This dissertation focuses on the development of computational models and algorithms related to diffuse reflectance spectroscopy. Specifically, this work aims to advance diffuse reflectance spectroscopy to a technique that is capable of measuring depth dependent properties in tissue. First, we introduce the Monte Carlo lookup table (MCLUT) method for extracting optical properties from diffuse reflectance spectra. Next, we extend this method to a two-layer tissue geometry so that it can extract depth dependent properties in tissue. We then develop a computational model that relates photon sampling depth to optical properties and probe geometry. This model can be used to aid in design of application specific diffuse reflectance probes. In order to provide justification for using a two-layer model for extracting tissue properties, we show that the use of a one-layer model can lead to significant errors in the extracted optical properties. Lastly, we use our two-layer MCLUT model and a probe that was designed based on our sampling depth model to extract tissue properties from the skin of 80 subjects at 5 anatomical locations. The results agree with previously published values for skin properties and show that can diffuse reflectance spectroscopy can be used to measured depth dependent properties in tissue. / text

The nature of visual properties used for object perception in mid- and high-level vision areas of the brain is poorly understood. Past studies have employed simplistic stimuli probing models limited in descriptive power and mathematical under-pinnings. Unfortunately, pursuit of more complex stimuli and properties requires searching through a wide, unknown space of models and of images. The difficulty of this pursuit is exacerbated in brain research by the limited number of stimulus responses that can be collected for a given human subject over the course of an experiment. To more quickly identify complex visual features underlying cortical object perception, I develop, test, and use a novel method in which stimuli for use in the ongoing study are selected in realtime based on fMRI-measured cortical responses to recently-selected and displayed stimuli. A variation of the simplex method controls this ongoing selection as part of a search in visual space for images producing maximal activity — measured in realtime — in a pre-determined 1 cm3 brain region. I probe cortical selectivities during this search using photographs of real-world objects and synthetic “Fribble” objects. Real-world objects are used to understand perception of naturally-occurring visual properties. These objects are characterized based on feature descriptors computed from the scale invariant feature transform (SIFT), a popular computer vision method that is well established in its utility for aiding in computer object recognition and that I recently found to account for intermediate-level representations in the visual object processing pathway in the brain. Fribble objects are used to study object perception in an arena in which visual properties are well defined a priori. They are constructed from multiple well-defined shapes, and variation of each of these component shapes produces a clear space of visual stimuli. I study the behavior of my novel realtime fMRI search method, to assess its value in the investigation of cortical visual perception, and I study the complex visual properties my method identifies as highly-activating selected brain regions in the visual object processing pathway. While there remain further technical and biological challenges to overcome, my method uncovers reliable and interesting cortical properties for most subjects — though only for selected searches performed for each subject. I identify brain regions selective for holistic and component object shapes and for varying surface properties, providing examples of more precise selectivities within classes of visual properties previously associated with cortical object representation. I also find examples of “surround suppression,” in which cortical activity is inhibited upon viewing stimuli slightly deviation from the visual properties preferred by a brain region, expanding on similar observations at lower levels of vision.

The Internet at the interdomain level is a complex network of approximately 50,000 Autonomous Systems (ASes). ASes interconnect through two types of links: (a) transit (customer-provider) and (b) peering links. Recent studies have shown that despite being optional for most ASes, a rich and dynamic peering fabric exists among ASes. Peering has also grown as one of the main instruments for catching up with asymmetric traffic due to CDNs, online video traffic, performance requirements, etc. Moreover, peering has been in the spotlight recently because of peering conflicts between major ISPs and Content Providers. Such conflicts have led to calls for intervention by communication regulators and legislation at the highest levels of government. Peering disputes have also sometimes resulted in partitioning of the Internet. Despite the broad interest and intense debate about peering, several fundamental questions remain elusive. The objective of this thesis is to study peering from a techno-economics perspective. We explore the following questions: 1- What are the main sources of complexity in Internet peering that defy the development of an automated approach to assess peering relationships? 2- What is the current state of the peering ecosystem, e.g., which categories of ASes are more inclined towards peering? What are the most popular peering strategies among ASes in the Internet? 3- What can we say about the economics of contemporary peering practices, e.g., what is the impact of using different peering traffic ratios as a strategy to choose peers? Is the general notion that peering saves network costs, always valid? 4- Can we propose novel methods for peering that result in more stable and fair peering interconnections? We have used game-theoretic modeling, large-scale computational agent-based modeling, and analysis of publicly available peering data to answer the above questions. The main contributions of this thesis include: 1- Identification of fundamental complexities underlying the evaluation of peers and formation of stable peering links in the interdomain network. 2- An empirical study of the state of the peering ecosystem from August 2010 to August 2013. 3- Development of a large-scale agent-based computational model to study the formation and evolution of Internet peering interconnections. 4- A plausible explanation for the gravitation of Internet transit providers towards Open peering and a prediction of its future consequences. 5- We propose a variant of the Open peering policy and a new policy based on cost-benefit analysis to replace the contemporary simplistic policies.

