The collection of biological data has been limited by instrumentation, the complexity of the systems themselves, and even the ability of graduate students to stay awake and record the data. However, increasing measurement capabilities and decreasing costs may soon enable the collection of reasonably sampled time course data characterizing biological systems, though in general only a subset of the system's species would be measured. This increase in data volume requires a corresponding increase in the use and interpretation of such data, specifically in the development of system identification techniques to identify parameter sets in proposed models. In this paper, we present the results of identifiability analysis on a small test system, including the identifiability of parameters with respect to different measurements (proteins and mRNA), and propose a working definition for "biologically meaningful estimation". We also analyze the correlations between parameters, and use this analysis to consider effective approaches to determining parameters with biological meaning. In addition, we look at other methods for determining relationships between parameters and their possible significance. Finally, we present potential biologically meaningful parameter groupings from the test system and present the results of our attempt to estimate the value of select groupings. / Master of Science

Humans are ubiquitously exposed to mixtures of per- and polyfluoroalkyl substances (PFAS). Exposure to well-studied PFAS including perfluorooctanoic acid (PFOA) is associated with multiple adverse health effects in humans including dysregulated lipid homeostasis. Evidence from epidemiological studies consistently shows a positive association between PFOA exposure and circulating total and low-density lipoprotein cholesterol levels with emerging evidence suggesting PFOA disrupts liver lipid homeostasis as well. Animal toxicity studies show that PFOA decreases, has no effect on, or increases circulating cholesterol levels in rodents and induces liver lipid accumulation. Mechanisms through which PFOA and other PFAS disrupt liver and whole body lipid homeostasis, and an explanation for the differences between species are poorly understood. The overarching hypothesis of this dissertation is that PFOA disrupts serum and liver lipid homeostasis through interactions with multiple hepatic nuclear receptors including peroxisome proliferator activated receptor α (PPARα). This hypothesis was tested with a focus on human-relevant experimental designs. In the first research aim, an in vivo exposure was used to test the hypothesis that the effects of PFOA on liver and serum triacylglyceride and cholesterol concentrations differ by PPARα genotype. Female mice expressing mouse PPARα, human PPARα (hPPARα), or no PPARα were exposed to PFOA (1.2, 3.4, or 14.8 μM) for 14 weeks via drinking water to achieve steady state exposure with co-exposure to a diet containing fat and cholesterol based on “What we eat in America.” PFOA increased liver and serum cholesterol content through PPARα-dependent mechanisms. Analysis of hepatic mRNA expression showed that the PPARα-dependent increase in serum and liver cholesterol was accompanied by a PPARα-dependent decrease in the mRNA expression of the rate-limiting enzyme that converts cholesterol to bile acids and represents an important source of cholesterol turnover in humans. In the second research aim, an in vivo exposure was used to test the hypothesis that PFOA disrupts liver lipid homeostasis by modulating multiple hepatic nuclear receptor pathways. Male and female hPPARα and PPARα null mice were exposed to PFOA (8 μM) for 6 weeks via drinking water in the context of a fat and cholesterol rich diet based on “What we eat in America.” PFOA exposure changed the abundance of multiple lipid classes in the liver with some changes depending on PPARα expression while others occurred via mechanisms independent of PPARα. Less than 60% of PFOA-induced transcriptomic changes depended on hPPARα. Signaling pathways for other nuclear receptors including CAR and PXR may account for the non- PPARα-dependent transcriptomic and lipidomic changes. Because the effects of PFOA on liver and whole-body lipid homeostasis are partially mediated by PPARα, the third research aim tested the hypothesis that hPPARα activation by PFAS mixtures can be predicted with the mathematical model Generalized Concentration Addition (GCA). Data generated using a full-length hPPARα-driven reporter assay showed that, in addition to differences in potency, PFAS differ in the efficacy with which they activate hPPARα. Perfluorinated carboxylic acids (PFCAs) tended to act as full hPPARα agonists while perfluorinated sulfonic acids (PFSAs) tended to act as partial hPPARα agonists. Because of these differences in efficacy, GCA more accurately predicted hPPARα activation by human-relevant PFAS mixtures than traditional mixtures modeling approaches that do not take into account differences in efficacy. Taken together, the research presented in this dissertation shows that hPPARα activation is one of several important molecular initiating events underlying PFOA-induced lipid dysregulation, including increased liver and serum cholesterol levels. Results from these studies support a causal association between PFOA exposure and increased serum cholesterol in humans. The data define important distinctions between PFCAs and PFSAs, which warrant consideration for regulatory agencies acting on the joint toxicity of PFAS mixtures. / 2026-01-03T00:00:00Z

The cell is a highly ordered and intricate machine within which a wide variety of chemical processes take place. The full scientific understanding of cellular physiology requires accurate mathematical models that depict the temporal dynamics of these chemical processes. Modelers build mathematical models of chemical processes primarily from systems of differential equations. Although developing new biological ideas is more of an art than a science, constructing a mathematical model from a biological idea is largely mechanical and automatable. This dissertation describes the practices and processes that biological modelers use for modeling and simulation. Computational biologists struggle with existing tools for creating models of complex eukaryotic cells. This dissertation develops new processes for biological modeling that make model creation, verification, validation, and testing less of a struggle. This dissertation introduces computational software that automates parts of the biological modeling process, including model building, transformation, execution, analysis, and evaluation. User and methodological requirements heavily affect the suitability of software for biological modeling. This dissertation examines the modeling software in terms of these requirements. Intelligent, automated model evaluation shows a tremendous potential to enable the rapid, repeatable, and cost-effective development of accurate models. This dissertation presents a case study that indicates that automated model evaluation can reduce the evaluation time for a budding yeast model from several hours to a few seconds, representing a more than 1000-fold improvement. Although constructing an automated model evaluation procedure requires considerable domain expertise and skill in modeling and simulation, applying an existing automated model evaluation procedure does not. With this automated model evaluation procedure, the computer can then search for and potentially discover models superior to those that the biological modelers developed previously. / Ph. D.

As a physical system, a cell interacts with its environment through physical and chemical processes. The cell can change these interactions through modification of its environment or its own composition. This dissertation presents the overarching hypothesis that both biochemical regulation of intercellular adhesion and physical interaction between cells are required to account for the emergence of cluster migration and collective dynamics observed in epithelial cells. Collective migration is defined as the displacement of a group of cells with transient or permanent cell-cell contacts. One mode, cluster migration, plays an important role during embryonic development and in cancer metastasis. Despite its importance, collective migration is a slow process and hard to visualize, and therefore it has not been thoroughly studied in three dimensions (3D). Based on known information about cluster migration from 2D studies of epithelial sheets and 3D single cell migration, this dissertation presents theoretical and experimental techniques to assess the independent contribution of physical and biochemical factors to 3D cluster migration. It first develops two computational models that explore the interaction between cells and the ECM and epithelial discohesion. These discrete mechanistic models reveal the need to account for intracellular regulation of adherens junctions in space and time within a cluster. Consequently, a differential algebraic model is developed that accounts for cross-reactivity of three pathways in a regulatory biochemical network: Wnt/β-catenin signaling, protein N-glycosylation, and E-cadherin adhesion. The model is tested by matching predictions to Wnt/β-catenin inhibition in MDCK cells. The model is then incorporated into a self-propelled particle (SPP) model, creating the first SPP model for study of adhesive mammalian cellular systems. MDCK cell clusters with fluorescent nuclei are grown, seeded, and tracked in 3D collagen gels using confocal microscopy. They provide data on individual cell dynamics within clusters. Borrowed from the field of complex systems, normalized velocity is used to quantify the order of both in vitro and simulated clusters. An analysis of sensitivity of cluster dynamics on factors describing physical and biochemical processes provides new quantitative insights into mechanisms underlying collective cell migration and explains temporal and spatial heterogeneity of cluster behavior.

Understanding the mechanisms of virus capsid assembly has been an important research objective over the past few decades. Determining critical points along the pathways by which virus capsids form could prove extremely beneficial in producing more stable DNA vectors or pinpointing targets for antiviral therapy. The inability of current experimental technology to address this objective has resulted in a need for alternative approaches. Theoretical and computational studies offer an unprecedented opportunity for detailed examination of capsid assembly. The Schwartz Lab has previously developed a discrete event stochastic simulator to model virus assembly based upon local rules detailing the geometry and interaction kinetics of individual capsid subunits. Applying numerical optimization methods to learn kinetic rate parameters that fit simulation output to in vitro static light scattering data has been a successful avenue to understand the details of virus assembly systems; however, information describing in vitro assembly processes does not necessarily translate to real virus assembly pathways in vivo. There are a number of important distinctions between experimental and realistic assembly environments that must be addressed to produce an accurate model. This thesis will describe work expanding upon previous parameter estimation algorithms for more complex data over three model icosahedral virus systems: human papillomavirus (HPV), hepatitis B virus (HBV) and cowpea chlorotic mottle virus (CCMV). Then it will consider two important modifications to assembly environment to more accurately reflect in vivo conditions: macromolecular crowding and the presence of nucleic acid about which viruses may assemble. The results of this work led to a number of surprising revelations about the variability in potential assembly rates and mechanisms discovered and insight into how assembly mechanisms are affected by changes in concentration, fluctuations in kinetic rates and adjustments to the assembly environment.

The hyoid apparatus in laryngeally echolocating bats is unique as it forms a mechanical connection between the larynx and auditory bullae which has been hypothesized to transfer the outgoing echolocation call to the middle ear during call emission. Previous finite element modeling (FEM) found that hyoid-borne sound can reach the bulla at an amplitude likely heard by echolocating bats; however, that study did not model how or if the signal could reach the inner ear (or cochlea). One route that sound could take is via stimulation of the eardrum – similarly to that of air-conducted sound. We used µCT data to build models of the hyoid apparatus and middle ear from six species of bats with variable morphology. Using FEM, we ran harmonic response analyses to measure the vibroacoustic response of the tympanic membrane to hyoid-borne sound generated during echolocation and found that hyoid-borne sound in all six species stimulated the eardrum within a range likely heard by bats. Although there was variation in the efficiency between models at higher frequencies, there are no obvious morphological patterns to account for it. This suggests that hyoid morphology in laryngeal echolocators is likely driven by other associated functions and warrants further inquiry. Note: This work was published open access in the Journal of Integrative Organismal Biology (https://doi.org/10.1093/iob/obad004)

This thesis' focus is on the use of Alfred North Whitehead's concept of Actual Entities as a computational tool for computer science and the introduction of a novel usage of Actual Entities as learning agents. Actual Entities are vector based agents that interact within their environment through a process called prehension. It is the combined effect of multiple Actual Entities working within a Colony of Prehending Entities that produces emergent, intelligent behavior. It is not always the case that prehension functions for desired behavior are known beforehand and frequently the functions are too complex to construct by hand. Through the use of Artificial Neural Networks and a technique called Observational Intelligence, Actual Entities can extract the characteristic behavior of observable phenomena. This behavior is then converted into a functional form and generalized to provide a knowledge base for how an observed object interacts with its surroundings.

This thesis aims to explore how to physically interact with computational models on an interactive tabletop display. The research began with the design and implementation of several prototype systems. The research of the prototype systems showed that tangible interactions on interactive tabletops have the potential to be more effective on some tasks than traditional interfaces that use screen displays, keyboards and mice. The prototype work shaped the research to focus on the effectiveness of adopting tangible interactions on interactive tabletops. To substantiate the thesis claims, this thesis develops an interactive tabletop application, Pathways, to support the fitting process in modeling biological systems. Pathways supports the concepts of Tangible User Interfaces (TUIs) and tabletop visualizations. It realizes real-time simulation of models and provides comparisons of simulation results with experimental data on the tabletop. It also visualizes the simulation of the model with animations. In addition to that, Pathways introduces a new visualization to help systems biologists quickly compare the simulation results. This thesis provides the quantitative and qualitative evaluation results of Pathways. The evidence showed that using tangible interactions to control numerical values is practical. The results also showed that in experimental conditions users achieved better fitting results and faster fitting results on Pathways than the control group, which used the systems biologists' current tools. The results further suggested that it is possible to recruit non-experts to perform the fitting tasks that are usually done by professional systems biologists.

145 p. Advisers: John Conery (Computer and Information Science)and Shawn Lockery (Biology) / A print copy of this title is available through the UO Libraries under the call number: SCIENCE QA76.87 .D96 2006 / This thesis makes two major contributions: it introduces a novel method for analysis of artificial neural networks and provides new models of the nematode Caenorhabditis elegans nervous system. The analysis method extracts neural network motifs,or subnetworks of recurring neuronal function, from optimized neural networks. The method first creates models for each neuron relating network stimulus to neuronal response, then clusters the model parameters, and finally combines the neurons into multi-neuron motifs based on their cluster category. To infer biological function, this analysis method was applied to neural networks optimized to reproduce C. elegans behavior, which converged upon a small number of motifs. This allowed both a quantitative exploration of network function as well as discovery of larger motifs. Neural network models of C. elegans anatomical connectivity were optimized to reproduce two C. elegans behaviors: chemotaxis (orientation towards a maximum chemical attractant concentration) and thermotaxis (orientation towards a set temperature). Three chemotaxis motifs were identified. Experimental evidence suggests that chemotaxis is driven by a differentiator motif with two important features. The first feature was a fast, excitatory pathway in parallel with one or more slow, inhibitory pathways. The second feature was inhibitory feedback on all self-connections and recurrent loops, which regulates neuronal response. Six thermotaxis motifs were identified. Every motif consisted of two circuits, each a previously discovered chemotaxis motif with most having a dedicated sensory neuron. One circuit was thermophilic (heat-seeking) and the other was cryophilic (cold-seeking). Experimental evidence suggests that the cryophilic circuit is a differentiator motif and the thermophilic circuit functions by klinokinesis. / NSF: IBN-0080068

