92-XX BIOLOGY AND OTHER NATURAL SCIENCES
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Eco-physiological processes are expressing the interaction of organisms within an environmental context of their habitat and their degree of adaptation, level of resistance as well as the limits of life in a changing environment. The present study focuses on observations achieved by methods used in this scientific discipline of “Ecophysiology” and to enlarge the scientific context in a broader range of understanding with universal character. The present eco-physiological work is building the basis for classifying and exploring the degree of habitability of another planet like Mars by a bio-driven experimentally approach. It offers also new ways of identifying key-molecules which are playing a specific role in physiological processes of tested organisms to serve as well as potential biosignatures in future space exploration missions with the goal to search for life. This has important implications for the new emerging scientific field of Astrobiology. Astrobiology addresses the study of the origin, evolution, distribution and future of life in the universe. The three fundamental questions which are hidden behind this definition are: how does life begin and evolve? Is there life beyond Earth and, if so, how can we detect it? What is the future of life on Earth and in the universe? It means that this multidisciplinary field encompasses the search for habitable environments in our Solar System and habitable planets outside our Solar System. It comprises the search for the evidence of prebiotic chemistry and life on Mars and other bodies in our Solar System like the icy moons of the Jovian and Saturnian system, laboratory and field research into the origins and early evolution of life on Earth, and studies of the potential for life to adapt to challenges on Earth and in space. For this purpose an integrated research strategy was applied, which connects field research, laboratory research allowing planetary simulation experiments with investigation enterprises performed in space (particularly performed in the low Earth Orbit.
Over the last years there is an increasing awareness that historical land cover changes and associated land use legacies may be important drivers for present-day species richness and biodiversity due to time-delayed extinctions or colonizations in response to historical environmental changes. Historically altered habitat patches may therefore exhibit an extinction debt or colonization credit and can be expected to lose or gain species in the future. However, extinction debts and colonization credits are difficult to detect and their actual magnitudes or payments have rarely been quantified because species richness patterns and dynamics are also shaped by recent environmental conditions and recent environmental changes.
In this thesis we aimed to determine patterns of herb-layer species richness and recent species richness dynamics of forest herb layer plants and link those patterns and dynamics to historical land cover changes and associated land use legacies. The study was conducted in the Prignitz, NE-Germany, where the forest distribution remained stable for the last ca. 100 years but where a) the deciduous forest area had declined by more than 90 per cent (leaving only remnants of "ancient forests"), b) small new forests had been established on former agricultural land ("post-agricultural forests"). Here, we analyzed the relative importance of land use history and associated historical land cover changes for herb layer species richness compared to recent environmental factors and determined magnitudes of extinction debt and colonization credit and their payment in ancient and post-agricultural forests, respectively.
We showed that present-day species richness patterns were still shaped by historical land cover changes that ranged back to more than a century. Although recent environmental conditions were largely comparable we found significantly more forest specialists, species with short-distance dispersal capabilities and clonals in ancient forests than in post-agricultural forests. Those species richness differences were largely contingent to a colonization credit in post-agricultural forests that ranged up to 9 species (average 4.7), while the extinction debt in ancient forests had almost completely been paid. Environmental legacies from historical agricultural land use played a minor role for species richness differences. Instead, patch connectivity was most important. Species richness in ancient forests was still dependent on historical connectivity, indicating a last glimpse of an extinction debt, and the colonization credit was highest in isolated post-agricultural forests. In post-agricultural forests that were better connected or directly adjacent to ancient forest patches the colonization credit was way smaller and we were able to verify a gradual payment of the colonization credit from 2.7 species to 1.5 species over the last six decades.
Predators can have numerical and behavioral effects on prey animals. While numerical effects are well explored, the impact of behavioral effects is unclear. Furthermore, behavioral effects are generally either analyzed with a focus on single individuals or with a focus on consequences for other trophic levels. Thereby, the impact of fear on the level of prey communities is overlooked, despite potential consequences for conservation and nature management. In order to improve our understanding of predator-prey interactions, an assessment of the consequences of fear in shaping prey community structures is crucial.
