Reproductive biology of Faidherbia albida (Del.) A. Chev.

Abstract

Mechanistic population Models in Biology: Model Derivation and Application

In general, models of ecological systems can be broadly categorized as ’top-down’ or ’bottom-up’ models, based on the hierarchical level that the model processes are formulated on. The structure of a top-down, also known as phenomenological, population model can be interpreted in terms of population characteristics, but it typically lacks an interpretation on a more basic level. In contrast, bottom-up, also known as mechanistic, population models are derived from assumptions and processes on a more basic level, which allows interpretation of the model parameters in terms of individual behavior. Both approaches, phenomenological and mechanistic modelling, can have their advantages and disadvantages in different situations. However, mechanistically derived models might be better at capturing the properties of the system at hand, and thus give more accurate predictions. In particular, when models are used for evolutionary studies, mechanistic models are more appropriate, since natural selection takes place on the individual level, and in mechanistic models the direct connection between model parameters and individual properties has already been established. The purpose of this thesis is twofold. Firstly, a systematical way to derive mechanistic discrete-time population models is presented. The derivation is based on combining explicitly modelled, continuous processes on the individual level within a reproductive period with a discrete-time maturation process between reproductive periods. Secondly, as an example of how evolutionary studies can be carried out in mechanistic models, the evolution of the timing of reproduction is investigated. Thus, these two lines of research, derivation of mechanistic population models and evolutionary studies, are complementary to each other.

A cell spot microarray method for high-throughput biology

High-throughput screening of cellular effects of RNA interference (RNAi) libraries is now being increasingly applied to explore the role of genes in specific cell biological processes and disease states. However, the technology is still limited to specialty laboratories, due to the requirements for robotic infrastructure, access to expensive reagent libraries, expertise in high-throughput screening assay development, standardization, data analysis and applications. In the future, alternative screening platforms will be required to expand functional large-scale experiments to include more RNAi constructs, allow combinatorial loss-of-function analyses (e.g. genegene or gene-drug interaction), gain-of-function screens, multi-parametric phenotypic readouts or comparative analysis of many different cell types. Such comprehensive perturbation of gene networks in cells will require a major increase in the flexibility of the screening platforms, throughput and reduction of costs. As an alternative for the conventional multi-well based high-throughput screening -platforms, here the development of a novel cell spot microarray method for production of high density siRNA reverse transfection arrays is described. The cell spot microarray platform is distinguished from the majority of other transfection cell microarray techniques by the spatially confined array layout that allow highly parallel screening of large-scale RNAi reagent libraries with assays otherwise difficult or not applicable to high-throughput screening. This study depicts the development of the cell spot microarray method along with biological application examples of high-content immunofluorescence and phenotype based cancer cell biological analyses focusing on the regulation of prostate cancer cell growth, maintenance of genomic integrity in breast cancer cells, and functional analysis of integrin protein-protein interactions in situ.

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.

Chemical Biology Screen for Prostate Cancer Therapeutics

Prostate cancer initially responds to hormone-based therapeutics such as anti-androgen treatment or chemotherapeutics but eventually becomes resistant. Novel treatment options are therefore urgently needed. This thesis study applied a high-throughput screen of 4910 known drugs and drug-like small molecules to identify compounds that selectively inhibit growth of prostate cancer cells. In addition, the mechanisms underlying the cellular sensitivity to potent cancer selective compounds were addressed. Surprisingly, many of the compounds currently used in the clinics or studied in clinical trials were not cancer-selective. Only four drugs, aldehyde dehydrogenase inhibitor disulfiram (Antabus), antibiotic ionophore monensin, histone deacetylase inhibitor tricostatin A and fungicide thiram inhibited prostate cancer cell growth at nanomolar concentrations without major effects on non-malignant prostate epithelial cells. Disulfiram, monensin and a structurally similar compound to monensin, salinomycin, induced oxidative stress and inhibited aldehyde dehydrogenase activity. Moreover, monensin and salinomycin reduced androgen receptor signalling and steroidogenesis, enforced cell differentiation and reduced the overall levels of cancer stem cells. Taken together, novel and potentially prostate cancer-selective therapeutic agents were identified in this study, including the description of a multitude of intoxicating mechanisms such as those relating to oxidative stress. The results provide novel insights into prostate cancer biology and exemplify useful means of considering novel approaches to cancer treatment.

