Search in the TU Delft Repository Collections
Recently Added Records
Cost of Drought
Distribution of drought-induced costs of foundation damage to housing among actors in the Netherlands
Due to drought, houses with wooden foundations in the Netherlands can start to rot. This causes damage to houses and a higher risk of subsidence. Approximately 425.000 houses are currently at risk, this can go upto 687.000 in the future. Foundation damage is not cheap, it can cost €50.000 to €150.000 per house. This thesis explores the possibility for policies to distribute the costs of foundation damage. A spatially explicit agent-based model (ABM) has been used to find the answer to this research question. The homeowner’s behaviour, the government, insurance companies and banks are included as agents. A nudging policy, insurance policy and subsidy policy have been implemented and evaluated. This thesis has been conducted at the strategic exploration department of Rijkswaterstaat.
...
Due to drought, houses with wooden foundations in the Netherlands can start to rot. This causes damage to houses and a higher risk of subsidence. Approximately 425.000 houses are currently at risk, this can go upto 687.000 in the future. Foundation damage is not cheap, it can cost €50.000 to €150.000 per house. This thesis explores the possibility for policies to distribute the costs of foundation damage. A spatially explicit agent-based model (ABM) has been used to find the answer to this research question. The homeowner’s behaviour, the government, insurance companies and banks are included as agents. A nudging policy, insurance policy and subsidy policy have been implemented and evaluated. This thesis has been conducted at the strategic exploration department of Rijkswaterstaat.
Adipic acid is a large-volume chemical, used mainly for nylon, and about 95 % of world production runs through nitric-acid oxidation of benzene-derived intermediates, a route whose nitrous oxide by-product dominates the product's emissions and whose benzene ties it to a fossil supply chain. Electrochemical oxidation and a biomass route via electrochemical hydrogenation are proposed against both problems, and all three are modelled together in Yeo (2025), the one published baseline that spans them in Aspen Plus at a consistent scale and boundary. This thesis re-evaluates that baseline under the European market and policy conditions it holds fixed, not to rank a fixed winner, but to establish the bottlenecks and viability conditions that decide each route.
The re-evaluation is built on one basis for a 100 kt/a European plant: a full pinch analysis on each flowsheet, cell degradation over service life, carbon priced under the EU ETS, and electricity charged at each Antwerp-Rotterdam-Rhine-Ruhr (ARRRA) country's tariff and grid carbon intensity in place of a single price and an assumed renewable supply. Around that basis every input the published work holds fixed is swept rather than assumed: the conventional route's N2O destruction efficiency, the carbon price, each country's grid intensity and electricity price, cell performance and degradation, feedstock prices and procurement, load factor, and both biomass endpoints, trans-3-hexenedioic acid (t3HDA) and adipic acid. Each is varied alone and in combination.
On central inputs the conventional route reaches a levelised cost of adipic acid (LCOA) of 1.87 $/kg at a cradle-to-gate greenhouse-gas (GHG) intensity of 3.39 kgCO2e/kg; the electrochemical route 2.19 $/kg at 3.79 kgCO2e/kg; and the biomass route 4.03 $/kg at 2.34 kgCO2e/kg to t3HDA, or 4.34 at 2.56 once catalytically hydrogenated to adipic acid. Against that baseline the electrochemical route's cost premium over the conventional route narrows from 47 to 17 %, and its GHG position reverses, from 54 % below the conventional route to 12 % above. No route is best on both measures.
The binding constraints differ by route. The conventional route turns mainly on the destruction efficiency it sustains and the carbon price on its residual N2O, the electrochemical route on the carbon intensity and price of its electricity, and the biomass route on the cost of a muconic-acid broth no market yet quotes and on whether a buyer pays for non-fossil origin or for the reactive double bond t3HDA retains. Both alternatives also rest on electrochemical cells demonstrated only in the laboratory. Across every case assessed here, abating the conventional process's N2O costs orders of magnitude less per tonne of CO2-equivalent than any change of route, which points to unabated capacity, most of it outside Europe, as the larger near-term lever on the adipic-acid sector's GHG emissions. ...
