Society by Numbers : Studies on Model-Based Explanations in the Social Sciences

The aim of this dissertation is to provide conceptual tools for the social scientist for clarifying, evaluating and comparing explanations of social phenomena based on formal mathematical models. The focus is on relatively simple theoretical models and simulations, not statistical models. These studies apply a theory of explanation according to which explanation is about tracing objective relations of dependence, knowledge of which enables answers to contrastive why and how-questions. This theory is developed further by delineating criteria for evaluating competing explanations and by applying the theory to social scientific modelling practices and to the key concepts of equilibrium and mechanism. The dissertation is comprised of an introductory essay and six published original research articles. The main theses about model-based explanations in the social sciences argued for in the articles are the following. 1) The concept of explanatory power, often used to argue for the superiority of one explanation over another, compasses five dimensions which are partially independent and involve some systematic trade-offs. 2) All equilibrium explanations do not causally explain the obtaining of the end equilibrium state with the multiple possible initial states. Instead, they often constitutively explain the macro property of the system with the micro properties of the parts (together with their organization). 3) There is an important ambivalence in the concept mechanism used in many model-based explanations and this difference corresponds to a difference between two alternative research heuristics. 4) Whether unrealistic assumptions in a model (such as a rational choice model) are detrimental to an explanation provided by the model depends on whether the representation of the explanatory dependency in the model is itself dependent on the particular unrealistic assumptions. Thus evaluating whether a literally false assumption in a model is problematic requires specifying exactly what is supposed to be explained and by what. 5) The question of whether an explanatory relationship depends on particular false assumptions can be explored with the process of derivational robustness analysis and the importance of robustness analysis accounts for some of the puzzling features of the tradition of model-building in economics. 6) The fact that economists have been relatively reluctant to use true agent-based simulations to formulate explanations can partially be explained by the specific ideal of scientific understanding implicit in the practise of orthodox economics.

Topological Quantum Computation - An Analysis of an Anyon Model Based on Quantum Double Symmetries

There exists various suggestions for building a functional and a fault-tolerant large-scale quantum computer. Topological quantum computation is a more exotic suggestion, which makes use of the properties of quasiparticles manifest only in certain two-dimensional systems. These so called anyons exhibit topological degrees of freedom, which, in principle, can be used to execute quantum computation with intrinsic fault-tolerance. This feature is the main incentive to study topological quantum computation. The objective of this thesis is to provide an accessible introduction to the theory. In this thesis one has considered the theory of anyons arising in two-dimensional quantum mechanical systems, which are described by gauge theories based on so called quantum double symmetries. The quasiparticles are shown to exhibit interactions and carry quantum numbers, which are both of topological nature. Particularly, it is found that the addition of the quantum numbers is not unique, but that the fusion of the quasiparticles is described by a non-trivial fusion algebra. It is discussed how this property can be used to encode quantum information in a manner which is intrinsically protected from decoherence and how one could, in principle, perform quantum computation by braiding the quasiparticles. As an example of the presented general discussion, the particle spectrum and the fusion algebra of an anyon model based on the gauge group S_3 are explicitly derived. The fusion algebra is found to branch into multiple proper subalgebras and the simplest one of them is chosen as a model for an illustrative demonstration. The different steps of a topological quantum computation are outlined and the computational power of the model is assessed. It turns out that the chosen model is not universal for quantum computation. However, because the objective was a demonstration of the theory with explicit calculations, none of the other more complicated fusion subalgebras were considered. Studying their applicability for quantum computation could be a topic of further research.

Where and how to conserve: Extending the scope of spatial reserve network design

Ongoing habitat loss and fragmentation threaten much of the biodiversity that we know today. As such, conservation efforts are required if we want to protect biodiversity. Conservation budgets are typically tight, making the cost-effective selection of protected areas difficult. Therefore, reserve design methods have been developed to identify sets of sites, that together represent the species of conservation interest in a cost-effective manner. To be able to select reserve networks, data on species distributions is needed. Such data is often incomplete, but species habitat distribution models (SHDMs) can be used to link the occurrence of the species at the surveyed sites to the environmental conditions at these locations (e.g. climatic, vegetation and soil conditions). The probability of the species occurring at unvisited location is next predicted by the model, based on the environmental conditions of those sites. The spatial configuration of reserve networks is important, because habitat loss around reserves can influence the persistence of species inside the network. Since species differ in their requirements for network configuration, the spatial cohesion of networks needs to be species-specific. A way to account for species-specific requirements is to use spatial variables in SHDMs. Spatial SHDMs allow the evaluation of the effect of reserve network configuration on the probability of occurrence of the species inside the network. Even though reserves are important for conservation, they are not the only option available to conservation planners. To enhance or maintain habitat quality, restoration or maintenance measures are sometimes required. As a result, the number of conservation options per site increases. Currently available reserve selection tools do however not offer the ability to handle multiple, alternative options per site. This thesis extends the existing methodology for reserve design, by offering methods to identify cost-effective conservation planning solutions when multiple, alternative conservation options are available per site. Although restoration and maintenance measures are beneficial to certain species, they can be harmful to other species with different requirements. This introduces trade-offs between species when identifying which conservation action is best applied to which site. The thesis describes how the strength of such trade-offs can be identified, which is useful for assessing consequences of conservation decisions regarding species priorities and budget. Furthermore, the results of the thesis indicate that spatial SHDMs can be successfully used to account for species-specific requirements for spatial cohesion - in the reserve selection (single-option) context as well as in the multi-option context. Accounting for the spatial requirements of multiple species and allowing for several conservation options is however complicated, due to trade-offs in species requirements. It is also shown that spatial SHDMs can be successfully used for gaining information on factors that drive a species spatial distribution. Such information is valuable to conservation planning, as better knowledge on species requirements facilitates the design of networks for species persistence. This methods and results described in this thesis aim to improve species probabilities of persistence, by taking better account of species habitat and spatial requirements. Many real-world conservation planning problems are characterised by a variety of conservation options related to protection, restoration and maintenance of habitat. Planning tools therefore need to be able to incorporate multiple conservation options per site, in order to continue the search for cost-effective conservation planning solutions. Simultaneously, the spatial requirements of species need to be considered. The methods described in this thesis offer a starting point for combining these two relevant aspects of conservation planning.

