Dynamics of Storage Carbohydrates Metabolism in Saccharomyces cerevisiae

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Dynamics of Storage Carbohydrates Metabolism in

Saccharomyces cerevisiae

:

A Quantitative Analysis

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. ir. K.Ch.A.M. Luyben, voorzitter van het College voor Promoties

in het openbaar te verdedigen op dinsdag 1 december 2015 om 10:00 uur

door

Camilo Alberto SUAREZ MENDEZ

ingenieur Biochemical Engineering geboren te Venecia, Colombia

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Promotor: Prof. dr. ir. J.J. Heijnen Copromotor: Dr. S.A. Wahl

Composition of the doctoral committee:

Rector Magnificus, chairperson

Prof. dr. ir. J.J. Heijnen, promotor

Dr. S.A. Wahl, copromotor

Independent members:

Prof. dr. H. Noorman TNW, TU Delft

Prof. dr. M. Oldiges, Forschungszentrum Jülich

Prof. dr. B. Teusink, VU Amsterdam

Prof. dr. M. J. Teixeira de Mattos, U-Amsterdam

Dr. L. Wu, DSM

Reserve member:

Prof. dr. I. W. C. E Arends, TNW, TU Delft

The research presented in this thesis was performed at the Cell Systems Engineering section, Department of Biotechnology, Faculty of Applied Sciences, Delft University of Technology (The Netherlands).

This project was carried out within the research programme of the Kluyver Centre for Genomics of Industrial Fermentation which is part of the Netherlands Genomics Initiative

/ Netherlands Organization for Scientific Research.

ISBN: 978-94-6299-237-5

Cover designed by Shokar@linkedin.com

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To those who dare to be warriors No matter how weak they are No matter how strong is the wind against No matter how uncertain is the path No matter what others could think In the end what prevails is the strength of heart and mind

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Summary

Production of chemicals via biotechnological routes are becoming rapidly an alternative to oil-based processes. Today, not only traditional products like beer, wine and bread are produced by exploiting microbial metabolism. Several microorganisms including yeast, bacteria, fungi and algae can transform feedstocks into high-value molecules at industrial scale. These molecules include biofuels, organic acids, and proteins, among others. Improvement of the bioprocess performance is a key factor for making this technology economically feasible. Despite the vast knowledge on microbial metabolism, some gaps still remain open. In Saccharomyces cerevisiae, metabolism of storage carbohydrates, trehalose and glycogen, is well documented. However, the lack of quantitative information limits our understanding on what is the actual role of these materials, especially regarding the in vivo reaction rates.

Like in nature, in industrial bioreactors microorganisms may encounter fluctuating environments that lead to diverse metabolic responses, especially due to large-scale substrate mixing limitations. These dynamic scenarios are known to trigger the recycle of glucose through the storage carbohydrate nodes depending on the growth rate. The cyclic nature of storage carbohydrate metabolism (synthesis and degradation) can lead to a waste of metabolic energy decreasing biomass and product yields. Thus, the biological system needs to be robust enough to withstand this adverse scenario and maintain its metabolic functions. To understand microbial metabolism, estimation of biochemical reaction rates is a critical goal of metabolic engineering. Unfortunately, the storage carbohydrate metabolism is usually not considered when estimating intracellular fluxes mainly due to its cyclic nature and simultaneous occurrence.

The scope of this thesis is to investigate the dynamics of storage carbohydrates, trehalose and glycogen, and its interaction with the central carbon metabolism in Saccharomyces cerevisiae from a quantitative perspective. To overcome the difficulty imposed by the cyclic nature of the storage carbohydrate metabolism, in this work it is proposed to use stimulus response experiments in combination

with 13_{C-labeling and mathematical modeling for identifying reaction rates. In this}

direction, novel feast/famine experiments are proposed as a robust experimental platform for dynamic metabolic studies and estimating dynamic fluxes in time by piecewise function approximations. Hence, the main contribution of this thesis is not only the better understanding of storage carbohydrate metabolism through

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dynamic metabolic rates but also the used experimental platforms and modeling approach.

By performing 13_{C labeling experiments in aerobic glucose limited cultures}

of Saccharomyces cerevisiae at four different growth rates (0.054; 0.101, 0.207, 0.307 h

-1_{) it is found that exchange fluxes between the central carbon metabolism and}

storage carbohydrates and amino acids are rearranged depending on the growth rate. At low growth rates the impact of the storage carbohydrate recycle is relatively more significant than at high growth rates due to a higher concentration of these materials in the cell and higher fluxes relative to the glucose uptake rate. In addition, experimental observations show that glucose is exported to the extracellular space whose source is related to storage carbohydrates, most likely via the export and subsequent extracellular breakdown of trehalose.

Implementation of mild dynamic feast/famine regimes with moderate changes in substrate availability produce stable patterns with repetitive and reproducible metabolic responses in time, thus providing a robust platform for studying microorganism’s physiology under such dynamic conditions. A slightly reduced biomass yield (−5%) is observed under this mild regime. In addition, the dynamic response of intracellular metabolites show differences in comparison to other dynamic experiments. Remarkably, the frequently reported ATP paradox observed in single pulse experiments is not present during repetitive perturbations. Moreover, dynamic profiles of intracellular metabolites obtained with the feast/famine suggest the presence of regulatory mechanisms.

The feast/famine setup in combination with 13_{C labeling serve also as a}

tool for estimating dynamic fluxes. A highly dynamic flux response has been

observed with the glucose uptake increasing from 203 to 5213 µmol gDW-1 h-1 in 15

s. Other fluxes also exhibit fast changes, like the striking increase of nearly 200,000-fold in T6P synthesis upon glucose addition. This response indicates that the storage metabolism is very sensitive to changes in glycolytic flux and counterbalances these rapid changes by diverting flux into large pools to subsequently refill the central metabolism when the substrate becomes scarce.

Using 13_{C-tracer we have found a dilution in the labeling of extracellular glucose,}

G6P, T6P and other metabolites, indicating an influx of unlabeled carbon.

Remarkably, a dramatic decrease in the 13_{C-profile of T6P has been observed}

suggesting a putative mechanism for trehalose phosphorylation. The results obtained in this work clearly highlight the relevance of storage carbohydrates and its interaction with the central carbon metabolism.

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Mutant strains lacking trehalase activity were grown under dynamic feast/famine conditions to test the hypothesis on the role of trehalose in glucose recycling through the extracellular space, and the results are compared with those obtained for a wild-type strain. Interestingly, it has been found that the major contribution to glucose recycle via trehalose is mediated by Ath1p, an enzyme that catalyzes the extracellular degradation of trehalose. This mechanism largely

explains the experimental observations related to 13_{C-enrichment dilution of}

extracellular glucose. Although trehalase knockout does not largely alter both the phenotype and metabolome of yeast cells, main differences are observed regarding

CO2 and O2 production and consumption rates, which are about 10% lower in the

mutant strain compared to the wild-type. The difference in qCO2 can be largely

explained by trehalose that is not degraded in the mutant strains.

