehydrogenase (XDH), a redox metabolism. However, due to the scale and complexity involved in GEMs, validation and refinement of GEMs have (i.e., excess amountdehydrogenase of NADHvsproduced) isofresulted been veryinchallenging (Österlund etrefinement al., 2012; McCloskey et imbalance involved GEMs, validation and of GEMs have have different cofactors preference (NADH NADPH) xylose reductase (XR) and xylitol (XDH), a redox al., 2013). To address this challenge, the authors reductase (XR) and xylitol (XDH), a of redox imbalance (i.e., amount of NADH produced) is resulted involved in GEMs, validation and etrefinement of GEMs have been very (Österlund al., McCloskey et xylose is excess converted to dehydrogenase xylulose. Currently, none the been2013). very challenging challenging (Österlund et(SID) al., 2012; 2012; et when al., address this challenge, the McCloskey authors have reductase (XR) and xylitol dehydrogenase (XDH), a redox imbalance (i.e., excess amount of NADH produced) is resulted developed a To system identification based framework, imbalance (i.e., excess amount of NADH produced) is resulted when xylose is converted to xylulose. Currently, none of the been2013). very challenging (Österlund et al., 2012; McCloskey et GEMs al., To address this challenge, the authors have of S. stipitis contain any gene regulatory mechanism. al., 2013). To address this challenge, the authors have developed a system identification (SID) based framework, imbalance (i.e., excess amount of NADH produced)none is resulted when is to xylulose. Currently, the which has abeen successfully applied to based GEM validation when xylose xylose is converted converted to any xylulose. Currently, none of ofwill the of S.without stipitis contain gene regulatory mechanism. al., 2013). To address this challenge, the authors have GEMs developed system identification (SID) framework, Therefore, additional constraints, the GEMs developed system identification (SID)to based framework, which hasetabeen successfully applied GEM validation when xylose is converted to any xylulose. Currently, mechanism. none of the GEMs of S. stipitis contain gene regulatory (Damiani al., 2015), refinement (Damiani et al., 2018) and GEMs of S.without stipitis contain any geneasregulatory mechanism. Therefore, additional constraints, the GEMshigher will developed abeen system identification (SID)to based framework, which has successfully applied GEM validation always pick the NADH route for XR, it will produce which hasetphase been successfully applied to GEM validation (Damiani al., 2015), refinement (Damiani et 2018a). al., 2018) of S.without stipitis contain any geneasregulatory mechanism. Therefore, additional GEMs will phenotype plane analysis (Hilliard et al., In and the GEMs always pick the NADH for constraints, XR, it willthe produce Therefore, without constraints, the GEMshigher will which haset been successfully applied to GEM validation (Damiani al., 2015), refinement (Damiani et al., 2018) and biomass growth due additional toroute the balanced redox. To address this (Damiani et al., 2015), refinement (Damiani et al., 2018) and phenotype phase plane analysis (Hilliard et al., 2018a). In the Therefore, without additional constraints, the GEMs will always pick the NADH route for XR, as it will produce higher SID-based framework, the knowledge captured by 2018) a GEM is always biomass growth due toroute the balanced To address this pick thethe NADH forconducted XR, asredox. it will produce higher (Damiani etphase al., 2015), refinement (Damiani et 2018a). al., and phenotype plane analysis (Hilliard et al., In the limitation, for simulations in this section, we phenotype phase plane analyzing analysis (Hilliard et al., 2018a). the SID-based framework, the knowledge captured by a GEM is always pick the NADH route for XR, asredox. it willTo produce higher biomass growth due to the balanced address this first extracted through a series of designed in In silico biomass growth due to the balanced redox. To address this limitation, for the simulations conducted in this section, we phenotype phase plane analysis (Hilliard et al., 2018a). In the SID-based framework, the knowledge captured by a GEM is added a constraint on the ratio between XR using NADPH and SID-based framework, the knowledge captured by aavailable GEM is biomass first extracted through a is series of designed in silico growth due to the balanced redox. To address this limitation, for the simulations conducted in this section, we experiments, then suchanalyzing knowledge compared with limitation, for the simulations conducted in this section, we added a constraint on the ratio between XR using NADPH and SID-based framework, the knowledge captured by a GEM is first extracted through analyzing a series of designed in silico (XR asthe theratio cofactor, to force the of experiments, then such knowledge compared with available first extracted through analyzing a is series of designed in guide silico NADH limitation, forratio) the on simulations conducted in thisutilization section, and we added a constraint between XR using NADPH knowledge on the strain to conduct model validation, a constraint on the ratio between XR using NADPH and NADH (XR ratio) as the cofactor, to force the utilization of first extractedthen through analyzing a is series of designed in silico added experiments, such knowledge compared with available NADPH to reflect the available knowledge on the XR reaction. experiments, then such