In this thesis, I evaluated how fear alters prey space use, community structure and composition, focusing on terrestrial mammals. By integrating landscapes of fear in an existing individual-based and spatially-explicit model, I simulated community assembly of prey animals via individual home range formation. The model comprises multiple hierarchical levels from individual home range behavior to patterns of prey community structure and composition. The mechanistic approach of the model allowed for the identification of underlying mechanism driving prey community responses under fear.
My results show that fear modified prey space use and community patterns. Under fear, prey animals shifted their home ranges towards safer areas of the landscape. Furthermore, fear decreased the total biomass and the diversity of the prey community and reinforced shifts in community composition towards smaller animals. These effects could be mediated by an increasing availability of refuges in the landscape. Under landscape changes, such as habitat loss and fragmentation, fear intensified negative effects on prey communities. Prey communities in risky environments were subject to a non-proportional diversity loss of up to 30% if fear was taken into account. Regarding habitat properties, I found that well-connected, large safe patches can reduce the negative consequences of habitat loss and fragmentation on prey communities. Including variation in risk perception between prey animals had consequences on prey space use. Animals with a high risk perception predominantly used safe areas of the landscape, while animals with a low risk perception preferred areas with a high food availability. On the community level, prey diversity was higher in heterogeneous landscapes of fear if individuals varied in their risk perception compared to scenarios in which all individuals had the same risk perception.
Overall, my findings give a first, comprehensive assessment of the role of fear in shaping prey communities. The linkage between individual home range behavior and patterns at the community level allows for a mechanistic understanding of the underlying processes. My results underline the importance of the structure of the landscape of fear as a key driver of prey community responses, especially if the habitat is threatened by landscape changes. Furthermore, I show that individual landscapes of fear can improve our understanding of the consequences of trait variation on community structures. Regarding conservation and nature management, my results support calls for modern conservation approaches that go beyond single species and address the protection of biotic interactions.
The concept of hydrologic connectivity summarizes all flow processes that link separate regions of a landscape. As such, it is a central theme in the field of catchment hydrology, with influence on neighboring disciplines such as ecology and geomorphology. It is widely acknowledged to be an important key in understanding the response behavior of a catchment and has at the same time inspired research on internal processes over a broad range of scales. From this process-hydrological point of view, hydrological connectivity is the conceptual framework to link local observations across space and scales.
This is the context in which the four studies this thesis comprises of were conducted. The focus was on structures and their spatial organization as important control on preferential subsurface flow. Each experiment covered a part of the conceptualized flow path from hillslopes to the stream: soil profile, hillslope, riparian zone, and stream.
For each study site, the most characteristic structures of the investigated domain and scale, such as slope deposits and peat layers were identified based on preliminary or previous investigations or literature reviews. Additionally, further structural data was collected and topographical analyses were carried out. Flow processes were observed either based on response observations (soil moisture changes or discharge patterns) or direct measurement (advective heat transport). Based on these data, the flow-relevance of the characteristic structures was evaluated, especially with regard to hillslope to stream connectivity.
Results of the four studies revealed a clear relationship between characteristic spatial structures and the hydrological behavior of the catchment. Especially the spatial distribution of structures throughout the study domain and their interconnectedness were crucial for the establishment of preferential flow paths and their relevance for large-scale processes. Plot and hillslope-scale irrigation experiments showed that the macropores of a heterogeneous, skeletal soil enabled preferential flow paths at the scale of centimeters through the otherwise unsaturated soil. These flow paths connected throughout the soil column and across the hillslope and facilitated substantial amounts of vertical and lateral flow through periglacial slope deposits.
In the riparian zone of the same headwater catchment, the connectivity between hillslopes and stream was controlled by topography and the dualism between characteristic subsurface structures and the geomorphological heterogeneity of the stream channel. At the small scale (1 m to 10 m) highest gains always occurred at steps along the longitudinal streambed profile, which also controlled discharge patterns at the large scale (100 m) during base flow conditions (number of steps per section). During medium and high flow conditions, however, the impact of topography and parafluvial flow through riparian zone structures prevailed and dominated the large-scale response patterns.
In the streambed of a lowland river, low permeability peat layers affected the connectivity between surface water and groundwater, but also between surface water and the hyporheic zone. The crucial factor was not the permeability of the streambed itself, but rather the spatial arrangement of flow-impeding peat layers, causing increased vertical flow through narrow “windows” in contrast to predominantly lateral flow in extended areas of high hydraulic conductivity sediments.
