A diversity-aware computational framework for systems biology

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A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

The application of agent-based modeling and fuzzy-logic controllers for the study of magnesium biomaterials

Agent-based modeling (ABM) is a powerful approach for studying complex systems and their underlying properties by explicitly modeling the actions and interactions of individual agents. Over the past decade, numerous software programs have been developed to address the needs of the ABM community. However, these solutions often suffer from limitations in design, a lack of comprehensive documentation, or poor performance. As the first objective of this thesis, we introduce CppyABM-a general-purpose software for ABM that provides simulation tools in both Python and C++. CppyABM also enables ABM development using a combination of C++ and Python, taking advantage of the computational performance of C++ and the data analysis and visualization tools of Python. We demonstrate the capabilities of CppyABM through its application to various problems in ecology, virology, and computational biology. As the second objective of this thesis, we use ABM and fuzzy logic controllers (FLCs) to numerically study the effects of magnesium (Mg2+) ions on osteogenesis. Mg-based materials have emerged as the next generation of biomaterials that degrade in the body after implantation and eliminate the need for secondary surgery. We develop two computer models using ABM and FLC and calibrate them based on cell culture experiments. The models were able to capture the regulatory effects of Mg2+ ions and other important factors such as inflammatory cytokines on mesenchymal stem cells (MSC) activities. The models were also able to shed light on the fundamental differences in the cells cultured in different experiments such as proliferation capacity and sensitivity to environmental factors

Algorithms in nature: the convergence of systems biology and computational thinking

Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. This Perspectives discusses the recent convergence of these two ways of thinking

Advancing the cell culture landscape:the instructive potential of artificial and natural geometries

This research focuses on how surface structures can influence the behaviour of cells. There is a great diversity of surface structures, which makes the identification of an optimal physical environment for a specific phenotype difficult. Therefore, platforms that allow screening of many different designs at the same time facilitate the identification of an optimal cultural environment. Using the TopoChip, which contains 2176 unique microtopographies, structures have been identified that support the tenocyte phenotype, the primary cell type of the tendon. In addition, this also applies to mesenchymal stem cells (MSCs), which experience an activation of tendon-related genes. Furthermore, the library has been creatively expanded by using natural surface topographies that cause unique cell behaviour, such as promoting osteogenesis

THE PHARMACOGENOMICS OF EGFR-DEPENDENT NSCLC: PREDICTING AND ENHANCING RESPONSE TO TARGETED EGFR THERAPY

The introduction of tyrosine kinase inhibitors (TKI) targeting the epidermal growth factor receptor (EGFR) inhibitors to the clinic has resulted in an improvement in the treatment of non small cell lung cancer (NSCLC). However, many patients treated with EGFR TKIs do not respond to therapy. The burden of failed treatment is largely placed on the healthcare field, limiting the effectiveness of EGFR TKIs. Furthermore, responses are hindered by the emergence of resistance. Thus, two questions must be addressed to achieve maximum benefit of EGFR inhibitors: How can patients who will benefit from EGFR TKIs be selected a priori? How can patients who respond achieve maximal benefit? To answer these questions, two hypotheses were formed. First, the EGFR-dependent phenotype, which is displayed by the tumors cells of those patients who respond clinically to EGFR TKIs, can be captured by genomic profiling of NSCLC cell lines stratified by sensitivity to EGFR TKIs. This gene signature may be used to predict the outcome of EGFR TKI therapy in unknown samples. Secondly, the predictive signature of response to EGFR TKI could provide insights into the underlying biology of the phenotype of EGFR-dependency. This information could be exploited to identify inhibitors which could be combined with EGFR inhibitors to elicit a greater effect, thereby minimizing resistance. The work herein describes the testing of these hypotheses. Pharmacogenomics was utilized to define a signature of EGFR-dependency which effectively predicted response to EGFR TKI in vitro and in vivo. Furthermore, the signature was analyzed by bioinformatic approaches to identify the RAS/MAPK pathway as a candidate target in EGFR-dependent NSCLC. The RAS/MAPK pathway regulates expression and activation of EGF-like ligands. Furthermore, the RAS/MAPK pathway modulates EGFR stability in the EGFR-dependent phenotype. Further biochemical analyses demonstrated that the RAS/MAPK pathway mediates proliferation and survival of EGFR-dependent NSCLC cells. Finally, combinatorial treatment of EGFR-dependent NSCLC cell lines with small molecules targeting EGFR and the RAS/MAPK pathway yielded cytotoxic synergy. Thus, we have used pharmacogenomics methods to potentially improve NSCLC treatment by developing a method of predicting response and identifying an additional target to combine with EGFR TKIs to maximize responses

