Matching on-the-fly in Sequential Experiments for Higher Power and Efficiency

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials. Subjects arrive iteratively and are either randomized or paired via a matching criterion to a previously randomized subject and administered the alternate treatment. We develop estimators for the average treatment effect that combine information from both the matched pairs and unmatched subjects as well as an exact test. Simulations illustrate the method's higher efficiency and power over competing allocation procedures in both controlled scenarios and historical experimental data.Comment: 20 pages, 1 algorithm, 2 figures, 8 table

Programming Synthetic Microbial Communities for Coexistence, Coordination, and Information Processing

Synthetic microbial communities offer a variety of potential advantages over single species approaches for many medical, industrial, and environmental applications. At the cellular level, metabolic pathways can be distributed amongst several community residents to lower the metabolic burden on individual cells and to enable optimization of reaction conditions for different parts of metabolic pathways. At the population level, diverse microbial communities in different natural contexts have been shown to be more productive, efficient, stable, and resistant to invasion by foreign agents. Along with these potential advantages, however, come a variety of new challenges as well. First, different species or cell types of interest must be able to coexist. Additionally, in many scenarios the relative abundance of each resident can impact the overall property of the community. Beyond coexistence and community composition, information processing and sharing is often essential to the types of complex, coordinated behavior that is required for many desired medical, industrial, and environmental applications. My dissertation has centered around the design and implementation of two novel systems which address some of the challenges discussed above that must be overcome to realize the potential of synthetic microbial communities for use in technological applications. In the first system our goal was to develop a tool that can be used to enable coexistence and program community composition within a synthetic microbial community. We use xvi temperature as a modality to enable coexistence of two microorganisms, Escherichia coli and Pseudomonas putida, with different thermal niches and to further program the composition of this model synthetic bi-culture. Specifically, I developed two different approaches, referred to as a constant temperature regime and a cycling temperature regime. Employing a combination of wet-lab experiments and mathematical modeling, I showed that a variety of parameters such as temperature, cycle duration, etc. can be manipulated to achieve desired community compositions. Building on this work, I then used a mathematical framework developed by ecologists to explore design principles and specific mechanisms underlying the observed relationship between culture temperature and coexistence. In the second system, I designed a novel synthetic microbial community with a distributed sensing and centralized reporting architecture that is enabled by what we have termed bacteriophage-mediated information transfer. Our goal is to explore a novel distributed sensing with centralized memory system architecture that is capable of addressing limitations of previously developed systems. A modular genetic circuit was developed that connects the input of an environmental signal of interest to activation of a lysogenic lambda bacteriophage which is used to transfer information about the sensing event from the sensor cell population to a reporter cell population. A variety of different ways to encode and store information were explored. While seemingly different, the lines of work described above are connected by a common thread of developing generalizable and modular approaches for engineering synthetic microbial communities to deliver the potential advantages they offer in a variety of medical, industrial, and environmental applications. Synthetic microbial communities are capable of xvii performing complex and varied functions within these contexts and this dissertation is contributing to the rapidly growing body of research work for addressing the challenges that must be overcome to realize that potential.PHDCellular & Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163080/1/agkrieg_1.pd

Matching On-the-Fly: Sequential Allocation with Higher Power and Efficiency

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in fixed sample randomized trials with sequential allocation. Subjects arrive iteratively and are either randomized or paired via a matching criterion to a previously randomized subject and administered the alternate treatment. We develop estimators for the average treatment effect that combine information from both the matched pairs and unmatched subjects as well as an exact test. Simulations illustrate the method\u27s higher efficiency and power over several competing allocation procedures in both simulations and in data from a clinical trial

Using Open Stack for an Open Cloud Exchange(OCX)

We are developing a new public cloud, the Massachusetts Open Cloud (MOC) based on the model of an Open Cloud eXchange (OCX). We discuss in this paper the vision of an OCX and how we intend to realize it using the OpenStack open-source cloud platform in the MOC. A limited form of an OCX can be achieved today by layering new services on top of OpenStack. We have performed an analysis of OpenStack to determine the changes needed in order to fully realize the OCX model. We describe these proposed changes, which although significant and requiring broad community involvement will provide functionality of value to both existing single-provider clouds as well as future multi-provider ones

The Optimality of Blocking Designs in Equally and Unequally Allocated Randomized Experiments with General Response

We consider the performance of the difference-in-means estimator in a two-arm randomized experiment under common experimental endpoints such as continuous (regression), incidence, proportion and survival. We examine performance under both equal and unequal allocation to treatment groups and we consider both the Neyman randomization model and the population model. We show that in the Neyman model, where the only source of randomness is the treatment manipulation, there is no free lunch: complete randomization is minimax for the estimator's mean squared error. In the population model, where each subject experiences response noise with zero mean, the optimal design is the deterministic perfect-balance allocation. However, this allocation is generally NP-hard to compute and moreover, depends on unknown response parameters. When considering the tail criterion of Kapelner et al. (2021), we show the optimal design is less random than complete randomization and more random than the deterministic perfect-balance allocation. We prove that Fisher's blocking design provides the asymptotically optimal degree of experimental randomness. Theoretical results are supported by simulations in all considered experimental settings.Comment: 33 pages, 1 figure, 2 table

Temperature regulation as a tool for enabling and programming synthetic microbial communities

As target applications grow more elaborate, researchers are developing new approaches to program increasingly complex functionality into synthetic biology platforms. One emerging approach is engineering cooperative, multi-species synthetic microbial communities, which offer significant potential advantages compared to single species systems for numerous applications such as biosynthesis of target compounds through complex pathways enabled by division of labor. However, population dynamics, inter-species interactions, and differing ecological niches of resident microorganisms also introduce complexities that must be addressed to achieve effective and robust synthetic microbial communities. One fundamental challenge is regulation of community composition. At the most basic level, maintaining coexistence of resident community members is required to enable the desired community level functionality. Additionally, community composition often needs to be tuned to optimize overall functionality. For example, when a complex pathway is divided into multiple components hosted by different community members, fluxes through different enzymatic reactions can be coordinated through modulation of each sub-population size to maximize overall efficiency. This type of microbial community manipulation has not been fully utilized in synthetic biology applications, likely due in part to limited available tools. Here we develop temperature regulation as a general tool to enable coexistence and control community composition in synthetic microbial communities. We demonstrate that rationally selected constant temperature regimes can be used to enable coexistence of species from distinct thermal niches. Furthermore, cycling temperature regimes can be used to regulate relative species abundance in microbial communities. We employ mathematical modeling to design cycling temperature regimes for desired community compositions and related features. As microbial communities are increasingly used in a variety of applications, we envision that tools for modulating community composition will continue to expand and we see temperature regulation as a powerful new approach in this area