The extracellular trehalose concentration increases up to 10-fold in cultivations with the mutant strains confirming that trehalose is exported to the extracellular space. In addition, during the dynamic feast/famine the extracellular glucose concentration is about 60% lower in the mutant strain compared to the wild-type, while extracellular trehalose is 24-fold higher. Moreover, a more sensitive ATP response is observed in the mutant strain leading to a decreased energy charge when glucose availability is low. Flux estimations show that the actual glucose uptake is about 20% higher in the wild-type strain, and is mainly due to extracellular glucose recycle through trehalose and glycogen.

Here, yeast robustness is analyzed based on the metabolic response of yeast to cyclic glucose perturbations of different magnitude. It is found that despite a 10-fold change in extracellular glucose concentration, the intracellular concentration and fluxes of glycolytic intermediates slightly respond (1.2 to 1.5 fold change), and remain close to the response under mild perturbations. In contrast, intermediates of the storage carbohydrate metabolism react more drastically. Remarkably, T6P is highly sensitive increasing its concentration by 66-fold in 120 s while the concentrations of other storage carbohydrate intermediates are reduced. At the same time, the metabolic flux significantly increases suggesting that storage carbohydrates are key metabolites for yeast robustness since these metabolites are involved in the regulation of the glycolytic flux. Diversion of the glycolytic flux towards storage carbohydrates is more significant when a strong perturbation is applied.

This work comprehensively presents the role of trehalose and glycogen metabolism in yeast from a quantitative point of view, and it is expected that this contribution will be of significant relevance for understanding yeast metabolism in both scientific and industrial environments.

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Samenvatting

Productie van chemicaliën via biotechnologische routes wordt in snel tempo een alternatief voor de op olie gebaseerde processen. Tegenwoordig, niet alleen traditionele producten, zoals bier, wijn en brood, worden geproduceerd via microbieel metabolisme. Verscheidene micro-organismen zoals gisten, bacteriën, schimmels en algen zetten grondstoffen om op industriële schaal in hoogwaardige moleculen. Deze moleculen omvatten onder andere biobrandstoffen, organische zuren en eiwitten. Verbetering van de bioproces opbrengst is een essentiële factor om deze technologie economisch haalbaar te maken. Ondanks de enorme kennis van het microbiële metabolisme, blijven enkele lacunes nog open. In Saccharomyces

cerevisiae, het metabolisme van koolhydratenopslag – trehalose en glycogeen – is

goed gedocumenteerd. Echter, het gebrek aan kwantitatieve informatie beperkt ons begrip van wat de feitelijke rol van deze materialen is, vooral wat betreft de in vivo reactiesnelheden.

Net als in de natuur, in de industriële bioreactoren micro-organismen kunnen fluctuerende omgevingen tegenkomen die leiden tot diverse metabolische reacties, vooral door de beperkingen bij het mengen van grootschalig substraat. Deze dynamische scenario’s zijn erom bekend dat zij de recirculatie van glucose triggeren via koolhydraatopslag nodes afhankelijk van de groeisnelheid. De cyclische aard van het opslagkoolhydraatmetabolisme (synthese en afbraak) kan leiden tot verspilling van metabole energie, tot afnemende biomassa en tot afnemende productopbrengsten. Dus het biologische systeem moet robuust genoeg zijn om dit ongunstige scenario te weerstaan en om de stofwisseling te handhaven. Om het microbiële metabolisme te begrijpen, is de schatting van biochemische reactiesnelheden een kritische doelstelling van metabolic engineering. Helaas is het opslagkoolhydraatmetabolisme meestal niet meegenomen bij het schatten intracellulaire fluxen vooral vanwege zijn cyclische aard en het gelijktijdig optreden ervan.

Het onderwerp van dit proefschrift is het kwantitatief onderzoek naar de dynamiek van de opslagkoolhydraten, trehalose en glycogeen, en hun interactie met het centrale koolstofmetabolisme in Saccharomyces cerevisiae. Om de moeilijkheid met de cyclische aard van het opslagkoolhydraatmetabolisme te ondervangen, wordt in dit werk voorgesteld om stimulus respons experimenten te gebruiken in

combinatie met 13_{C-labeling en wiskundig modellen voor het identificeren van}

reactiesnelheden. In deze richting, zijn nieuwe feast/famine experimenten voorgesteld als een robuust experimenteel platform voor dynamische metabole studies en het schatten van dynamische fluxen door stuksgewijze functie

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benaderingen. Vandaar dat de belangrijkste bijdrage van dit proefschrift niet alleen een beter begrip is van de opslag koolhydraat metabolisme via dynamische metabolische reacties, maar dat ook het nieuwe is in de gebruikte experimentele platformen en de modelmatige benadering.

Door het uitvoeren van 13_{C labeling experimenten met aerobe glucose}

gelimiteerde culturen van Saccharomyces cerevisiae bij vier verschillende groeisnelheden

(0,054, 0,101, 0,207 en 0,307 h-1_{), werd vastgesteld dat uitwisselingsfluxen}

herschikken tussen de centrale koolstof metabolisme en opslag koolhydraten en aminozuren afhankelijk van de groeisnelheid. Bij lage groeisnelheden is het effect van de koolhydraatopslag recycle relatief belangrijker dan bij hoge groeisnelheden vanwege de hogere concentratie van deze stoffen in de cel en de hogere fluxen opzichte van de glucose opnamesnelheid. Daarnaast laten de experimentele waarnemingen zien, dat glucose wordt geëxporteerd naar de extracellulaire ruimte, waarvan de bron is gerelateerd aan opslagkoolhydraten, waarschijnlijk via de export en de daaropvolgende extracellulaire afbraak van trehalose.

De realisatie van milde dynamische feast/famine regimes met een matige veranderingen in de beschikbaarheid van substraat produceert stabiele patronen met repetitieve en reproduceerbare metabole responses in de tijd, waardoor een robuust platform ontstaat voor het bestuderen van de fysiologie van micro-organismen onder dergelijke dynamische omstandigheden. Een iets lagere biomassa opbrengst (-5%) wordt waargenomen onder dit milde regime. Bovendien, de dynamische respons van intracellulaire metabolieten hier verschilt van die van andere dynamische proeven. Opmerkelijk is dat de frequent gemelde ATP paradox waargenomen in enkelvoudige puls experimenten niet aanwezig is tijdens repetitieve verstoringen. Bovendien, de dynamische profielen van intracellulaire metabolieten verkregen met het feast/famine experimenten suggereren de aanwezigheid van regulerende mechanismen.

