@phdthesis{Synodinos2016,
  author    = {Synodinos, Alexios D.},
  title     = {Savanna dynamics under extreme conditions},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-395000},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 168},
  year      = {2016},
  abstract  = {Savannas cover a broad geographical range across continents and are a biome best described by a mix of herbaceous and woody plants. The former create a more or less continuous layer while the latter should be sparse enough to leave an open canopy. What has long intrigued ecologists is how these two competing plant life forms of vegetation coexist. Initially attributed to resource competition, coexistence was considered the stable outcome of a root niche differentiation between trees and grasses. The importance of environmental factors became evident later, when data from moister environments demonstrated that tree cover was often lower than what the rainfall conditions would allow for. Our current understanding relies on the interaction of competition and disturbances in space and time. Hence, the influence of grazing and fire and the corresponding feedbacks they generate have been keenly investigated. Grazing removes grass cover, initiating a self-reinforcing process propagating tree cover expansion. This is known as the encroachment phenomenon. Fire, on the other hand, imposes a bottleneck on the tree population by halting the recruitment of young trees into adulthood. Since grasses fuel fires, a feedback linking grazing, grass cover, fire, and tree cover is created. In African savannas, which are the focus of this dissertation, these feedbacks play a major role in the dynamics. The importance of these feedbacks came into sharp focus when the notion of alternative states began to be applied to savannas. Alternative states in ecology arise when different states of an ecosystem can occur under the same conditions. According to this an open savanna and a tree-dominated savanna can be classified as alternative states, since they can both occur under the same climatic conditions. The aforementioned feedbacks are critical in the creation of alternative states. The grass-fire feedback can preserve an open canopy as long as fire intensity and frequency remain above a certain threshold. Conversely, crossing a grazing threshold can force an open savanna to shift to a tree-dominated state. Critically, transitions between such alternative states can produce hysteresis, where a return to pre-transition conditions will not suffice to restore the ecosystem to its original state. In the chapters that follow, I will cover aspects relating to the coexistence mechanisms and the role of feedbacks in tree-grass interactions. Coming back to the coexistence question, due to the overwhelming focus on competition and disturbance another important ecological process was neglected: facilitation. Therefore, in the first study within this dissertation I examine how facilitation can expand the tree-grass coexistence range into drier conditions. For the second study I focus on another aspect of savanna dynamics which remains underrepresented in the literature: the impacts of inter-annual rainfall variability upon savanna trees and the resilience of the savanna state. In the third and final study within this dissertation I approach the well-researched encroachment phenomenon from a new perspective: I search for an early warning indicator of the process to be used as a prevention tool for savanna conservation. In order to perform all this work I developed a mathematical ecohydrological model of Ordinary Differential Equations (ODEs) with three variables: soil moisture content, grass cover and tree cover. Facilitation: Results showed that the removal of grass cover through grazing was detrimental to trees under arid conditions, contrary to expectation based on resource competition. The reason was that grasses preserved moisture in the soil through infiltration and shading, thus ameliorating the harsh conditions for trees in accordance with the Stress Gradient Hypothesis. The exclusion of grasses from the model further demonstrated this: tree cover was lower in the absence of grasses, indicating that the benefits of grass facilitation outweighed the costs of grass competition for trees. Thus, facilitation expanded the climatic range where savannas persisted into drier conditions. Rainfall variability: By adjusting the model to current rainfall patterns in East Africa, I simulated conditions of increasing inter-annual rainfall variability for two distinct mean rainfall scenarios: semi-arid and mesic. Alternative states of tree-less grassland and tree-dominated savanna emerged in both cases. Increasing variability reduced semi-arid savanna tree cover to the point that at high variability the savanna state was eliminated, because variability intensified resource competition and strengthened the fire disturbance during high rainfall years. Mesic savannas, on the other hand, became more resilient along the variability gradient: increasing rainfall variability created more opportunities for the rapid growth of trees to overcome the fire disturbance, boosting the chances of savannas persisting and thus increasing mesic savanna resilience. Preventing encroachment: The breakdown in the grass-fire feedback caused by heavy grazing promoted the expansion of woody cover. This could be irreversible due to the presence of alternative states of encroached and open savanna, which I found along a simulated grazing gradient. When I simulated different short term heavy grazing treatments followed by a reduction to the original grazing conditions, certain cases converged to the encroached state. Utilising woody cover changes only during the heavy grazing treatment, I developed an early warning indicator which identified these cases with a high risk of such hysteresis and successfully distinguished them from those with a low risk. Furthermore, after validating the indicator on encroachment data, I demonstrated that it appeared early enough for encroachment to be prevented through realistic grazing-reduction treatments. Though this dissertation is rooted in the theory of savanna dynamics, its results can have significant applications in savanna conservation. Facilitation has only recently become a topic of interest within savanna literature. Given the threat of increasing droughts and a general anticipation of drier conditions in parts of Africa, insights stemming from this research may provide clues for preserving arid savannas. The impacts of rainfall variability on savannas have not yet been thoroughly studied, either. Conflicting results appear as a result of the lack of a robust theoretical understanding of plant interactions under variable conditions. . My work and other recent studies argue that such conditions may increase the importance of fast resource acquisition creating a 'temporal niche'. Woody encroachment has been extensively studied as phenomenon, though not from the perspective of its early identification and prevention. The development of an encroachment forecasting tool, as the one presented in this work, could protect both the savanna biome and societies dependent upon it for (economic) survival. All studies which follow are bound by the attempt to broaden the horizons of savanna-related research in order to deal with extreme conditions and phenomena; be it through the enhancement of the coexistence debate or the study of an imminent external threat or the development of a management-oriented tool for the conservation of savannas.},
