Advancements in genetically based technologies have begun to allow us to better understand the relationships between underlying neural activity and the patterns of measurable behavior that can be reproducibly studied in the laboratory. As this field develops, there are key limitations to the currently available technologies hindering their full potential to deliver meaningful datasets. The limitations which are most critical to advancement of these technologies in behavioral neuroscience are: the temporal resolution at which physiological events can be windowed, the divergent molecular pathways in signal transduction that introduce ambiguity into the output of activity sensors, and the impractical size of the tool’s genetic material—requiring 3-4 separate AAV vectors to deliver a fully functional system into a cell. To address these limitations and help bring the potential of these types of technologies into better realization, we have engineered a nucleus localized light-sensitive Ca2+-dependent gene expression system based on AsLOV2 and the downstream responsive element antagonist modulator (DREAM). The design and engineering of each component was performed in such a way to: 1) preserve behaviorally relevant temporal dynamics, 2) preserve signal fidelity appropriate for studying experience-driven neural activity patterns and their relationship to specific animal responses, and 3) have full delivery of the system’s genetic material via a single AAV vector. The system was tested in vitro and subsequently in vivo with neural activity induced by Channelrhodopsin, and could be used in the future with behaviorally-driven neural activity. To our knowledge this is the first optogenetic tool for the practical use of linking activity-dependent gene activation in response to direct nuclear calcium transduction.

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Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves (Casey, Jones, and Hare, 2008), often choosing among options about which they have little knowledge and experience. This process of individuation and independence is reflected as real-world risk taking behaviors (Silveri et al., 2004), including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death (Casey et al., 2008). The extent to which adolescents continue to display increased behaviors with negative consequences during this period of life depends critically on their ability to explore and learn potential consequences of actions within novel environments. This learning is not limited to the value of the outcome associated with making choices, but extends to the levels of risk taken in making those choices. While the existing adolescence literature has focused on neural substrates of risk preferences, how adolescents behaviorally and neurally learn about risks remain unknown. Success or failure to learn the potential variability of these consequences, or the risks involved, in ambiguous decisions is hypothesized to be a crucial process to allow the individuals to make decisions based on their risk preferences. The alternative in which adolescents fail to learn about the risks involved in their decisions leaves the adolescent in a state of continued exploration of the ambiguity, reflected as continued risk-taking behavior. This dissertation comprises 2 papers. The first paper is a perspective paper outlining a paradigm that risk taking behavior observed during adolescents may be a product of each adolescent's abilities to learn about risk. The second paper builds on the hypothesis of the perspective paper by first examining neural correlates of risk learning and quantifying individual risk learning abilities and then examining longitudinal risk learning developmental trajectories in relation to real-world risk-trajectories in adolescent individuals. / Doctor of Philosophy / Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves, often choosing among options about which they have little knowledge and experience. This process of individuation and independence begins with the adolescent exploring their world and those options they are ignorant of. This is reflected as real-world risk-taking behaviors, including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death. We hypothesized and tested the premise that whether adolescents who succeeded or fail to learn about the negative consequences of their actions while exploring will continue to partake in behaviors with negative consequences. This learning is not limited to the value of the outcome associated with making choices, but extends to the range of possible outcomes of the choices or the risks involved. Indeed, the failure to learn the risks involved in decisions with no known information show continued and greater risk-taking behavior, perhaps remaining in a state of continued exploration of the unknown.