Het feast/famine setup in combinatie met 13_{C labeling dient ook als een}

instrument voor het schatten van dynamische fluxen. Een zeer dynamische flux respons werd waargenomen met de opname van glucose toenemende van 203 tot

5213 µmol GDW-1_h-1_{in 15 s. Andere fluxen vertonen ook snelle veranderingen,}

zoals de opvallende stijging met een factor 200.000 in de T6P synthese na glucose toevoeging. Deze response geeft aan dat het opslagmetabolisme zeer gevoelig is voor veranderingen in glycolytische flux en compenseert deze snelle veranderingen door flux naar grote pools te leiden en achteraf het centrale metabolisme aan te

vullen, wanneer het substraat schaars is. Met behulp van 13_{C-tracer hebben we een}

verdunning waargenomen in de labeling van extracellulaire glucose, G6P, T6P en andere metabolieten, wat wijst op een instroom van niet-gelabelde koolstof.

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Opmerkelijk is de observatie van een dramatische afname van het C-profiel van T6P wat op een mogelijk mechanisme voor trehalose fosforylering duidt. De in dit

werk verkregen resultaten markeren duidelijk het belang van de

opslagkoolhydraten en hun interactie met het centrale koolstofmetabolisme. Mutantstammen, die trehalase-activiteit missen, werden gecultiveerd onder dynamische feast/famine condities om de hypothese te testen over de rol van trehalose in glucose recycling via de extracellulaire ruimte, en de resultaten te vergelijken met die verkregen voor een wildtype stam. Het is interessant, dat gevonden is, dat de belangrijkste bijdrage aan recycle glucose via trehalose wordt gemedieerd door Ath1p, een enzym dat de afbraak van extracellulair trehalose katalyseert. Dit mechanisme verklaart grotendeels de experimentele waarnemingen

met betrekking tot 13_{C labeling verdunning van extracellulaire glucose. Hoewel}

trehalase knockout niet veel verandert aan zowel het fenotype als het metaboloom van de gistcellen, worden toch belangrijke verschillen waargenomen betreffende

CO2 en O2 productie- en consumptiesnelheden, die ongeveer 10% lager waren in

de mutante stam vergeleken met het wildtype. Het verschil in qCO2 kan grotendeels

verklaard worden door de aanwezigheid van trehalose die niet wordt afgebroken in de mutante stammen.

De extracellulaire trehalose concentratie neemt 10-voudig toe in cultures van mutant stammen, wat bevestigt dat trehalose wordt geëxporteerd naar de extracellulaire ruimte. Bovendien, tijdens de dynamische feast/famine cyclus, is de extracellulaire glucose-concentratie ongeveer 60% lager in de mutante stam vergeleken met de wildtype stam, terwijl extracellulair trehalose 24 keer hoger is. Overigens is een gevoeliger ATP response waargenomen in de mutante stam die tot een verminderde energie behoefte leidt wanneer de glucose beschikbaarheid laag is. Flux schattingen tonen aan dat de werkelijke opname van glucose ongeveer 20% hoger is in de wildtype stam, en vooral veroorzaakt wordt door recyclage van extracellulair glucose via trehalose en glycogeen.

Hier wordt gist robuustheid geanalyseerd op basis van de metabole respons van gist op cyclische glucose verstoringen van verschillende amplitude. Het blijkt dat ondanks een 10-voudige verandering in de extracellulaire glucose-concentratie, de intracellulaire concentraties en fluxen van glycolytische tussenproducten licht reageren (1,2 tot 1,5 voudige verandering) en dichtbij de reactie onder milde

verstoringen blijven. Daarentegen tussenproducten van het

opslagkoolhydraatmetabolisme reageren drastischer. Opmerkelijk is dat T6P zeer gevoelig is en zijn concentratie 66-voudig vergroot in 120 s, terwijl de concentraties van andere koolhydraatopslag tussenproducten worden gereduceerd. Tegelijkertijd is de metabole flux significant verhoogd wat suggereert dat opslag koolhydraten

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belangrijke metabolieten zijn voor gist robuustheid aangezien deze metabolieten zijn betrokken bij de regulering van de glycolytische flux. Omleiding van de glycolytische flux in de richting van de opslag koolhydraten is belangrijk wanneer een sterke verstoring wordt toegepast.

Dit werk geeft uitgebreid de rol van trehalose en glycogeen metabolisme in gist uit een kwantitatief oogpunt, en de verwachting is, dat deze bijdrage van groot relevantie is voor het begrijpen van gist metabolisme in zowel wetenschappelijke als industriële omgevingen.

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CHAPTER

1

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General Introduction

Background

Biotechnological routes to chemicals are becoming rapidly an alternative to petrochemical dependent processes. Apart from those historically made products like beer, wine and bread, more high-value molecules like biofuels, organic acids among others are produced by means of microorganisms like yeast, bacteria and algae (Steensels et al., 2014). Industrial bioprocesses pursue to take advantage of metabolic activities of living cells, which are accomplished by a regulated network of enzyme-catalyzed reactions and membrane transport systems (Bailey, 1991). Despite the vast natural metabolic diversity, metabolic networks have evolved in natural environments rather than in industrial plants. Hence, it is necessary to implement genetic improvement for better bioprocess performance aiming to obtain an increased production of a desired metabolite or chemical (Kerkhoven et al., 2015). To this aim, metabolic engineering emerged as a field for a directed modulation of metabolic pathways using methods of recombinant technology for the purpose of

overproducing fuels and chemical and pharmaceutical products (Bailey, 1991).

Classical metabolic engineering approaches are common in industry and allow for gradual improvements, but are still limited in describing the multilayered metabolic regulation present in biological systems (Kerkhoven et al., 2015). More recently, systems biology became a discipline that aims at integrating different biological levels such as transcriptome, proteome and metabolome by means of mathematical modeling in order to better identify metabolic engineering targets (Kerkhoven et al., 2015). System-level understanding of a biological entity focuses on understanding its system’s structure and dynamics (Kitano, 2002). Sauer (2006) argued that intracellular reaction rates (i.e., fluxes) are the functional end points of the multiple interactions in metabolic networks, and are highly relevant for understanding a biological system. Similarly, Kerkhoven et al. (2015) considered that a microorganism’s phenotype can be described by its pattern of metabolic fluxes. In this thesis the approach of metabolic engineering and the strategy of systems biology are combined in order to understand the role of storage carbohydrates in Saccharomyces cerevisiae by measuring metabolite concentrations and intracellular flux estimations. Thus, experimental platforms for dynamic experiments in conjunction with proven metabolomics techniques and mathematical modeling have been developed to investigate the interplay of

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trehalose and glycogen with the central carbon metabolism, aiming at an holistic understanding of regulatory mechanisms.

Role of storage carbohydrates

The yeast Saccharomyces cerevisiae is one of the major workhorses used in biotechnology for producing pharmaceuticals, biofuels (Hashem and Darwish, 2010), and bulk chemicals (Steensels et al., 2014; Willke and Vorlop, 2004). S.

cerevisiae is also an important model organism for studying physiology (Pereira et al.,

2001), genetics and metabolic mechanisms of eukaryotic cells that may also be used for understanding human cells (Castrillo et al., 2007). Despite the vast knowledge on S. cerevisae, there are yet many gaps in our understanding, especially regarding the kinetics of some parts of its metabolism (Steensels et al., 2014). For instance, in understanding the kinetics of carbon storage nodes, which have recently received more attention due to its interaction with the central carbon metabolism because it can introduce a bias when estimating flux distributions and studying intracellular dynamics (Aboka et al., 2009; van Heerden et al., 2014).