These results show that the spatial organization of structures was an important control for hydrological processes at all scales and study areas. In a final step, the observations from different scales and catchment elements were put in relation and compared. The main focus was on the theoretical analysis of the scale hierarchies of structures and processes and the direction of causal dependencies in this context. Based on the resulting hierarchical structure, a conceptual framework was developed which is capable of representing the system’s complexity while allowing for adequate simplifications.
The resulting concept of the parabolic scale series is based on the insight that flow processes in the terrestrial part of the catchment (soil and hillslopes) converge. This means that small-scale processes assemble and form large-scale processes and responses. Processes in the riparian zone and the streambed, however, are not well represented by the idea of convergence. Here, the large-scale catchment signal arrives and is modified by structures in the riparian zone, stream morphology, and the small-scale interactions between surface water and groundwater. Flow paths diverge and processes can better be represented by proceeding from large scales to smaller ones. The catchment-scale representation of processes and structures is thus the conceptual link between terrestrial hillslope processes and processes in the riparian corridor.
Neuroinflammatory and neurodegenerative diseases such as Parkinson's (PD) and multiple sclerosis (MS) often result in a severe impairment of the patient´s quality of life. Effective therapies for the treatment are currently not available, which results in a high socio-economic burden. Due to the heterogeneity of the disease subtypes, stratification is particularly difficult in the early phase of the disease and is mainly based on clinical parameters such as neurophysiological tests and central nervous imaging. Due to good accessibility and stability, blood and cerebrospinal fluid metabolite markers could serve as surrogates for neurodegenerative processes. This can lead to an improved mechanistic understanding of these diseases and further be used as "treatment response" biomarkers in preclinical and clinical development programs. Therefore, plasma and CSF metabolite profiles will be identified that allow differentiation of PD from healthy controls, association of PD with dementia (PDD) and differentiation of PD subtypes such as akinetic rigid and tremor dominant PD patients. In addition, plasma metabolites for the diagnosis of primary progressive MS (PPMS) should be investigated and tested for their specificity to relapsing-remitting MS (RRMS) and their development during PPMS progression.
By applying untargeted high-resolution metabolomics of PD patient samples and in using random forest and partial least square machine learning algorithms, this study identified 20 plasma metabolites and 14 CSF metabolite biomarkers. These differentiate against healthy individuals with an AUC of 0.8 and 0.9 in PD, respectively. We also identify ten PDD specific serum metabolites, which differentiate against healthy individuals and PD patients without dementia with an AUC of 1.0, respectively. Furthermore, 23 akinetic-rigid specific plasma markers were identified, which differentiate against tremor-dominant PD patients with an AUC of 0.94 and against healthy individuals with an AUC of 0.98. These findings also suggest more severe disease pathology in the akinetic-rigid PD than in tremor dominant PD. In the analysis of MS patient samples a partial least square analysis yielded predictive models for the classification of PPMS and resulted in 20 PPMS specific metabolites. In another MS study unknown changes in human metabolism were identified after administration of the multiple sclerosis drug dimethylfumarate, which is used for the treatment of RRMS. These results allow to describe and understand the hitherto completely unknown mechanism of action of this new drug and to use these findings for the further development of new drugs and targets against RRMS.
In conclusion, these results have the potential for improved diagnosis of these diseases and improvement of mechanistic understandings, as multiple deregulated pathways were identified. Moreover, novel Dimethylfumarate targets can be used to aid drug development and treatment efficiency. Overall, metabolite profiling in combination with machine learning identified as a promising approach for biomarker discovery and mode of action elucidation.
Biological invasions are the dispersal and following establishment of species outside their native habitat. Due to globalisation, connectivity of regions and climate changes the number of invasive species and their successful establishment is rising. The impact of these species is mostly negative, can induce community and habitat alterations, and is one main cause for biodiversity loss. This impact is particularly high and less researched in aquatic systems and microbial organisms and despite the high impact, the knowledge about overall mechanisms and specific factors affecting invasions are not fully understood. In general, the characteristics of the habitat, native community and invader determine the invasiveness.