knowledge is compared with available knowledge on the strain to conduct model validation, guide a (XR constraint on the ratio between XR using NADPH and NADH ratio) as the cofactor, to force the utilization of model refinement, andknowledge analyze different phenotypes. By added NADH (XR ratio) as the cofactor, to force the utilization of NADPH to reflect the available knowledge on the XR reaction. experiments, then such is compared with available knowledge on the strain to conduct model validation, guide 1 (XR shows that agreement between the model knowledge on SID-based the strain conduct model validation, guide model refinement, and to analyze different phenotypes. By Table NADH ratio) asexcellent the cofactor, to force the utilization of NADPH to reflect the available knowledge on the XR reaction. applying the framework, the authors recently NADPH toxylitol reflect theexcellent available knowledge on the XRthe reaction. Table 1 shows that agreement between model knowledge on the strain to conduct model validation, guide model refinement, and analyze different phenotypes. By predicted yields and the reported experimental values model and analyze different phenotypes. By NADPH applyingrefinement, the SID-based framework, the authors recently to reflect theexcellent availableagreement knowledgebetween on the XRthe reaction. Table 1 shows that model 1 shows excellent between the values model predicted xylitolthat yields and theagreement reported experimental model refinement, and analyze different phenotypes. By Table applying applying the the SID-based SID-based framework, framework, the the authors authors recently recently Table 1 shows excellent between the values model predicted xylitolthat yields and theagreement reported experimental applying the SID-based framework, the authors recently predicted xylitol yields and the reported experimental values predicted xylitol yields and the reported experimental values Copyright © 2018 IFAC 110 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright 2018 responsibility IFAC 110Control. Peer review©under of International Federation of Automatic Copyright © 2018 IFAC 110 10.1016/j.ifacol.2018.09.018 Copyright © 2018 IFAC 110 Copyright © 2018 IFAC 110
IFAC FOSBE 2018 Chicago, Illinois, USA, August 5-8, 2018 Matthew Hilliard et al. / IFAC PapersOnLine 51-19 (2018) 110–111
(Farias et al.,2014) can be achieved through the tuning of the oxygen uptake rate (OUR) and XR ratio.
slightly increased by 6% (0.67mmol/gDCW/hr), while the total production/consumption of cytosolic NADPH decreased significantly by 46% (1.138 mmol/gDCW/hr). Additional analysis suggests that to cope with the stress caused by reduced oxygen supply, cells rely on increased production of NADH, as well as converting cytosolic NADPH to mitochondrial NADH to achieve the drastic shift from NADPH to NADH as the reducing factor.
Table 1 Model Predictions vs Experimental Values Case 1 2 3 4 *
YE/S Exp.* 0.328 0.358 0.376 0.437
Xylitol Exp.* 0.1408 0.0934 0.0115 0.0172
Xylitol Pred. * 0.1411 0.0937 0.0118 0.0176
XR Ratio
OUR*
1.06 0.79 0.51 0
2.00 1.71 1.46 0.28
111
unit: mmol/gCDW/hr,
3. SYSTEM-LEVEL ANALYSIS ON THE REDOX SHIFT To gain a better understanding on how the cellular redox balance shifts when OUR decreases, we applied the SID-based framework to investigate the cellular response to the change in OUR. First, a series of in silico experiments were designed and carried out to generate intracellular flux data. Because iDH814 does not contain the gene regulatory mechanism on cofactor utilization, in silico experiments were carefully designed to reflect what is going on within the cell by using experimental results. Further analysis showed that the first 3 conditions listed in Table 1 share the same phenotype phase (Damiani et al., 2018a). Based on this, we designed the following in silico experiments: as OUR was reduced linearly from 2.0 to 1.46 mmol/gDCW/hr, the XR ratio (NADPH:NADH) was decreased linearly from 1.058 to 0.502, while keeping a constant xylose uptake rate (5 mmol/gDCW/hr). Then principal component analysis (PCA) was applied to analyze the resulted flux distribution matrix. One principal component (PC) captured 100% of the variance, which confirms that in silico experiment conditions belong to the same phenotype. Fig. 1 visualizes the PC loadings against the metabolic network map to better understand the intracellular metabolic response predicted by the model, where blue arrows indicate that the reaction fluxes are upregulated, red down regulated, and black no change in flux through the reaction. Fig. 1 shows that corresponding to the reduced OUR and shifted XR cofactor preference, PPP, TCA and ETC pathways are all down regulated, while glycolysis and ethanol production are upregulated. Such responses agree with our understandings. In addition, because the inner membrane of the mitochondria is impermeable to NADH, electron shuttles, metabolic pathways that facilitate the transfer of electrons (in the form of NADH) produced in the cytosol to the electron transport chain in mitochondria, are needed. Our analysis results show that S. stipitis primarily utilizes the GLYC3P shuttle (highlighted in blue box) and/or the NADHDH shuttle (highlighted in green box), and their down-regulation confirms the reduction in excess NADH. Note that the use of GLYC3P and NADHDH shuttles by iDH814 were obtained as the alternative optimal solutions of FBA, indicating both shuttles could be in operation simultaneously.