S. cerevisiae mainly accumulates two types of carbon stores, trehalose and

glycogen (François and Parrou, 2001). Trehalose is a non-reducing disaccharide

composed of two glucose molecules linked by one α (1,1) glycosidic bond, while

glycogen is a polysaccharide of high molecular mass in which glucose molecules

are chained by α (1,4) and α (1,6) glycosidic bonds. Glycogen and trehalose are

considered part of the metabolic machinery that provide S. cerevisiae its ability to adapt and respond to nutrient fluctuations in the environment because they can either accumulate or be mobilized as response to those changing conditions (Guillou et al., 2004). Glycogen and trehalose are not only carbon and energy reserves in yeast, but also serve as stress protectants, particularly trehalose (François and Parrou, 2001; Parrou et al., 2005; Stambuk et al., 1996). More importantly, trehalose cycling seems to have an influence in controlling glycolytic flux at the level of hexose phosphorylation (Guillou et al., 2004; van Heerden et al., 2014). In addition, metabolism of trehalose and glycogen seems to be associated with the cell growth cycle (Paalman et al., 2003; Silljé et al., 1999).

Glycogen and trehalose metabolism

In glycogen biosynthesis and degradation, a number of steps are involved (Figure 1.1). The former consists of initiation, elongation and ramification (François and

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Parrou, 2001). Glycogen synthesis is initiated by a protein encoded by

GLG1/GLG2 and designated as ‘glycogenin’, which produces short α

(1,4)-glucosyl chains from UDP-Glucose (UDPG). The second step is elongation,

catalyzed by GSY1/GSY2 that forms an α (1,4)-glucosidic bond between UDPG

and the non-reducing end of the linear α (1,4)-glucosyl chains. The active form of

glycogen synthase is a non-phosphorylated protein, which is allosterically activated by G6P and reversible covalent phosphorylation in which protein kinases and phosphatases play a role. Glycogen branching is initiated by a protein encoded by

GLC3. In this reaction the linear α (1,4)-glucosyl chains are ramified forming α

(1,6)-glycosidic bonds. It is estimated that yeast glycogen is more branched than in

other species with up to 10% of α (1,6)-glycosidic linkages (François and Parrou,

2001).

Figure 1 .1 Metabol ic reactions of the st orage carbohydrat es pathw ay in S. cer evisiae. Letters in it alics represent the enzyme/r eaction. All met abolites ar e intr acell ul ar ex cept thos e ident if ied with –ec, which stands for extr acell ul ar. Doubl e arrows indicate bidirectional reactio ns.

Glycogen degradation occurs either by amylolysis or phosphorolysis. The

former is via a hydrolase (α-glucosidase) producing glucose, while the latter is

carried out by a phosphorylase producing G1P and glucose. Yeast glycogen phosphorolysis is mediated by GPH1 and a debranching enzyme by GDB1. The

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action of these two proteins seem to be coordinated since disruption of either gene generates a failure in glycogen degradation (François and Parrou, 2001) and G6P appears to be the main regulator in controlling activation of glycogen phosphorylase. On the other hand, glycogen amylolysis is carried out by a protein

encoded by SGA1, which possesses α (1,4)- and α (1,6)- activities.

Trehalose is synthesized by a trehalose synthase complex composed of four different subunits encoded by TPS1, TPS2, TSL1 and TPS3. The first one catalyzes the production of T6P from UDP-glucose and G6P while TPS2 catalyzes a phosphatase reaction that produces trehalose from T6P. TSL1 and TPS3 seems to function only in stabilizing the trehalose synthase complex. Contrary to other enzyme reactions involved in reserve carbohydrate metabolism, the trehalose synthase complex is not subject to reversible phosphorylation and its activations seems to depend strongly on temperature with an optimum at 42-45°C. Trehalose synthesis is most likely influenced by the concentration of G6P and UDPG, temperature and the steady-state levels of the proteins (François and Parrou, 2001). On the other hand, trehalose degradation is mostly mediated by two enzymes (Jules et al., 2008):

i. A neutral trehalase encoded by NTH1/NTH2 in the cytosol;

ii. An acid trehalase encoded by ATH1 in the vacuole or in the

extracellular space.

When yeast grows on trehalose as co-substrate with hexoses, trehalose is taken up and accumulated in the intracellular space. Interestingly, in response to a sudden depletion of the hexose, the accumulated trehalose is exported by means of an

H+_{/trehalose transporter encoded by AGT1 in order to be used as carbon source.}

Jules et al. (2008) suggested that this mechanism exist in yeast in order to assimilate exogenous trehalose in batch cultivations. In addition, these authors found that trehalose mobilization via Ath1p was prior to that of glycogen, however, when this mechanism was impaired, glycogen was mobilized earlier and faster in what they interpreted as a fine-tuning control in carbon storage management during periods of carbon and energy restriction. Reported main facts about trehalose and glycogen are described below:

i. Metabolism of trehalose and glycogen are cyclic and they occur

simultaneously (François and Parrou, 2001), starting from glucose to yield back glucose in the end (Jules et al., 2008).

ii. Because of its cyclic nature, it is usually considered that storage

carbohydrate metabolism results in a net loss of ATP, thus becoming a futile cycle (Mashego et al., 2004). In addition, a high flux of simultaneous

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synthesis and mobilization must have an influence on fluxes in central carbon metabolism, specially under dynamic conditions(Aboka et al., 2009).

iii. These carbon stores may accumulate up to 30% of the cell dry weight,

which strongly depends on growth conditions (Parrou et al., 2005; Wang et al., 2001). In addition, glycogen and trehalose levels seem to be negatively correlated with growth rate (Wang et al., 2001).

iv. Both carbohydrates respond rapidly to fluctuations in substrate

concentration. For example, Aboka et al. (2009) found that in chemostat cultivations a 50% shift-up in glucose uptake (i.e. increased growth rate

from 0.05 to 0.075 h-1_{) counter-intuitively triggered a temporary}

mobilization of storage carbohydrates into glycolysis that was about one third of the glycolytic flux.

v. Upon a step increase in inflow CO2 concentration from 0.04% to 1% to a

yeast aerobic cultivation, Mashego (2005) observed an increased glycolytic flux while there was a corresponding mobilization of trehalose and glycogen.

Estimating fluxes of metabolic network reactions accurately is crucial for metabolic applications in science and industrial purposes. Although there is a considerable knowledge on storage carbohydrates, some questions remain open. Especially, the rate of synthesis and degradation at different growth rates and/or under dynamic conditions have not yet been fully determined, thus it remains unclear up to what extent this recycle influences the central carbon metabolism and the corresponding flux estimations. Even more relevant is the interaction in the natural dynamic environment, but also at large scale industrial cultivations, where organisms are exposed to rapid dynamic conditions (Sweere et al., 1988), which are usually repetitive (Larsson et al., 1996).

