@phdthesis{RobainaEstevez2017,
  author    = {Robaina Estevez, Semidan},
  title     = {Context-specific metabolic predictions},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401365},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vi, 158},
  year      = {2017},
  abstract  = {All life-sustaining processes are ultimately driven by thousands of biochemical reactions occurring in the cells: the metabolism. These reactions form an intricate network which produces all required chemical compounds, i.e., metabolites, from a set of input molecules. Cells regulate the activity through metabolic reactions in a context-specific way; only reactions that are required in a cellular context, e.g., cell type, developmental stage or environmental condition, are usually active, while the rest remain inactive. The context-specificity of metabolism can be captured by several kinds of experimental data, such as by gene and protein expression or metabolite profiles. In addition, these context-specific data can be assimilated into computational models of metabolism, which then provide context-specific metabolic predictions. This thesis is composed of three individual studies focussing on context-specific experimental data integration into computational models of metabolism. The first study presents an optimization-based method to obtain context-specific metabolic predictions, and offers the advantage of being fully automated, i.e., free of user defined parameters. The second study explores the effects of alternative optimal solutions arising during the generation of context-specific metabolic predictions. These alternative optimal solutions are metabolic model predictions that represent equally well the integrated data, but that can markedly differ. This study proposes algorithms to analyze the space of alternative solutions, as well as some ways to cope with their impact in the predictions. Finally, the third study investigates the metabolic specialization of the guard cells of the plant Arabidopsis thaliana, and compares it with that of a different cell type, the mesophyll cells. To this end, the computational methods developed in this thesis are applied to obtain metabolic predictions specific to guard cell and mesophyll cells. These cell-specific predictions are then compared to explore the differences in metabolic activity between the two cell types. In addition, the effects of alternative optima are taken into consideration when comparing the two cell types. The computational results indicate a major reorganization of the primary metabolism in guard cells. These results are supported by an independent 13C labelling experiment.},
  language  = {en}
}
@phdthesis{Basler2012,
  author    = {Basler, Georg},
  title     = {Mass-balanced randomization : a significance measure for metabolic networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-62037},
  school      = {Universit{\"a}t Potsdam},
  year      = {2012},
  abstract  = {Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, including small average path length, large clustering coefficient, heavy-tail degree distribution, and hierarchical organization, viewed as requirements for efficient and robust system architectures. Existing studies estimate the significance of network properties using a generic randomization scheme - a Markov-chain switching algorithm - which generates unrealistic reactions in metabolic networks, as it does not account for the physical principles underlying metabolism. Therefore, it is unclear whether the properties identified with this generic approach are related to the functions of metabolic networks. Within this doctoral thesis, I have developed an algorithm for mass-balanced randomization of metabolic networks, which runs in polynomial time and samples networks almost uniformly at random. The properties of biological systems result from two fundamental origins: ubiquitous physical principles and a complex history of evolutionary pressure. The latter determines the cellular functions and abilities required for an organism's survival. Consequently, the functionally important properties of biological systems result from evolutionary pressure. By employing randomization under physical constraints, the salient structural properties, i.e., the smallworld property, degree distributions, and biosynthetic capabilities of six metabolic networks from all kingdoms of life are shown to be independent of physical constraints, and thus likely to be related to evolution and functional organization of metabolism. This stands in stark contrast to the results obtained from the commonly applied switching algorithm. In addition, a novel network property is devised to quantify the importance of reactions by simulating the impact of their knockout. The relevance of the identified reactions is verified by the findings of existing experimental studies demonstrating the severity of the respective knockouts. The results suggest that the novel property may be used to determine the reactions important for viability of organisms. Next, the algorithm is employed to analyze the dependence between mass balance and thermodynamic properties of Escherichia coli metabolism. The thermodynamic landscape in the vicinity of the metabolic network reveals two regimes of randomized networks: those with thermodynamically favorable reactions, similar to the original network, and those with less favorable reactions. The results suggest that there is an intrinsic dependency between thermodynamic favorability and evolutionary optimization. The method is further extended to optimizing metabolic pathways by introducing novel chemically feasibly reactions. The results suggest that, in three organisms of biotechnological importance, introduction of the identified reactions may allow for optimizing their growth. The approach is general and allows identifying chemical reactions which modulate the performance with respect to any given objective function, such as the production of valuable compounds or the targeted suppression of pathway activity. These theoretical developments can find applications in metabolic engineering or disease treatment. The developed randomization method proposes a novel approach to measuring the significance of biological network properties, and establishes a connection between large-scale approaches and biological function. The results may provide important insights into the functional principles of metabolic networks, and open up new possibilities for their engineering.},