Figure 1 Loading values plotted against central carbon network map for S. stipitis
Table 2 Balance of Major Electron Carriers Metabolite Cond. 1 Cond. 3 Diff NADH 1.029 0.631 -0.398 NADPH 0.162 0.144 -0.018 FADH2 2.742 2.085 -0.656 Q6H2 3.771 2.717 -1.054 6. ACKNOWLEDGEMENT This work was supported by National Science Foundation (1069004 for MH, 1264861 for JW and QHE) and Department of Education (P200A150074 for MH and JW). 7. REFERENCES Caspeta, L., Shoaie, S., Agren, R., Nookaew, I., Nielsen, J. [2012]. BMC Syst Biol, 6(1). Damiani, A.L., He, Q.P., Jeffries, T.W., Wang, J. [2015]. Biotechnol Bioeng, 112(6), 1250-1262. Damiani, A.L., Hilliard, M., He, Q.P., Wang, J. [2018]. Microb Cell Fact, submitted. Farias, D., de Andrade, R.R., Maugeri-Filho, F. [2014]. Appl Biochem Biotech, 172(1), 361-379. Hilliard, M., Damiani, A.L., He, Q.P., Wang, J. [2018a]. Proceedings of the 13th International Symposium on Process Systems Engineering, accepted. Hilliard, M., He, Q.P., Wang, J. [2018b]. Foundations of Systems Biology in Engineering, submitted. Jeffries, T.W., Grigoriev, I.V., Grimwood, J., Laplaza, J.M., Aerts, A., Salamov, A., Richardson, P.M. [2007]. Nat Biotechnol, 25(3), 319-326. McCloskey, D., Palsson, B.Ø., Feist, A.M. [2013]. Mol Syst Biol, 9(1), 661. Österlund, T., Nookaew, I., Nielsen, J. [2012]. Biotechnol Adv, 30(5), 979-988.
To further examine the specific role redox (im)balance plays in the fermentation of xylose, in Table 2 we tabulated total NADH, NADPH production/consumption in the cytosol and the total Q6H2 production/consumption in mitochondria when the cells transition from condition 1 to condition 3. As OUR reduces, the total production/consumption of cytosolic NADH 111
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IFAC PapersOnLine 51-19 (2018) 110–111 Using a System Identification based Framework to Elucidate How Using a System Identification Framework to Elucidate How Using a System Identification based Framework to Elucidate How Scheffersomyces stipitis Shifts Redoxbased in Response to Reduced Oxygen Supply Using a System Identification based Framework to Elucidate How Scheffersomyces stipitis Shifts Redox in Response to Reduced Oxygen Supply Using a System Identification based Framework to Elucidate How Scheffersomyces stipitis Shifts Redox in Response to Reduced Oxygen Supply + Scheffersomyces stipitis Shifts Redox in Response to Reduced Oxygen Andrew in Damiani*, Q. Peter Jin Wang* Scheffersomyces Matthew stipitis Hilliard*, Shifts Redox Response toHe*, Reduced Oxygen Supply Supply
Matthew Hilliard*, Andrew Damiani*, Q. Peter He*, Jin Wang*++ Matthew Hilliard*, Andrew Damiani*, Q. Peter Peter He*, He*, Jin Jin Wang* Wang*+ Matthew Hilliard*, Andrew Damiani*, Q. Matthew Hilliard*, Andrew Damiani*, Q. Peter He*, Jin Wang*+ *Auburn University, Auburn, AL 36849 USA (e-mails:[email protected]; [email protected]; [email protected]; *Auburn University, Auburn, AL 36849 USA (e-mails:[email protected]; [email protected]; [email protected]; [email protected]) *Auburn [email protected]; *Auburn University, University, Auburn, Auburn, AL AL 36849 36849 USA USA (e-mails:[email protected]; (e-mails:[email protected]; [email protected]; [email protected]; [email protected]; [email protected]) *Auburn University, Auburn, AL 36849 USA (e-mails:[email protected]; [email protected]; [email protected]; [email protected]) [email protected]) [email protected]) Abstract: Scheffersomyces stipitis has been recognized as an important yeast species in the field of Abstract: Scheffersomyces stipitis has been as an important species in the of biorenewables due to its native capacity forrecognized utilizing xylose. It has beenyeast well-recognized thatfield redox Abstract: Scheffersomyces stipitis has been recognized as an important yeast species in the field of biorenewables due to its native capacity for utilizing xylose. It has been well-recognized that redox Abstract: Scheffersomyces stipitis has been recognized as an important yeast species in the field of (im)balance plays antoimportant role forbeen S. for stipitis underxylose. oxygen limited conditions, in terms of field ethanol Abstract: Scheffersomyces stipitis has recognized as an important yeast species in the of biorenewables due its native capacity utilizing It has been well-recognized that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, in terms of ethanol biorenewables due to its native capacity for utilizing xylose. It has been well-recognized that redox production and xylitol (anative by-product) production. However, there has not been in any systems level biorenewables due to its capacity for utilizing xylose. It has been well-recognized that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, terms of ethanol production (a shift by-product) However, there hasconditions, not been shift any level (im)balance and plays an important role for production. S. stipitiscontribute under oxygen limited in terms ethanol understanding onxylitol how the in redox balance to the overall metabolic insystems S.of stipitis to (im)balance and plays an important role for production. S. stipitiscontribute under oxygen limited conditions, in terms ofstipitis ethanol production xylitol (a by-product) However, there has not been any systems level understanding on how the shift in redox balance to the overall metabolic shift in S. to production and xylitol (a by-product) production. However, there has not been any systems level cope with reduced oxygen uptake. In this work, with our recently developed genome-scale metabolic model production andonxylitol (a shift by-product) production. there has genome-scale