Relevant cultivation parameters, like dissolved oxygen, substrate

concentration, pH and CO2 concentration vary at different regimes encountered in

large scale bioreactors (Schmalzriedt et al., 2003) mainly due to long mixing times (Larsson et al., 1996). Substrate concentrations in large reactors, for instance, can vary from feed solution in the order of hundreds of grams per liter to very limiting concentrations (in the order of mg/L) in areas far away from the feed inflow. Thus, microorganisms in bioreactors experience rapid changing environments that may have detrimental effects, including decrease in product and biomass yield (de Jonge et al., 2013; Sweere et al., 1988). In S. cerevisiae substrate fluctuations, depending on the frequency and magnitude, may decrease the biomass yield up to 25% (Van Kleeff et al., 1996). Under rapid dynamic conditions rearrangement of

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the metabolic fluxes is expected, leading to a different physiology. Neglecting the presence of storage carbohydrates under these conditions will lead to a misleading interpretation of the carbon flux distribution.

Research approach

Quantification of intracellular fluxes under dynamic conditions requires a well-defined experimental setup as well as a model-based calculation as they cannot be measured using a sensor. Theobald et al. (1997) first proposed an interesting approach that consists in collecting metabolic data from dynamic experiments to describe the dynamic response of a pathway to an extracellular stimulus. Due to its features, this sound approach has been used by other people until now (Chou and Voit, 2012; Mashego et al., 2006). In the present research a hypothesis-driven approach is used (Kitano, 2002), by which a set of hypotheses regarding the interaction of storage carbohydrates with the central carbon metabolism is studied (Figure 1.2).

Figure 1.2. Sch em e of the research approach used in t his work bas ed on Kit ano (2002). The process can s tart with ‘w et’ exp erim ents that usual ly r esult in contradictor y issues for cing t o a generat io n of new hypoth eses. Fo llow ing, these h ypothes es are impl emented in a m athem at ical model that is test ed by comput at io nal s imul at ions . New theor ies can aris e leading to a new round o f experim ents unt il the mo del can descr ibe the exp erim ental r esults properl y.

From those hypotheses a number of experiments were designed, and evaluated through modeling. This approach usually leads to novel insights that are

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validated by new experiments using for example specific mutants or modified culture conditions like different substrates.

Experimental platforms

Mainly two different approaches are available to study cellular metabolism: 1- steady-state experiments, and 2- dynamic experiments. Dynamic conditions allow the identification of intracellular, in vivo kinetics that can be used later to develop predictive metabolic models, i.e., they can be applied to new conditions. In contrast, steady-state approaches are mainly restricted to static pictures of the metabolism though are suitable for phenotypic identification (i.e., stoichiometry and fluxes) at a given growth rate. Whereas metabolic stationary approaches mainly aim to quantify intracellular fluxes, non-stationary experiments are more frequently used to identify reaction kinetic properties like maximal in vivo rates, substrate affinities and allosteric inhibition (Wahl et al., 2008; Wiechert and Noack, 2011). In order to reproduce such dynamic environments a number of approaches have been applied:

i. Step and pulse perturbations of glucose limited cultivations (Guillou et al.,

2004; Mashego et al., 2006; Sweere et al., 1988; Theobald et al., 1997; Van Kleeff et al., 1996; Weber et al., 2005), and

ii. Periodic perturbations (Buziol et al., 2008).

Wahl et al. (2008) summarized some stimulus-response strategies aiming at the identification of in vivo enzyme kinetics. The authors suggested that this type of experiments were limited since it was not possible to determine all kinetic parameters, though massive quantities of data could be produced during a time window of seconds. In line with Wahl et al. (2008), Heijnen and Verheijen (2013) suggested that main limitations are related to parallel and cyclic reactions. To

overcome this limitation, 13_{C-labeling experiments where the system is at both,}

metabolic and isotopic non-stationary state are proposed to significantly increase the accuracy of kinetic parameter estimation.

In a classical labeling experiment, 13_{C labelled atoms replace the ‘unlabeled’}

12_{C atoms, and a steady-state has to be reached for fluxes, metabolites and labeling}

enrichments (e.g., isotopomers). In particular, reaching a steady-state for the labeling enrichment requires several generation times that demand long waiting times before useful data can be obtained. Today, given the advances in analytical sensitivity and computational algorithms (Nöh et al., 2007; Noh et al., 2006; van Winden et al., 2002; Wahl et al., 2008; Zhao et al., 2008), it is possible to perform

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isotopic non-stationary experiments where C enrichment of intracellular intermediates can be measured instead of biomass components. Transient labelled data delivers information about flux distribution within a micro-organism (Noh et

al., 2006). Fractions of 13_{C enrichment can be distinguished by MS,}

GC-MS/MS and LC-MS-MS.

Here, two types of experimental setups were used:

i. 13_{C wash-in experiments where Saccharomyces cerevisiae cells were grown in a}

chemostat culture until steady-state, followed by 13_{C-labeling while}

keeping the same dilution rate (Figure 1.3A). Under these conditions the biological system was kept at metabolic (flux) steady-state while the isotopic state was transient.

ii. Dynamic experiments in which the biological system was perturbed by

cyclic addition of glucose, labeled and unlabeled (Figure 1.3B). This type of experiment is later referred to as Feast/Famine where concentrations

changed in time and at the same time 13_{C labeling was applied.}

Figure 1.3. Exper iment al s etups used in this th esis. A) 1 3_{C Wash- in exp erim ent.}

The biolo gical syst em was at metabol ic steady-st ate while isotop omers were transient. B) F east /famine. Both flux es, m et abo lit es and isotopom ers wer e transient. Vert ical dot ted lines represent the t im e at w hich l abel ing start ed

In both cases fluxes are estimated by means of mathematical modeling from

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Modeling approach

The metabolic operation of a living cell may be better analyzed through fluxes and metabolite concentrations, especially under dynamic conditions (Dauner, 2010). Typically, metabolic flux analysis (MFA) has been used to characterize the in vivo steady-state (i.e., fluxes) of a metabolic network (Vallino and Stephanopoulos, 1993; Van Gulik and Heijnen, 1995; Varma and Palsson, 1994). Although frequently applied, MFA has intrinsic limitations that are compensated by additional assumptions like biomass yield optimization, redox potential minimization or ATP yield maximization. In practice, MFA does not predict the phenotype of microorganisms correctly, most likely because the organism’s objective is more complex (Fischer and Sauer, 2005; Schmidt et al., 1998; Schuetz

et al., 2007). 13_{C-MFA was proposed to minimize optimization assumptions, and}

quantify fluxes through parallel pathways, intracellular cycles, and bidirectional

reactions. 13_{C-MFA combines experiments with a tracer like}13_{C glucose and the}

classical MFA approach, thus it relies on the 13_{C-enrichment of metabolites}

(Dauner et al., 2000; van Winden et al., 2002; Wiechert, 2001; Wiechert and De

Graaf, 1997). Moreover, state of the art 13_{C MFA relies on transient}13_C-labeling

and rigorous modeling (Nöh et al., 2007; Noh et al., 2006; Wahl et al., 2008). Although these advances have led to a minimization of the a priori assumptions, some are still needed in relation to anaplerotic and transamination reactions, storage metabolism, compartmentalization, protein turnover, and mRNA degradation (Wiechert and Nöh, 2013).