The re-evaluation is built on one basis for a 100 kt/a European plant: a full pinch analysis on each flowsheet, cell degradation over service life, carbon priced under the EU ETS, and electricity charged at each Antwerp-Rotterdam-Rhine-Ruhr (ARRRA) country's tariff and grid carbon intensity in place of a single price and an assumed renewable supply. Around that basis every input the published work holds fixed is swept rather than assumed: the conventional route's N2O destruction efficiency, the carbon price, each country's grid intensity and electricity price, cell performance and degradation, feedstock prices and procurement, load factor, and both biomass endpoints, trans-3-hexenedioic acid (t3HDA) and adipic acid. Each is varied alone and in combination.
On central inputs the conventional route reaches a levelised cost of adipic acid (LCOA) of 1.87 $/kg at a cradle-to-gate greenhouse-gas (GHG) intensity of 3.39 kgCO2e/kg; the electrochemical route 2.19 $/kg at 3.79 kgCO2e/kg; and the biomass route 4.03 $/kg at 2.34 kgCO2e/kg to t3HDA, or 4.34 at 2.56 once catalytically hydrogenated to adipic acid. Against that baseline the electrochemical route's cost premium over the conventional route narrows from 47 to 17 %, and its GHG position reverses, from 54 % below the conventional route to 12 % above. No route is best on both measures.
The binding constraints differ by route. The conventional route turns mainly on the destruction efficiency it sustains and the carbon price on its residual N2O, the electrochemical route on the carbon intensity and price of its electricity, and the biomass route on the cost of a muconic-acid broth no market yet quotes and on whether a buyer pays for non-fossil origin or for the reactive double bond t3HDA retains. Both alternatives also rest on electrochemical cells demonstrated only in the laboratory. Across every case assessed here, abating the conventional process's N2O costs orders of magnitude less per tonne of CO2-equivalent than any change of route, which points to unabated capacity, most of it outside Europe, as the larger near-term lever on the adipic-acid sector's GHG emissions. ...
Adipic acid is a large-volume chemical, used mainly for nylon, and about 95 % of world production runs through nitric-acid oxidation of benzene-derived intermediates, a route whose nitrous oxide by-product dominates the product's emissions and whose benzene ties it to a fossil supply chain. Electrochemical oxidation and a biomass route via electrochemical hydrogenation are proposed against both problems, and all three are modelled together in Yeo (2025), the one published baseline that spans them in Aspen Plus at a consistent scale and boundary. This thesis re-evaluates that baseline under the European market and policy conditions it holds fixed, not to rank a fixed winner, but to establish the bottlenecks and viability conditions that decide each route.
The re-evaluation is built on one basis for a 100 kt/a European plant: a full pinch analysis on each flowsheet, cell degradation over service life, carbon priced under the EU ETS, and electricity charged at each Antwerp-Rotterdam-Rhine-Ruhr (ARRRA) country's tariff and grid carbon intensity in place of a single price and an assumed renewable supply. Around that basis every input the published work holds fixed is swept rather than assumed: the conventional route's N2O destruction efficiency, the carbon price, each country's grid intensity and electricity price, cell performance and degradation, feedstock prices and procurement, load factor, and both biomass endpoints, trans-3-hexenedioic acid (t3HDA) and adipic acid. Each is varied alone and in combination.
On central inputs the conventional route reaches a levelised cost of adipic acid (LCOA) of 1.87 $/kg at a cradle-to-gate greenhouse-gas (GHG) intensity of 3.39 kgCO2e/kg; the electrochemical route 2.19 $/kg at 3.79 kgCO2e/kg; and the biomass route 4.03 $/kg at 2.34 kgCO2e/kg to t3HDA, or 4.34 at 2.56 once catalytically hydrogenated to adipic acid. Against that baseline the electrochemical route's cost premium over the conventional route narrows from 47 to 17 %, and its GHG position reverses, from 54 % below the conventional route to 12 % above. No route is best on both measures.