A Farewell to Flat Biology. Three-dimensional Cell Culture Models in Cancer Drug Target Identification and Validation

Cells of epithelial origin, e.g. from breast and prostate cancers, effectively differentiate into complex multicellular structures when cultured in three-dimensions (3D) instead of conventional two-dimensional (2D) adherent surfaces. The spectrum of different organotypic morphologies is highly dependent on the culture environment that can be either non-adherent or scaffold-based. When embedded in physiological extracellular matrices (ECMs), such as laminin-rich basement membrane extracts, normal epithelial cells differentiate into acinar spheroids reminiscent of glandular ductal structures. Transformed cancer cells, in contrast, typically fail to undergo acinar morphogenic patterns, forming poorly differentiated or invasive multicellular structures. The 3D cancer spheroids are widely accepted to better recapitulate various tumorigenic processes and drug responses. So far, however, 3D models have been employed predominantly in the Academia, whereas the pharmaceutical industry has yet to adopt a more widely and routine use. This is mainly due to poor characterisation of cell models, lack of standardised workflows and high throughput cell culture platforms, and the availability of proper readout and quantification tools. In this thesis, a complete workflow has been established entailing well-characterised 3D cell culture models for prostate cancer, a standardised 3D cell culture routine based on high-throughput-ready platform, automated image acquisition with concomitant morphometric image analysis, and data visualisation, in order to enable large-scale high-content screens. Our integrated suite of software and statistical analysis tools were optimised and validated using a comprehensive panel of prostate cancer cell lines and 3D models. The tools quantify multiple key cancer-relevant morphological features, ranging from cancer cell invasion through multicellular differentiation to growth, and detect dynamic changes both in morphology and function, such as cell death and apoptosis, in response to experimental perturbations including RNA interference and small molecule inhibitors. Our panel of cell lines included many non-transformed and most currently available classic prostate cancer cell lines, which were characterised for their morphogenetic properties in 3D laminin-rich ECM. The phenotypes and gene expression profiles were evaluated concerning their relevance for pre-clinical drug discovery, disease modelling and basic research. In addition, a spontaneous model for invasive transformation was discovered, displaying a highdegree of epithelial plasticity. This plasticity is mediated by an abundant bioactive serum lipid, lysophosphatidic acid (LPA), and its receptor LPAR1. The invasive transformation was caused by abrupt cytoskeletal rearrangement through impaired G protein alpha 12/13 and RhoA/ROCK, and mediated by upregulated adenylyl cyclase/cyclic AMP (cAMP)/protein kinase A, and Rac/ PAK pathways. The spontaneous invasion model tangibly exemplifies the biological relevance of organotypic cell culture models. Overall, this thesis work underlines the power of novel morphometric screening tools in drug discovery.

Libyan breast cancer: Health services and biology. Diagnosis delay and prognostic value of DNA pploidy, S-phase fraction, and Ki-67 and Bcl-2 immunohistochemistry

The aim of this study was to investigate the diagnosis delay and its impact on the stage of disease. The study also evaluated a nuclear DNA content, immunohistochemical expression of Ki-67 and bcl-2, and the correlation of these biological features with the clinicopathological features and patient outcome. 200 Libyan women, diagnosed during 2008–2009 were interviewed about the period from the first symptoms to the final histological diagnosis of breast cancer. Also retrospective preclinical and clinical data were collected from medical records on a form (questionnaire) in association with the interview. Tumor material of the patients was collected and nuclear DNA content analysed using DNA image cytometry. The expression of Ki-67 and bcl-2 were assessed using immunohistochemistry (IHC). The studies described in this thesis show that the median of diagnosis time for women with breast cancer was 7.5 months and 56% of patients were diagnosed within a period longer than 6 months. Inappropriate reassurance that the lump was benign was an important reason for prolongation of the diagnosis time. Diagnosis delay was also associated with initial breast symptom(s) that did not include a lump, old age, illiteracy, and history of benign fibrocystic disease. The patients who showed diagnosis delay had bigger tumour size (p<0.0001), positive lymph nodes (p<0.0001), and high incidence of late clinical stages (p<0.0001). Biologically, 82.7% of tumors were aneuploid and 17.3% were diploid. The median SPF of tumors was 11% while the median positivity of Ki-67 was 27.5%. High Ki-67 expression was found in 76% of patients, and high SPF values in 56% of patients. Positive bcl-2 expression was found in 62.4% of tumors. 72.2% of the bcl-2 positive samples were ER-positive. Patients who had tumor with DNA aneuploidy, high proliferative activity and negative bcl-2 expression were associated with a high grade of malignancy and short survival. The SPF value is useful cell proliferation marker in assessing prognosis, and the decision cut point of 11% for SPF in the Libyan material was clearly significant (p<0.0001). Bcl-2 is a powerful prognosticator and an independent predictor of breast cancer outcome in the Libyan material (p<0.0001). Libyan breast cancer was investigated in these studies from two different aspects: health services and biology. The results show that diagnosis delay is a very serious problem in Libya and is associated with complex interactions between many factors leading to advanced stages, and potentially to high mortality. Cytometric DNA variables, proliferative markers (Ki-67 and SPF), and oncoprotein bcl-2 negativity reflect the aggressive behavior of Libyan breast cancer and could be used with traditional factors to predict the outcome of individual patients, and to select appropriate therapy.

