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ResearcherID
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In this work models for molecular networks consisting of ordinary differential equations are extended by terms that include the interaction of the corresponding molecular network with the environment that the molecular network is embedded in. These terms model the effects of the external stimuli on the molecular network. The usability of this extension is demonstrated with a model of a circadian clock that is extended with certain terms and reproduces data from several experiments at the same time.
Once the model including external stimuli is set up, a framework is developed in order to calculate external stimuli that have a predefined desired effect on the molecular network. For this purpose the task of finding appropriate external stimuli is formulated as a mathematical optimal control problem for which in order to solve it a lot of mathematical methods are available. Several methods are discussed and worked out in order to calculate a solution for the corresponding optimal control problem. The application of the framework to find pharmacological intervention points or effective drug combinations is pointed out and discussed. Furthermore the framework is related to existing network analysis tools and their combination for network analysis in order to find dedicated external stimuli is discussed.
The total framework is verified with biological examples by comparing the calculated results with data from literature. For this purpose platelet aggregation is investigated based on a corresponding gene regulatory network and associated receptors are detected. Furthermore a transition from one to another type of T-helper cell is analyzed in a tumor setting where missing agents are calculated to induce the corresponding switch in vitro. Next a gene regulatory network of a myocardiocyte is investigated where it is shown how the presented framework can be used to compare different treatment strategies with respect to their beneficial effects and side effects quantitatively. Moreover a constitutively activated signaling pathway, which thus causes maleficent effects, is modeled and intervention points with corresponding treatment strategies are determined that steer the gene regulatory network from a pathological expression pattern to physiological one again.
The culture of human induced pluripotent stem cells (hiPSCs) at large-scale becomes feasible with the aid of scalable suspension setups in continuously stirred tank reactors (CSTRs). Suspension cul- tures of hiPSCs are characterized by the self-aggregation of single cells into macroscopic cell aggre- gates that increase in size over time. The development of these free-floating aggregates is dependent on the culture vessel and thus represents a novel process parameter that is of particular interest for hiPSC suspension culture scaling. Further, aggregates surpassing a critical size are prone to spon- taneous differentiation or cell viability loss. In this regard, and, for the first time, a hiPSC-specific suspension culture unit was developed that utilizes in situ microscope imaging to monitor and to characterize hiPSC aggregation in one specific CSTR setup to a statistically significant degree while omitting the need for error-prone and time-intensive sampling. For this purpose, a small-scale CSTR system was designed and fabricated by fused deposition modeling (FDM) using an in-house 3D- printer. To provide a suitable cell culture environment for the CSTR system and in situ microscope, a custom-built incubator was constructed to accommodate all culture vessels and process control devices. Prior to manufacture, the CSTR design was characterized in silico for standard engineering parameters such as the specific power input, mixing time, and shear stress using computational fluid dynamics (CFD) simulations. The established computational model was successfully validated by comparing CFD-derived mixing time data to manual measurements. Proof for system functionality was provided in the context of long-term expansion (4 passages) of hiPSCs. Thereby, hiPSC aggregate size development was successfully tracked by in situ imaging of CSTR suspensions and subsequent automated image processing. Further, the suitability of the developed hiPSC culture unit was proven by demonstrating the preservation of CSTR-cultured hiPSC pluripotency on RNA level by qRT-PCR and PluriTest, and on protein level by flow cytometry.
The measurement of the mass of the $W$ boson is currently one of the most promising precision analyses of the Standard Model, that could ultimately reveal a hint for new physics.
The mass of the $W$ boson is determined by comparing the $W$ boson, which cannot be reconstructed directly, to the $Z$ boson, where the full decay signature is available. With the help of Monte Carlo simulations one can extrapolate from the $Z$ boson to the $W$ boson.
Technically speaking, the measurement of the $W$ boson mass is performed by comparing data taken by the ATLAS experiment to a set of calibrated Monte Carlo simulations, which reflect different mass hypotheses.\
A dedicated calibration of the reconstructed objects in the simulations is crucial for a high precision of the measured value.
The comparison of simulated $Z$ boson events to reconstructed $Z$ boson candidates in data allows to derive event weights and scale factors for the calibration.