not been shift anymetabolic systems level understanding the in balance contribute to overall metabolic in S. to cope with oxygen this work, with ourHowever, recently developed model understanding on how how theuptake. shiftwe inInredox redox contribute to the the overall metabolic shiftto inelucidate S. stipitis stipitis to (GEM) forreduced S. stipitis, iDH814, apply abalance system identification (SID) based framework how understanding on how the shift in redox balance contribute to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. In this work, with our recently developed genome-scale metabolic model (GEM) forreduced S. stipitis, iDH814, weInapply ainsystem (SID) based framework to elucidate how cope with oxygen uptake. this work, withidentification our recently oxygen developed genome-scale metabolic model the cellular metabolism of S. stipitis shifts response to reduced supply. The systems level analysis cope with reduced oxygen uptake. In this work, with our recently developed genome-scale metabolic model (GEM) for S. stipitis, iDH814, we apply a system identification (SID) based framework to elucidate how the cellular metabolism of S. stipitis shiftsapproach response reduced oxygen supply. The systems level analysis (GEM) forthat S. S. stipitis, we apply ainsystem identification (SID) based framework elucidate how indicates stipitisiDH814, uses a concerted to to cope with the stress associated withto reduced oxygen (GEM) forthat S. S. stipitis, iDH814, we apply ainsystem identification (SID) based framework toreduced elucidate how the cellular metabolism of S. stipitis shifts response to reduced oxygen supply. The systems level analysis indicates stipitis uses a concerted approach to cope with the stress associated with oxygen the cellular metabolism of S. stipitis shifts in response to reduced oxygen supply. The systems level analysis supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the cellular metabolism of S. stipitis shifts in response to reduced oxygen supply. The systems level analysis indicates that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and theS.shift of reducing power from NADPH to NADH seems toassociated be the center theme that oxygen directs indicates that stipitis uses astates. concerted approach to cope with the stress with reduced the overall shift in metabolic indicates that S. in stipitis uses astates. concerted approach to cope with the stress associated with reduced oxygen supply, and the of power the overall metabolic supply, andshift the shift shift of reducing reducing power from from NADPH NADPH to to NADH NADH seems seems to to be be the the center center theme theme that that directs directs supply, and the(International shift of reducing poweroffrom NADPH to NADH seems to be theLtd. center that directs the overall shift in states. © 2018, IFAC Federation Automatic Control) by Elsevier All theme rightsphase reserved. Keywords: System identification, modeling, fluxHosting balance analysis, phenotype plane the overall shift in metabolic metabolic states. metabolic the overall shift in component metabolic states. Keywords: System identification, metabolic modeling, flux balanceoptimal analysis, phenotype phase plane analysis, principal analysis, linear programing, alternative solution. Keywords: System identification, metabolic modeling, flux balance analysis, phenotype phase plane analysis, principal analysis, linear programing, alternative solution. Keywords: Systemcomponent identification, metabolic modeling, flux balanceoptimal analysis, phenotype phase plane Keywords: Systemcomponent identification, metabolic modeling, flux balanceoptimal analysis, phenotype phase plane analysis, principal analysis, linear programing, alternative solution. analysis, principal component analysis, linear programing, alternative optimal solution. analysis, principal component analysis, linear programing, alternative optimal solution. developed an improved GEM of S. stipitis, iDH814, based on 1. INTRODUCTION developed an improved GEM S. stipitis, iDH814, based on 1. INTRODUCTION iSS884 (Caspeta at al., 2012).of In this work, the SID-based developed an improved GEM of S. stipitis, iDH814, based on 1. INTRODUCTION developed an improved GEM of S. stipitis, iDH814, on iSS884 (Caspeta at al., 2012). In this work, the SID-based Scheffersomyces stipitis has been recognized as an important framework is adapted to analyze iDH814 to help based 1. INTRODUCTION us gain developed an improved GEM of S. stipitis, iDH814, based on iSS884 (Caspeta at al., 2012). In this work, the SID-based 1. INTRODUCTION Scheffersomyces stipitis has been recognizeddue as an important iSS884 (Caspeta at al., 2012). In this work, the SID-based framework is adapted to analyze iDH814 to help us gain yeast species in the field of biorenewables to its native systems level understanding on how S. stipitis copes with the Scheffersomyces stipitis has been recognized as an important iSS884 (Caspeta at al., 2012). In this work, the SID-based framework is adapted to analyze iDH814 to help us gain Scheffersomyces stipitis has been recognized as an yeast species in the field of (Jeffries biorenewables due to important its second native framework is understanding adapted to analyze help with us gain systems level on redox howiDH814 S.shift. stipitistocopes the capacity for utilizing xylose et al., 2007), reduced oxygen supply through Scheffersomyces stipitis has been recognized as anthe important yeast species in the of biorenewables due its native is understanding adapted to analyze iDH814 tocopes help with us gain systems level on how S. stipitis the capacity for utilizing xylose (Jeffries et al.,biomass. 