In this thesis, I aimed to provide a better understanding of aquatic invasions focusing on the invader and its traits and identity. This thesis used a set of 12 strains of the invasive cyanobacterium <i>Cylindrospermopsis raciborskii</i> to examine the effect and impact of the invaders’ identity and genetic diversity. Further, the effect of timing on the invasion potential and success was determined, because aquatic systems in particular undergo seasonal fluctuations.
Most studies revealed a higher invasion success with increasing genetic diversity. Here, the increase of the genetic diversity, by either strain richness or phylogenetic dissimilarity, is not firstly driving the invasion, but the strain-identity. The high variability among the strains in traits important for invasions led to the highly varying strain-specific invasion success. This success was most dependent on nitrogen uptake and efficient resource use. The lower invasion success into communities comprising further N-fixing species indicates <i>C. raciborskii</i> can use this advantage only without the presence of competitive species. The relief of grazing pressure, which is suggested to be more important in aquatic invasions, was only promoting the invasion when unselective and larger consumers were present. High abundances of unselective consumers hampered the invasion success.
This indicates a more complex and temporal interplay of competitive and consumptive resistance mechanisms during the invasion process. Further, the fluctuation abundance and presence of competitors (= primary producers) and consumers (= zooplankton) in lakes can open certain ‘invasion windows’.
Remarkably, the composition of the resident community was also strain-specific affected and altered, independent of a high or low invasion success. Prior, this was only documented on the species level. Further, investigations on the population of invasive strains can reveal more about the invasion patterns and how multiple strain invasions change resident communities.
The present dissertation emphasises the importance of invader-addition experiments with a community context and the importance of the strain-level for microbial invasions and in general, e.g. for community assemblies and the outcome of experiments. The strain-specific community changes, also after days, may explain some sudden changes in communities, which have not been explained yet. This and further knowledge may also facilitate earlier and less cost-intensive management to step in, because these species are rarely tracked until they reach a high abundance or bloom, because of their small size.
Concluded for <i>C. raciborskii</i>, it shows that this species is no ‘generalistic’ invader and its invasion success depends more on the competitor presence than grazing pressure. This may explain its, still unknown, invasion pattern, as <i>C. raciborskii</i> is not found in all lakes of a region.
The interdisciplinary workshop STOCHASTIC PROCESSES WITH APPLICATIONS IN THE NATURAL SCIENCES was held in Bogotá, at Universidad de los Andes from December 5 to December 9, 2016. It brought together researchers from Colombia, Germany, France, Italy, Ukraine, who communicated recent progress in the mathematical research related to stochastic processes with application in biophysics.
The present volume collects three of the four courses held at this meeting by Angelo Valleriani, Sylvie Rœlly and Alexei Kulik.
A particular aim of this collection is to inspire young scientists in setting up research goals within the wide scope of fields represented in this volume.
Angelo Valleriani, PhD in high energy physics, is group leader of the team "Stochastic processes in complex and biological systems" from the Max-Planck-Institute of Colloids and Interfaces, Potsdam.
Sylvie Rœlly, Docteur en Mathématiques, is the head of the chair of Probability at the University of Potsdam.
Alexei Kulik, Doctor of Sciences, is a Leading researcher at the Institute of Mathematics of Ukrainian National Academy of Sciences.
Estuarine marshes are ecosystems that are situated at the transition zone between land and water and are thus controlled by physical and biological interactions. Marsh vegetation offers important ecosystem services by filtrating solid and dissolved substances from the water and providing habitat. By buffering a large part of the arriving flow velocity, attenuating wave energy and serving as erosion control for riverbanks, tidal marshes furthermore reduce the destructive effects of storm surges and storm waves and thus contribute to ecosystem-based shore protection. However, in many estuaries, extensive embankments, artificial bank protection, river dredging and agriculture threaten tidal marshes. Global warming might entail additional risks, such as changes in water levels, an increase of the tidal amplitude and a resulting shift of the salinity zones. This can affect the dynamics of the shore and foreland vegetation, and vegetation belts can be narrowed or fragmented. Against this background, it is crucial to gain a better understanding of the processes underlying the spatio temporal vegetation dynamics in brackish marshes. Furthermore, a better understanding of how plant-habitat relationships generate patterns in tidal marsh vegetation is vital to maintain ecosystem functions and assess the response of marshes to environmental change as well as the success of engineering and restoration projects.