The binding constraints differ by route. The conventional route turns mainly on the destruction efficiency it sustains and the carbon price on its residual N2O, the electrochemical route on the carbon intensity and price of its electricity, and the biomass route on the cost of a muconic-acid broth no market yet quotes and on whether a buyer pays for non-fossil origin or for the reactive double bond t3HDA retains. Both alternatives also rest on electrochemical cells demonstrated only in the laboratory. Across every case assessed here, abating the conventional process's N2O costs orders of magnitude less per tonne of CO2-equivalent than any change of route, which points to unabated capacity, most of it outside Europe, as the larger near-term lever on the adipic-acid sector's GHG emissions.
The re-evaluation is built on one basis for a 100 kt/a European plant: a full pinch analysis on each flowsheet, cell degradation over service life, carbon priced under the EU ETS, and electricity charged at each Antwerp-Rotterdam-Rhine-Ruhr (ARRRA) country's tariff and grid carbon intensity in place of a single price and an assumed renewable supply. Around that basis every input the published work holds fixed is swept rather than assumed: the conventional route's N2O destruction efficiency, the carbon price, each country's grid intensity and electricity price, cell performance and degradation, feedstock prices and procurement, load factor, and both biomass endpoints, trans-3-hexenedioic acid (t3HDA) and adipic acid. Each is varied alone and in combination.
On central inputs the conventional route reaches a levelised cost of adipic acid (LCOA) of 1.87 $/kg at a cradle-to-gate greenhouse-gas (GHG) intensity of 3.39 kgCO2e/kg; the electrochemical route 2.19 $/kg at 3.79 kgCO2e/kg; and the biomass route 4.03 $/kg at 2.34 kgCO2e/kg to t3HDA, or 4.34 at 2.56 once catalytically hydrogenated to adipic acid. Against that baseline the electrochemical route's cost premium over the conventional route narrows from 47 to 17 %, and its GHG position reverses, from 54 % below the conventional route to 12 % above. No route is best on both measures.
The binding constraints differ by route. The conventional route turns mainly on the destruction efficiency it sustains and the carbon price on its residual N2O, the electrochemical route on the carbon intensity and price of its electricity, and the biomass route on the cost of a muconic-acid broth no market yet quotes and on whether a buyer pays for non-fossil origin or for the reactive double bond t3HDA retains. Both alternatives also rest on electrochemical cells demonstrated only in the laboratory. Across every case assessed here, abating the conventional process's N2O costs orders of magnitude less per tonne of CO2-equivalent than any change of route, which points to unabated capacity, most of it outside Europe, as the larger near-term lever on the adipic-acid sector's GHG emissions.
Material Requirements Planning under uncertainty
Optimising safety stock levels of changeover materials using simulation-based optimisation
Material Requirements Planning (MRP) is widely used to translate production plans into procurement and inventory decisions. Its performance can deteriorate in environments characterised by demand uncertainty and frequent material changeovers. In such settings, deterministic MRP models often set safety stocks for changeover materials to zero in order to minimise material obsolescence. This increases vulnerability to stockouts, minimum-order-quantity rush orders and last-minute procurement costs. This thesis investigates whether simulation-based optimisation can be used to parametrise safety stock levels for changeover materials in a deterministic mixed-integer programming MRP model to improve rolling-horizon performance in a stochastic production environment.
A framework is developed in which the manufacturer’s existing MRP optimisation model is treated as a black box and is embedded in an Optimisation with Simulation-based Iterations (OSI) structure. To better represent robustness in the MRP logic, a hard safety stock constraint is introduced through which safety stock levels are optimised for grouped materials. Demand uncertainty is modelled using historical weekly production plans, analysed through a method based on block bootstrapping and kernel density estimation (KDE). This analysis showed that nearly all observed variability in the production plans is demand-driven rather than induced by planning decisions.