2007), second yeast species in sugar the field field of biorenewables due to tothe its nativea framework systems level understanding on how S. stipitis copes with the reduced oxygen supply through redox shift. most abundant in lignocellulosic Being yeast species in the field of (Jeffries biorenewables due tothe its second native systems capacity for utilizing xylose et al., 2007), level2.understanding on how S. stipitis copes with the reduced oxygen supply through redox shift. capacity for utilizing xylose (Jeffries et al., 2007), the second most abundant sugar in lignocellulosic biomass. Being a THE IMPROVED GEM iDH814 reduced oxygen supply through redox shift. Crabtree negative yeast strain, ethanol production by S. stipitis capacity for utilizing xylose (Jeffries et al.,biomass. 2007), theBeing seconda reduced oxygen most abundant sugar in lignocellulosic supply through redox shift. 2. THE IMPROVED GEM iDH814 most abundant sugar in lignocellulosic biomass. Being a Crabtree negative yeast strain, ethanol production by S. stipitis is triggered by oxygen limitation. It has been well-recognized 2. IMPROVED most abundant sugar in lignocellulosic biomass. Being a Crabtree negative yeast strain, ethanol production by S. stipitis of 972 metabolites,GEM 1380iDH814 reactions, 814 genes 2. THE THE IMPROVED GEM iDH814 Crabtree negative yeastlimitation. strain, ethanol production by S. S. stipitis stipitis iDH814 consists is triggered by oxygen It has beenrole well-recognized that redox (im)balance plays an important for 2. THE IMPROVED GEM consists of 972 metabolites, 1380iDH814 reactions, 814 genes Crabtree negative yeastlimitation. strain, ethanol production by S. stipitis iDH814 is triggered by oxygen It has been well-recognized and three compartments: cytosol, exchange and mitochondria. is triggered by oxygen It in has beenrole well-recognized that redox (im)balance plays an important S. stipitis of metabolites, 1380 reactions, 814 genes under oxygen limited limitation. conditions, of for ethanol and iDH814 and threeconsists compartments: cytosol, exchange and mitochondria. iDH814 consists of 972 972 metabolites, 1380 reactions, 814 is triggered by oxygen limitation. It hasterms beenrole well-recognized that redox (im)balance plays an important for S. stipitis In Damiani et al. (2018), we show that iDH814 notgenes only that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, in terms of ethanol and iDH814 consists of 972 metabolites, 1380 reactions, 814 genes and three compartments: cytosol, exchange and mitochondria. xylitol (a by-product) production. Itinhas been suggested that and threepredictions compartments: cytosol, exchange mitochondria. In Damiani et al. (2018), webetter show that and iDH814 not only that redox (im)balance plays an important role for S. stipitis under oxygen limited conditions, terms of ethanol and provides that show agreement with reported under oxygen limited conditions, terms ofsuggested ethanol and and xylitol (ainby-product) production. Itinhas that three compartments: cytosol, exchange and mitochondria. In Damiani et al. (2018), we show that iDH814 not only the shift redox balance is the result ofbeen oxygen limitation. Damiani et al. (2018), we show that iDH814 not only provides predictions that show better agreement with reported under oxygen limited conditions, inhas terms ofsuggested ethanol and In xylitol (a by-product) production. It been that experimental but also knowledge that align xylitol production. Itbeen hasofbeen suggested that In the shift(ainby-product) redox balance is thenot result oxygen limitation. Damiani etdata, al. (2018), wecaptures showagreement that iDH814 not only provides predictions that show better with reported However, to date, there has any systems level provides predictions that show better agreement with reported experimental data, but also captures knowledge that align xylitol (a by-product) production. It has been suggested that the shift in redox balance is the result of oxygen limitation. better with available understanding S.knowledge stipitis.with Specifically, However, date, there has not beenbalance systems level the shift intoredox balance is the result of any oxygen limitation. provides predictions that showcaptures betterofagreement reported experimental data, but also that align understanding on how the shift in redox contributes to experimental data, but also predicted captures knowledge that align better withproduction available understanding of S. stipitis. the shift intoredox balance is thenot result of any oxygen limitation. However, date, there has been systems level xylitol pattern by iDH814Specifically, agrees with However, date, there has not beenbalance level understanding on how the shift redox contributes to the experimental data, but also captures knowledge that align better with available understanding of S. stipitis. Specifically, the overall to metabolic shift in S.in stipitis to any copesystems with reduced better with available understanding of S. stipitis. Specifically, the xylitol production pattern predicted by iDH814 agrees with However, to date, there has not beenbalance any systems level understanding on how the shift in redox contributes to experimental observations. As iDH814 is able to predict understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced better withproduction available understanding of S. stipitis. Specifically, the xylitol pattern predicted by iDH814 agrees with oxygen uptake of ethanol. Genome-scale xylitol production pattern predicted by iDH814 agrees with experimental observations. As