@phdthesis{Videla2014,
  author    = {Videla, Santiago},
  title     = {Reasoning on the response of logical signaling networks with answer set programming},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-71890},
  school      = {Universit{\"a}t Potsdam},
  year      = {2014},
  abstract  = {Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.},
  language  = {en}
}
@phdthesis{Thiele2011,
  author    = {Thiele, Sven},
  title     = {Modeling biological systems with Answer Set Programming},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-59383},
  school      = {Universit{\"a}t Potsdam},
  year      = {2011},
  abstract  = {Biology has made great progress in identifying and measuring the building blocks of life. The availability of high-throughput methods in molecular biology has dramatically accelerated the growth of biological knowledge for various organisms. The advancements in genomic, proteomic and metabolomic technologies allow for constructing complex models of biological systems. An increasing number of biological repositories is available on the web, incorporating thousands of biochemical reactions and genetic regulations. Systems Biology is a recent research trend in life science, which fosters a systemic view on biology. In Systems Biology one is interested in integrating the knowledge from all these different sources into models that capture the interaction of these entities. By studying these models one wants to understand the emerging properties of the whole system, such as robustness. However, both measurements as well as biological networks are prone to considerable incompleteness, heterogeneity and mutual inconsistency, which makes it highly non-trivial to draw biologically meaningful conclusions in an automated way. Therefore, we want to promote Answer Set Programming (ASP) as a tool for discrete modeling in Systems Biology. ASP is a declarative problem solving paradigm, in which a problem is encoded as a logic program such that its answer sets represent solutions to the problem. ASP has intrinsic features to cope with incompleteness, offers a rich modeling language and highly efficient solving technology. We present ASP solutions, for the analysis of genetic regulatory networks, determining consistency with observed measurements and identifying minimal causes for inconsistency. We extend this approach for computing minimal repairs on model and data that restore consistency. This method allows for predicting unobserved data even in case of inconsistency. Further, we present an ASP approach to metabolic network expansion. This approach exploits the easy characterization of reachability in ASP and its various reasoning methods, to explore the biosynthetic capabilities of metabolic reaction networks and generate hypotheses for extending the network. Finally, we present the BioASP library, a Python library which encapsulates our ASP solutions into the imperative programming paradigm. The library allows for an easy integration of ASP solution into system rich environments, as they exist in Systems Biology.},
  language  = {en}
}
@phdthesis{Stuebler2023,
  author    = {St{\"u}bler, Sabine},
  title     = {Mathematical model of the mucosal immune response to study inflammatory bowel diseases and their treatments},
  doi       = {10.25932/publishup-61230},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-612301},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 194},
  year      = {2023},
  abstract  = {Inflammatory bowel diseases (IBD), characterised by a chronic inflammation of the gut wall, develop as consequence of an overreacting immune response to commensal bacteria, caused by a combination of genetic and environmental conditions. Large inter-individual differences in the outcome of currently available therapies complicate the decision for the best option for an individual patient. Predicting the prospects of therapeutic success for an individual patient is currently only possible to a limited extent; for this, a better understanding of possible differences between responders and non-responders is needed. In this thesis, we have developed a mathematical model describing the most important processes of the gut mucosal immune system on the cellular level. The model is based on literature data, which were on the one hand used (qualitatively) to choose which cell types and processes to incorporate and to derive the model structure, and on the other hand (quantitatively) to derive the parameter values. Using ordinary differential equations, it describes the concentration-time course of neutrophils, macrophages, dendritic cells, T cells and bacteria, each subdivided into different cell types and activation states, in the lamina propria and mesenteric lymph nodes. We evaluate the model by means of simulations of the healthy immune response to salmonella infection and mucosal injury. A virtual population includes IBD patients, which we define through their initially asymptomatic, but after a trigger chronically inflamed gut wall. We demonstrate the model's usefulness in different analyses: (i) The comparison of virtual IBD patients with virtual healthy individuals shows that the disease is elicited by many small or fewer large changes, and allows to make hypotheses about dispositions relevant for development of the disease. (ii) We simulate the effects of different therapeutic targets and make