The framework is tested using a Full Enumeration optimisation technique, and experimented on different simulation settings, material grouping strategies, uncertainty variants and safety stock variants. Although the proposed approach is technically feasible and yields a reproducible method for evaluating safety stock policies under uncertainty, the final optimisation does not deliver an obsolete stock cost improvement caused by the addition of safety stock. Low safety stock levels consistently performed best, suggesting that a generic safety stock policy for changeover materials is not effective under the current objective structure and framework setup. The main contribution of this thesis is methodological: it demonstrates how simulation-based optimisation can be applied to an industry-scale, black-box MRP environment. It also highlights the challenges of translating robustness into measurable system-wide gains when only obsolete stock costs are optimised. The findings indicate that future research should adopt broader objective functions and further refine material grouping and uncertainty scenarios to better capture material- and scenario-specific trade-offs.
...
A framework is developed in which the manufacturer’s existing MRP optimisation model is treated as a black box and is embedded in an Optimisation with Simulation-based Iterations (OSI) structure. To better represent robustness in the MRP logic, a hard safety stock constraint is introduced through which safety stock levels are optimised for grouped materials. Demand uncertainty is modelled using historical weekly production plans, analysed through a method based on block bootstrapping and kernel density estimation (KDE). This analysis showed that nearly all observed variability in the production plans is demand-driven rather than induced by planning decisions.
The framework is tested using a Full Enumeration optimisation technique, and experimented on different simulation settings, material grouping strategies, uncertainty variants and safety stock variants. Although the proposed approach is technically feasible and yields a reproducible method for evaluating safety stock policies under uncertainty, the final optimisation does not deliver an obsolete stock cost improvement caused by the addition of safety stock. Low safety stock levels consistently performed best, suggesting that a generic safety stock policy for changeover materials is not effective under the current objective structure and framework setup. The main contribution of this thesis is methodological: it demonstrates how simulation-based optimisation can be applied to an industry-scale, black-box MRP environment. It also highlights the challenges of translating robustness into measurable system-wide gains when only obsolete stock costs are optimised. The findings indicate that future research should adopt broader objective functions and further refine material grouping and uncertainty scenarios to better capture material- and scenario-specific trade-offs.
...
Material Requirements Planning (MRP) is widely used to translate production plans into procurement and inventory decisions. Its performance can deteriorate in environments characterised by demand uncertainty and frequent material changeovers. In such settings, deterministic MRP models often set safety stocks for changeover materials to zero in order to minimise material obsolescence. This increases vulnerability to stockouts, minimum-order-quantity rush orders and last-minute procurement costs. This thesis investigates whether simulation-based optimisation can be used to parametrise safety stock levels for changeover materials in a deterministic mixed-integer programming MRP model to improve rolling-horizon performance in a stochastic production environment.
A framework is developed in which the manufacturer’s existing MRP optimisation model is treated as a black box and is embedded in an Optimisation with Simulation-based Iterations (OSI) structure. To better represent robustness in the MRP logic, a hard safety stock constraint is introduced through which safety stock levels are optimised for grouped materials. Demand uncertainty is modelled using historical weekly production plans, analysed through a method based on block bootstrapping and kernel density estimation (KDE). This analysis showed that nearly all observed variability in the production plans is demand-driven rather than induced by planning decisions.
The framework is tested using a Full Enumeration optimisation technique, and experimented on different simulation settings, material grouping strategies, uncertainty variants and safety stock variants. Although the proposed approach is technically feasible and yields a reproducible method for evaluating safety stock policies under uncertainty, the final optimisation does not deliver an obsolete stock cost improvement caused by the addition of safety stock. Low safety stock levels consistently performed best, suggesting that a generic safety stock policy for changeover materials is not effective under the current objective structure and framework setup. The main contribution of this thesis is methodological: it demonstrates how simulation-based optimisation can be applied to an industry-scale, black-box MRP environment. It also highlights the challenges of translating robustness into measurable system-wide gains when only obsolete stock costs are optimised. The findings indicate that future research should adopt broader objective functions and further refine material grouping and uncertainty scenarios to better capture material- and scenario-specific trade-offs.