This thesis presents a new approach to reweight the hadronic recoil in the simulations. The focus of the calibration is on the average hadronic activity visible in the mean of the scalar sum of the hadronic recoil $\Sigma E_T$ as a function of pileup. In contrast to the standard method, which directly reweights the scalar sum, the dependency to the transverse boson momentum is less strongly affected here.
The $\Sigma E_T$ distribution is modeled first by means of its pileup dependency. Then, the remaining differences in the resolution of the vector sum of the hadronic recoil are scaled. This is done separately for the parallel and the pterpendicular component of the hadronic recoil with respect to the reconstructed boson.
This calibration was developed for the dataset taken by the ATLAS experiment at a center of mass energy of $8\,\textrm{TeV}$ in 2012. In addition, the same reweighting procedure is applied to the recent dataset with a low pileup contribution, the \textit{lowMu} runs at $5\,\textrm{TeV}$ and at $13\,\textrm{TeV}$, taken by ATLAS in November 2017. The dedicated aspects of the reweighting procedure are presented in this thesis. It can be shown that this reweighting approach improves the agreement between data and the simulations effectively for all datasets.
The uncertainties of this reweighting approach as well as the statistical errors are evaluated for a $W$ mass measurement by a template fit to pseudodata for the \textit{lowMu} dataset. A first estimate of these uncertainties is given here. For the pfoEM algorithm a statistical uncertainty of $17\,\text{MeV}$ for the $5\,\textrm{TeV}$ dataset and of $18\,\text{MeV}$ for the $13\,\textrm{TeV}$ are found for the $W \rightarrow \mu \nu$ analysis. The systematic uncertainty introduced by the resolution scaling has the largest effect, a value of $15\,\text{MeV}$ is estimated for the $13\,\textrm{TeV}$ dataset in the muon channel.
This thesis deals with a new so-called sequential quadratic Hamiltonian (SQH) iterative scheme to solve optimal control problems with differential models and cost functionals ranging from smooth to discontinuous and non-convex. This scheme is based on the Pontryagin maximum principle (PMP) that provides necessary optimality conditions for an optimal solution. In this framework, a Hamiltonian function is defined that attains its minimum pointwise at the optimal solution of the corresponding optimal control problem. In the SQH scheme, this Hamiltonian function is augmented by a quadratic penalty term consisting of the current control function and the control function from the previous iteration. The heart of the SQH scheme is to minimize this augmented Hamiltonian function pointwise in order to determine a control update. Since the PMP does not require any differ- entiability with respect to the control argument, the SQH scheme can be used to solve optimal control problems with both smooth and non-convex or even discontinuous cost functionals. The main achievement of the thesis is the formulation of a robust and efficient SQH scheme and a framework in which the convergence analysis of the SQH scheme can be carried out. In this framework, convergence of the scheme means that the calculated solution fulfills the PMP condition. The governing differential models of the considered optimal control problems are ordinary differential equations (ODEs) and partial differential equations (PDEs). In the PDE case, elliptic and parabolic equations as well as the Fokker-Planck (FP) equation are considered. For both the ODE and the PDE cases, assumptions are formulated for which it can be proved that a solution to an optimal control problem has to fulfill the PMP. The obtained results are essential for the discussion of the convergence analysis of the SQH scheme. This analysis has two parts. The first one is the well-posedness of the scheme which means that all steps of the scheme can be carried out and provide a result in finite time. The second part part is the PMP consistency of the solution. This means that the solution of the SQH scheme fulfills the PMP conditions. In the ODE case, the following results are obtained that state well-posedness of the SQH scheme and the PMP consistency of the corresponding solution. Lemma 7 states the existence of a pointwise minimum of the augmented Hamiltonian. Lemma 11 proves the existence of a weight of the quadratic penalty term such that the minimization of the corresponding augmented Hamiltonian results in a control updated that reduces the value of the cost functional. Lemma 12 states that the SQH scheme stops if an iterate is PMP optimal. Theorem 13 proves the cost functional reducing properties of the SQH control updates. The main result is given in Theorem 14, which states the pointwise convergence of the SQH scheme towards a PMP consistent solution. In this ODE framework, the SQH method is applied to two optimal control problems. The first one is an optimal quantum control problem where it is shown that the SQH method converges much faster to an optimal solution than a globalized Newton method. The second optimal control problem is an