For this purpose, three research objectives were addressed within this thesis: (1) to explore the possibility of vegetation serving as self-adaptive shore protection by quantifying the reduction of current velocity in the vegetation belt and the morphologic plasticity of a brackish marsh pioneer, (2) to disentangle the roles of abiotic factors and interspecific competition on species distribution and stand characteristics in brackish marshes, and (3) to develop a mechanistic vegetation model that helps analysing the influence of habitat conditions on the spatio-temporal dynamic of tidal marsh vegetation. These aspects were investigated using a combination of field studies and statistical as well as process-based modelling.
To explore the possibility of vegetation serving as self-adaptive coastal protection, in the first study, we measured current velocity with and without living vegetation, recorded ramet density and plant thickness during two growing periods at two locations in the Elbe estuary and assessed the adaptive value of a larger stem diameter of plants at locations with higher mechanical stress by biomechanical measurements. The results of this study show that under non-storm conditions, the vegetation belt of the marsh pioneer Bolboschoenus maritimus is able to buffer a large proportion of the flow velocity. We were furthermore able to show that morphological traits of plant species are adapted to hydrodynamic forces by demonstrating a positive correlation between ramet thickness and cross-shore current. In addition, our measurements revealed that thicker ramets growing at the front of the vegetation belt have a significantly higher stability than ramets inside the vegetation belt. This self-adaptive effect improves the ability of B. maritimus to grow and persist in the pioneer zone and could provide an adaptive value in habitats with high mechanical stress.
In the second study, we assessed the distribution of the two marsh species and a set of stand characteristics, namely aboveground and belowground biomass, ramet density, ramet height and the percentage of flowering ramets. Furthermore, we collected information on several abiotic habitat factors to test their effect on plant growth and zonation with generalised linear models (GLMs). Our results demonstrate that flow velocity is the main factor controlling the distribution of Bolboschoenus maritimus and Phragmites australis. Additionally, inundation height and duration, as well as intraspecific competition affect distribution patterns. This study furthermore shows that cross-shore flow velocity does not only directly influence the distribution of the two marsh species, but also alters the plants’ occurrence relative to inun-dation height and duration. This suggests an effect of cross-shore flow velocity on their tolerance to inundation. The analysis of the measured stand characteristics revealed a negative effect of total flow velocity on all measured parameters of B. maritimus and thus confirmed our expectation that flow velocity is a decisive stressor which influences the growth of this species.
To gain a better understanding of the processes and habitat factors influencing the spatio-temporal vegetation dynamics in brackish marshes, I built a spatially explicit, mechanistic model applying a pattern-oriented modelling approach. A sensitivity analysis of the para-meters of this dynamic habitat-macrophyte model HaMac suggests that rhizome growth is the key process for the lateral dynamics of brackish marshes. From the analysed habitat factors, P. australis patterns were mainly influenced by flow velocity. The competition with P. australis was of key importance for the belowground biomass of B. maritimus. Concerning vegetation dynamics, the model results emphasise that without the effect of flow velocity the B. maritimus vegetation belt would expand into the tidal flat at locations with present vegetation recession, suggesting that flow velocity is the main reason for vegetation recession at exposed locations.
Overall, the results of this thesis demonstrate that brackish marsh vegetation considerably contributes to flow reduction under average flow conditions and can hence be a valuable component of shore-protection schemes. At the same time, the distribution, growth and expansion of tidal marsh vegetation is substantially influenced by flow. Altogether, this thesis provides a clear step forward in understanding plant-habitat interactions in tidal marshes. Future research should integrate studies of vertical marsh accretion with research on the factors that control the lateral position of marshes.
Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles.
Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations.
Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years.
Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect.
Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication.
Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified.
Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk.
Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures.
Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.
