@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}
}
@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}
}
@article{HoffmannHaoShearwinetal.2019,
  author    = {Hoffmann, Stefan A. and Hao, Nan and Shearwin, Keith E. and Arndt, Katja Maren},
  title     = {Characterizing transcriptional interference between converging genes in bacteria},
  series = {ACS synthetic biology},
  volume    = {8},
  journal   = {ACS synthetic biology},
  number    = {3},
  publisher = {American Chemical Society},
  address   = {Washington},
  issn      = {2161-5063},
  doi       = {10.1021/acssynbio.8b00477},
  pages     = {466 -- 473},
  year      = {2019},
  abstract  = {Antisense transcription is common in naturally occurring genomes and is increasingly being used in synthetic genetic circuitry as a tool for gene expression control. Mutual influence on the expression of convergent genes can be mediated by antisense RNA effects and by transcriptional interference (TI). We aimed to quantitatively characterize long-range TI between convergent genes with untranslated intergenic spacers of increasing length. After controlling for antisense RNA-mediated effects, which contributed about half of the observed total expression inhibition, the TI effect was modeled. To achieve model convergence, RNA polymerase processivity and collision resistance were assumed to be modulated by ribosome trailing. The spontaneous transcription termination rate in regions of untranslated DNA was experimentally determined. Our modeling suggests that an elongating RNA polymerase with a trailing ribosome is about 13 times more likely to resume transcription than an opposing RNA polymerase without a trailing ribosome, upon head-on collision of the two.},
  language  = {en}
}
@phdthesis{Sin2016,
  author    = {Sin, Celine},
  title     = {Post-transcriptional control of gene expression},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-102469},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxv, 238},
  year      = {2016},
  abstract  = {Gene expression describes the process of making functional gene products (e.g. proteins or special RNAs) from instructions encoded in the genetic information (e.g. DNA). This process is heavily regulated, allowing cells to produce the appropriate gene products necessary for cell survival, adapting production as necessary for different cell environments. Gene expression is subject to regulation at several levels, including transcription, mRNA degradation, translation and protein degradation. When intact, this system maintains cell homeostasis, keeping the cell alive and adaptable to different environments. Malfunction in the system can result in disease states and cell death. In this dissertation, we explore several aspects of gene expression control by analyzing data from biological experiments. Most of the work following uses a common mathematical model framework based on Markov chain models to test hypotheses, predict system dynamics or elucidate network topology. Our work lies in the intersection between mathematics and biology and showcases the power of statistical data analysis and math modeling for validation and discovery of biological phenomena.},
  language  = {en}
}