predictions about the therapeutic outcome based on the pre-treatment state. (iii) From the analysis of differences between virtual responders and non-responders, we derive hypotheses about reasons for the inter-individual variability in treatment outcome. (iv) For the example of anti-TNF-alpha therapy, we analyse, which alternative therapies are most promising in case of therapeutic failure, and which therapies are most suited for combination therapies: For drugs also directly targeting the cytokine levels or inhibiting the recruitment of innate immune cells, we predict a low probability of success when used as alternative treatment, but a large gain when used in a combination treatment. For drugs with direct effects on T cells, via modulation of the sphingosine-1-phosphate receptor or inhibition of T cell proliferation, we predict a considerably larger probability of success when used as alternative treatment, but only a small additional gain when used in a combination therapy.},
  language  = {en}
}
@phdthesis{Schwahn2018,
  author    = {Schwahn, Kevin},
  title     = {Data driven approaches to infer the regulatory mechanism shaping and constraining levels of metabolites in metabolic networks},
  doi       = {10.25932/publishup-42324},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423240},
  school      = {Universit{\"a}t Potsdam},
  pages     = {109},
  year      = {2018},
  abstract  = {Systems biology aims at investigating biological systems in its entirety by gathering and analyzing large-scale data sets about the underlying components. Computational systems biology approaches use these large-scale data sets to create models at different scales and cellular levels. In addition, it is concerned with generating and testing hypotheses about biological processes. However, such approaches are inevitably leading to computational challenges due to the high dimensionality of the data and the differences in the dimension of data from different cellular layers. This thesis focuses on the investigation and development of computational approaches to analyze metabolite profiles in the context of cellular networks. This leads to determining what aspects of the network functionality are reflected in the metabolite levels. With these methods at hand, this thesis aims to answer three questions: (1) how observability of biological systems is manifested in metabolite profiles and if it can be used for phenotypical comparisons; (2) how to identify couplings of reaction rates from metabolic profiles alone; and (3) which regulatory mechanism that affect metabolite levels can be distinguished by integrating transcriptomics and metabolomics read-outs. I showed that sensor metabolites, identified by an approach from observability theory, are more correlated to each other than non-sensors. The greater correlations between sensor metabolites were detected both with publicly available metabolite profiles and synthetic data simulated from a medium-scale kinetic model. I demonstrated through robustness analysis that correlation was due to the position of the sensor metabolites in the network and persisted irrespectively of the experimental conditions. Sensor metabolites are therefore potential candidates for phenotypical comparisons between conditions through targeted metabolic analysis. Furthermore, I demonstrated that the coupling of metabolic reaction rates can be investigated from a purely data-driven perspective, assuming that metabolic reactions can be described by mass action kinetics. Employing metabolite profiles from domesticated and wild wheat and tomato species, I showed that the process of domestication is associated with a loss of regulatory control on the level of reaction rate coupling. I also found that the same metabolic pathways in Arabidopsis thaliana and Escherichia coli exhibit differences in the number of reaction rate couplings. I designed a novel method for the identification and categorization of transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approach determines the partial correlation of metabolites with control by the principal components of the transcript levels. The principle components contain the majority of the transcriptomic information allowing to partial out the effect of the transcriptional layer from the metabolite profiles. Depending whether the correlation between metabolites persists upon controlling for the effect of the transcriptional layer, the approach allows us to group metabolite pairs into being associated due to post-transcriptional or transcriptional regulation, respectively. I showed that the classification of metabolite pairs into those that are associated due to transcriptional or post-transcriptional regulation are in agreement with existing literature and findings from a Bayesian inference approach. The approaches developed, implemented, and investigated in this thesis open novel ways to jointly study metabolomics and transcriptomics data as well as to place metabolic profiles in the network context. The results from these approaches have the potential to provide further insights into the regulatory machinery in a biological system.},
  language  = {en}
}
@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{Mauri2014,
  author    = {Mauri, Marco},
  title     = {A model for sigma factor competition in bacterial cells},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-72098},