optimal tumor treatment problem with a system of coupled highly non-linear state equations that describe the tumor growth. It is shown that the framework in which the convergence of the SQH scheme is proved is applicable for this highly non-linear case. Next, the case of PDE control problems is considered. First a general framework is discussed in which a solution to the corresponding optimal control problem fulfills the PMP conditions. In this case, many theoretical estimates are presented in Theorem 59 and Theorem 64 to prove in particular the essential boundedness of the state and adjoint variables. The steps for the convergence analysis of the SQH scheme are analogous to that of the ODE case and result in Theorem 27 that states the PMP consistency of the solution obtained with the SQH scheme. This framework is applied to different elliptic and parabolic optimal control problems, including linear and bilinear control mechanisms, as well as non-linear state equations. Moreover, the SQH method is discussed for solving a state-constrained optimal control problem in an augmented formulation. In this case, it is shown in Theorem 30 that for increasing the weight of the augmentation term, which penalizes the violation of the state constraint, the measure of this state constraint violation by the corresponding solution converges to zero. Furthermore, an optimal control problem with a non-smooth L\(^1\)-tracking term and a non-smooth state equation is investigated. For this purpose, an adjoint equation is defined and the SQH method is used to solve the corresponding optimal control problem. The final part of this thesis is devoted to a class of FP models related to specific stochastic processes. The discussion starts with a focus on random walks where also jumps are included. This framework allows a derivation of a discrete FP model corresponding to a continuous FP model with jumps and boundary conditions ranging from absorbing to totally reflecting. This discussion allows the consideration of the drift-control resulting from an anisotropic probability of the steps of the random walk. Thereafter, in the PMP framework, two drift-diffusion processes and the corresponding FP models with two different control strategies for an optimal control problem with an expectation functional are considered. In the first strategy, the controls depend on time and in the second one, the controls depend on space and time. In both cases a solution to the corresponding optimal control problem is characterized with the PMP conditions, stated in Theorem 48 and Theorem 49. The well-posedness of the SQH scheme is shown in both cases and further conditions are discussed that ensure the convergence of the SQH scheme to a PMP consistent solution. The case of a space and time dependent control strategy results in a special structure of the corresponding PMP conditions that is exploited in another solution method, the so-called direct Hamiltonian (DH) method.
This work deals with the development and application of novel quantum Monte Carlo methods to simulate fermion-boson models. Our developments are based on the path-integral formalism, where the bosonic degrees of freedom are integrated out exactly to obtain a retarded fermionic interaction. We give an overview of three methods that can be used to simulate retarded interactions. In particular, we develop a novel quantum Monte Carlo method with global directed-loop updates that solves the autocorrelation problem of previous approaches and scales linearly with system size. We demonstrate its efficiency for the Peierls transition in the Holstein model and discuss extensions to other fermion-boson models as well as spin-boson models. Furthermore, we show how with the help of generating functionals bosonic observables can be recovered directly from the Monte Carlo configurations. This includes estimators for the boson propagator, the fidelity susceptibility, and the specific heat of the Holstein model. The algorithmic developments of this work allow us to study the specific heat of the spinless Holstein model covering its entire parameter range. Its key features are explained from the single-particle spectral functions of electrons and phonons. In the adiabatic limit, the spectral properties are calculated exactly as a function of temperature using a classical Monte Carlo method and compared to results for the Su-Schrieffer-Heeger model.
The importance of Clinical Data Warehouses (CDW) has increased significantly in recent years as they support or enable many applications such as clinical trials, data mining, and decision making.
CDWs integrate Electronic Health Records which still contain a large amount of text data, such as discharge letters or reports on diagnostic findings in addition to structured and coded data like ICD-codes of diagnoses.
Existing CDWs hardly support features to gain information covered in texts.
Information extraction methods offer a solution for this problem but they have a high and long development effort, which can only be carried out by computer scientists.
Moreover, such systems only exist for a few medical domains.