Molecular Modelling of Estrogen-Producing Enzymes CYP450 Aromatase and 17beta-Hydroxysteroid Dehydrogenase Type 1

Breast cancer is the most common cancer in women in the western countries. Approximately two-thirds of breast cancer tumours are hormone dependent, requiring estrogens to grow. Estrogens are formed in the human body via a multistep route starting from cholesterol. The final steps in the biosynthesis include the CYP450 aromatase enzyme, converting the male hormones androgens (preferred substrate androstenedione ASD) into estrogens(estrone E1), and the 17beta-HSD1 enzyme, converting the biologically less active E1 into the active hormone 17beta-hydroxyestradiol E2. E2 is bound to the nuclear estrogen receptors causing a cascade of biochemical reactions leading to cell proliferation in normal tissue, and to tumour growth in cancer tissue. Aromatase and 17beta-HSD1 are expressed in or near the breast tumour, locally providing the tissue with estrogens. One approach in treating hormone dependent breast tumours is to block the local estrogen production by inhibiting these two enzymes. Aromatase inhibitors are already on the market in treating breast cancer, despite the lack of an experimentally solved structure. The structure of 17beta-HSD1, on the other hand, has been solved, but no commercial drugs have emerged from the drug discovery projects reported in the literature. Computer-assisted molecular modelling is an invaluable tool in modern drug design projects. Modelling techniques can be used to generate a model of the target protein and to design novel inhibitors for them even if the target protein structure is unknown. Molecular modelling has applications in predicting the activities of theoretical inhibitors and in finding possible active inhibitors from a compound database. Inhibitor binding at atomic level can also be studied with molecular modelling. To clarify the interactions between the aromatase enzyme and its substrate and inhibitors, we generated a homology model based on a mammalian CYP450 enzyme, rabbit progesterone 21-hydroxylase CYP2C5. The model was carefully validated using molecular dynamics simulations (MDS) with and without the natural substrate ASD. Binding orientation of the inhibitors was based on the hypothesis that the inhibitors coordinate to the heme iron, and were studied using MDS. The inhibitors were dietary phytoestrogens, which have been shown to reduce the risk for breast cancer. To further validate the model, the interactions of a commercial breast cancer drug were studied with MDS and ligand–protein docking. In the case of 17beta-HSD1, a 3D QSAR model was generated on the basis of MDS of an enzyme complex with active inhibitor and ligand–protein docking, employing a compound library synthesised in our laboratory. Furthermore, four pharmacophore hypotheses with and without a bound substrate or an inhibitor were developed and used in screening a commercial database of drug-like compounds. The homology model of aromatase showed stable behaviour in MDS and was capable of explaining most of the results from mutagenesis studies. We were able to identify the active site residues contributing to the inhibitor binding, and explain differences in coordination geometry corresponding to the inhibitory activity. Interactions between the inhibitors and aromatase were in agreement with the mutagenesis studies reported for aromatase. Simulations of 17beta-HSD1 with inhibitors revealed an inhibitor binding mode with hydrogen bond interactions previously not reported, and a hydrophobic pocket capable of accommodating a bulky side chain. Pharmacophore hypothesis generation, followed by virtual screening, was able to identify several compounds that can be used in lead compound generation. The visualisation of the interaction fields from the QSAR model and the pharmacophores provided us with novel ideas for inhibitor development in our drug discovery project.