During the drug discovery & development process, several phases encompassing a number of preclinical and clinical studies have to be successfully passed to demonstrate safety and efficacy of a new drug candidate. As part of these studies, the characterization of the drug's pharmacokinetics (PK) is an important aspect, since the PK is assumed to strongly impact safety and efficacy. To this end, drug concentrations are measured repeatedly over time in a study population. The objectives of such studies are to describe the typical PK time-course and the associated variability between subjects. Furthermore, underlying sources significantly contributing to this variability, e.g. the use of comedication, should be identified. The most commonly used statistical framework to analyse repeated measurement data is the nonlinear mixed effect (NLME) approach. At the same time, ample knowledge about the drug's properties already exists and has been accumulating during the discovery & development process: Before any drug is tested in humans, detailed knowledge about the PK in different animal species has to be collected. This drug-specific knowledge and general knowledge about the species' physiology is exploited in mechanistic physiological based PK (PBPK) modeling approaches -it is, however, ignored in the classical NLME modeling approach.
Mechanistic physiological based models aim to incorporate relevant and known physiological processes which contribute to the overlying process of interest. In comparison to data--driven models they are usually more complex from a mathematical perspective. For example, in many situations, the number of model parameters outrange the number of measurements and thus reliable parameter estimation becomes more complex and partly impossible. As a consequence, the integration of powerful mathematical estimation approaches like the NLME modeling approach -which is widely used in data-driven modeling -and the mechanistic modeling approach is not well established; the observed data is rather used as a confirming instead of a model informing and building input.
Another aggravating circumstance of an integrated approach is the inaccessibility to the details of the NLME methodology so that these approaches can be adapted to the specifics and needs of mechanistic modeling. Despite the fact that the NLME modeling approach exists for several decades, details of the mathematical methodology is scattered around a wide range of literature and a comprehensive, rigorous derivation is lacking. Available literature usually only covers selected parts of the mathematical methodology. Sometimes, important steps are not described or are only heuristically motivated, e.g. the iterative algorithm to finally determine the parameter estimates.
Thus, in the present thesis the mathematical methodology of NLME modeling is systemically described and complemented to a comprehensive description,
comprising the common theme from ideas and motivation to the final parameter estimation. Therein, new insights for the interpretation of different approximation methods used in the context of the NLME modeling approach are given and illustrated; furthermore, similarities and differences between them are outlined. Based on these findings, an expectation-maximization (EM) algorithm to determine estimates of a NLME model is described.
Using the EM algorithm and the lumping methodology by Pilari2010, a new approach on how PBPK and NLME modeling can be combined is presented and exemplified for the antibiotic levofloxacin. Therein, the lumping identifies which processes are informed by the available data and the respective model reduction improves the robustness in parameter estimation. Furthermore, it is shown how apriori known factors influencing the variability and apriori known unexplained variability is incorporated to further mechanistically drive the model development. Concludingly, correlation between parameters and between covariates is automatically accounted for due to the mechanistic derivation of the lumping and the covariate relationships.
A useful feature of PBPK models compared to classical data-driven PK models is in the possibility to predict drug concentration within all organs and tissue in the body. Thus, the resulting PBPK model for levofloxacin is used to predict drug concentrations and their variability within soft tissues which are the site of action for levofloxacin. These predictions are compared with data of muscle and adipose tissue obtained by microdialysis, which is an invasive technique to measure a proportion of drug in the tissue, allowing to approximate the concentrations in the interstitial fluid of tissues. Because, so far, comparing human in vivo tissue PK and PBPK predictions are not established, a new conceptual framework is derived. The comparison of PBPK model predictions and microdialysis measurements shows an adequate agreement and reveals further strengths of the presented new approach.
We demonstrated how mechanistic PBPK models, which are usually developed in the early stage of drug development, can be used as basis for model building in the analysis of later stages, i.e. in clinical studies. As a consequence, the extensively collected and accumulated knowledge about species and drug are utilized and updated with specific volunteer or patient data. The NLME approach combined with mechanistic modeling reveals new insights for the mechanistic model, for example identification and quantification of variability in mechanistic processes. This represents a further contribution to the learn & confirm paradigm across different stages of drug development.
Finally, the applicability of mechanism--driven model development is demonstrated on an example from the field of Quantitative Psycholinguistics to analyse repeated eye movement data. Our approach gives new insight into the interpretation of these experiments and the processes behind.