Latent Space and Social Psychological Models of Diffusion

The problem of social diffusion has animated sociological thinking on topics ranging from the spread of an idea, an innovation or a disease, to the foundations of collective behavior and political polarization. While network diffusion has been a productive metaphor, the reality of diffusion processes is often muddier. Ideas and innovations diffuse differently from diseases, but, with a few exceptions, the diffusion of ideas and innovations has been modeled under the same assumptions as the diffusion of disease. In this dissertation, I develop two new diffusion models for "socially meaningful" contagions that address two of the most significant problems with current diffusion models: (1) that contagions can only spread along observed ties, and (2) that contagions do not change as they spread between people. I augment insights from these statistical and simulation models with an analysis of an empirical case of diffusion - the use of enterprise collaboration software in a large technology company. I focus the empirical study on when people abandon innovations, a crucial, and understudied aspect of the diffusion of innovations. Using timestamped posts, I analyze when people abandon software to a high degree of detail.

To address the first problem, I suggest a latent space diffusion model. Rather than treating ties as stable conduits for information, the latent space diffusion model treats ties as random draws from an underlying social space, and simulates diffusion over the social space. Theoretically, the social space model integrates both actor ties and attributes simultaneously in a single social plane, while incorporating schemas into diffusion processes gives an explicit form to the reciprocal influences that cognition and social environment have on each other. Practically, the latent space diffusion model produces statistically consistent diffusion estimates where using the network alone does not, and the diffusion with schemas model shows that introducing some cognitive processing into diffusion processes changes the rate and ultimate distribution of the spreading information. To address the second problem, I suggest a diffusion model with schemas. Rather than treating information as though it is spread without changes, the schema diffusion model allows people to modify information they receive to fit an underlying mental model of the information before they pass the information to others. Combining the latent space models with a schema notion for actors improves our models for social diffusion both theoretically and practically.

The empirical case study focuses on how the changing value of an innovation, introduced by the innovations' network externalities, influences when people abandon the innovation. In it, I find that people are least likely to abandon an innovation when other people in their neighborhood currently use the software as well. The effect is particularly pronounced for supervisors' current use and number of supervisory team members who currently use the software. This case study not only points to an important process in the diffusion of innovation, but also suggests a new approach -- computerized collaboration systems -- to collecting and analyzing data on organizational processes.

Gaussian Process Kernels for Cross-Spectrum Analysis in Electrophysiological Time Series

Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, the spectral mixture (SM) kernel was proposed to model the spectral density of a single task in a Gaussian process framework. This work develops a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. The expressive capabilities of the CSM kernel are demonstrated through implementation of 1) a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel, and 2) a Gaussian process factor analysis model, where factor scores represent the utilization of cross-spectral neural circuits. Results are presented for measured multi-region electrophysiological data.

Resonance Energy Transfer-Based Molecular Switch Designed Using a Systematic Design Process Based on Monte Carlo Methods and Markov Chains

A RET network consists of a network of photo-active molecules called chromophores that can participate in inter-molecular energy transfer called resonance energy transfer (RET). RET networks are used in a variety of applications including cryptographic devices, storage systems, light harvesting complexes, biological sensors, and molecular rulers. In this dissertation, we focus on creating a RET device called closed-diffusive exciton valve (C-DEV) in which the input to output transfer function is controlled by an external energy source, similar to a semiconductor transistor like the MOSFET. Due to their biocompatibility, molecular devices like the C-DEVs can be used to introduce computing power in biological, organic, and aqueous environments such as living cells. Furthermore, the underlying physics in RET devices are stochastic in nature, making them suitable for stochastic computing in which true random distribution generation is critical.

In order to determine a valid configuration of chromophores for the C-DEV, we developed a systematic process based on user-guided design space pruning techniques and built-in simulation tools. We show that our C-DEV is 15x better than C-DEVs designed using ad hoc methods that rely on limited data from prior experiments. We also show ways in which the C-DEV can be improved further and how different varieties of C-DEVs can be combined to form more complex logic circuits. Moreover, the systematic design process can be used to search for valid chromophore network configurations for a variety of RET applications.

We also describe a feasibility study for a technique used to control the orientation of chromophores attached to DNA. Being able to control the orientation can expand the design space for RET networks because it provides another parameter to tune their collective behavior. While results showed limited control over orientation, the analysis required the development of a mathematical model that can be used to determine the distribution of dipoles in a given sample of chromophore constructs. The model can be used to evaluate the feasibility of other potential orientation control techniques.

Role of Vinculin in Regulating Force-Sensitive Dynamics of Adherens Junctions During Collective Cell Migration

Dynamic processes such as morphogenesis and tissue patterning require the precise control of many cellular processes, especially cell migration. Historically, these processes are thought to be mediated by genetic and biochemical signaling pathways. However, recent advances have unraveled a previously unappreciated role of mechanical forces in regulating these homeostatic processes in of multicellular systems. In multicellular systems cells adhere to both deformable extracellular matrix (ECM) and other cells, which are sources of applied forces and means of mechanical support. Cells detect and respond to these mechanical signals through a poorly understood process called mechanotransduction, which can have profound effects on processes such as cell migration. These effects are largely mediated by the sub cellular structures that link cells to the ECM, called focal adhesions (FAs), or cells to other cells, termed adherens junctions (AJs).

Overall this thesis is comprised of my work on identifying a novel force dependent function of vinculin, a protein which resides in both FAs and AJs - in dynamic process of collective migration. Using a collective migration assay as a model for collective cell behavior and a fluorescence resonance energy transfer (FRET) based molecular tension sensor for vinculin I demonstrated a spatial gradient of tension across vinculin in the direction of migration. To define this novel force-dependent role of vinculin in collective migration I took advantage of previously established shRNA based vinculin knock down Marin-Darby Canine Kidney (MDCK) epithelial cells.

The first part of my thesis comprises of my work demonstrating the mechanosensitive role of vinculin at AJ’s in collectively migrating cells. Using vinculin knockdown cells and vinculin mutants, which specifically disrupt vinculin’s ability to bind actin (VinI997A) or disrupt its ability to localize to AJs without affecting its localization at FAs (VinY822F), I establish a role of force across vinculin in E-cadherin internalization and clipping. Furthermore by measuring E-cadherin dynamics using fluorescence recovery after bleaching (FRAP) analysis I show that vinculin inhibition affects the turnover of E-cadherin at AJs. Together these data reveal a novel mechanosensitive role of vinculin in E-cadherin internalization and turnover in a migrating cell layer, which is contrary to the previously identified role of vinculin in potentiating E-cadherin junctions in a static monolayer.

For the last part of my thesis I designed a novel tension sensor to probe tension across N-cadherin (NTS). N-cadherin plays a critical role in cardiomyocytes, vascular smooth muscle cells, neurons and neural crest cells. Similar to E-cadherin, N-cadherin is also believed to bear tension and play a role in mechanotransduction pathways. To identify the role of tension across N-cadherin I designed a novel FRET-based molecular tension sensor for N-cadherin. I tested the ability of NTS to sense molecular tension in vascular smooth muscle cells, cardiomyocytes and cancer cells. Finally in collaboration with the Horwitz lab we have been able to show a role of tension across N-cadherin in synaptogenesis of neurons.