  language  = {en}
}
@phdthesis{Hethey2017,
  author    = {Hethey, Christoph Philipp},
  title     = {Cell physiology based pharmacodynamic modeling of antimicrobial drug combinations},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401056},
  school      = {Universit{\"a}t Potsdam},
  pages     = {102},
  year      = {2017},
  abstract  = {Mathematical models of bacterial growth have been successfully applied to study the relationship between antibiotic drug exposure and the antibacterial effect. Since these models typically lack a representation of cellular processes and cell physiology, the mechanistic integration of drug action is not possible on the cellular level. The cellular mechanisms of drug action, however, are particularly relevant for the prediction, analysis and understanding of interactions between antibiotics. Interactions are also studied experimentally, however, a lacking consent on the experimental protocol hinders direct comparison of results. As a consequence, contradictory classifications as additive, synergistic or antagonistic are reported in literature. In the present thesis we developed a novel mathematical model for bacterial growth that integrates cell-level processes into the population growth level. The scope of the model is to predict bacterial growth under antimicrobial perturbation by multiple antibiotics in vitro. To this end, we combined cell-level data from literature with population growth data for Bacillus subtilis, Escherichia coli and Staphylococcus aureus. The cell-level data described growth-determining characteristics of a reference cell, including the ribosomal concentration and efficiency. The population growth data comprised extensive time-kill curves for clinically relevant antibiotics (tetracycline, chloramphenicol, vancomycin, meropenem, linezolid, including dual combinations). The new cell-level approach allowed for the first time to simultaneously describe single and combined effects of the aforementioned antibiotics for different experimental protocols, in particular different growth phases (lag and exponential phase). Consideration of ribosomal dynamics and persisting sub-populations explained the decreased potency of linezolid on cultures in the lag phase compared to exponential phase cultures. The model captured growth rate dependent killing and auto-inhibition of meropenem and - also for vancomycin exposure - regrowth of the bacterial cultures due to adaptive resistance development. Stochastic interaction surface analysis demonstrated the pronounced antagonism between meropenem and linezolid to be robust against variation in the growth phase and pharmacodynamic endpoint definition, but sensitive to a change in the experimental duration. Furthermore, the developed approach included a detailed representation of the bacterial cell-cycle. We used this representation to describe septation dynamics during the transition of a bacterial culture from the exponential to stationary growth phase. Resulting from a new mechanistic understanding of transition processes, we explained the lag time between the increase in cell number and bacterial biomass during the transition from the lag to exponential growth phase. Furthermore, our model reproduces the increased intracellular RNA mass fraction during long term exposure of bacteria to chloramphenicol. In summary, we contribute a new approach to disentangle the impact of drug effects, assay readout and experimental protocol on antibiotic interactions. In the absence of a consensus on the corresponding experimental protocols, this disentanglement is key to translate information between heterogeneous experiments and also ultimately to the clinical setting.},
  language  = {en}
}
@phdthesis{Gopalakrishnan2016,
  author    = {Gopalakrishnan, Sathej},
  title     = {Mathematical modelling of host-disease-drug interactions in HIV disease},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-100100},
  school      = {Universit{\"a}t Potsdam},
  pages     = {121},
  year      = {2016},
  abstract  = {The human immunodeficiency virus (HIV) has resisted nearly three decades of efforts targeting a cure. Sustained suppression of the virus has remained a challenge, mainly due to the remarkable evolutionary adaptation that the virus exhibits by the accumulation of drug-resistant mutations in its genome. Current therapeutic strategies aim at achieving and maintaining a low viral burden and typically involve multiple drugs. The choice of optimal combinations of these drugs is crucial, particularly in the background of treatment failure having occurred previously with certain other drugs. An understanding of the dynamics of viral mutant genotypes aids in the assessment of treatment failure with a certain drug combination, and exploring potential salvage treatment regimens. Mathematical models of viral dynamics have proved invaluable in understanding the viral life cycle and the impact of antiretroviral drugs. However, such models typically use simplified and coarse-grained mutation schemes, that curbs the extent of their application to drug-specific clinical mutation data, in order to assess potential next-line therapies. Statistical models of mutation accumulation have served well in dissecting mechanisms of resistance evolution by reconstructing mutation pathways under different drug-environments. While these models perform well in predicting treatment outcomes by statistical learning, they do not incorporate drug effect mechanistically. Additionally, due to an inherent lack of temporal features in such models, they are less informative on aspects such as predicting mutational abundance at treatment failure. This limits their application in analyzing the pharmacology of antiretroviral drugs, in particular, time-dependent characteristics of HIV therapy such as pharmacokinetics and pharmacodynamics, and also in understanding the impact of drug efficacy on mutation dynamics. In this thesis, we develop an integrated model of in vivo viral dynamics incorporating drug-specific mutation schemes learned from clinical data. Our combined modelling approach enables us to study the dynamics of different mutant genotypes and assess mutational abundance at virological failure. As an application of our model, we estimate in vivo fitness characteristics of viral mutants under different drug environments. Our approach also extends naturally to multiple-drug therapies. Further, we demonstrate the versatility of our model by showing how it can be modified to incorporate recently elucidated mechanisms of drug action including molecules that target host factors. Additionally, we address another important aspect in the clinical management of HIV disease, namely drug pharmacokinetics. It is clear that time-dependent changes in in vivo drug concentration could have an impact on the antiviral effect, and also influence decisions on dosing intervals. We present a framework that provides an integrated understanding of key characteristics of multiple-dosing regimens including drug accumulation ratios and half-lifes, and then explore the impact of drug pharmacokinetics on viral suppression. Finally, parameter identifiability in such nonlinear models of viral dynamics is always a concern, and we investigate techniques that alleviate this issue in our setting.},
  language  = {en}
}