@phdthesis{HashemiRanjbar2023,
  author    = {Hashemi Ranjbar, Seirana},
  title     = {Plasticity and trade-offs in plant metabolic networks},
  school      = {Universit{\"a}t Potsdam},
  pages     = {112},
  year      = {2023},
  abstract  = {A biological trade-off situation denotes the dependence between traits whereby an increase in the value of one of the traits leads to a decrease in the value of at least one of the others. Understanding trade-offs in cellular systems is relevant to understanding the limits and constraints to tuning desired phenotypes. Therefore, it is mainly the case for rates (i.e. fluxes) of biochemical reactions that shape not only molecular traits, like metabolite concentrations but also determine physiological traits, like growth. Intracellular fluxes are the final phenotype from transcriptional and (post)translational regulation. Quantifying intracellular fluxes provides insights into cellular physiology under particular growth conditions and can be used to characterize the metabolic activity of different pathways. However, estimating fluxes from labelling experiments is labour-intensive; therefore, developing approaches to accurately and precisely predict intracellular fluxes is essential. This thesis addresses two main problems: (i) identifying flux trade-offs and (ii) predicting accurate and precise reaction flux at a genome-scale level. To this end, the concept of an absolute flux trade-off is defined, and a constraint-based approach, termed FluTO, was developed to identify absolute flux trade-offs. FluTO is cast as a mixed integer programming approach applied to genome-scale metabolic models of E. coli, S. cerevisiae, and A. thaliana, imposing realistic constraints on growth and nutrient uptake.. The findings showed that trade-offs are not only species-specific but also specific to carbon sources. In addition, we found that different models of a single species have a different number of reactions in trade-offs. We also showed that absolute flux trade-offs depend on the biomass reaction used to model the growth of A. thaliana under different carbon and nitrogen conditions. Findings reflect the strong relation between nitrogen, carbon, and sulphur metabolisms in the leaves of C3 plants. The concept of relative trade-offs was introduced to further study trade-offs in metabolic networks. A constraint-based approach, FluTOr, was proposed to identify reactions whose fluxes are in relative trade-off concerning an optimized fitness-related cellular task, like growth. FluTOr was employed to find the relative flux trade-offsin the genome-scale metabolic networks of E. coli, S. cerevisiae, and A. thaliana. The results showed that in contrast to the A. thaliana model, the relative trade-offs in the two microorganisms depend on the carbon source, reflecting the differences in the underlying metabolic network. Furthermore, applying FluTOr also showed that reactions that participated in relative trade-offs were implicated in cofactor biosynthesis in the two microorganisms. Prediction of reaction fluxes in the constraint-based metabolic framework is usually performed by parsimonious flux balance analysis (pFBA), employing the principle of efficient usage of protein resources. However, we argued that principles related to the coordination of flux values, neglected in previous studies, provide other means to predict intracellular fluxes. To this end, we designed a constraint-based approach, termed complex-balanced FBA (cbFBA), to predict steady-state flux distributions that maximize the number of balanced complexes in a flux distribution, whereby multi-reaction dependencies are maximized. The comparative analysis showed a better agreement of the flux distributions resulting from cbFBA compared to pFBA with experimentally measured fluxes from 17 E. coli strains and 26 S. cerevisiae knock-out mutants. The results also showed that the predictions from cbFBA are more precise than those from pFBA since cbFBA results in a smaller space of alternative solutions than pFBA.},
  language  = {en}
}
@article{FriouxSchaubSchellhornetal.2019,
  author    = {Frioux, Cl{\´e}mence and Schaub, Torsten H. and Schellhorn, Sebastian and Siegel, Anne and Wanko, Philipp},
  title     = {Hybrid metabolic network completion},
  series = {Theory and practice of logic programming},
  volume    = {19},
  journal   = {Theory and practice of logic programming},
  number    = {1},
  publisher = {Cambridge University Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068418000455},
  pages     = {83 -- 108},
  year      = {2019},
  abstract  = {Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system dingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.},
  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}
}