Modeling gene regulatory networks has become a problem of great interest in biology and medical research. Most common methods for learning regulatory dependencies rely on observations in the form of gene expression data. In this dissertation, computational models for gene regulation have been developed based on constrained regression by integrating comprehensive gene expression data for M. tuberculosis with genome-scale ChIP-Seq interaction data. The resulting models confirmed predictive power for expression in independent stress conditions and identified mechanisms driving hypoxic adaptation and lipid metabolism in M. tuberculosis. I then used the regulatory network model for M. tuberculosis to identify factors responding to stress conditions and drug treatments, revealing drug synergies and conditions that potentiate drug treatments. These results can guide and optimize design of drug treatments for this pathogen. I took the next step in this direction, by proposing a new probabilistic framework for learning modular structures in gene regulatory networks from gene expression and protein-DNA interaction data, combining the ideas of module networks and stochastic blockmodels. These models also capture combinatorial interactions between regulators. Comparisons with other network modeling methods that rely solely on expression data, showed the essentiality of integrating ChIP-Seq data in identifying direct regulatory links in M. tuberculosis. Moreover, this work demonstrates the theoretical advantages of integrating ChIP-Seq data for the class of widely-used module network models. The systems approach and statistical modeling presented in this dissertation can also be applied to problems in other organisms. A similar approach was taken to model the regulatory network controlling genes with circadian gene expression in Neurospora crassa, through integrating time-course expression data with ChIP-Seq data. The models explained combinatorial regulations leading to different phase differences in circadian rhythms. The Neurospora crassa network model also works as a tool to manipulate the phases of target genes.

The self-organized nest construction behaviors of European paper wasps (Polistes dominula) show potential for adoption in artificial intelligence and robotic systems where centralized control proves challenging. However, P. dominula nest construction mechanisms are not fully understood. The goal of this research was to investigate how P. dominula nest structures stimulate worker actions. Simulation utilities were constructed in C++, C#, and Python. Two models from previous work, a three-dimensional model with weighted actions and a two-dimensional model with simple rule-based actions, were combined in a three-dimensional model with simple rules. Nest construction was simulated with a random selection rule, an age-based rule, a height requirement rule, and a height difference rule. Real and idealized nest data were used to evaluate simulated nests. Structures generated with age- and height-based rules showed more correlation with real and idealized nest structures than randomly-generated structures.