Recent Approaches Encompassing the Phenotypic Cell Heterogeneity for Anticancer Drug Efficacy Evaluation

Despite the advancements in biomarker-based personalized cancer therapy, the inadequacy of molecular and genetic profiling in identifying effective drug combinations was defined in most cases. Drug resistance remains a major limitation of the current predictive oncology. Emerging reports indicate that the success of anticancer therapy is usually limited by intratumoral heterogeneity which is not captured by the existing cancer cell biomarker-based approaches. Cell heterogeneity, not only genetic but also phenotypic, is considered to be the root cause of resistance to anticancer treatment, cancer progression, and the presence of cancer stem cells. Therefore, functional testing of live cells representing various cell types within the tumor exposed to potential therapies is needed for identification of effective drug combinations. Here we look at the different existing model systems, including ex vivo models of the patient’s tumor cells, 2D/3D in vitro cultures/cocultures, patient-derived cellular organoids, single-cell models, ex vivo tumor platforms containing tumor microenvironment and extracellular matrix, etc., scoping at drug efficacy evaluation and solving the problem of cancer resistance

An object-oriented database for the compilation of signal transduction pathways

Transpath ist ein Informationssystem fuer Signaltransduktionsnetze. Der Fokus liegt auf Signalpfaden und -kaskaden, die an der Regulation von Transkriptionsfaktoren beteiligt sind. Molekuele und Reaktionenen werden als Knoten in einem Signalgraphen aufgefasst und, zusammen mit Informationen ueber ihre Lokalisation, Qualitaet, Familienhierarchien und Signalmotive, in einer objekt-orientierten Databank gespeichert. Weiterhin werden Verweise zu anderen Datenbanken und der Originalliteratur gespeichert. Transpath unterscheidet zwischen den Zuständen eines Signalmoleküles und kann die Reaktionsmechanismen der Signalinteraktionen angemessen beschreiben. Transpath is über das World Wide Web (http://transpath.gbf.de) verfügbar durch ein Servlet-basiertes Interface, das dynamische Sichten direkt aus dem Inhalt der Datenbank erzeugt. Signalpfad-Abfragen und verschiedene Arten der Visualisierung werden zusammen mit textbasierenden Abfragen und Detailinformationen zu einzelnen Eintraegen unterstuetzt. Es wird gezeigt dass die Datenbank zur Analyse des Signalnetzes von Nutzen ist und die Grundlage fuer Simulationen liefern kann.Transpath is an information system on signal-transduction networks. It focuses on pathways involved in the regulation of transcription factors. Molecules and reactions are the nodes in a signaling graph. They are stored in an object-oriented database, together with information about their location, quality, family relationships and signaling motifs. Also stored are links to other databases and references to the original literature. Transpath differentiates between the states of a signal molecule, and can adequately describe the reaction mechanisms of signaling interactions. It is available over the web (http://transpath.gbf.de) through a Servlet-based interface, that creates dynamic content directly from the contents of the database. Pathway query mechanisms and several kinds of display are provided for the database in addition to text-based queries and information on single entries. We show that the database is useful for analysis of the signaling network and can provide the basis for simulations

EMPLOYING QUANTITATIVE SYSTEMS PHARMACOLOGY TO CHARACTERIZE DIFFERENCES IN IGF1 AND INSULIN SIGNALING PATHWAYS IN BREAST CANCER

Insulin and insulin-like growth factor I (IGF1) have been shown to influence cancer risk and progression through poorly understood mechanisms. Here, new insights on the mechanisms of differential MAPK and Akt activation are revealed by an iterative quantitative systems pharmacology approach. In the first iteration, I combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array of 21 breast cancer cell lines treated with a time course of IGF1 and insulin, I constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. Both of the knock-down perturbations caused phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. In the second iteration, mechanistic representation of IGF1 and insulin dual signaling cascades by a set of ODEs is generated by rule-based modeling. The mechanistic network modeling provided a framework to elucidate experimental targets downstream of two receptors, which were treated as indistinguishable in previous models. The model included cascades of both mitogen-activated protein kinase (MAPK) and Akt signaling, as well as the crosstalk and feedback loops in between. The parameter perturbation scanning employed for seven different models of seven cell lines yielded new experimental hypotheses on how differential responses of MAPK and Akt originate. Complementary to the first iteration, the results in this part suggested that regulation of insulin receptor substrate 1 (IRS1) is critical in inducing differential MAPK or Akt activation. Compensation and activating feedback mechanisms collectively depressed the efficacy of anti-IGF1R/InsR therapies. With the quantitative systems pharmacologic approach, the networks of signal transduction constructed in this thesis are aimed to discern novel downstream components of the IGF1R/InsR system, and to direct patients with suitable tumor subclasses to efficient personalized clinical interventions