Refine
Year of publication
- 2014 (222) (remove)
Document Type
- Doctoral Thesis (222) (remove)
Keywords
- Gammastrahlungsastronomie (4)
- data analysis (4)
- gamma-ray astronomy (4)
- Crab Nebula (3)
- Datenanalyse (3)
- Krebsnebel (3)
- Synchronisation (3)
- Systembiologie (3)
- synchronization (3)
- systems biology (3)
Institute
- Institut für Biochemie und Biologie (37)
- Institut für Physik und Astronomie (25)
- Institut für Geowissenschaften (19)
- Wirtschaftswissenschaften (15)
- Institut für Chemie (13)
- Historisches Institut (12)
- Institut für Informatik und Computational Science (10)
- Department Psychologie (9)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (9)
- Institut für Ernährungswissenschaft (8)
Monoclonal antibodies (mAbs) are engineered immunoglobulins G (IgG) used for more than 20 years as targeted therapy in oncology, infectious diseases and (auto-)immune disorders. Their protein nature greatly influences their pharmacokinetics (PK), presenting typical linear and non-linear behaviors.
While it is common to use empirical modeling to analyze clinical PK data of mAbs, there is neither clear consensus nor guidance to, on one hand, select the structure of classical compartment models and on the other hand, interpret mechanistically PK parameters. The mechanistic knowledge present in physiologically-based PK (PBPK) models is likely to support rational classical model selection and thus, a methodology to link empirical and PBPK models is desirable. However, published PBPK models for mAbs are quite diverse in respect to the physiology of distribution spaces and the parameterization of the non-specific elimination involving the neonatal Fc receptor (FcRn) and endogenous IgG (IgGendo). The remarkable discrepancy between the simplicity of biodistribution data and the complexity of published PBPK models translates in parameter identifiability issues.
In this thesis, we address this problem with a simplified PBPK model—derived from a hierarchy of more detailed PBPK models and based on simplifications of tissue distribution model. With the novel tissue model, we are breaking new grounds in mechanistic modeling of mAbs disposition: We demonstrate that binding to FcRn is indeed linear and that it is not possible to infer which tissues are involved in the unspecific elimination of wild-type mAbs. We also provide a new approach to predict tissue partition coefficients based on mechanistic insights: We directly link tissue partition coefficients (Ktis) to data-driven and species-independent published antibody biodistribution coefficients (ABCtis) and thus, we ensure the extrapolation from pre-clinical species to human with the simplified PBPK model. We further extend the simplified PBPK model to account for a target, relevant to characterize the non-linear clearance due to mAb-target interaction.
With model reduction techniques, we reduce the dimensionality of the simplified PBPK model to design 2-compartment models, thus guiding classical model development with physiological and mechanistic interpretation of the PK parameters. We finally derive a new scaling approach for anatomical and physiological parameters in PBPK models that translates the inter-individual variability into the design of mechanistic covariate models with direct link to classical compartment models, specially useful for PK population analysis during clinical development.
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.
During this work I built a four wave mixing setup for the time-resolved femtosecond spectroscopy of Raman-active lattice modes. This setup enables to study the selective excitation of phonon polaritons. These quasi-particles arise from the coupling of electro-magnetic waves and transverse optical lattice modes, the so-called phonons. The phonon polaritons were investigated in the optically non-linear, ferroelectric crystals LiNbO₃ and LiTaO₃.
The direct observation of the frequency shift of the scattered narrow bandwidth probe pulses proofs the role of the Raman interaction during the probe and excitation process of phonon polaritons. I compare this experimental method with the measurement where ultra-short laser pulses are used. The frequency shift remains obscured by the relative broad bandwidth of these laser pulses. In an experiment with narrow bandwidth probe pulses, the Stokes and anti-Stokes intensities are spectrally separated. They are assigned to the corresponding counter-propagating wavepackets of phonon polaritons. Thus, the dynamics of these wavepackets was separately studied. Based on these findings, I develop the mathematical description of the so-called homodyne detection of light for the case of light scattering from counter propagating phonon polaritons.
Further, I modified the broad bandwidth of the ultra-short pump pulses using bandpass filters to generate two pump pulses with non-overlapping spectra. This enables the frequency-selective excitation of polariton modes in the sample, which allows me to observe even very weak polariton modes in LiNbO₃ or LiTaO₃ that belong to the higher branches of the dispersion relation of phonon polaritons. The experimentally determined dispersion relation of the phonon polaritons could therefore be extended and compared to theoretical models. In addition, I determined the frequency-dependent damping of phonon polaritons.
Scientific inquiry requires that we formulate not only what we know, but also what we do not know and by how much. In climate data analysis, this involves an accurate specification of measured quantities and a consequent analysis that consciously propagates the measurement errors at each step. The dissertation presents a thorough analytical method to quantify errors of measurement inherent in paleoclimate data. An additional focus are the uncertainties in assessing the coupling between different factors that influence the global mean temperature (GMT).
Paleoclimate studies critically rely on `proxy variables' that record climatic signals in natural archives. However, such proxy records inherently involve uncertainties in determining the age of the signal. We present a generic Bayesian approach to analytically determine the proxy record along with its associated uncertainty, resulting in a time-ordered sequence of correlated probability distributions rather than a precise time series. We further develop a recurrence based method to detect dynamical events from the proxy probability distributions. The methods are validated with synthetic examples and
demonstrated with real-world proxy records. The proxy estimation step reveals the interrelations between proxy variability and uncertainty. The recurrence analysis of the East Asian Summer Monsoon during the last 9000 years confirms the well-known `dry' events at 8200 and 4400 BP, plus an additional significantly dry event at 6900 BP.
We also analyze the network of dependencies surrounding GMT. We find an intricate, directed network with multiple links between the different factors at multiple time delays. We further uncover a significant feedback from the GMT to the El Niño Southern Oscillation at quasi-biennial timescales. The analysis highlights the need of a more nuanced formulation of influences between different climatic factors, as well as the limitations in trying to estimate such dependencies.