CD4+ T cells mediate and orchestrate a tremendous panoply of lymphoid cell subsets in the human immune system. CD4+ T cells are able to differentiate into either effector pro-inflammatory or regulatory anti-inflammatory subsets depending on the cytokine milieu in their environment. This complex process is mediated through a variety of cytokines and soluble factors. Yet, the mechanisms of action underlying the process of differentiation and plasticity of this interesting immune subset are incompletely understood. To gain a better understanding of the CD4+ T cell differentiation and function, here we present an array of different strategies to model and validate CD4+ T cell differentiation and heterogeneity. The approaches presented here vary from ordinary-differential equation-based to agent-based simulations, from data-driven to theory-based approaches, and from intracellular mathematical to tissue-level or cellular modeling. The knowledge generated throughout this dissertation exemplifies how a combination of computational modeling with experimental immunology can efficiently advance the scene on CD4+ T cell differentiation. In this thesis I present i) an overview on CD4+ T cell differentiation and an introduction to which computational strategies have been adopted in the field to tackle with this problem, ii) ODE-based modeling and predictions on Th17 plasticity modulated by PPARγ, iii) ODE- and ABM-based cellular level modeling of immune responses towards Helicobacter pylori and the role of CD4+ T cell subsets on it, iv) Intracellular strategies to validate a potential therapeutic target within a CD4+ T cell to treat H. pylori infection, and finally v) data-driven strategies to model Th17 differentiation based on sequencing or microarray data to generate novel predictions on specific components. I present both mathematical and computational work as well as experimental work, in vitro and in vivo with animal models, to demonstrate how computational immunology and immunoinformatics can help, not only in understanding this complex process, but also in the development of immune therapeutics for infectious, allergic and immune-mediated diseases. / Ph. D.

Better visualization of micro-level structures and processes can greatly enhance student understanding of key biological functions such as the central dogma. Previous research has demonstrated a need of introducing novel methods to increase student understanding of these concepts. The intention of this report is to show how computational modeling programs (CMPs) can be successfully used as an innovative method of teaching biology concepts that occur at a molecular level. The use of computers and web-based lessons are not new topics in secondary education studies but there is not an abundance of research related to computational modeling alone. We began by researching the many studies that have already indicated the benefits of using computers in the classroom with an emphasis on CMPs and simulations. Of these, we focused mostly on the ones that showed increased student engagement and influenced understanding of core science concepts. Based on the literature reviewed, a framework for curriculum designed around CMPs is proposed. Lastly, a model lesson is discussed to provide an example of how these professional grade tools can be employed in the classroom. This report provides a basis for the continued development of constructivist curriculum built around the use of professional grade computational tools in secondary science classrooms. / text

Understanding how human and animal visual systems work is an important and still largely unsolved problem. The neural mechanisms for low-level visual processing have been studied in detail, focusing on early visual areas. Much less is known about the neural basis of high-level perception, particularly in humans. An important issue is whether and how lessons learned from low-level studies, such as how neurons in the primary visual cortex respond to oriented edges, can be applied to understanding highlevel perception, such as human processing of faces. Visual aftereffects are a useful tool for investigating how stimuli are represented, because they reveal aspects of the underlying neural organisation. This thesis focuses on identifying neural mechanisms involved in high-level visual processing, by studying the relationship between low- and high-level visual aftereffects. Previous psychophysical studies have shown that humans exhibit reliable orientation (tilt) aftereffects, wherein prolonged exposure to an oriented visual pattern systematically biases perception of other orientations. Humans also show face identity aftereffects, wherein prolonged exposure to one face systematically biases perception of other faces. Despite these apparent similarities, previous studies have argued that the two effects reflect different mechanisms, in part because tilt aftereffects show a characteristic S-shaped curve, with the effect magnitude increasing and then decreasing with orientation difference, while face aftereffects appeared to increase monotonically (in various units of face morphing strengths) with difference from a norm (average) face. Using computational models of orientation and face processing in the visual cortex, I show that the same computational mechanisms derived from early cortical processing, applied to either orientation-selective or face-selective neurons, are sufficient to replicate both types of effects. However, the models predict that face aftereffects would also be S-shaped, if tested on a sufficiently wide range of face stimuli. Based on the modelling work, I designed psychophysical experiments to test this theory. An identical experimental paradigm was used to test both face gender and tilt aftereffects, with strikingly similar S-shape curves obtained for both conditions. Combined with the modelling results, this result provides evidence that low- and high level visual adaptation reflect similar neural mechanisms. Other psychophysical experiments have recently shown interactions between low and high-level aftereffects, whereby orientation and line curvature processing (in early visual area) can influence judgements of facial emotion (by high-level face-selective neurons). An extended multi-level version of the face processing model replicates this interaction across levels, but again predicts that the cross-level effects will show similar S-shaped aftereffect curves. Future psychophysical experiments can test these predictions. Together, these results help us to understand how stimuli are represented and processed at each level of the visual cortex. They suggest that similar adaptation mechanisms may underlie both high-level and low-level visual processing, which would allow us to apply much of what we know from low-level studies to help understand high-level processing.