This paper presents a method empowering clinicians to extract information from texts on their own. Medical concepts can be extracted ad hoc from e.g. discharge letters, thus physicians can work promptly and autonomously. The proposed system achieves these improvements by efficient data storage, preprocessing, and with powerful query features. Negations in texts are recognized and automatically excluded, as well as the context of information is determined and undesired facts are filtered, such as historical events or references to other persons (family history).
Context-sensitive queries ensure the semantic integrity of the concepts to be extracted.
A new feature not available in other CDWs is to query numerical concepts in texts and even filter them (e.g. BMI > 25).
The retrieved values can be extracted and exported for further analysis.
This technique is implemented within the efficient architecture of the PaDaWaN CDW and evaluated with comprehensive and complex tests.
The results outperform similar approaches reported in the literature.
Ad hoc IE determines the results in a few (milli-) seconds and a user friendly GUI enables interactive working, allowing flexible adaptation of the extraction.
In addition, the applicability of this system is demonstrated in three real-world applications at the Würzburg University Hospital (UKW).
Several drug trend studies are replicated: Findings of five studies on high blood pressure, atrial fibrillation and chronic renal failure can be partially or completely confirmed in the UKW. Another case study evaluates the prevalence of heart failure in inpatient hospitals using an algorithm that extracts information with ad hoc IE from discharge letters and echocardiogram report (e.g. LVEF < 45 ) and other sources of the hospital information system.
This study reveals that the use of ICD codes leads to a significant underestimation (31%) of the true prevalence of heart failure.
The third case study evaluates the consistency of diagnoses by comparing structured ICD-10-coded diagnoses with the diagnoses described in the diagnostic section of the discharge letter.
These diagnoses are extracted from texts with ad hoc IE, using synonyms generated with a novel method.
The developed approach can extract diagnoses from the discharge letter with a high accuracy and furthermore it can prove the degree of consistency between the coded and reported diagnoses.
Insulin-like growth factor-I (IGF-I) is a 70-amino acid polypeptide with a molecular weight of approximately 7.6 kDa acting as an anabolic effector. It is essential for tissue growth and remodeling. Clinically, it is used for the treatment of growth disorders and has been proposed for various other applications including musculoskeletal diseases. Unlike insulin, IGF-I is complexed to at least six high-affinity binding proteins (IGFBPs) exerting homeostatic effects by modulating IGF-I availability to its receptor (IGF-IR) on most cells in the body as well as changing the distribution of the growth factor within the organism.1-3 Short half-lived IGF-I have been the driving forces for the design of localized IGF-I depot systems or protein modification with enhanced pharmacokinetic properties. In this thesis, we endeavor to present a versatile biologic into which galenical properties were engineered through chemical synthesis, e.g., by site-specific coupling of biomaterials or complex composites to IGF-I. For that, we redesigned the therapeutic via genetic codon expansion resulting in an alkyne introduced IGF-I, thereby becoming a substrate for biorthogonal click chemistries yielding a site-specific decoration.
In this approach, an orthogonal pyrrolysine tRNA synthetase (PylRS)/tRNAPyl CUA pair was employed to direct the co-translational incorporation of an unnatural amino acid—¬propargyl-L-lysine (plk)—bearing a clickable alkyne functional handle into IGF-I in response to the amber stop codon (UAG) introduced into the defined position in the gene of interest. We summarized the systematic optimization of upstream and downstream process alike with the ultimate goal to increase the yield of plk modified IGF-I therapeutic, from the construction of gene fusions resulting in (i) Trx-plk-IGF-I fusion variants, (ii) naturally occurring pro-IGF-I protein (IGF-I + Ea peptide) (plk-IGF-I Ea), over the subsequent bacterial cultivation and protein extraction to the final chromatographic purification. The opportunities and hurdles of all of the above strategies were discussed. Evidence was provided that the wild-type IGF-I yields were pure by exploiting the advantages of the pHisTrx expression vector system in concert with a thrombin enzyme with its highly specific proteolytic digestion site and multiple-chromatography steps. The alkyne functionality was successfully introduced into IGF-I by amber codon suppression. The proper folding of plk-IGF-I Ea was assessed by WST-1 proliferation assay and the detection of phosphorylated AKT in MG-63 cell lysate. The purity of plk-IGF-I Ea was monitored with RP-HPLC and SDS-PAGE analysis. This work also showed site-specific coupling an alkyne in plk-IGF-I Ea by copper (I)-catalyzed azide-alkyne cycloaddition (CuAAC) with potent activities in vitro. The site-specific immobilization of plk-IGF-I Ea to the model carrier (i.e., agarose beads) resulted in enhanced cell proliferation and adhesion surrounding the IGF-I-presenting particles. Cell proliferation and differentiation were enhanced in the accessibility of IGF-I decorated beads, reflecting the multivalence on cellular performance.