  language  = {en}
}
@phdthesis{Grimbs2009,
  author    = {Grimbs, Sergio},
  title     = {Towards structure and dynamics of metabolic networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-32397},
  school      = {Universit{\"a}t Potsdam},
  year      = {2009},
  abstract  = {This work presents mathematical and computational approaches to cover various aspects of metabolic network modelling, especially regarding the limited availability of detailed kinetic knowledge on reaction rates. It is shown that precise mathematical formulations of problems are needed i) to find appropriate and, if possible, efficient algorithms to solve them, and ii) to determine the quality of the found approximate solutions. Furthermore, some means are introduced to gain insights on dynamic properties of metabolic networks either directly from the network structure or by additionally incorporating steady-state information. Finally, an approach to identify key reactions in a metabolic networks is introduced, which helps to develop simple yet useful kinetic models. The rise of novel techniques renders genome sequencing increasingly fast and cheap. In the near future, this will allow to analyze biological networks not only for species but also for individuals. Hence, automatic reconstruction of metabolic networks provides itself as a means for evaluating this huge amount of experimental data. A mathematical formulation as an optimization problem is presented, taking into account existing knowledge and experimental data as well as the probabilistic predictions of various bioinformatical methods. The reconstructed networks are optimized for having large connected components of high accuracy, hence avoiding fragmentation into small isolated subnetworks. The usefulness of this formalism is exemplified on the reconstruction of the sucrose biosynthesis pathway in Chlamydomonas reinhardtii. The problem is shown to be computationally demanding and therefore necessitates efficient approximation algorithms. The problem of minimal nutrient requirements for genome-scale metabolic networks is analyzed. Given a metabolic network and a set of target metabolites, the inverse scope problem has as it objective determining a minimal set of metabolites that have to be provided in order to produce the target metabolites. These target metabolites might stem from experimental measurements and therefore are known to be produced by the metabolic network under study, or are given as the desired end-products of a biotechological application. The inverse scope problem is shown to be computationally hard to solve. However, I assume that the complexity strongly depends on the number of directed cycles within the metabolic network. This might guide the development of efficient approximation algorithms. Assuming mass-action kinetics, chemical reaction network theory (CRNT) allows for eliciting conclusions about multistability directly from the structure of metabolic networks. Although CRNT is based on mass-action kinetics originally, it is shown how to incorporate further reaction schemes by emulating molecular enzyme mechanisms. CRNT is used to compare several models of the Calvin cycle, which differ in size and level of abstraction. Definite results are obtained for small models, but the available set of theorems and algorithms provided by CRNT can not be applied to larger models due to the computational limitations of the currently available implementations of the provided algorithms. Given the stoichiometry of a metabolic network together with steady-state fluxes and concentrations, structural kinetic modelling allows to analyze the dynamic behavior of the metabolic network, even if the explicit rate equations are not known. In particular, this sampling approach is used to study the stabilizing effects of allosteric regulation in a model of human erythrocytes. Furthermore, the reactions of that model can be ranked according to their impact on stability of the steady state. The most important reactions in that respect are identified as hexokinase, phosphofructokinase and pyruvate kinase, which are known to be highly regulated and almost irreversible. Kinetic modelling approaches using standard rate equations are compared and evaluated against reference models for erythrocytes and hepatocytes. The results from this simplified kinetic models can simulate acceptably the temporal behavior for small changes around a given steady state, but fail to capture important characteristics for larger changes. The aforementioned approach to rank reactions according to their influence on stability is used to identify a small number of key reactions. These reactions are modelled in detail, including knowledge about allosteric regulation, while all other reactions were still described by simplified reaction rates. These so-called hybrid models can capture the characteristics of the reference models significantly better than the simplified models alone. The resulting hybrid models might serve as a good starting point for kinetic modelling of genome-scale metabolic networks, as they provide reasonable results in the absence of experimental data, regarding, for instance, allosteric regulations, for a vast majority of enzymatic reactions.},
  language  = {en}
}
@phdthesis{Scholz2006,
  author    = {Scholz, Matthias},
  title     = {Approaches to analyse and interpret biological profile data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-7839},
  school      = {Universit{\"a}t Potsdam},
  year      = {2006},