  school      = {Universit{\"a}t Potsdam},
  pages     = {167},
  year      = {2014},
  abstract  = {Bacteria respond to changing environmental conditions by switching the global pattern of expressed genes. In response to specific environmental stresses the cell activates several stress-specific molecules such as sigma factors. They reversibly bind the RNA polymerase to form the so-called holoenzyme and direct it towards the appropriate stress response genes. In exponentially growing E. coli cells, the majority of the transcriptional activity is carried out by the housekeeping sigma factor, while stress responses are often under the control of alternative sigma factors. Different sigma factors compete for binding to a limited pool of RNA polymerase (RNAP) core enzymes, providing a mechanism for cross talk between genes or gene classes via the sharing of expression machinery. To quantitatively analyze the contribution of sigma factor competition to global changes in gene expression, we develop a thermodynamic model that describes binding between sigma factors and core RNAP at equilibrium, transcription, non-specific binding to DNA and the modulation of the availability of the molecular components. Association of housekeeping sigma factor to RNAP is generally favored by its abundance and higher binding affinity to the core. In order to promote transcription by alternative sigma subunits, the bacterial cell modulates the transcriptional efficiency in a reversible manner through several strategies such as anti-sigma factors, 6S RNA and generally any kind of transcriptional regulators (e.g. activators or inhibitors). By shifting the outcome of sigma factor competition for the core, these modulators bias the transcriptional program of the cell. The model is validated by comparison with in vitro competition experiments, with which excellent agreement is found. We observe that transcription is affected via the modulation of the concentrations of the different types of holoenzymes, so saturated promoters are only weakly affected by sigma factor competition. However, in case of overlapping promoters or promoters recognized by two types of sigma factors, we find that even saturated promoters are strongly affected. Active transcription effectively lowers the affinity between the sigma factor driving it and the core RNAP, resulting in complex cross talk effects and raising the question of how their in vitro measure is relevant in the cell. We also estimate that sigma factor competition is not strongly affected by non-specific binding of core RNAPs, sigma factors, and holoenzymes to DNA. Finally, we analyze the role of increased core RNAP availability upon the shut-down of ribosomal RNA transcription during stringent response. We find that passive up-regulation of alternative sigma-dependent transcription is not only possible, but also displays hypersensitivity based on the sigma factor competition. Our theoretical analysis thus provides support for a significant role of passive control during that global switch of the gene expression program and gives new insights into RNAP partitioning in the cell.},
  language  = {en}
}
@phdthesis{Girbig2014,
  author    = {Girbig, Dorothee},
  title     = {Analysing concerted criteria for local dynamic properties of metabolic systems},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-72017},
  school      = {Universit{\"a}t Potsdam},
  year      = {2014},
  abstract  = {Metabolic systems tend to exhibit steady states that can be measured in terms of their concentrations and fluxes. These measurements can be regarded as a phenotypic representation of all the complex interactions and regulatory mechanisms taking place in the underlying metabolic network. Such interactions determine the system's response to external perturbations and are responsible, for example, for its asymptotic stability or for oscillatory trajectories around the steady state. However, determining these perturbation responses in the absence of fully specified kinetic models remains an important challenge of computational systems biology. Structural kinetic modeling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a parameterised representation of the system's Jacobian matrix in which the model parameters encode information about the enzyme-metabolite interactions. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. The parameter space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Because the sampled parameters are equivalent to the elasticities used in metabolic control analysis (MCA), the results are easy to interpret biologically. In this project, the SKM framework was extended by several novel methodological improvements. These improvements were evaluated in a simulation study using a set of small example pathways with simple Michaelis Menten rate laws. Afterwards, a detailed analysis of the dynamic properties of the neuronal TCA cycle was performed in order to demonstrate how the new insights obtained in this work could be used for the study of complex metabolic systems. The first improvement was achieved by examining the biological feasibility of the elasticity combinations created during Monte Carlo sampling. Using a set of small example systems, the findings showed that the majority of sampled SK-models would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion was formulated that mitigates