Next, we aimed at effectively showing the disease environment by co-delivery of fibroblast growth factor 2 (FGF2) and IGF-I, deploying localized matrix metalloproteinases (MMPs) upregulation as a surrogate marker driving the response of the drug delivery system. For this purpose, we genetically engineered FGF2 variant containing an (S)-2-amino-6-(((2-azidoethoxy)carbonyl)amino)hexanoic acid incorporated at its N-terminus, followed by an MMPs-cleavable linker (PCL) and FGF2 sequence, thereby allowing site-directed, specific decoration of the resultant azide-PCL-FGF2 with the previously mentioned plk-IGF-I Ea to generate defined protein-protein conjugates with a PCL in between. The click reaction between plk-IGF-I Ea and azide-PCL-FGF2 was systematically optimized to increase the yield of IGF-FGF conjugates, including reaction temperature, incubation duration, the addition of anionic detergent, and different ratios of the participating biopharmaceutics. The challenge here was that CuAAC reaction components or conditions might oxidize free cysteines of azide-PCL-FGF2 and future work needs to present the extent of activity retention after conjugation. Furthermore, our study provides potential options for dual-labeling of IGF-I either by the introduction of unnatural amino acids within two distinct positions of the protein of interest for parallel “double-click” labeling of the resultant plk-IGF-I Ea-plk or by using a combination of enzymatic-catalyzed and CuAAC bioorthogonal coupling strategies for sequentially dual-labeling of plk-IGF-I Ea.
In conclusion, genetic code expansion in combination with click-chemistry provides the fundament for novel IGF-I analogs allowing unprecedented site specificity for decoration. Considerable progress towards IGF-I based therapies with enhanced pharmacological properties was made by demonstrating the feasibility of the expression of plk incorporated IGF-I using E. coli and retained activity of unconjugated and conjugated IGF-I variant. Dual-labeling of IGF-I provides further insights into the functional requirements of IGF-I. Still, further investigation warrants to develop precise IGF-I therapy through unmatched temporal and spatial regulation of the pleiotropic IGF-I.
Advanced Analytics in Operations Management and Information Systems: Methods and Applications
(2019)
The digital transformation of business and society presents enormous potentials for companies across all sectors. Fueled by massive advances in data generation, computing power, and connectivity, modern organizations have access to gigantic amounts of data. Companies seek to establish data-driven decision cultures to leverage competitive advantages in terms of efficiency and effectiveness. While most companies focus on descriptive tools such as reporting, dashboards, and advanced visualization, only a small fraction already leverages advanced analytics (i.e., predictive and prescriptive analytics) to foster data-driven decision-making today. Therefore, this thesis set out to investigate potential opportunities to leverage prescriptive analytics in four different independent parts.
As predictive models are an essential prerequisite for prescriptive analytics, the first two parts of this work focus on predictive analytics. Building on state-of-the-art machine learning techniques, we showcase the development of a predictive model in the context of capacity planning and staffing at an IT consulting company. Subsequently, we focus on predictive analytics applications in the manufacturing sector. More specifically, we present a data science toolbox providing guidelines and best practices for modeling, feature engineering, and model interpretation to manufacturing decision-makers. We showcase the application of this toolbox on a large data-set from a German manufacturing company.
Merely using the improved forecasts provided by powerful predictive models enables decision-makers to generate additional business value in some situations. However, many complex tasks require elaborate operational planning procedures. Here, transforming additional information into valuable actions requires new planning algorithms. Therefore, the latter two parts of this thesis focus on prescriptive analytics. To this end, we analyze how prescriptive analytics can be utilized to determine policies for an optimal searcher path problem based on predictive models. While rapid advances in artificial intelligence research boost the predictive power of machine learning models, a model uncertainty remains in most settings. The last part of this work proposes a prescriptive approach that accounts for the fact that predictions are imperfect and that the arising uncertainty needs to be considered. More specifically, it presents a data-driven approach to sales-force scheduling. Based on a large data set, a model to predictive the benefit of additional sales effort is trained. Subsequently, the predictions, as well as the prediction quality, are embedded into the underlying team orienteering problem to determine optimized schedules.