Gene Expression : From Microarrays to Functional Genomics

The time of the large sequencing projects has enabled unprecedented possibilities of investigating more complex aspects of living organisms. Among the high-throughput technologies based on the genomic sequences, the DNA microarrays are widely used for many purposes, including the measurement of the relative quantity of the messenger RNAs. However, the reliability of microarrays has been strongly doubted as robust analysis of the complex microarray output data has been developed only after the technology had already been spread in the community. An objective of this study consisted of increasing the performance of microarrays, and was measured by the successful validation of the results by independent techniques. To this end, emphasis has been given to the possibility of selecting candidate genes with remarkable biological significance within specific experimental design. Along with literature evidence, the re-annotation of the probes and model-based normalization algorithms were found to be beneficial when analyzing Affymetrix GeneChip data. Typically, the analysis of microarrays aims at selecting genes whose expression is significantly different in different conditions followed by grouping them in functional categories, enabling a biological interpretation of the results. Another approach investigates the global differences in the expression of functionally related groups of genes. Here, this technique has been effective in discovering patterns related to temporal changes during infection of human cells. Another aspect explored in this thesis is related to the possibility of combining independent gene expression data for creating a catalog of genes that are selectively expressed in healthy human tissues. Not all the genes present in human cells are active; some involved in basic activities (named housekeeping genes) are expressed ubiquitously. Other genes (named tissue-selective genes) provide more specific functions and they are expressed preferably in certain cell types or tissues. Defining the tissue-selective genes is also important as these genes can cause disease with phenotype in the tissues where they are expressed. The hypothesis that gene expression could be used as a measure of the relatedness of the tissues has been also proved. Microarray experiments provide long lists of candidate genes that are often difficult to interpret and prioritize. Extending the power of microarray results is possible by inferring the relationships of genes under certain conditions. Gene transcription is constantly regulated by the coordinated binding of proteins, named transcription factors, to specific portions of the its promoter sequence. In this study, the analysis of promoters from groups of candidate genes has been utilized for predicting gene networks and highlighting modules of transcription factors playing a central role in the regulation of their transcription. Specific modules have been found regulating the expression of genes selectively expressed in the hippocampus, an area of the brain having a central role in the Major Depression Disorder. Similarly, gene networks derived from microarray results have elucidated aspects of the development of the mesencephalon, another region of the brain involved in Parkinson Disease.

Country-scale carbon accounting of the vegetation and mineral soils of Finland

The increase in global temperature has been attributed to increased atmospheric concentrations of greenhouse gases (GHG), mainly that of CO2. The threat of severe and complex socio-economic and ecological implications of climate change have initiated an international process that aims to reduce emissions, to increase C sinks, and to protect existing C reservoirs. The famous Kyoto protocol is an offspring of this process. The Kyoto protocol and its accords state that signatory countries need to monitor their forest C pools, and to follow the guidelines set by the IPCC in the preparation, reporting and quality assessment of the C pool change estimates. The aims of this thesis were i) to estimate the changes in carbon stocks vegetation and soil in the forests in Finnish forests from 1922 to 2004, ii) to evaluate the applied methodology by using empirical data, iii) to assess the reliability of the estimates by means of uncertainty analysis, iv) to assess the effect of forest C sinks on the reliability of the entire national GHG inventory, and finally, v) to present an application of model-based stratification to a large-scale sampling design of soil C stock changes. The applied methodology builds on the forest inventory measured data (or modelled stand data), and uses statistical modelling to predict biomasses and litter productions, as well as a dynamic soil C model to predict the decomposition of litter. The mean vegetation C sink of Finnish forests from 1922 to 2004 was 3.3 Tg C a-1, and in soil was 0.7 Tg C a-1. Soil is slowly accumulating C as a consequence of increased growing stock and unsaturated soil C stocks in relation to current detritus input to soil that is higher than in the beginning of the period. Annual estimates of vegetation and soil C stock changes fluctuated considerably during the period, were frequently opposite (e.g. vegetation was a sink but soil was a source). The inclusion of vegetation sinks into the national GHG inventory of 2003 increased its uncertainty from between -4% and 9% to ± 19% (95% CI), and further inclusion of upland mineral soils increased it to ± 24%. The uncertainties of annual sinks can be reduced most efficiently by concentrating on the quality of the model input data. Despite the decreased precision of the national GHG inventory, the inclusion of uncertain sinks improves its accuracy due to the larger sectoral coverage of the inventory. If the national soil sink estimates were prepared by repeated soil sampling of model-stratified sample plots, the uncertainties would be accounted for in the stratum formation and sample allocation. Otherwise, the increases of sampling efficiency by stratification remain smaller. The highly variable and frequently opposite annual changes in ecosystem C pools imply the importance of full ecosystem C accounting. If forest C sink estimates will be used in practice average sink estimates seem a more reasonable basis than the annual estimates. This is due to the fact that annual forest sinks vary considerably and annual estimates are uncertain, and they have severe consequences for the reliability of the total national GHG balance. The estimation of average sinks should still be based on annual or even more frequent data due to the non-linear decomposition process that is influenced by the annual climate. The methodology used in this study to predict forest C sinks can be transferred to other countries with some modifications. The ultimate verification of sink estimates should be based on comparison to empirical data, in which case the model-based stratification presented in this study can serve to improve the efficiency of the sampling design.