A framework is developed in which the manufacturer’s existing MRP optimisation model is treated as a black box and is embedded in an Optimisation with Simulation-based Iterations (OSI) structure. To better represent robustness in the MRP logic, a hard safety stock constraint is introduced through which safety stock levels are optimised for grouped materials. Demand uncertainty is modelled using historical weekly production plans, analysed through a method based on block bootstrapping and kernel density estimation (KDE). This analysis showed that nearly all observed variability in the production plans is demand-driven rather than induced by planning decisions.
The framework is tested using a Full Enumeration optimisation technique, and experimented on different simulation settings, material grouping strategies, uncertainty variants and safety stock variants. Although the proposed approach is technically feasible and yields a reproducible method for evaluating safety stock policies under uncertainty, the final optimisation does not deliver an obsolete stock cost improvement caused by the addition of safety stock. Low safety stock levels consistently performed best, suggesting that a generic safety stock policy for changeover materials is not effective under the current objective structure and framework setup. The main contribution of this thesis is methodological: it demonstrates how simulation-based optimisation can be applied to an industry-scale, black-box MRP environment. It also highlights the challenges of translating robustness into measurable system-wide gains when only obsolete stock costs are optimised. The findings indicate that future research should adopt broader objective functions and further refine material grouping and uncertainty scenarios to better capture material- and scenario-specific trade-offs.
Beneath the Sweet Surface
A Deep Dive into Microbial Glycomolecule Precursors and Their Enzymes
The surface of microorganisms is often decorated with a wide variety of glycans and glycoconjugates. These biomolecules play key roles in structural support, cellular communication, immune recognition, and host–pathogen interactions. Despite their importance, glycan analysis still lags far behind that of DNA and proteins. This is mainly because glycans are not directly encoded in the genome but arise from the combined action of glycosyltransferases and glycosidases. Their characterization is further complicated by extensive structural diversity, as glycans consist of a large and expanding repertoire of monosaccharides. In addition, restricted access to these building blocks hinders functional studies and the exploitation of glycans as antimicrobial targets. This thesis aims to advance the discovery of microbial metabolites, glycomolecule precursors, and carbohydrate-active enzymes using mass spectrometry and bioinformatic approaches.
Chapter 2 reviews advanced mass spectrometry-based methods for the identification and quantification of monosaccharides and their nucleotide-activated forms. Key analytical challenges are highlighted, including the differentiation of stereoisomers and the limited availability of reference standards. Complementary strategies, such as high-performance separation techniques, advanced fragmentation methods, and isotope tracing, are discussed for improved structural characterization and pathway analysis.
Building on these analytical foundations, Chapter 3 presents SugarBase, a mass spectrometry and bioinformatic pipeline for untargeted exploration of microbial nucleotide sugar networks. SugarBase combines narrow-window DIA fragmentation with a theoretical sugar composition database for parent ion annotation. The platform revealed species-specific nucleotide sugar profiles, including distinct nonulosonic acid profiles in Campylobacter jejuni strains and previously unannotated compounds across diverse microbes, including a higher-carbon ulosonic acid in Magnetospirillum.
Extending this work to complex microbial systems, Chapter 4 includes a collaborative study adapting the Chapter 3 methodology into a targeted metabolomics workflow to quantify nucleotides in granular biofilms involved in enhanced biological phosphorus removal. Using fast quenching, boiling-water extraction, and high-resolution mass spectrometry with ¹³C-labeled internal standards, the approach enabled measurement of adenylate and uridylate energy charge dynamics. Applied to a lab-scale system, it revealed energy pool fluctuations during acetate uptake and polyphosphate degradation, demonstrating that energy imbalances can underlie metabolic switching in biofilms.