Cette thèse porte sur la modélisation et l’analyse de stabilité de certains mécanismes biologiques complexes en rapport avec le cancer. Un intérêt particulier est porté au cas de l’hématopoïèse et de la leucémie aiguë myéloblastique (LAM). Les modèles utilisés et/ou introduits dans cette thèse se décrivent par des équations aux dérivées partielles structurées en âge, qui se réduisent à des systèmes à retards de plusieurs types (retards ponctuels ou distribués, à support fini ou infini). Ces modèles à retards sont parfois couplés à des équations aux différences, et possiblement avec des paramètres variant dans le temps. Un des principaux challenges dans ce travail consiste à développer des méthodes temporelles, qui se basent sur la construction de fonctionnelles de Lyapunov-Krasovskii strictes, pour les systèmes non-linéaires à retards étudiés. Les principales notions abordées dans ces travaux incluent : l’analyse de stabilité/stabilisation et de robustesse, l’emploi de techniques de modélisation des populations cellulaires saines et malades, l’étude de différentes classes de systèmes dynamiques, (possiblement à temps variant ou à commutation), et également l’introduction de quelques outils issus de l’intelligence artificielle (planification et recherche de solution) dans un contexte de modèles biologiques. Ainsi, les méthodes de modélisation et d’analyse employées dans ce travail ont permis d’une part d’étendre les résultats de stabilité de cette classe de systèmes biologiques, et d’autre part de mieux comprendre certains mécanismes biologiques liés au cancer et sa thérapie. Plus précisément, certains concepts récemment établis en biologie et en médecine sont mis en évidence dans ce travail pour la première fois dans cette classe de systèmes, telles que : la dédifférenciation des cellules (plasticité), ou encore la dormance des cellules cancéreuses dans des modèles tenant compte de la cohabitation entre cellules saines et mutées. Les résultats obtenus sont interprétés dans le cas de l’hématopoïèse et de la LAM, mais ce travail s’applique également à d’autres types de tissus où le cycle cellulaire se produit de façon similaire. / Medical research is looking for new combined targeted therapies against cancer. Our research project -which involves intensive collaboration with hematologists from Saint-Antoine Hospital in Paris- is imbued within a similar spirit and fits the expectations of a better understanding of the behavior of blood cell dynamics. In fact, hematopoiesis provides a paradigm for studying all the mammalian stem cells, as well as all the mechanisms involved in the cell cycle. We address multiple issues related to the modeling and analysis of the cell cycle, with particular insights into the hematopoietic systems. Stability features of the models are highlighted, since systems trajectories reflect the most prominent healthy or unhealthy behaviors of the biological process under study. We indeed perform stability analysis of systems describing healthy and unhealthy situations, with a particular interest in the case of acute myeloblastic leukemia (AML). Thus, we pursue the objectives of understanding the interactions between the various parameters and functions involved in the mechanisms of interest. For that purpose, an advanced stability analysis of the cell fate evolution in treated or untreated leukemia is performed in several modeling frameworks, and our study suggests new anti-leukemic combined chemotherapy. Throughout the thesis, we cover many biological evidences that are currently undergoing intensive biological research, such as: cell plasticity, mutations accumulation, cohabitation between ordinary and mutated cells, control or eradication of cancer cells, cancer dormancy, etc.Among the contributions of Part I of the thesis, we can mention the extension of both modeling and analysis aspects in order to take into account a proliferating phase in which most of the cells may divide, or die, while few of them may be arrested during their cycle for unlimited time. We also introduce for the first time cell-plasticity features to the class of systems that we are focusing on.Next, in Part II, stability analyses of some differential-difference cell population models are performed through several time-domain techniques, including tools of Comparative and Positive Systems approaches. Then, a new age-structured model describing the coexistence between cancer and ordinary stem cells is introduced. This model is transformed into a nonlinear time-delay system that describes the dynamics of healthy cells, coupled to a nonlinear differential-difference system governing the dynamics of unhealthy cells. The main features of the coupled system are highlighted and an advanced stability analysis of several coexisting steady states is performed through a Lyapunov-like approach for descriptor-type systems. We pursue an analysis that provides a theoretical treatment framework following different medical orientations, among which: i) the case where therapy aims to eradicate cancer cells while preserving healthy ones, and ii) a less demanding, more realistic, scenario that consists in maintaining healthy and unhealthy cells in a controlled stable dormancy steady-state. Mainly, sufficient conditions for the regional exponential stability, estimate of the decay rate of the solutions, and subsets of the basins of attraction of the steady states of interest are provided. Biological interpretations and therapeutic strategies in light of emerging AML-drugs are discussed according to our findings.Finally, in Part III, an original formulation of what can be interpreted as a stabilization issue of population cell dynamics through artificial intelligence planning tools is provided. In that framework, an optimal solution is discovered via planning and scheduling algorithms. For unhealthy hematopoiesis, we address the treatment issue through multiple drug infusions. In that case, we determine the best therapeutic strategy that restores normal blood count as in an ordinary hematopoietic system.