Doctor of Philosophy

dissertationOver the past few decades, synthetic biology has generated great interest to biologists and engineers alike. Synthetic biology combines the research of biology with the engineering principles of standards, abstraction, and automated construction with the ultimate goal of being able to design and build useful biological systems. To realize this goal, researchers are actively working on better ways to model and analyze synthetic genetic circuits, groupings of genes that influence the expression of each other through the use of proteins. When designing and analyzing genetic circuits, researchers are often interested in building circuits that exhibit a particular behavior. Usually, this involves simulating their models to produce some time series data and analyzing this data to discern whether or not the circuit behaves appropriately. This method becomes less attractive as circuits grow in complexity because it becomes very time consuming to generate a sufficient amount of runs for analysis. In addition, trying to select representative runs out of a large data set is tedious and error-prone thereby motivating methods of automating this analysis. This has led to the need for design space exploration techniques that allow synthetic biologists to easily explore the effect of varying parameters and efficiently consider alternative designs of their systems. This dissertation attempts to address this need by proposing new analysis and verification techniques for synthetic genetic circuits. In particular, it applies formal methods such as model checking techniques to models of genetic circuits in order to ensure that they behave correctly and are as robust as possible for a variety of different inputs and/or parameter settings. However, model checking stochastic systems is not as simple as model checking deterministic systems where it is always known what the next state of the system will be at any given step. Stochastic systems can exhibit a variety of different behaviors that are chosen randomly with different probabilities at each time step. Therefore, model checking a stochastic system involves calculating the probability that the system will exhibit a desired behavior. Although it is often more difficult to work with the probabilities that stochastic systems introduce, stochastic systems and the models that represent them are becoming commonplace in many disciplines including electronic circuit design where as parts are being made smaller and smaller, they are becoming less reliable. In addition to stochastic model checking, this dissertation proposes a new incremental stochastic simulation algorithm (iSSA) based on Gillespie's stochastic simulation algorithm (SSA) that is capable of presenting a researcher with a simulation trace of the typical behavior of the system. Before the development of this algorithm, discerning this information was extremely error-prone as it involved performing many simulations and attempting to wade through the massive amounts of data. This algorithm greatly aids researchers in designing genetic circuits as it efficiently shows the researcher the most likely behavior of the circuit. Both the iSSA and stochastic model checking can be used in concert to give a researcher the likelihood that the system will exhibit its most typical behavior. Once the typical behavior is known, properties for nontypical behaviors can be constructed and their likelihoods can also be computed. This methodology is applied to several genetic circuits leading to new understanding of the effects of various parameters on the behavior of these circuits

Utilizing stochastic model checking to analyze genetic circuits

pre-printWhen designing and analyzing genetic circuits, researchers are often interested in the probability of the system reaching a given state within a certain amount of time. Usually, this involves simulating the system to produce some time series data and analyzing this data to discern the state probabilities. However, as the complexity of models of genetic circuits grow, it becomes more difficult for researchers to reason about the different states by looking only at time series simulation results of the models. To address this problem, this paper employs the use of stochastic model checking, a method for determining the likelihood that certain events occur in a system, with continuous stochastic logic (CSL) properties to obtain similar results. This goal is accomplished by the introduction of a methodology for converting a genetic circuit model (GCM) into a continuous-time Markov chain (CTMC). This CTMC is analyzed using transient Markov chain analysis to determine the likelihood that the circuit satisfies a given CSL property in a finite amount of time. This paper illustrates a use of this methodology to determine the likelihood of failure in a genetic toggle switch and compares these results to stochastic simulation-based analysis of this same circuit. Our results show that this method results in a substantial speedup as compared with conventional simulation-based approaches

Farm Machinery Costs: Own Lease or Custom Hire

One of the largest annual costs on South Dakota farms and ranches today is that of owning and operating machinery. Total costs associated with farm machinery have increased as farm operators have expanded their operations and substituted machines for labor. This trend does not appear to have run its course because new and larger machines are continuously being developed and adopted. Along with these changes have been corresponding increases in productivity per worker, average farm size, and total machinery investment. In many cases, increased investment in machinery has occurred at the expense of operating capital. Farmers have had to invest heavily in modern machinery without being able to expand production enough to justify the added investment. To add to the problem, rising production costs, variable farm prices, and the increasingly competitive nature of all agricultural production have combined to exert important economic pressures on farmers. These trends are causing farmers to take a closer look at the alternatives to machine ownership in order to release scarce capital for investment in other phases of the farm business where a higher return can be realized

The International Critical Commentary. Chronicles

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Approximation Techniques for Stochastic Analysis of Biological Systems

There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to scale limits its applicability in practice. This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking. The proposed method is evaluated on a design of a genetic toggle switch. Comparisons with another state-of-the-art tool demonstrate both accuracy and efficiency of the presented method

Livestock Contract Feeding Arrangements

The “share or contract feeding” arrangements have been a favorable alternative for both livestock owners and feeders for a number of years. Some feeders prefer to risk only their labor and possibly their feed, others are willing to risk the entire cost of feeding livestock but lack the necessary capital or credit. Some livestock owners find it more convenient to contract with a second party to finish their livestock for market. As in all contracts livestock feeding contracts should be fair and understood by both parties. The contract should be designed to meet specific conditions important to the livestock owner and to the feeder. In a contract feeding agreement, the livestock owner usually agrees to supply the livestock to be fed. The feeder agrees to furnish the feed, equipment and labor for wintering, and/or pasturing or fattening the animals. The purpose of the contract is to make provisions for: • Handling and feeding. • Division of profit or loss. • Marketing the livestock. A thorough understanding of the contract should be reached before the plan is completed and signed. The agreement should always be in writing and each party should have a signed copy

Frequency-Domain Photon Migration in Turbid Media

An analytical model is presented for the propagation of diffuse photon density waves in turbid media. The frequency- and wavelength-dependence of photon density waves are measured using Frequency-domain Photon Migration (FDPM). Media optical properties, including absorption, transport, and fluorescence relaxation times are calculated from experimental results

Metrics for Signal Temporal Logic Formulae

Signal Temporal Logic (STL) is a formal language for describing a broad range of real-valued, temporal properties in cyber-physical systems. While there has been extensive research on verification and control synthesis from STL requirements, there is no formal framework for comparing two STL formulae. In this paper, we show that under mild assumptions, STL formulae admit a metric space. We propose two metrics over this space based on i) the Pompeiu-Hausdorff distance and ii) the symmetric difference measure, and present algorithms to compute them. Alongside illustrative examples, we present applications of these metrics for two fundamental problems: a) design quality measures: to compare all the temporal behaviors of a designed system, such as a synthetic genetic circuit, with the "desired" specification, and b) loss functions: to quantify errors in Temporal Logic Inference (TLI) as a first step to establish formal performance guarantees of TLI algorithms.Comment: This paper has been accepted for presentation at, and publication in the proceedings of, the 2018 IEEE Conference on Decision and Control (CDC), to be held in Fontainebleau, Miami Beach, FL, USA on Dec. 17-19, 201

BBF RFC 108: Synthetic Biology Open Language (SBOL) Version 2.0.0

The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards