Computational models of complex biological systems can provide a better understanding of how living systems function but need to be validated before they are employed for real-life (e.g. clinical) applications. One of the most frequently employed in silico approaches for validating such models is model checking. Traditional model checking approaches are limited to uniscale non-spatial computational models because they do not explicitly distinguish between different scales, and do not take properties of (emergent) spatial structures (e.g. density of multicellular population) into account. This thesis defines a novel multiscale multidimensional spatio-temporal meta model checking methodology which enables validating multiscale (spatial) computational models of biological systems relative to how both numeric (e.g. concentrations) and spatial system properties are expected to change over time and across multiple scales. The methodology has two important advantages. First it supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to produce them. Secondly the methodology is generic because it can be automatically reconfigured according to case study specific types of spatial structures and properties using the meta model checking approach. In addition the methodology could be employed for multiple domains of science, but we illustrate its applicability here only against biological case studies. To automate the computational model validation process, the approach was implemented in software tools, which are made freely available online. Their efficacy is illustrated against two uniscale and four multiscale quantitative computational models encoding phase variation in bacterial colonies and the chemotactic aggregation of cells, respectively the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. This novel model checking approach will enable the efficient construction of reliable multiscale computational models of complex systems.

We develop a computational framework for risk mitigation in high population density events. With increased global population, the frequency of high population density events is naturally increased. Therefore, risk-free crowd management plans are critical for efficient mobility, convenient daily life, resource management and most importantly mitigation of any inadvertent incidents and accidents such as stampedes. The status-quo for crowd management plans is the use of human experience/expert advice. However, most often such dependency on human experience is insufficient, flawed and results in inconvenience and tragic events. Motivated by these issues, we propose an agent-based mathematical model describing realistic human motion and simulating large dense crowds in a wide variety of events as a potential simulation testbed to trial crowd management plans. The developed model incorporates stylized mindset characteristics as an internal drive for physical behavior such as walking, running, and pushing. Furthermore, the model is combined with a visualisation of crowd movement. We develop analytic tools to quantify crowd dynamic features. The analytic tools will enable verification and validation to empirical evidence and surveillance video feed in both local and holistic representations of the crowd. This work addresses research problems in computational modeling of crowd dynamics, specifically: understanding and modeling the impact of a collective mindset on crowd dynamics versus mixtures of heterogeneous mindsets, the effect of social contagion of behaviors and decisions within the crowd, the competitive and aggressive pushing behaviors, and torso and steering dynamics.

Heat shock protein 90 (HSP90) is a molecular chaperone protein that protects proteins from degradation, repairs damaged proteins, and assists proteins in carrying out their functions. HSP90 has hundreds of clients, many of which are inflammatory signaling kinases. The mechanism by which HSP90 enables inflammatory pathways is an active area of investigation. The HSP90 inhibitors such as geldanamycin (GA) and its derivative 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) have been shown to reduce inflammation. It was hypothesized that inhibiting HSP90 would reduce inflammatory signal cascade levels. To test this, J774 mouse macrophage cells were treated with 17-DMAG and immune-stimulated with lipopolysaccharide (LPS). 17-DMAG treatment reduced nitric oxide (NO) production and the expression of pro-inflammatory cytokines interleukin (IL)-6, IL-12, and TNF-α. Inhibition of HSP90 also prevented nuclear translocation of NF-κB. To investigate the anti-inflammatory effects of HSP90 inhibition in vivo, MRL/lpr lupus mice were administered 5 mg/kg 17-DMAG for six weeks via intraperitoneal injection. Mice treated with 17-DMAG were found to have reduced proteinuria and reduced splenomegaly. Flow cytometric analysis of splenocytes showed that 17-DMAG decreased double negative T (DNT) cells. Renal expression of HSP90 was also measured and found to be increased in MRL/lpr mice that did not receive 17-DMAG. The mechanistic interactions between HSP90 and the pro-inflammatory nuclear factor-κB (NF-κB) pathway were studied and a computational model was developed. The model predicts cellular response of inhibitor of κB kinase (IKK) activation and NF-κB activation to LPS stimulation. Model parameters were fit to IKK activation data. Parameter sensitivity was assessed through simulation studies and showed a strong dependence on IKK-HSP90 binding. The model also accounts for the effect of a general HSP90 inhibitor to disrupt the IKK-HSP90 interaction for reduced activation of NF-κB. Model simulations were validated with experimental data. In conclusion, HSP90 facilitates inflammation through multiple signal pathways including Akt and IKK. Inhibition of HSP90 by 17-DMAG reduced disease in the MRL/lpr lupus mouse model. A computational model supported the hypothesis that HSP90 is required for IKK to activate the NF-κB pathway. Taken together, HSP90 is a prime target for therapeutic regulation of many inflammatory processes and warrants further study to understand its mechanism of regulating cell signaling cascades. / Ph. D.

This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, we construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.

[pt] Com o objetivo de introduzir a heterogeneidade e o efeito de escala na modelagem numérica de estruturas de concreto, este trabalho contribui para um melhor entendimento de alguns aspectos inerentes a este tipo de estudo. Apresentam-se uma formalização e generalização do modelo de dano distribuído, a partir da identificação do processo de evolução do modelo, desde os trabalhos iniciais até a adoção de relações de compliância inversa. A referida formalização contempla as várias possibilidades para a relação de compliância inicial, variação da direção de ocorrência de dano, aproximações do tensor tangente do referido modelo e para as leis de evolução de dano em tração e em compressão. Discutem-se as limitações dos métodos incrementais-iterativos clássicos na solução de problemas fisicamente não-lineares. As formulações secante e tangente destes métodos, bem como uma revisão dos procedimentos de controle mais utilizados são apresentadas. Um método que tem a variação das deformações como parâmetro controlador é proposto e suas formulações secante e tangente são mostradas. As evidências experimentais dos fenômenos associados com a heterogeneidade e efeito de tamanho em estruturas de concreto, bem como os estudos teóricos destinados a representá-los,são discutidos, a partir de um resgate histórico dos mesmos. Um modelo para consideração dos efeitos de tamanho e heterogeneidade, via método dos elementos finitos, que se baseia na aleatoriedade da ocorrência de fases sólidas no volume estrutural, bem como nas diferenças das respostas constitutivas associadas a diferentes tamanhos estruturais, é proposto. Através de exemplos, os aspectos do trabalho relacionados com o modelo constitutivo adotado, com os processos de obtenção de trajetórias de equilíbrio e com o modelo proposto para tratar a heterogeneidade e o efeito de tamanho, são discutidos. / [en] This study is a contribution to a better understanding of some aspects in the numerical modeling of the heterogeneity and size effects in the analysis of concrete structures. A general format of the smeared damage model is presented based on a historical perspective, since the first studies to the adoption of the inverse compliance relationship. This format includes diverse initial compliance relations, change in the direction of the damage approximations to the tangent tensor and different evolution damage laws for tension and compression.The limitations of the classical incremental-iterative methods for the solution of nonlinear physical problems are discussed. The secant and tangent formulations of these methods are shown as well as a review of the most commonly used controlling procedures. A new method is introduced in which the controlling parameter is based on the strain variation. Its secant and tangent formulations are also indicated.A historical review of the experimental and theoretical studies concerning the aspects of heterogeneity and size effects in concrete structures is done. A finite element model is then proposed which includes both the heterogeneity and size effects. It is based on the random occurrence of solid phases in the structural volume as well as on the differences of constitutive responses due to structural size.Examples problems are solved which illustrate the employed constitutive model, the procedures for the determination of the equilibrium paths and the model to heterogeneity and size effect.

The main body of this thesis describes how to calculate scanning tunneling microscopy (STM) images from first-principles methods. The theory is based on localized orbital density functional theory (DFT), whose limitations for large-vacuum STM models are resolved by propagating localized-basis wave functions close to the surface into the vacuum region in real space. A finite difference approximation is used to define the vacuum Hamiltonian, from which accurate vacuum wave functions are calculated using equations based on standard single-particle Green’s function techniques, and ultimately used to compute the conductance. By averaging over the lateral reciprocal space, the theory is compared to a series of high-quality experiments in the low- bias limit, concerning copper surfaces with adsorbed carbon monoxide (CO) species and adsorbate atoms, scanned by pure and CO-functionalized copper tips. The theory compares well to the experiments, and allows for further insights into the elastic tunneling regime. A second significant project in this thesis concerns first-principles calculations of a simple chemical reaction of a hydroxyl (oxygen-deuterium) monomer adsorbed on a copper surface. The reaction mechanism is provided by tunneling electrons that, via a finite electron-vibration coupling, trigger the deuterium atom to flip between two nearly identical configurational states along a frustrated rotational motion. The theory suggests that the reaction primarily occurs via nuclear tunneling for the deuterium atom through the estimated reaction barrier, and that over-barrier ladder climbing processes are unlikely.

During tissue invasion individual tumor cells exhibit two interconvertible migration modes, namely mesenchymal and amoeboid migration. The cellular microenvironment triggers the switch between both modes, thereby allowing adaptation to dynamic conditions. It is, however, unclear if this amoeboid-mesenchymal migration plasticity contributes to a more effective tumor invasion. We address this question with a mathematical model, where the amoeboid-mesenchymal migration plasticity is regulated in response to local extracellular matrix resistance. Our numerical analysis reveals that extracellular matrix structure and presence of a chemotactic gradient are key determinants of the model behavior. Only in complex microenvironments, if the extracellular matrix is highly heterogeneous and a chemotactic gradient directs migration, the amoeboid-mesenchymal migration plasticity allows a more widespread invasion compared to the non-switching amoeboid and mesenchymal modes. Importantly, these specific conditions are characteristic for in vivo tumor invasion. Thus, our study suggests that in vitro systems aiming at unraveling the underlying molecular mechanisms of tumor invasion should take into account the complexity of the microenvironment by considering the combined effects of structural heterogeneities and chemical gradients on cell migration.

Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives. My work has given computers awareness of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a non-trivial task, because people rarely tell one story at a time. People don’t specifically announce when we are starting or stopping our stories: We interrupt each other. We tell stories within stories. Before my work, computers had no awareness of narrative boundaries, essentially where stories begin and end. My programs can extract narrative boundaries from novels and short stories with an F1 of 0.65. Before this I worked on teaching computers to identify which paragraphs of text have story content, with an F1 of 0.75 (which is state of the art). Additionally, I have taught computers to identify the narrative point of view (POV; how the narrator identifies themselves) and diegesis (how involved in the story’s action is the narrator) with F1 of over 0.90 for both narrative characteristics. For the narrative POV, diegesis, and narrative level extractors I ran annotation studies, with high agreement, that allowed me to teach computational models to identify structural elements of narrative through supervised machine learning. My work has given computers the ability to find where stories begin and end in raw text. This allows for further, automatic analysis, like extraction of plot, intent, event causality, and event coreference. These tasks are impossible when the computer can’t distinguish between which stories are told in what spans of text. There are two key contributions in my work: 1) my identification of features that accurately extract elements of narrative structure and 2) the gold-standard data and reports generated from running annotation studies on identifying narrative structure.

Cycling as an active mode of transport is increasing across all Europe [1]. Multiple benefits are coming from cycling both for the single user and the society as a whole. With increasing cycling, we expect more conflicts to happen between cyclists and vehicles, as it is also shown by the increasing cyclists' share of fatalities, contrary to the passenger cars' share [2]. Understanding cyclists' behavioral patterns can help automated vehicles (AVs) to predict cyclist's behavior, and then behave safely and comfortably when they encounter them. As a result, developing reliable predictive models of cyclist behavior will help AVs to interact safely with cyclists.

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