iDH814 is we ablecompared to predict understanding onand how production the shift instipitis redox balance contributes to the the overall metabolic shift in S. to cope with reduced xylitol production in a more reasonable way, its the overall metabolic shift have in S. been stipitis to cope with reduced the oxygen uptake and production of ethanol. Genome-scale xylitol production pattern As predicted by iDH814 agrees with experimental observations. iDH814 is able to predict metabolic models (GEMs) shown to be a powerful observations. Asdata iDH814 is we able to predict xylitol production in a more reasonable way, compared its the overall metabolic shift in S. stipitis to copeGenome-scale with reduced experimental oxygen uptake and production of ethanol. prediction with experimental available in literature on oxygento uptake production of ethanol. metabolic modelsand (GEMs) have been shown to Genome-scale beona powerful Asdata iDH814 is we able to predict xylitol production in reasonable way, its tool gain genome-wide understanding cellular experimental prediction withobservations. experimental available in compared literature on xylitol production in aa more more reasonable way, we compared its oxygen uptake and production of ethanol. Genome-scale metabolic models (GEMs) have been shown to be a powerful xylitol production (Farias et al., 2014). In S. stipitis, due to the metabolic models (GEMs) have been shown to be a powerful tool to gain genome-wide understanding on cellular xylitol production in a more reasonable way, we compared its prediction with experimental data available in literature on metabolism. However, due to been the shown scale to andbeon complexity prediction with experimental data available in literature on xylitol production (Farias et al.,(NADH 2014). In S. stipitis, due to the metabolic models (GEMs) have a powerful tool to gain genome-wide understanding cellular different cofactors preference vs NADPH) of xylose tool to ingain genome-wide understanding on cellular metabolism. However, due to the scale and complexity prediction with experimental data available in literature on xylitol production (Farias et al., 2014). In S. stipitis, due to the involved GEMs, validation and refinement of GEMs have production (Farias et al., 2014). In stipitis, to the different cofactors preference (NADH vs S. NADPH) ofa xylose tool to gainHowever, genome-wide understanding on cellular xylitol metabolism. due the scale and complexity reductase (XR) and xylitol dehydrogenase (XDH),due redox metabolism. However, due to to scale and complexity involved GEMs, validation andthe of GEMs have production (Farias et al., 2014). In S. stipitis, due to the different cofactors preference (NADH vs NADPH) of xylose been veryin challenging (Österlund etrefinement al., 2012; McCloskey et xylitol different cofactors preference (NADH vs NADPH) of xylose reductase (XR) and xylitol dehydrogenase (XDH), a redox metabolism. However, due to the scale and complexity involved in GEMs, validation and refinement of GEMs have (i.e., excess amountdehydrogenase of NADHvsproduced) isofresulted been veryinchallenging (Österlund etrefinement al., 2012; McCloskey et imbalance involved GEMs, validation and of GEMs have have different cofactors preference (NADH NADPH) xylose reductase (XR) and xylitol (XDH), a redox al., 2013). To address this challenge, the authors reductase (XR) and xylitol (XDH), a of redox imbalance (i.e., amount of NADH produced) is resulted involved in GEMs, validation and etrefinement of GEMs have been very (Österlund al., McCloskey et xylose is excess converted to dehydrogenase xylulose. Currently, none the been2013). very challenging challenging (Österlund et(SID) al., 2012; 2012; et when al., address this challenge, the McCloskey authors have reductase (XR) and xylitol dehydrogenase (XDH), a redox imbalance (i.e., excess amount of NADH produced) is resulted developed a To system identification based framework, imbalance (i.e., excess amount of NADH produced) is resulted when xylose is converted to xylulose. Currently, none of the been2013). very challenging (Österlund et al., 2012; McCloskey et GEMs al., To address this challenge, the authors have of S. stipitis contain any gene regulatory mechanism. al., 2013). To address this challenge, the authors have developed a system identification (SID) based framework, imbalance (i.e., excess amount of NADH produced)none is resulted when is to xylulose. Currently, the which has abeen successfully applied to based GEM validation when xylose xylose is converted converted to any xylulose. Currently, none of ofwill the of S.without stipitis contain gene regulatory mechanism. al., 2013). To address this challenge, the authors have GEMs developed system identification (SID) framework, Therefore, additional constraints, the GEMs developed system identification (SID)to based framework, which hasetabeen successfully applied GEM validation when xylose is converted to any xylulose. Currently, mechanism. none of the GEMs of S. stipitis contain gene regulatory (Damiani al., 2015), refinement (Damiani et al., 2018) and GEMs of S.without stipitis contain any geneasregulatory mechanism. Therefore, additional constraints, the GEMshigher will developed abeen system identification (SID)to based framework, which has successfully applied GEM validation always pick the NADH route for XR, it will produce which hasetphase been successfully applied to GEM validation (Damiani al., 2015), refinement (Damiani et 2018a). al., 2018) of S.without stipitis contain any geneasregulatory mechanism. Therefore, additional GEMs will phenotype plane