@phdthesis{Kueken2020,
  author    = {K{\"u}ken, Anika},
  title     = {Predictions from constraint-based approaches including enzyme kinetics},
  school      = {Universit{\"a}t Potsdam},
  pages     = {116, A-16, B-7, C-8},
  year      = {2020},
  abstract  = {The metabolic state of an organism reflects the entire phenotype that is jointly affected by genetic and environmental changes. Due to the complexity of metabolism, system-level modelling approaches have become indispensable tools to obtain new insights into biological functions. In particular, simulation and analysis of metabolic networks using constraint-based modelling approaches have helped the analysis of metabolic fluxes. However, despite ongoing improvements in prediction of reaction flux through a system, approaches to directly predict metabolite concentrations from large-scale metabolic networks remain elusive. In this thesis, we present a computational approach for inferring concentration ranges from genome-scale metabolic models endowed with mass action kinetics. The findings specify a molecular mechanism underling facile control of concentration ranges for components in large-scale metabolic networks. Most importantly, an extended version of the approach can be used to predict concentration ranges without knowledge of kinetic parameters, provided measurements of concentrations in a reference state. We show that the approach is applicable with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life. By challenging the predictions of concentration ranges in the genome-scale metabolic network of Escherichia coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach. To predict concentration ranges in other species, e.g. model plant species Arabidopsis thaliana, we would rely on estimates of kinetic parameters (i.e. enzyme catalytic rates) since plant-specific enzyme catalytic rates are poorly documented. Using the constraint-based approach of Davidi et al. for estimation of enzyme catalytic rates, we obtain values for 168 plant enzymes. The approach depends on quantitative proteomics data and flux estimates obtained from constraint-based model of plant leaf metabolism integrating maximal rates of selected enzymes, plant-specific constraints on fluxes through canonical pathways, and growth measurements from Arabidopsis thaliana rosette under ten conditions. We demonstrate a low degree of plant enzyme saturation, supported by the agreement between concentrations of nicotinamide adenine dinucleotide, adenosine triphosphate, and glyceraldehyde 3-phosphate, based on our maximal in vivo catalytic rates, and available quantitative metabolomics data. Hence, our results show genome-wide estimation for plant-specific enzyme catalytic rates is feasible. These can now be readily employed to study resource allocation, to predict enzyme and metabolite concentrations using recent constrained-based modelling approaches. Constraint-based methods do not directly account for kinetic mechanisms and corresponding parameters. Therefore, a number of workflows have already been proposed to approximate reaction kinetics and to parameterize genome-scale kinetic models. We present a systems biology strategy to build a fully parameterized large-scale model of Chlamydomonas reinhardtii accounting for microcompartmentalization in the chloroplast stroma. Eukaryotic algae comprise a microcompartment, the pyrenoid, essential for the carbon concentrating mechanism (CCM) that improves their photosynthetic performance. Since the experimental study of the effects of microcompartmentation on metabolic pathways is challenging, we employ our model to investigate compartmentation of fluxes through the Calvin-Benson cycle between pyrenoid and stroma. Our model predicts that ribulose-1,5-bisphosphate, the substrate of Rubisco, and 3-phosphoglycerate, its product, diffuse in and out of the pyrenoid. We also find that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Therefore, our computational approach represents a stepping stone towards understanding of microcompartmentalized CCM in other organisms. This thesis provides novel strategies to use genome-scale metabolic networks to predict and integrate metabolite concentrations. Therefore, the presented approaches represent an important step in broadening the applicability of large-scale metabolic models to a range of biotechnological and medical applications.},
  language  = {en}
}
@article{KuekenGennermannNikoloski2020,
  author    = {K{\"u}ken, Anika and Gennermann, Kristin and Nikoloski, Zoran},
  title     = {Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana},
  series = {The plant journal},
  volume    = {103},
  journal   = {The plant journal},
  number    = {6},
  publisher = {Wiley},
  address   = {Oxford},
  issn      = {0960-7412},
  doi       = {10.1111/tpj.14890},
  pages     = {2168 -- 2177},
  year      = {2020},
  abstract  = {Availability of plant-specific enzyme kinetic data is scarce, limiting the predictive power of metabolic models and precluding identification of genetic factors of enzyme properties. Enzyme kinetic data are measuredin vitro, often under non-physiological conditions, and conclusions elicited from modeling warrant caution. Here we estimate maximalin vivocatalytic rates for 168 plant enzymes, including photosystems I and II, cytochrome-b6f complex, ATP-citrate synthase, sucrose-phosphate synthase as well as enzymes from amino acid synthesis with previously undocumented enzyme kinetic data in BRENDA. The estimations are obtained by integrating condition-specific quantitative proteomics data, maximal rates of selected enzymes, growth measurements fromArabidopsis thalianarosette with and fluxes through canonical pathways in a constraint-based model of leaf metabolism. In comparison to findings inEscherichia coli, we demonstrate weaker concordance between the plant-specificin vitroandin vivoenzyme catalytic rates due to a low degree of enzyme saturation. This is supported by the finding that concentrations of nicotinamide adenine dinucleotide (phosphate), adenosine triphosphate and uridine triphosphate, calculated based on our maximalin vivocatalytic rates, and available quantitative metabolomics data are below reportedKMvalues and, therefore, indicate undersaturation of respective enzymes. Our findings show that genome-wide profiling of enzyme kinetic properties is feasible in plants, paving the way for understanding resource allocation.},
  language  = {en}
}