Mathematical description of the modeling approach

Relation between intracellular metabolite concentrations, c, and fluxes (i.e., reaction rates), v, in a metabolic reaction network can be described mathematically as:

(

, ,

)

dc

N v c c

dt = ⋅

α β

− ⋅

µ

(1.1)

Where N is an m x n stoichiometric matrix in which rows correspond to mass balances of metabolites, and columns corresponds to the stoichiometry of metabolic reactions. v is a n x 1 vector containing flux values of all reactions that

depend on metabolite concentrations, c, and a set of parameters, α and β. Whereas

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affinity constraints, maximal conversion rates), β contains parameters mainly set by the experiments like feed regime, dilution rate, etc. The rates of change of intracellular concentrations (left hand side of equation 1.1) are described by balancing the corresponding in- and out-fluxes for each metabolite pool on the right hand side of equation 1.1.

From the general model depicted in equation 1.1 and its inherent complexity, certain situations are encountered depending on the experimental setup (Figure 1.4). The simplest situation occurs when metabolite concentrations are constant, therefore fluxes are defined to a constant value. In case metabolite concentrations change in time, kinetic expressions are required for flux determination. These expressions are typically complex and nonlinear functions, hence, simplifications are often made. For instance, piecewise affine (PWA) functions can be used for estimating fluxes as a function of time.

Figure 1.4. Main featur es of math em atical mo dels fo r estim ating fl uxes. Two levels of inform at io n are avail able: 1- Metabol ite concentrat io ns, which define the met abol ic st at e, 2- Is otopomers, wh ich define t he isot opomer ic st eady-state. Data on isot opo mers incr eas e th e inf o rmation co ntent from meas urem ents, and ar e used to bet ter est im ate f lux values.

Equation 1.1 can be extended with information from labeling experiments

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metabolite includes the 2 equations of all possible labeling states or isotopomers (Wiechert et al., 1999). If a molecule’s backbone containing p carbon atoms is

composed of either 12_{C and}13_{C atoms (Figure 1.5), it can be assumed that the total}

concentration in time of an intermediate metabolite is the sum of all of its isotopomer concentrations. The labeling state or enrichment, x, of a metabolite can be described as follows:

(

0

)

, , , inp dx f c v x x dt = (1.2)

Where the labeling state in time is a function of metabolite concentrations,

c, and fluxes, v, the initial labeling state at time 0, x0, and the enrichment of the

labeled substrate used as tracer, xinp. Depending on the experimental setup,

equation 1.2 will have some features as shown in figure 1.4. In general, solving a

non-stationary 13_{C-MFA problem involves the solution of the two differential}

equations 1.1 and 1.2.

Figure 1.5. Possible isot opo mers ( i.e. , l abel ing st ates) for a mol ecul e co nsist ing of 3 carbon atoms. Th e number of possible isotopo mers is equal to 23_{= 8. Open}

and filled cir cles repres ent 1 2_{C and} 1 3_{C atoms, r espectively. The r elative}

amo unt of each isotopo meric species is th e fraction, x, of a part icul ar isotopom er in a tot al m etabolite pool .

Because of the high complexity of the isotopomer balance equations and limitations in knowing the actual position of a labeled carbon atom in a molecule, especially if chromatography/mass spectrometry techniques are used, a representation for the labeling state of a metabolite intermediate is typically used. Wiechert et al. (1999) proposed the concept of cumomer as a transformation of the isotopomer fraction variable. The word cumomer stands for “cumulated isotopomer fraction”, and its actual meaning is a certain sum of isotopomer fractions of a particular metabolite. It has been shown that even when this concept is applied, the mathematical framework remains equivalent to the one developed for balancing isotopomers (Noack et al., 2011; Wiechert et al., 1999)

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Systems using piecewise affine approximations

Flux values change in time when a biological system is at both, non-stationary

metabolic and isotopic states (i.e., metabolite concentration and 13_{C-labeling state}

change in time). This is the case depicted in figure 1.3B for the feast/famine experiment. Because of the intrinsic complexity of the ordinary differential equations (ODE), especially regarding the high nonlinearity in the concentration domain of the flux functions, further simplifications are required. For a system at metabolic and isotopomeric transient states the approach proposed by Abate et al. (2012) can be used for reconstruction of dynamic fluxes in time using piecewise affine (PWA) functions in the time domain. Later, time-dependent profiles can be embedded in the concentration space for enzyme kinetic identification.

In particular, the nonlinear flux functions are simplified by continuous piecewise linear functions, and the system of ODE’s for balancing metabolite concentrations become algebraic. This approach was recently applied by de Jonge et al. (2013). PWA functions are defined in time by a set of j + 1 breakpoints (switch times), and the whole time domain is divided into a j time intervals or domains. Equation 1.3 shows the case for fluxes that are defined by three switches like the feast/famine sketched in figure 1.6.

Figure 1.6 Sketch of a P WA approximat io n for deter mining fl uxes in tim e fro m feast /f amine exp erim ents

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( )

(

)

(

)

1 0 0 1 1 2 1 1 1 1 2 2 1 3 2 2 2 2 3 3 2

v

t

v

v t

v

t

v

t t

t



₋

+

≤



₋

=



+

−

< ≤

−



₋

+

−

< ≤



−



(1.3)

Where the absolute values of the fluxes, v, at each breakpoint are the parameters to estimate. Thus, a flux value is calculated locally by linear interpolation of two adjacent breakpoints. For a feast/famine problem with i fluxes and j domains the number of parameters is equal to i x j because fluxes at the beginning and at the end of the time domain are declared to be the same because concentrations are the same at these two extreme points. On the other hand, the change of concentration in time can be defined as:

( )

dc u t

dt = (1.4)

Where u t( ) is the slope of the concentration profiles in time at each breakpoint,

and can be calculated by a linear interpolation in a similar manner as v t

( )

in

equation 1.3. Integrating equation 1.4 leads to the calculation of concentrations in time, which can be compared with the measured experimental concentrations. Hence, linear least square optimization of concentrations would lead to an optimized flux value. Knowing the differential term in equation 1.4, it is possible to reformulate equation 1.1 as follows:

( )

dc

u t N v t

dt = = ⋅ (1.5)

The parameter space can be reduced by setting a number of constraints (free fluxes calculated from concentrations in time, which are assumed to be true). Thus, the system in equation 1.5 can be rewritten as follows:

( )

(

)

f

_{( )}

( )

f d d v t dc t N N dt v t   =       (1.6)

Where vf is a vector of free fluxes, and

v

d is a vector of dependent fluxes.