In Chapter 5, we demonstrate that microbial metabolite extracts can serve as effective and readily accessible substrate sources for glycosyltransferase assays, without further purification. Using a Campylobacter jejuni extract containing diverse nonulosonic acid derivatives, we show substrate promiscuity of the previously identified pseudaminyltransferase from Acinetobacter baumannii, currently the only confirmed enzyme of its kind. These findings support the production of glycans and glycoconjugates relevant for antimicrobial and vaccine development.
While glycans and glycoconjugates are central to many biological processes, carbohydrate-active enzymes are of similar interest due to their potential to replace harsh chemical processes and their ability to enable new products. A key challenge remains the targeted identification of enzymes with desired activities and characteristics. In Chapter 6, we combine enrichment cultures with metagenomics and metaproteomics to discover glycoside hydrolases. This strategy enables functional enrichment under customized conditions and yields a focused set of enzyme candidates. One of the candidates was subsequently confirmed as a pullulan-degrading enzyme using a mass spectrometry-based activity assay.
Finally, Chapter 7 summarizes some of the main findings, discusses main challenges and outlines future research directions.
...
Chapter 2 reviews advanced mass spectrometry-based methods for the identification and quantification of monosaccharides and their nucleotide-activated forms. Key analytical challenges are highlighted, including the differentiation of stereoisomers and the limited availability of reference standards. Complementary strategies, such as high-performance separation techniques, advanced fragmentation methods, and isotope tracing, are discussed for improved structural characterization and pathway analysis.
Building on these analytical foundations, Chapter 3 presents SugarBase, a mass spectrometry and bioinformatic pipeline for untargeted exploration of microbial nucleotide sugar networks. SugarBase combines narrow-window DIA fragmentation with a theoretical sugar composition database for parent ion annotation. The platform revealed species-specific nucleotide sugar profiles, including distinct nonulosonic acid profiles in Campylobacter jejuni strains and previously unannotated compounds across diverse microbes, including a higher-carbon ulosonic acid in Magnetospirillum.
Extending this work to complex microbial systems, Chapter 4 includes a collaborative study adapting the Chapter 3 methodology into a targeted metabolomics workflow to quantify nucleotides in granular biofilms involved in enhanced biological phosphorus removal. Using fast quenching, boiling-water extraction, and high-resolution mass spectrometry with ¹³C-labeled internal standards, the approach enabled measurement of adenylate and uridylate energy charge dynamics. Applied to a lab-scale system, it revealed energy pool fluctuations during acetate uptake and polyphosphate degradation, demonstrating that energy imbalances can underlie metabolic switching in biofilms.
In Chapter 5, we demonstrate that microbial metabolite extracts can serve as effective and readily accessible substrate sources for glycosyltransferase assays, without further purification. Using a Campylobacter jejuni extract containing diverse nonulosonic acid derivatives, we show substrate promiscuity of the previously identified pseudaminyltransferase from Acinetobacter baumannii, currently the only confirmed enzyme of its kind. These findings support the production of glycans and glycoconjugates relevant for antimicrobial and vaccine development.
While glycans and glycoconjugates are central to many biological processes, carbohydrate-active enzymes are of similar interest due to their potential to replace harsh chemical processes and their ability to enable new products. A key challenge remains the targeted identification of enzymes with desired activities and characteristics. In Chapter 6, we combine enrichment cultures with metagenomics and metaproteomics to discover glycoside hydrolases. This strategy enables functional enrichment under customized conditions and yields a focused set of enzyme candidates. One of the candidates was subsequently confirmed as a pullulan-degrading enzyme using a mass spectrometry-based activity assay.
Finally, Chapter 7 summarizes some of the main findings, discusses main challenges and outlines future research directions.
...