<div>Understanding the fundamental physics involved in the high strain rate deformation of high explosives (HE) is critical for developing more efficient, reliable, and safer energetic materials. When HE are impacted at high velocities, several thermo-mechanical processes are activated, which are responsible for the ignition of these materials. These processes occur at different time and length scales, some of them inaccessible by experimentation. Therefore, computational modeling is an excellent alternative to study multiscale phenomena responsible for the ignition and initiation of HE. This thesis aims to develop a continuum model of HMX to study the anisotropic behavior of HE at the mesoscale, including fracture evolution and plastic deformation. This thesis focus on three types of simulations. First, we investigate dynamic fracture and hotspot formation in HMX particles embedded in Sylgard binder undergoing high strain rate compression and harmonic excitation. We use the phase field damage model (PFDM) to simulate dynamic fracture. Also, we implement a thermal model to capture temperature increase due to fracture dissipation and friction at both cracks and debonded HMX/Sylgard interface. In our simulations, we observe that crack patterns are strongly dominated by initial defects such as pre-existing cracks and interface debonding. Regions with initial debonding between HMX particles and the polymer are critical sites where cracks nucleate and propagate. Heating due to friction generates in these regions too and caused the formation of critical hotspots. We also run simulations of a HMX particle under high-frequency harmonic excitation. As expected, higher frequencies and larger amplitudes lead to an increase in the damage growth rate. The simulations suggest that the intensity of the thermal localization can be controlled more readily by modifying the bonding properties between the particle and the binder rather than reducing the content of bulk defects in the particle. </div><div><br></div><div>Second, we present simulations of shock compression in HMX single crystals. For this purpose, we implemented a constitutive model that simulates the elastoplastic anisotropic response of this type of material. The continuum model includes a rate-dependent crystal plasticity model and the Mie-Gruneisen equation of state to obtain the pressure due to shock. Temperature evolves in the material due to plastic dissipation, shock, and thermo-elastic coupling. The model is calibrated with non-reactive atomistic simulations to make sure the model obeys the Rankine-Hugoniot jump conditions. We compare finite element (FE) and molecular dynamic (MD) simulations to study the formation of hot spots during the collapse of nano-size void in a HMX energetic crystal. The FE simulations captured the transition from viscoelastic collapse for relatively weak shocks to a hydrodynamic regime for strong shocks. The overall temperature distributions and the rate of pore collapse are similar to MD simulations. We observe that the void collapse rate and temperature field are strongly dependent on the plasticity model, and we quantify these effects. We also studied the collapse of a micron size void in HMX impacted at different crystal orientations and impact velocities. The simulation results of void collapse are in good agreement with a gas gun void collapse experiment. While the void size and crystal orientation do not affect the area ratio rate, they strongly affect the void collapse regime and temperature. Also, increased plastic activity when the crystal is impacted on the plane (110) renders higher temperature fields.</div><div><br></div><div>Finally, we studied shock compression and dynamic fracture in polycrystalline HMX using the same model implemented for shocks in single crystals. The goal of this study is to understand the role of crystal anisotropy and how it affects other hotspot formation mechanisms such as frictional heating. To simulate fracture, we used a phase field damage model implemented for large deformations. We first perform simulations of sustained shocks in polycrystalline HMX, where the grains are perfectly bonded to understand the effect of plastic deformation and hotspot formation due to plastic heating. Then, we simulate shocks in polycrystalline HMX with dynamic fracture. Simulations capture fracture evolution and frictional heating at cracks. In the polycrystalline case, we study heat generation due to shock and plastic deformation. A heterogeneous temperature field forms when the shock wave travels in the material. Temperature increases more in crystals that showed a higher magnitude of accumulated slip. When weak grain boundaries are included in the simulations, frictional heating becomes the dominant hotspot formation mechanism. As the crystals' interfaces break and crack surface sliding occurs, temperature increases due to friction at cracks. Hotspots tend to form at cracks oriented 45 deg from the shock direction. For this case, crystal anisotropy does not play an important role in temperature generation due to plastic dissipation. However, the random orientation of the crystals creates heterogeneous deformation and stress fields that cause the formation of a higher number of hotspots than the case where all the grains are oriented in the same direction.</div>

Synthetic biology endeavors to forward engineer genetic circuits with novel function. A major inspiration for the field has been the enormous success in the engineering of digital electronic circuits over the past half century. This dissertation approaches synthetic biology from the perspective of the engineering design cycle, a concept ubiquitous across many engineering disciplines. First, an analysis of the state of the engineering design cycle in synthetic biology is presented, pointing out the most limiting challenges currently facing the field. Second, a principle commonly used in electronics to weigh the tradeoffs between hardware and software implementations of a function, called co-design, is applied to synthetic biology. Designs to implement a specific logical function in three distinct domains are proposed and their pros and cons weighed. Third, automatic transitioning between an abstract design, its physical implementation, and accurate models of the corresponding system are critical for success in synthetic biology. We present a framework for accomplishing this task and demonstrate how it can be used to explore a design space. A major limitation of the aforementioned approach is that adequate parameter values for the performance of genetic components do not yet exist. Thus far, it has not been possible to uniquely attribute the function of a device to the function of the individual components in a way that enables accurate prediction of the function of new devices assembled from the same components. This lack presents a major challenge to rapid progression through the design cycle. We address this challenge by first collecting high time-resolution fluorescence trajectories of individual cells expressing a fluorescent protein, as well as snapshots of the number of corresponding mRNA molecules per cell. We then leverage the information embedded in the cell-cell variability of the population to extract parameter values for a stochastic model of gene expression more complex than typically used. Such analysis opens the door for models of genetic components that can more reliably predict the function of new combinations of these basic components. / Ph. D.

Magnetic nanoparticles are used in nanotechnologies and biomedical applications, such as drug targeting, hyperthermia, MRI contrasting agents, and bio-separation of compound solutions. Magnetite (Fe3O4) nanoparticles stand to be effective in these roles due to the non-toxic nature of magnetite and its ease of manufacture. To this end, a greater understanding of the magnetic behavior of the individual magnetite nanoparticles is needed when a collection of them is used. This research seeks to discover the local magnetic ordering of ensembles of magnetite nanoparticles at various stages of the magnetization process, temperatures above and below their blocking temperature, and for various particle sizes. We use x-ray circular dichroism and x-ray resonant magnetic scattering (XRMS), which provides information about the magnetic orders in the samples. Here we discuss the modeling of the magnetic scattering data using a one-dimensional chain of nanoparticles in real space as well as an empirical Gaussian packet model in reciprocal space. We find that at low temperature, and field values close to the coercive point, magnetite nanoparticles experience a significant amount of antiferromagnetic ordering that increases with particle size.

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