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( )

1

( )

1

( )

d d d f f dc v t N t N N v t dt − − = − (1.7)

Modeling

1 3

C enrichment states for feast/famine

experiments

Estimation of 13_{C labeling states are derived from mass balance of isotopomers}

following equation 1.2 as follows:

( )

(

0

)

, , , inp

dx

D c f c v x x

dt = (1.8)

Where D(c) is a diagonal matrix accounting for metabolite concentrations, x is a vector containing all the isotopomer distributions. To reduce complexity of equation 1.8 (i.e., the number of equations), labeling states can be represented as C-molar average enrichment fractions as follows:

1 , 1 n i i m k k y kn x− ₊ = =

∑

(1.9)

Where yi denotes the average enrichment fraction of metabolite i, k counts the number of carbon atoms, n, in the measured fragment of the metabolite, and

, i m k

x ₊ denotes the mass fraction of metabolite i with mass

m k

+

. Solving

equation 1.8 subject to equation 1.9, and comparing estimates with the corresponding labeling data it is possible to further optimize flux values by linear least square optimization.

In summary, figure 1.7 shows the work flow for estimating flux values. The problem starts with defining the metabolic network. Following, free fluxes are defined based on a set of measured metabolite concentrations that are considered “error-free”. These measurements are chosen based on the maturity and reliability of the method for determining a metabolite (e.g., G6P). Finally, having an initial guess for the free fluxes at each switch points, the optimization process proceeds in order to obtain the best approximation to the measured data (both

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Figure 1.7 . Sket ch of th e w ork flow for estim at ing flux values in t im e. St arting with the network definit io n and a s et of concentrat ion m easur em ents a set of linear co nstraints are def ined in th e syst em (free f luxes). Having an init ial guess, flux values are op timiz ed by gett ing the best approxim at ion t o th e experim ent al dat a (i. e., both concentr ation and 1 3_{C- enrichm ent}

meas urem ents). Adapt ed fr omSchum acher et al. ( Manuscript in prepar at ion)

Scope and outline of this thesis

The scope of this thesis is to investigate the dynamics of storage carbohydrates (trehalose and glycogen) and its interaction with the central carbon metabolism in

Saccharomyces cerevisiae from a quantitative perspective. Storage carbohydrate

metabolism (synthesis and degradation) is usually not considered when estimating intracellular fluxes mainly due to its cyclic nature and simultaneous occurrence. To overcome this difficulty, in this work we propose to use stimulus response

experiments in combination with 13_{C-labeling and modeling for identifying}

reaction rates. In this direction, we propose novel feast/famine experiments as a robust experimental platform for dynamic metabolic studies and the estimation of dynamic fluxes in time by piecewise function approximations. Hence, the main contribution of this thesis is not only the better understanding of storage carbohydrate metabolism through dynamic metabolic rates but also the experimental platforms and modeling approach used. We are confident that these contributions are of significant relevance for understanding yeast metabolism in both scientific and industrial environments.

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By conducting a hypothesis-driven research we obtained the results described in the outline below:

Chapter 2 deals with the presence of cyclic fluxes in yeast metabolism, in particular, exchange fluxes between storage carbohydrates and free amino acids with metabolites of the central carbon metabolism. Fluxes are determined under aerobic chemostat conditions at four different growth rates (0.054, 0.101, 0.207

and 0.207 h-1_{). The biological system is tested at metabolic steady-state and}

transient isotopomeric state. It is found that these fluxes are rearranged with growth rate becoming more significant at low growth rates for storage carbohydrates, and at high growth rates for amino acids. Because of their magnitude, these exchange fluxes buffer other metabolic fluxes in yeast. For instance, the impact of storage carbohydrate recycle is more significant at low growth rates obtaining higher intracellular concentrations (up to 560-fold) and higher fluxes relative to the glucose uptake rate (up to 16%). In addition, it is suggested that trehalose export and its subsequent extracellular degradation play a major role in glucose recycle in yeast.

In chapter 3, a novel experimental platform is presented for studying dynamic metabolism. The so called feast/famine experiment is used to study the question of metabolic flexibility under cyclic perturbations in substrate availability. Perturbations are induced by a block-wise feeding regime that leads to time-dependent substrate concentration. By applying this approach cells become ‘trained’ to cope with these dynamic environments and its metabolic performance is analyzed. The proposed experimental platform shows itself to be robust for the identification of metabolic states at dynamic conditions leading to reproducible and repetitive metabolic responses. ‘Trained’ cells do not exhibit the typical ATP paradox behavior reported for single pulse experiments. Moreover, the reproducible feast/famine approach allows for identification of regulatory mechanisms by time-scale analysis.

The challenge of dynamic intracellular flux identification in time is approached in chapter 4 by applying piecewise affine approximations. Hence, the metabolic response to cyclic perturbations can be described quantitatively. The

feast/famine approach is now used in combination with 13_{C-labeling to determine}

dynamic fluxes. It is shown that there is simultaneous synthesis and degradation of both, trehalose and glycogen. In addition, there is experimental evidence that glucose recycle through trehalose also occurs via extracellular degradation of trehalose. Flux estimations under these dynamic conditions suggest that some reactions of the non-oxidative pentose phosphate pathway run backwards when

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unknown reactions or processes. Here, a putative reaction producing T6P from trehalose, and the export of glucose from the intracellular space are reported.

Chapter 5 describes an experimental validation of a hypothesis derived

from findings in chapter 4. It is found that extracellular degradation of trehalose back to glucose is significant. Here, the respective genes were knocked out to validate the hypothesis of the enzyme dependent degradation. It is shown that Ath1p is indeed a major contributor to trehalose degradation activity. Nevertheless, there is only a minor change in phenotype in terms of metabolome and fluxome. A significant impact is found on the labeling dynamics of glucose when comparing results from mutant lacking trehalase activity and wild type strains.

In chapter 6, yeast robustness is evaluated in terms of its metabolic response to strong changes in substrate availability. We consider robustness as the persistence of the system to maintain its performance (i.e., phenotypic stability or characteristic behavior) when exposed to changing conditions. In particular, fold-changes in concentrations and fluxes are determined. The metabolic response clearly points out to yeast robustness in which glycolytic fluxes are most likely controlled by the metabolism of storage carbohydrates, trehalose and glycogen. Thermodynamic analysis (e.g., displacement from equilibrium) suggest changes in flux direction and magnitude that are later contrasted with estimated fluxes.

Chapter 7 contains a collection of supporting sidelines that contribute to

further validation of hypothesis generated in this thesis. Especially, glucose cycling and compartmentalization is addressed using alternative substrates and experimental conditions. In addition, trehalose transport aspects reinforcing the export of trehalose and its subsequent degradation in the extracellular space are covered. Experimental data are presented suggesting a putative mechanism by which trehalose is phosphorylated to T6P. It is also shown that trehalose

mobilization has a significant impact on the 13_{C-enrichment profiles of glycolytic}

metabolites.