Since its first experimental implementation in 2005, single-molecule localization microscopy (SMLM) emerged as a versatile and powerful imaging tool for biological structures with nanometer resolution. By now, SMLM has compiled an extensive track-record of novel insights in sub- and inter- cellular organization.\\
Moreover, since all SMLM techniques rely on the analysis of emission patterns from isolated fluorophores, they inherently allocate molecular information $per$ $definitionem$.\\
Consequently, SMLM transitioned from its origin as pure high-resolution imaging instrument towards quantitative microscopy, where the key information medium is no longer the highly resolved image itself, but the raw localization data set.\\
The work presented in this thesis is part of the ongoing effort to translate those $per$ $se$ molecular information gained by SMLM imaging to insights into the structural organization of the targeted protein or even beyond. Although largely consistent in their objectives, the general distinction between global or segmentation clustering approaches on one side and particle averaging or meta-analyses techniques on the other is usually made.\\
During the course of my thesis, I designed, implemented and employed numerous quantitative approaches with varying degrees of complexity and fields of application.\\ \\
In my first major project, I analyzed the localization distribution of the integral protein gp210 of the nuclear pore complex (NPC) with an iterative \textit{k}-means algorithm. Relating the distinct localization statistics of separated gp210 domains to isolated fluorescent signals led, among others, to the conclusion that the anchoring ring of the NPC consists of 8 homo-dimers of gp210.\\
This is of particular significance, both because it answered a decades long standing question about the nature of the gp210 ring and it showcased the possibility to gain structural information well beyond the resolution capabilities of SMLM by crafty quantification approaches.\\ \\
The second major project reported comprises an extensive study of the synaptonemal complex (SNC) and linked cohesin complexes. Here, I employed a multi-level meta-analysis of the localization sets of various SNC proteins to facilitate the compilation of a novel model of the molecular organization of the major SNC components with so far unmatched extend and detail with isotropic three-dimensional resolution.\\
In a second venture, the two murine cohesin components SMC3 and STAG3 connected to the SNC were analyzed. Applying an adapted algorithm, considering the disperse nature of cohesins, led to the realization that there is an apparent polarization of those cohesin complexes in the SNC, as well as a possible sub-structure of STAG3 beyond the resolution capabilities of SMLM.\\ \\
Other minor projects connected to localization quantification included the study of plasma membrane glycans regarding their overall localization distribution and particular homogeneity as well as the investigation of two flotillin proteins in the membrane of bacteria, forming clusters of distinct shapes and sizes.\\ \\
Finally, a novel approach to three-dimensional SMLM is presented, employing the precise quantification of single molecule emitter intensities. This method, named TRABI, relies on the principles of aperture photometry which were improved for SMLM.\\
With TRABI it was shown, that widely used Gaussian fitting based localization software underestimates photon counts significantly. This mismatch was utilized as a $z$-dependent parameter, enabling the conversion of 2D SMLM data to a virtual 3D space. Furthermore it was demonstrated, that TRABI can be combined beneficially with a multi-plane detection scheme, resulting in superior performance regarding axial localization precision and resolution.\\
Additionally, TRABI has been subsequently employed to photometrically characterize a novel dye for SMLM, revealing superior photo-physical properties at the single-molecule level.\\
Following the conclusion of this thesis, the TRABI method and its applications remains subject of diverse ongoing research.
Maps are the main tool to represent geographical information. Users often zoom in and out to access maps at different scales. Continuous map generalization tries to make the changes between different scales smooth, which is essential to provide users with comfortable zooming experience.
In order to achieve continuous map generalization with high quality, we optimize some important aspects of maps. In this book, we have used optimization in the generalization of land-cover areas, administrative boundaries, buildings, and coastlines. According to our experiments, continuous map generalization indeed benefits from optimization.