The surface of microorganisms is often decorated with a wide variety of glycans and glycoconjugates. These biomolecules play key roles in structural support, cellular communication, immune recognition, and host–pathogen interactions. Despite their importance, glycan analysis still lags far behind that of DNA and proteins. This is mainly because glycans are not directly encoded in the genome but arise from the combined action of glycosyltransferases and glycosidases. Their characterization is further complicated by extensive structural diversity, as glycans consist of a large and expanding repertoire of monosaccharides. In addition, restricted access to these building blocks hinders functional studies and the exploitation of glycans as antimicrobial targets. This thesis aims to advance the discovery of microbial metabolites, glycomolecule precursors, and carbohydrate-active enzymes using mass spectrometry and bioinformatic approaches.
Chapter 2 reviews advanced mass spectrometry-based methods for the identification and quantification of monosaccharides and their nucleotide-activated forms. Key analytical challenges are highlighted, including the differentiation of stereoisomers and the limited availability of reference standards. Complementary strategies, such as high-performance separation techniques, advanced fragmentation methods, and isotope tracing, are discussed for improved structural characterization and pathway analysis.
Building on these analytical foundations, Chapter 3 presents SugarBase, a mass spectrometry and bioinformatic pipeline for untargeted exploration of microbial nucleotide sugar networks. SugarBase combines narrow-window DIA fragmentation with a theoretical sugar composition database for parent ion annotation. The platform revealed species-specific nucleotide sugar profiles, including distinct nonulosonic acid profiles in Campylobacter jejuni strains and previously unannotated compounds across diverse microbes, including a higher-carbon ulosonic acid in Magnetospirillum.
Extending this work to complex microbial systems, Chapter 4 includes a collaborative study adapting the Chapter 3 methodology into a targeted metabolomics workflow to quantify nucleotides in granular biofilms involved in enhanced biological phosphorus removal. Using fast quenching, boiling-water extraction, and high-resolution mass spectrometry with ¹³C-labeled internal standards, the approach enabled measurement of adenylate and uridylate energy charge dynamics. Applied to a lab-scale system, it revealed energy pool fluctuations during acetate uptake and polyphosphate degradation, demonstrating that energy imbalances can underlie metabolic switching in biofilms.
In Chapter 5, we demonstrate that microbial metabolite extracts can serve as effective and readily accessible substrate sources for glycosyltransferase assays, without further purification. Using a Campylobacter jejuni extract containing diverse nonulosonic acid derivatives, we show substrate promiscuity of the previously identified pseudaminyltransferase from Acinetobacter baumannii, currently the only confirmed enzyme of its kind. These findings support the production of glycans and glycoconjugates relevant for antimicrobial and vaccine development.
While glycans and glycoconjugates are central to many biological processes, carbohydrate-active enzymes are of similar interest due to their potential to replace harsh chemical processes and their ability to enable new products. A key challenge remains the targeted identification of enzymes with desired activities and characteristics. In Chapter 6, we combine enrichment cultures with metagenomics and metaproteomics to discover glycoside hydrolases. This strategy enables functional enrichment under customized conditions and yields a focused set of enzyme candidates. One of the candidates was subsequently confirmed as a pullulan-degrading enzyme using a mass spectrometry-based activity assay.
Finally, Chapter 7 summarizes some of the main findings, discusses main challenges and outlines future research directions.
Chapter 2 reviews advanced mass spectrometry-based methods for the identification and quantification of monosaccharides and their nucleotide-activated forms. Key analytical challenges are highlighted, including the differentiation of stereoisomers and the limited availability of reference standards. Complementary strategies, such as high-performance separation techniques, advanced fragmentation methods, and isotope tracing, are discussed for improved structural characterization and pathway analysis.
Building on these analytical foundations, Chapter 3 presents SugarBase, a mass spectrometry and bioinformatic pipeline for untargeted exploration of microbial nucleotide sugar networks. SugarBase combines narrow-window DIA fragmentation with a theoretical sugar composition database for parent ion annotation. The platform revealed species-specific nucleotide sugar profiles, including distinct nonulosonic acid profiles in Campylobacter jejuni strains and previously unannotated compounds across diverse microbes, including a higher-carbon ulosonic acid in Magnetospirillum.