Finally, general conclusions from this thesis and major contributions are summarized in chapter 8. In addition, some prospects for future research are proposed.

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References

Abate, A., Hillen, R. C., Aljoscha Wahl, S., 2012. Piecewise affine approximations of fluxes and enzyme kinetics from in vivo 13C labeling experiments. Int. J. Robust. Nonlinear Control.

Aboka, F. O., Heijnen, J. J., Van Winden, W. A., 2009. Dynamic 13C-tracer study of storage carbohydrate pools in aerobic glucose-limited Saccharomyces cerevisiae confirms a rapid steady-state turnover and fast mobilization during a modest stepup in the glucose uptake rate. FEMS Yeast Res. 9, 191-201.

Bailey, J. E., 1991. Toward a science of metabolic engineering. Science. 252, 1668-1675. Buziol, S., Warth, L., Magario, I., Freund, A., Siemann-Herzberg, M., Reuss, M., 2008.

Dynamic response of the expression of hxt1, hxt5 and hxt7 transport proteins in Saccharomyces cerevisiae to perturbations in the extracellular glucose concentration. J. Biotechnol. 134, 203-210.

Castrillo, J., Zeef, L., Hoyle, D., Zhang, N., Hayes, A., Gardner, D., Cornell, M., Petty, J., Hakes, L., Wardleworth, L., 2007. Growth control of the eukaryote cell: a systems biology study in yeast. Journal of Biology. 6, 4.

Chou, I. C., Voit, E. O., 2012. Estimation of dynamic flux profiles from metabolic time series data. BMC Syst. Biol. 6.

Dauner, M., 2010. From fluxes and isotope labeling patterns towards in silico cells. Curr. Opin. Biotechnol. 21, 55-62.

Dauner, M., Bailey, I., Wiechert, W., Witholt, B., Sauer, U., 2000. Intracellular carbon flux analysis by 13C-tracer experiments. ETH Zurich, Zurich.

de Jonge, L., Buijs, N. A. A., Heijnen, J., van Gulik, W. M., Abate, A., Wahl, A., 2013. Flux Response of Glycolysis and Storage Metabolism during rapid feast/famine conditions in Penicillium chrysogenum using dynamic 13C labeling. Biotechnol. J. Submitted.

Fischer, E., Sauer, U., 2005. Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nature Genetics. 37, 636-640. François, J., Parrou, J. L., 2001. Reserve carbohydrates metabolism in the yeast

Saccharomyces cerevisiae. FEMS Microbiol. Rev. 25, 125-145.

Guillou, V., Plourde-Owobi, L., Parrou, J. L., Goma, G., François, J., 2004. Role of reserve carbohydrates in the growth dynamics of Saccharomyces cerevisiae. FEMS Yeast Res. 4, 773-787.

Hashem, M., Darwish, S. M. I., 2010. Production of bioethanol and associated by-products from potato starch residue stream by Saccharomyces cerevisiae. Biomass and Bioenergy. 34, 953-959.

Heijnen, J. J., Verheijen, P. J. T., 2013. Parameter identification of in vivo kinetic models: Limitations and challenges. Biotechnol. J. 8, 768-775.

Jules, M., Beltran, G., François, J., Parrou, J. L., 2008. New insights into trehalose metabolism by Saccharomyces cerevisiae: NTH2 encodes a functional cytosolic

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trehalase, and deletion of TPS1 reveals Ath1p-dependent trehalose mobilization. Appl. Environ. Microbiol. 74, 605-614.

Kerkhoven, E. J., Lahtvee, P.-J., Nielsen, J., 2015. Applications of computational modeling in metabolic engineering of yeast.

Kitano, H., 2002. Systems biology: A brief overview. Science. 295, 1662-1664.

Larsson, G., Törnkvist, M., Wernersson, E. S., Trägådh, C., Noorman, H., Enfors, S. O., 1996. Substrate gradients in bioreactors: origin and consequences. Bioprocess Biosyst. Eng. 14, 281-289.

Mashego, M., 2005. Robust experimental methods to study pre-steady state kinetics of primary metabolism in Saccharomyces cerevisiae. PhD Thesis, Delft University of Technology. Chapter 7.

Mashego, M. R., van Gulik, W. M., Vinke, J. L., Visser, D., Heijnen, J. J., 2006. In vivo kinetics with rapid perturbation experiments in Saccharomyces cerevisiae using a second-generation BioScope. Metab. Eng. 8, 370-383.

Mashego, M. R., Wu, L., van Dam, J. C., Ras, C., Vinke, J. L., Van Winden, W. A., van Gulik, W. M., Heijnen, J. J., 2004. MIRACLE: Mass Isotopomer Ratio Analysis of U-13C-Labeled Extracts. A New Method for Accurate Quantification of Changes in Concentrations of Intracellular Metabolites. Biotechnol. Bioeng. 85, 620-628. Noack, S., Nöh, K., Moch, M., Oldiges, M., Wiechert, W., 2011. Stationary versus

non-stationary 13C-MFA: A comparison using a consistent dataset. J. Biotechnol. 154, 179-190.

Nöh, K., Grönke, K., Luo, B., Takors, R., Oldiges, M., Wiechert, W., 2007. Metabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments. J. Biotechnol. 129, 249-267.

Noh, K., Wahl, A., Wiechert, W., 2006. Computational tools for isotopically instationary 13C labeling experiments under metabolic steady state conditions. Metabolic engineering. 8, 554-577.

Paalman, J. W. G., Verwaal, R., Slofstra, S. H., Verkleij, A. J., Boonstra, J., Verrips, C. T., 2003. Trehalose and glycogen accumulation is related to the duration of the G1 phase of Saccharomyces cerevisiae. FEMS yeast research. 3, 261-268.

Parrou, J. L., Jules, M., Beltran, G., François, J., 2005. Acid trehalase in yeasts and filamentous fungi: Localization, regulation and physiological function. FEMS Yeast Res. 5, 503-511.

Pereira, M., Eleutherio, E., Panek, A., 2001. Acquisition of tolerance against oxidative damage in Saccharomyces cerevisiae. BMC microbiology. 1, 11.

Sauer, U., 2006. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2. Schmalzriedt, S., Jenne, M., Mauch, K., Reuss, M., 2003. Integration of physiology and fluid

dynamics. Adv. Biochem. Eng./Biotechnol. 80, 19-68.

Schmidt, K., Marx, A., de Graaf, A. A., Wiechert, W., Sahm, H., Nielsen, J., Villadsen, J., 1998. 13C tracer experiments and metabolite balancing for metabolic flux analysis: comparing two approaches. Biotechnology and bioengineering. 58, 254-257.

Dynamics of Storage Carbohydrates Metabolism in Saccharomyces cerevisiae