As computational power has increased, computational modeling has become a very promising tool to model the biomechanics of complex joint systems. Musculoskeletal computational models have become more complex when compared to original iterations which utilized a number of simplifications. This thesis utilized a three-dimensional computational model of the wrist joint structure to investigate scapholunate kinematics. The model accurately represented the bony anatomy of the wrist and hand and represented soft tissue structures such as ligaments, tendons, and other surrounding tissues. Creation of the model was done using commercially available computer-aided design and medical image processing software, and utilized the rigid body modeling methodology. It was validated for scapholunate kinematics against a cadaver study and then utilized to investigate further measures and surgical procedures. The simulations performed by the model demonstrated an accurate anatomical response of wrist function. As better understanding of the biomechanics of the wrist joint is achieved, this model could prove to be an important tool to further investigate wrist mechanics.

Biological systems exhibit complex behaviors through coordinated responses of individual biological components. With the advent of genome-scale techniques, one focus has been to develop methods to model interactions between components to accurately describe intact system function. Mathematical modeling techniques such as constraint-based modeling, agent-based modeling, cellular automata (CA) modeling and differential equation modeling are employed as computational tools to study biological phenomenon. We have shown that cellular automata simulations can be used as a computational tool for 12 predicting the dynamics of biological systems with stochastic behavior. The basic premise for the research was the observations made during a study of biologically important feed-forward motifs where CA simulations were compared with differential equation simulations. It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. The study which comprised of CA simulations compared with differential equation modeling show reasonable agreement in the predictability of system dynamics, which provided enough support to model biological systems at cellular level to observe dynamic system evolution. The great promise shown by CA simulations to model biochemical systems was then employed to elucidate evolutionary clues as to why biological networks show preference for certain types of motifs and preserve them with higher frequency during evolution. It was followed by modeling apoptotic networks to shed light on the efficacy of inhibitors and to model cellulose hydrolysis to evaluate efficiency of different enzyme systems used by cellulytic bacteria.

Additive manufacturing technologies place materials at the direct point of need of the warfighter, enabling the development of optimal, situation-specific means to produce and repair parts of Army and DoD weapons systems. In the case of solid-state AM, a full understanding of the metallic powder is critical with producing ideal consolidated material properties reliably and repeatably. By way of iteratively coupling computational models with supportive experimental testing, one can rapidly archetype differences in processing methods, alloy compositions, and heat treatments for metallic powders that serve as feedstock for these AM technologies. Through the combination of thermodynamic models, advanced characterization, and dynamic nano-indentation, representative correlations are established between microstructural features and mechanical properties, enabling the development of enhanced feedstock materials that can achieve the specific needs of the warfighter efficiently without forfeiting quality. This represents both a holistic and a materials-by-design approach to AM through the deliberate use of computation to drive down the discovery process and allow feedstock powders to be engineered with specific properties dictated by Army requirements for performance. In a case study of Al 6061, unique observations were made through the combination of modeling and experimentation. It was discovered that the precipitation kinetics were greatly accelerated in powders and therefore, typical heat treatment processes used for cast-aluminum alloys were not valid. Due to this shift in precipitation sequences, high-temperature treatment was limited to discourage precipitate and grain coarsening. Additionally, when compared to typical cast Al 6061, the main precipitation hardening phase shifts from Mg2Si to Al4Cu2Mg8Si7, changing how aging mechanisms were accounted for. These conclusions were supported by both the computational models and experimental results. Through the generation of numerous data, the models were calibrated, enabling more efficient and precise development of tailored material characteristics from specific microstructural features to serve as an input in a holistic through-process model for a solid-state AM process and guide future experimentation.