Computational models for and from biology : simple gene assembly and reaction systems

The advancement of science and technology makes it clear that no single perspective is any longer sufficient to describe the true nature of any phenomenon. That is why the interdisciplinary research is gaining more attention overtime. An excellent example of this type of research is natural computing which stands on the borderline between biology and computer science. The contribution of research done in natural computing is twofold: on one hand, it sheds light into how nature works and how it processes information and, on the other hand, it provides some guidelines on how to design bio-inspired technologies. The first direction in this thesis focuses on a nature-inspired process called gene assembly in ciliates. The second one studies reaction systems, as a modeling framework with its rationale built upon the biochemical interactions happening within a cell. The process of gene assembly in ciliates has attracted a lot of attention as a research topic in the past 15 years. Two main modelling frameworks have been initially proposed in the end of 1990s to capture ciliates’ gene assembly process, namely the intermolecular model and the intramolecular model. They were followed by other model proposals such as templatebased assembly and DNA rearrangement pathways recombination models. In this thesis we are interested in a variation of the intramolecular model called simple gene assembly model, which focuses on the simplest possible folds in the assembly process. We propose a new framework called directed overlap-inclusion (DOI) graphs to overcome the limitations that previously introduced models faced in capturing all the combinatorial details of the simple gene assembly process. We investigate a number of combinatorial properties of these graphs, including a necessary property in terms of forbidden induced subgraphs. We also introduce DOI graph-based rewriting rules that capture all the operations of the simple gene assembly model and prove that they are equivalent to the string-based formalization of the model. Reaction systems (RS) is another nature-inspired modeling framework that is studied in this thesis. Reaction systems’ rationale is based upon two main regulation mechanisms, facilitation and inhibition, which control the interactions between biochemical reactions. Reaction systems is a complementary modeling framework to traditional quantitative frameworks, focusing on explicit cause-effect relationships between reactions. The explicit formulation of facilitation and inhibition mechanisms behind reactions, as well as the focus on interactions between reactions (rather than dynamics of concentrations) makes their applicability potentially wide and useful beyond biological case studies. In this thesis, we construct a reaction system model corresponding to the heat shock response mechanism based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We also introduce for RS various concepts inspired by biology, e.g., mass conservation, steady state, periodicity, etc., to do model checking of the reaction systems based models. We prove that the complexity of the decision problems related to these properties varies from P to NP- and coNP-complete to PSPACE-complete. We further focus on the mass conservation relation in an RS and introduce the conservation dependency graph to capture the relation between the species and also propose an algorithm to list the conserved sets of a given reaction system.

Targeting oncogenic signaling proteins : new insights using a FRET-based chemical biology approach

Cancer affects more than 20 million people each year and this rate is increasing globally. The Ras/MAPK-pathway is one of the best-studied cancer signaling pathways. Ras proteins are mutated in almost 20% of all human cancers and despite numerous efforts, no effective therapy that specifically targets Ras is available to date. It is now well established that Ras proteins laterally segregate on the plasma membrane into transient nanoscale signaling complexes called nanoclusters. These Ras nanoclusters are essential for the high-fidelity signal transmission. Disruption of nanoclustering leads to reduction in Ras activity and signaling, therefore targeting nanoclusters opens up important new therapeutic possibilities in cancer. This work describes three different studies exploring the idea of membrane protein nanoclusters as novel anti-cancer drug targets. It is focused on the design and implementation of a simple, cell-based Förster Resonance Energy Transfer (FRET)-biosensor screening platform to identify compounds that affect Ras membrane organization and nanoclustering. Chemical libraries from different sources were tested and a number of potential hit molecules were validated on full-length oncogenic proteins using a combination of imaging, biochemical and transformation assays. In the first study, a small chemical library was screened using H-ras derived FRET-biosensors. Surprisingly from this screen, commonly used protein synthesis inhibitors (PSIs) were found to specifically increase H-ras nanoclustering and downstream signalling in a H-ras dependent manner. Using a representative PSI, increase in H-ras activity was shown to induce cancer stem cell (CSC)-enriched mammosphere formation and tumor growth of breast cancer cells. Moreover, PSIs do not increase K-ras nanoclustering, making this screening approach suitable for identifying Ras isoform-specific inhibitors. In the second study, a nanoncluster-directed screen using both H- and K-ras derived FRET biosensors identified CSC inhibitor salinomycin to specifically inhibit K-ras nanocluster organization and downstream signaling. A K-ras nanoclusteringassociated gene signature was established that predicts the drug sensitivity of cancer cells to CSC inhibitors. Interestingly, almost 8% of patient tumor samples in the The Cancer Genome Atlas (TCGA) database had the above gene signature and were associated with a significantly higher mortality. From this mechanistic insight, an additional microbial metabolite screen on H- and K-ras biosensors identified ophiobolin A and conglobatin A to specifically affect K-ras nanoclustering and to act as potential breast CSC inhibitors. In the third study, the Ras FRET-biosensor principle was used to investigate membrane anchorage and nanoclustering of myristoylated proteins such as heterotrimeric G-proteins, Yes- and Src-kinases. Furthermore, Yes-biosensor was validated to be a suitable platform for performing chemical and genetic screens to identify myristoylation inhibitors. The results of this thesis demonstrate the potential of the Ras-derived FRETbiosensor platform to differentiate and identify Ras-isoform specfic inhibitors. The results also highlight that most of the inhibitors identified predominantly perturb Ras subcellular distribution and membrane organization through some novel and yet unknown mechanisms. The results give new insights into the role of Ras nanoclusters as promising new molecular targets in cancer and in stem cells.