  abstract  = {Advances in biotechnologies rapidly increase the number of molecules of a cell which can be observed simultaneously. This includes expression levels of thousands or ten-thousands of genes as well as concentration levels of metabolites or proteins. Such Profile data, observed at different times or at different experimental conditions (e.g., heat or dry stress), show how the biological experiment is reflected on the molecular level. This information is helpful to understand the molecular behaviour and to identify molecules or combination of molecules that characterise specific biological condition (e.g., disease). This work shows the potentials of component extraction algorithms to identify the major factors which influenced the observed data. This can be the expected experimental factors such as the time or temperature as well as unexpected factors such as technical artefacts or even unknown biological behaviour. Extracting components means to reduce the very high-dimensional data to a small set of new variables termed components. Each component is a combination of all original variables. The classical approach for that purpose is the principal component analysis (PCA). It is shown that, in contrast to PCA which maximises the variance only, modern approaches such as independent component analysis (ICA) are more suitable for analysing molecular data. The condition of independence between components of ICA fits more naturally our assumption of individual (independent) factors which influence the data. This higher potential of ICA is demonstrated by a crossing experiment of the model plant Arabidopsis thaliana (Thale Cress). The experimental factors could be well identified and, in addition, ICA could even detect a technical artefact. However, in continuously observations such as in time experiments, the data show, in general, a nonlinear distribution. To analyse such nonlinear data, a nonlinear extension of PCA is used. This nonlinear PCA (NLPCA) is based on a neural network algorithm. The algorithm is adapted to be applicable to incomplete molecular data sets. Thus, it provides also the ability to estimate the missing data. The potential of nonlinear PCA to identify nonlinear factors is demonstrated by a cold stress experiment of Arabidopsis thaliana. The results of component analysis can be used to build a molecular network model. Since it includes functional dependencies it is termed functional network. Applied to the cold stress data, it is shown that functional networks are appropriate to visualise biological processes and thereby reveals molecular dynamics.},
  subject      = {Bioinformatik},
  language  = {en}
}
@phdthesis{GomezPorras2005,
  author    = {G{\´o}mez-Porras, Judith Lucia},
  title     = {In silico identification of genes regulated by abscisic acid in Arabidopsis thaliana (L.) Heynh.},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-7401},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {Abscisic acid (ABA) is a major plant hormone that plays an important role during plant growth and development. During vegetative growth ABA mediates (in part) responses to various environmental stresses such as cold, drought and high salinity. The response triggered by ABA includes changes in the transcript level of genes involved in stress tolerance. The aim of this project was the In silico identification of genes putatively regulated by ABA in A. thaliana. In silico predictions were combined with experimental data in order to evaluate the reliability of computational predictions. Taking advantage of the genome sequence of A. thaliana publicly available since 2000, 1 kb upstream sequences were screened for combinations of cis-elements known to be involved in the regulation of ABA-responsive genes. It was found that around 10 to 20 percent of the genes of A. thaliana might be regulated by ABA. Further analyses of the predictions revealed that certain combinations of cis-elements that confer ABA-responsiveness were significantly over-represented compared with results in random sequences and with random expectations. In addition, it was observed that other combinations that confer ABA-responsiveness in monocotyledonous species might not be functional in A. thaliana. It is proposed that ABA-responsive genes in A. thaliana show pairs of ABRE (abscisic acid responsive element) with MYB binding sites, DRE (dehydration responsive element) or with itself. The analysis of the distances between pairs of cis-elements suggested that pairs of ABREs are bound by homodimers of ABRE binding proteins. In contrast, pairs between MYB binding sites and ABRE, or DRE and ABRE showed a distance between cis-elements that suggested that the binding proteins interact through protein complexes and not directly. The comparison of computational predictions with experimental data confirmed that the regulatory mechanisms leading to the induction or repression of genes by ABA is very incompletely understood. It became evident that besides the cis-elements proposed in this study to be present in ABA-responsive genes, other known and unknown cis-elements might play an important role in the transcriptional regulation of ABA-responsive genes. For example, auxin-related cis elements, or the cis-elements recognized by the NAM-family of transcription factors (Non-Apical meristem). This work documents the use of computational and experimental approaches to analyse possible interactions between cis-elements involved in the regulation of ABA-responsive genes. The computational predictions allowed the distinction between putatively relevant combinations of cis-elements from irrelevant combinations of cis-elements in ABA-responsive genes. The comparison with experimental data allowed to identify certain cis-elements that have not been previously associated to the ABA-mediated transcriptional regulation, but that might be present in ABA-responsive genes (e.g. auxin responsive elements). Moreover, the efforts to unravel the gene regulatory network associated with the ABA-signalling pathway revealed that NAM-transcription factors and their corresponding binding sequences are important components of this network.},
  subject      = {Bioinformatik},
  language  = {en}
}