analysis (Hilliard et al., In and the GEMs always pick the NADH for constraints, XR, it willthe produce Therefore, without constraints, the GEMshigher will which haset been successfully applied to GEM validation (Damiani al., 2015), refinement (Damiani et al., 2018) and biomass growth due additional toroute the balanced redox. To address this (Damiani et al., 2015), refinement (Damiani et al., 2018) and phenotype phase plane analysis (Hilliard et al., 2018a). In the Therefore, without additional constraints, the GEMs will always pick the NADH route for XR, as it will produce higher SID-based framework, the knowledge captured by 2018) a GEM is always biomass growth due toroute the balanced To address this pick thethe NADH forconducted XR, asredox. it will produce higher (Damiani etphase al., 2015), refinement (Damiani et 2018a). al., and phenotype plane analysis (Hilliard et al., In the limitation, for simulations in this section, we phenotype phase plane analyzing analysis (Hilliard et al., 2018a). the SID-based framework, the knowledge captured by a GEM is always pick the NADH route for XR, asredox. it willTo produce higher biomass growth due to the balanced address this first extracted through a series of designed in In silico biomass growth due to the balanced redox. To address this limitation, for the simulations conducted in this section, we phenotype phase plane analysis (Hilliard et al., 2018a). In the SID-based framework, the knowledge captured by a GEM is added a constraint on the ratio between XR using NADPH and SID-based framework, the knowledge captured by aavailable GEM is biomass first extracted through a is series of designed in silico growth due to the balanced redox. To address this limitation, for the simulations conducted in this section, we experiments, then suchanalyzing knowledge compared with limitation, for the simulations conducted in this section, we added a constraint on the ratio between XR using NADPH and SID-based framework, the knowledge captured by a GEM is first extracted through analyzing a series of designed in silico (XR asthe theratio cofactor, to force the of experiments, then such knowledge compared with available first extracted through analyzing a is series of designed in guide silico NADH limitation, forratio) the on simulations conducted in thisutilization section, and we added a constraint between XR using NADPH knowledge on the strain to conduct model validation, a constraint on the ratio between XR using NADPH and NADH (XR ratio) as the cofactor, to force the utilization of first extractedthen through analyzing a is series of designed in silico added experiments, such knowledge compared with available NADPH to reflect the available knowledge on the XR reaction. experiments, then such knowledge is compared with available knowledge on the strain to conduct model validation, guide a (XR constraint on the ratio between XR using NADPH and NADH ratio) as the cofactor, to force the utilization of model refinement, andknowledge analyze different phenotypes. By added NADH (XR ratio) as the cofactor, to force the utilization of NADPH to reflect the available knowledge on the XR reaction. experiments, then such is compared with available knowledge on the strain to conduct model validation, guide 1 (XR shows that agreement between the model knowledge on SID-based the strain conduct model validation, guide model refinement, and to analyze different phenotypes. By Table NADH ratio) asexcellent the cofactor, to force the utilization of NADPH to reflect the available knowledge on the XR reaction. applying the framework, the authors recently NADPH toxylitol reflect theexcellent available knowledge on the XRthe reaction. Table 1 shows that agreement between model knowledge on the strain to conduct model validation, guide model refinement, and analyze different phenotypes. By predicted yields and the reported experimental values model and analyze different phenotypes. By NADPH applyingrefinement, the SID-based framework, the authors recently to reflect theexcellent availableagreement knowledgebetween on the XRthe reaction. Table 1 shows that model 1 shows excellent between the values model predicted xylitolthat yields and theagreement reported experimental model refinement, and analyze different phenotypes. By Table applying applying the the SID-based SID-based framework, framework, the the authors authors recently recently Table 1 shows excellent between the values model predicted xylitolthat yields and theagreement reported experimental applying the SID-based framework, the authors recently predicted xylitol yields and the reported experimental values predicted xylitol yields and the reported experimental values Copyright © 2018 IFAC 110 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright 2018 responsibility IFAC 110Control. Peer review©under of International Federation of Automatic Copyright © 2018 IFAC 110 10.1016/j.ifacol.2018.09.018 Copyright © 2018 IFAC 110 Copyright © 2018 IFAC 110
IFAC FOSBE 2018 Chicago, Illinois, USA, August 5-8, 2018 Matthew Hilliard et al. / IFAC PapersOnLine 51-19 (2018) 110–111
(Farias et al.,2014) can be achieved through the tuning of the oxygen uptake rate (OUR) and XR ratio.
slightly increased by 6% (0.67mmol/gDCW/hr), while the total production/consumption of cytosolic NADPH decreased significantly by 46% (1.138 mmol/gDCW/hr). Additional analysis suggests that to cope with the stress caused by reduced oxygen supply, cells rely on increased production of NADH, as well as converting cytosolic NADPH to mitochondrial NADH to achieve the drastic shift from NADPH to NADH as the reducing factor.