Extending this work to complex microbial systems, Chapter 4 includes a collaborative study adapting the Chapter 3 methodology into a targeted metabolomics workflow to quantify nucleotides in granular biofilms involved in enhanced biological phosphorus removal. Using fast quenching, boiling-water extraction, and high-resolution mass spectrometry with ¹³C-labeled internal standards, the approach enabled measurement of adenylate and uridylate energy charge dynamics. Applied to a lab-scale system, it revealed energy pool fluctuations during acetate uptake and polyphosphate degradation, demonstrating that energy imbalances can underlie metabolic switching in biofilms.
In Chapter 5, we demonstrate that microbial metabolite extracts can serve as effective and readily accessible substrate sources for glycosyltransferase assays, without further purification. Using a Campylobacter jejuni extract containing diverse nonulosonic acid derivatives, we show substrate promiscuity of the previously identified pseudaminyltransferase from Acinetobacter baumannii, currently the only confirmed enzyme of its kind. These findings support the production of glycans and glycoconjugates relevant for antimicrobial and vaccine development.
While glycans and glycoconjugates are central to many biological processes, carbohydrate-active enzymes are of similar interest due to their potential to replace harsh chemical processes and their ability to enable new products. A key challenge remains the targeted identification of enzymes with desired activities and characteristics. In Chapter 6, we combine enrichment cultures with metagenomics and metaproteomics to discover glycoside hydrolases. This strategy enables functional enrichment under customized conditions and yields a focused set of enzyme candidates. One of the candidates was subsequently confirmed as a pullulan-degrading enzyme using a mass spectrometry-based activity assay.
Finally, Chapter 7 summarizes some of the main findings, discusses main challenges and outlines future research directions.
This study reflects on the probability of observing an extreme event of interest within a finite dataset, whether derived from observations or model simulations, to inform risk assessment or climate adaptation efforts. To do so, we adopt the concept of engineering reliability, which is defined as the probability that a system remains in a satisfactory state, to assess the reliability of extreme events inferred from a dataset, whether this is from observations or model simulations. This assessment links the number of available observations or simulations to the low frequency of the event, providing a quantitative measure of confidence in our ability to observe or simulate such events over a given time horizon. This approach offers a fresh perspective on the interpretation of an extreme event, where the rarity of an event is considered not only in terms of its frequency but also relative to the length of the dataset used. Our reflections aim to guide preparedness for future extremes and highlight the scientific challenges inherent in their prediction and projection. We emphasize that while large ensembles are essential to overcome the limitations of historical observations, they should be used with caution to avoid overconfidence arising from underlying modeling assumptions. Finally, we stress that statistical extrapolation, whether it is parametric or non-parametric, is unavoidable, as the link between event frequency and the definition of extremes cannot be eliminated.
...
This study reflects on the probability of observing an extreme event of interest within a finite dataset, whether derived from observations or model simulations, to inform risk assessment or climate adaptation efforts. To do so, we adopt the concept of engineering reliability, which is defined as the probability that a system remains in a satisfactory state, to assess the reliability of extreme events inferred from a dataset, whether this is from observations or model simulations. This assessment links the number of available observations or simulations to the low frequency of the event, providing a quantitative measure of confidence in our ability to observe or simulate such events over a given time horizon. This approach offers a fresh perspective on the interpretation of an extreme event, where the rarity of an event is considered not only in terms of its frequency but also relative to the length of the dataset used. Our reflections aim to guide preparedness for future extremes and highlight the scientific challenges inherent in their prediction and projection. We emphasize that while large ensembles are essential to overcome the limitations of historical observations, they should be used with caution to avoid overconfidence arising from underlying modeling assumptions. Finally, we stress that statistical extrapolation, whether it is parametric or non-parametric, is unavoidable, as the link between event frequency and the definition of extremes cannot be eliminated.