BioScript: programming safe chemistry on laboratories-on-a-chip

This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry which executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contributions of the paper include the BioScript type system and relevant portions of its compiler. The type system ensures that certain types of errors, specific to biochemistry, do not occur, including the interaction of chemicals that may be unsafe. The compiler includes novel optimizations that place biochemical operations to execute concurrently on a spatial 2D array platform on the granularity of a control flow graph, as opposed to individual basic blocks. Results are obtained using both a cycle-accurate microfluidic simulator and a software interface to a real-world platform

The Phage Toolbox: Automating Phage Discovery Using Novel Software, Devices, and High-Throughput Methodology

With reports of antibiotic resistance on the rise, the need for alternative therapeutic options is of high importance. Bacteriophage (or phage) therapy is a valuable alternative strategy that holds high promise for treating bacterial infections not responsive to traditional antibiotics. Furthermore, the ability to automate phage therapy research would push the field forward by speeding the time for development, discovery, and reproducible outcomes. This dissertation provides solutions to these challenges by delivering a range of software and device-related tools to streamline and automate phage research. Together, these tools converge to create a &ldquo;phage toolbox&rdquo;, where each tool can be used independently or collectively, paving the way for more automated solutions in phage discovery. The tools presented in this dissertation include (1) PhageBox, (2) PhageFilter, (3) PhageScanner, and (4) DebruijnExtend. PhageBox is an open-source digital microfluidics extension that offers magnetic and temperature control. Furthermore, the proof-of-concept applications show specialized utility towards bacteriophages. PhageFilter is an ultralow memory software tool that introduces a Genome Sequence Bloom Tree (gSBT) to assign short or long-read sequencing data to genomes, enabling accurate DNA filtering, binning, and abundance estimation. Potentially using these filtered sequences, PhageScanner is a machine learning pipeline and software tool that predicts phage virion proteins (PVPs) from metagenomic sequencing data. This extends upon preceding work in this area by using both oligonucleotide and protein sequence information. Lastly, DebruijnExtend presents a tool and algorithm for predicting the secondary structure of proteins from the primary sequence, which can assist with feature extraction and determining subtle differences in the secondary structures of phage structural proteins. The collection of tools presented here offers a comprehensive toolbox for automating phage research from a multidisciplinary perspective, including microfluidics, wet lab biology, and bioinformatics. As we witness the rise of the Internet of Things, Artificial Intelligence, and Synthetic Biology, we are beginning to realize the potential for making significant advances in biology through a multidisciplinary approach. These tools contribute to this paradigm shift by introducing opensource projects that leverage concepts from each of these areas. We envision a future where a consortium of companies and researchers will concentrate on developing bioinformatics software and open-source devices to automate phage biology.</p

Towards Microfluidic Design Automation

Microfluidic chips, lab-on-a-chip devices that have channels transporting liquids instead of wires carrying electrons, have attracted considerable attention recently from the bio-medical industry because of their application in testing assay and large-scale chemical reaction automation. These chips promise dramatic reduction in the cost of large-scale reactions and bio-chemical sensors. Just like in traditional chip design, there is an acute need for automation tools that can assist with design, testing and verification of microfluidics chips. We propose a design methodology and tool to design microfluidic chips based on SMT solvers. The design of these chips is expressed using the language of partial differential equations (PDEs) and non-linear multi-variate polynomials over the reals. We convert such designs into SMT2 format through appropriate approximations, and invoke Z3 and dReal solver on them. Through our experiments we show that using SMT solvers is a not only a viable strategy to address the microfluidics design problem, but likely will be key component of any future development environment. In addition to analysis of Microfluidic Chip design, we discuss the new area of Microhydraulics; a new technology being developed for the purposes of macking dynamic molds and dies for manufacturing. By contrast, Microhydraulics is more concerned on creating designs that will satisfy the dynamic requirements of manufacturers, as opposed to microfludics which is more concerned about the chemical reactions taking place in a chip. We develop the background of the technology as well as the models required for SMT solvers such as Z3 and dReal to solve them

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Virginia Commonwealth University Courses

Listing of courses for the 2019-2020 year

Virginia Commonwealth University Courses

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Virginia Commonwealth University Courses

Listing of courses for the 2022-2023 year