Table 1 Model Predictions vs Experimental Values Case 1 2 3 4 *
YE/S Exp.* 0.328 0.358 0.376 0.437
Xylitol Exp.* 0.1408 0.0934 0.0115 0.0172
Xylitol Pred. * 0.1411 0.0937 0.0118 0.0176
XR Ratio
OUR*
1.06 0.79 0.51 0
2.00 1.71 1.46 0.28
111
unit: mmol/gCDW/hr,
3. SYSTEM-LEVEL ANALYSIS ON THE REDOX SHIFT To gain a better understanding on how the cellular redox balance shifts when OUR decreases, we applied the SID-based framework to investigate the cellular response to the change in OUR. First, a series of in silico experiments were designed and carried out to generate intracellular flux data. Because iDH814 does not contain the gene regulatory mechanism on cofactor utilization, in silico experiments were carefully designed to reflect what is going on within the cell by using experimental results. Further analysis showed that the first 3 conditions listed in Table 1 share the same phenotype phase (Damiani et al., 2018a). Based on this, we designed the following in silico experiments: as OUR was reduced linearly from 2.0 to 1.46 mmol/gDCW/hr, the XR ratio (NADPH:NADH) was decreased linearly from 1.058 to 0.502, while keeping a constant xylose uptake rate (5 mmol/gDCW/hr). Then principal component analysis (PCA) was applied to analyze the resulted flux distribution matrix. One principal component (PC) captured 100% of the variance, which confirms that in silico experiment conditions belong to the same phenotype. Fig. 1 visualizes the PC loadings against the metabolic network map to better understand the intracellular metabolic response predicted by the model, where blue arrows indicate that the reaction fluxes are upregulated, red down regulated, and black no change in flux through the reaction. Fig. 1 shows that corresponding to the reduced OUR and shifted XR cofactor preference, PPP, TCA and ETC pathways are all down regulated, while glycolysis and ethanol production are upregulated. Such responses agree with our understandings. In addition, because the inner membrane of the mitochondria is impermeable to NADH, electron shuttles, metabolic pathways that facilitate the transfer of electrons (in the form of NADH) produced in the cytosol to the electron transport chain in mitochondria, are needed. Our analysis results show that S. stipitis primarily utilizes the GLYC3P shuttle (highlighted in blue box) and/or the NADHDH shuttle (highlighted in green box), and their down-regulation confirms the reduction in excess NADH. Note that the use of GLYC3P and NADHDH shuttles by iDH814 were obtained as the alternative optimal solutions of FBA, indicating both shuttles could be in operation simultaneously.
Figure 1 Loading values plotted against central carbon network map for S. stipitis
Table 2 Balance of Major Electron Carriers Metabolite Cond. 1 Cond. 3 Diff NADH 1.029 0.631 -0.398 NADPH 0.162 0.144 -0.018 FADH2 2.742 2.085 -0.656 Q6H2 3.771 2.717 -1.054 6. ACKNOWLEDGEMENT This work was supported by National Science Foundation (1069004 for MH, 1264861 for JW and QHE) and Department of Education (P200A150074 for MH and JW). 7. REFERENCES Caspeta, L., Shoaie, S., Agren, R., Nookaew, I., Nielsen, J. [2012]. BMC Syst Biol, 6(1). Damiani, A.L., He, Q.P., Jeffries, T.W., Wang, J. [2015]. Biotechnol Bioeng, 112(6), 1250-1262. Damiani, A.L., Hilliard, M., He, Q.P., Wang, J. [2018]. Microb Cell Fact, submitted. Farias, D., de Andrade, R.R., Maugeri-Filho, F. [2014]. Appl Biochem Biotech, 172(1), 361-379. Hilliard, M., Damiani, A.L., He, Q.P., Wang, J. [2018a]. Proceedings of the 13th International Symposium on Process Systems Engineering, accepted. Hilliard, M., He, Q.P., Wang, J. [2018b]. Foundations of Systems Biology in Engineering, submitted. Jeffries, T.W., Grigoriev, I.V., Grimwood, J., Laplaza, J.M., Aerts, A., Salamov, A., Richardson, P.M. [2007]. Nat Biotechnol, 25(3), 319-326. McCloskey, D., Palsson, B.Ø., Feist, A.M. [2013]. Mol Syst Biol, 9(1), 661. Österlund, T., Nookaew, I., Nielsen, J. [2012]. Biotechnol Adv, 30(5), 979-988.
To further examine the specific role redox (im)balance plays in the fermentation of xylose, in Table 2 we tabulated total NADH, NADPH production/consumption in the cytosol and the total Q6H2 production/consumption in mitochondria when the cells transition from condition 1 to condition 3. As OUR reduces, the total production/consumption of cytosolic NADH 111