such infeasible models and the application of this criterion changed the conclusions of the SKM experiment. The second improvement of this work was the application of supervised machine-learning approaches in order to analyse SKM experiments. So far, SKM experiments have focused on the detection of individual enzymes to identify single reactions important for maintaining the stability or oscillatory trajectories. In this work, this approach was extended by demonstrating how SKM enables the detection of ensembles of enzymes or metabolites that act together in an orchestrated manner to coordinate the pathways response to perturbations. In doing so, stable and unstable states served as class labels, and classifiers were trained to detect elasticity regions associated with stability and instability. Classification was performed using decision trees and relevance vector machines (RVMs). The decision trees produced good classification accuracy in terms of model bias and generalizability. RVMs outperformed decision trees when applied to small models, but encountered severe problems when applied to larger systems because of their high runtime requirements. The decision tree rulesets were analysed statistically and individually in order to explore the role of individual enzymes or metabolites in controlling the system's trajectories around steady states. The third improvement of this work was the establishment of a relationship between the SKM framework and the related field of MCA. In particular, it was shown how the sampled elasticities could be converted to flux control coefficients, which were then investigated for their predictive information content in classifier training. After evaluation on the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle with respect to their intrinsic mechanisms responsible for stability or instability. The findings showed that several elasticities were jointly coordinated to control stability and that the main source for potential instabilities were mutations in the enzyme alpha-ketoglutarate dehydrogenase.},
  language  = {en}
}
@phdthesis{Breuer2016,
  author    = {Breuer, David},
  title     = {The plant cytoskeleton as a transportation network},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-93583},
  school      = {Universit{\"a}t Potsdam},
  pages     = {164},
  year      = {2016},
  abstract  = {The cytoskeleton is an essential component of living cells. It is composed of different types of protein filaments that form complex, dynamically rearranging, and interconnected networks. The cytoskeleton serves a multitude of cellular functions which further depend on the cell context. In animal cells, the cytoskeleton prominently shapes the cell's mechanical properties and movement. In plant cells, in contrast, the presence of a rigid cell wall as well as their larger sizes highlight the role of the cytoskeleton in long-distance intracellular transport. As it provides the basis for cell growth and biomass production, cytoskeletal transport in plant cells is of direct environmental and economical relevance. However, while knowledge about the molecular details of the cytoskeletal transport is growing rapidly, the organizational principles that shape these processes on a whole-cell level remain elusive. This thesis is devoted to the following question: How does the complex architecture of the plant cytoskeleton relate to its transport functionality? The answer requires a systems level perspective of plant cytoskeletal structure and transport. To this end, I combined state-of-the-art confocal microscopy, quantitative digital image analysis, and mathematically powerful, intuitively accessible graph-theoretical approaches. This thesis summarizes five of my publications that shed light on the plant cytoskeleton as a transportation network: (1) I developed network-based frameworks for accurate, automated quantification of cytoskeletal structures, applicable in, e.g., genetic or chemical screens; (2) I showed that the actin cytoskeleton displays properties of efficient transport networks, hinting at its biological design principles; (3) Using multi-objective optimization, I demonstrated that different plant cell types sustain cytoskeletal networks with cell-type specific and near-optimal organization; (4) By investigating actual transport of organelles through the cell, I showed that properties of the actin cytoskeleton are predictive of organelle flow and provided quantitative evidence for a coordination of transport at a cellular level; (5) I devised a robust, optimization-based method to identify individual cytoskeletal filaments from a given network representation, allowing the investigation of single filament properties in the network context. The developed methods were made publicly available as open-source software tools. Altogether, my findings and proposed frameworks provide quantitative, system-level insights into intracellular transport in living cells. Despite my focus on the plant cytoskeleton, the established combination of experimental and theoretical approaches is readily applicable to different organisms. Despite the necessity of detailed molecular studies, only a complementary, systemic perspective, as presented here, enables both understanding of cytoskeletal function in its evolutionary context as well as its future technological control and utilization.},
  language  = {en}
}