Tissues comprise a multiplicity of specialized cell types that must coordinate state changes in order to function collectively. These state changes are orchestrated by coordinated direct cellular interactions and indirect responses to microenvironmental and systemic cues. Consequently, chronic perturbations to this collective behavior can result in disease states that are difficult to reprogram such as autoimmunity and cancer. As such, studying the self-reinforced dynamics of tissue function can benefit from a systems biology approach where the aim is to understand how individual components of biological systems interact to give rise to emergent properties. The recent growth in the availability of single-cell resolution genomics platforms has further expanded biologists’ ability to do this kind of unbiased inquiry. However, despite the increasing ease of generating these high-dimensional datasets, analyzing these data still presents significant computational challenges because of their noise and sparsity, which are further exacerbated on the level of individual cells and genes. As such, there is a need to develop computational methods that enable scientists to extract systems-level biological insight from noisy high dimensional data. This dissertation introduces DECIPHER, a machine learning framework tailored for network inference in systems biology, with a focus on applications to single-cell RNA sequencing data. Chapter 2 details the DECIPHER algorithm and its implementation for the R computing environment, deciphR, that is designed to reconstruct cell state networks from high-dimensional molecular profiles. Chapter 3 applies DECIPHER to unveil cell-cell interaction networks in the human breast, elucidating how state changes on the cell-level propagate throughout tissue in response to hormonal fluctuations. Chapter 4 extends DECIPHER's application to investigate peripheral immune dysregulation in a rare pediatric autoimmune disease, revealing underlying immune imbalances that persist even in disease remission and potential therapeutic targets. Overall, this dissertation presents a generalizable approach to network inference for systems biology and demonstrates its utility in multiple biological contexts for unravelling cellular coordination in tissue homeostasis and disease.

We model non-binding retail-price recommendations (RPRs) as a communication device facilitating coordination in vertical supply relations. Assuming both repeated vertical trade and asymmetric information about production costs, we show that RPRs may be part of a relational contract, communicating private information from manufacturer to retailer that is indispensable for maximizing joint surplus. We show that this contract is self-enforcing if the retailer’s profit is independent of production costs and punishment strategies are chosen appropriately. We also extend our analysis to settings where consumer demand is variable or depends directly on the manufacturer’s RPRs. Keywords: vertical relationships, relational contracts, asymmetric information, price recommendations. JEL Classification: D23; D43; L14; L15.

Tissue-level biology is an important, but experimentally challenging field within the biological sciences. Generation of correct tissue models in vitro is limited by the lack of scientific techniques available to fully control the correct physical arrangement of cells, extracellular matrix (ECM), and diffusible factors that are vital for homeostasis and proper function. This dissertation presents a method that combines a top-down patterning technique with the bottom-up assembly of cells to generate tissue models, called Surface Templated DNA Programmed Assembly of Cells (STDPAC). At its core, the method uses cell-surfaced grafted oligonucleotides as “zip codes” to direct cell-surface and cell-cell interactions through Watson-Crick base pairing. Herein we demonstrate the method, a showcase of technological applications, and complex tissue models generated to probe the biological function of tissues within the mammary gland.

The Notch signaling pathway is required in sensing the local cellular environment during multicellular development. Because Notch utilizes juxtacrine contact between two cells and relies upon a set of protease cleavage events, understanding the distribution and dynamics of the receptor during activation are of particular importance in order to understand its regulation. Notch must not just be expressed and present at the cell surface, but must be at the right place at the right time for proper activation to occur. Here we invent and then use monovalent Quantum Dots (mQDs) to target and monitor human Notch1 on the surface of live mammalian cells. We track and observe the dynamics of the receptor and compare the diffusion and distribution of Notch with other structural features at the surface of the cell. We find Notch to be slow and confined in its diffusion at the cell surface. In comparing temporal dynamics with static high-resolution static microscopy of the receptor we find Notch to be excluded from particular regions on the surface. These regions of exclusion include focal adhesions as determined by co-imaging Paxillin and Notch. In an attempt to determine the mechanism of exclusion we systematically pared domains from the receptor and identify the extracellular Notch regulatory region (NRR) as playing a significant role in distributing the receptor at the cell surface.

Additional work tries to quantitate the emergent behaviors observed in intercellular interactions taking place in heterogeneous collections of tissues. We repurpose single molecule tracking techniques used to track single molecules in order to track and identify the distribution and dynamics of cells to arrive at quantitative descriptions of multicellular interactions. We find two emergent behaviors in mosaic microtissues: cells with activated H-Ras are basally extruded or lead motile multicellular protrusions that direct the collective motility of their wild-type neighbors. Our results directly demonstrate that cell-to-cell variability in pathway activation within local populations of epithelial cells can drive emergent behaviors during epithelial morphogenesis.