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

Bridging Gaps in Programmable Laboratories-on-a-Chip Workflows and MediSyn: A Modular Pharmaceutical Discovery and Synthesis Framework

Life scientists have a need for making their work more efficient, cost-effective, and reproducible. The ongoing reproducibility crisis underscores the need for efforts to improve and transform existing methods, and the normative 10-15 years and $2.6 billion cost to develop new life-saving drugs is harrowing. This dissertation consists of two parts that aim to partially address these concerns: the first reviews programmable microfluidic labs-on-a-chip (pLoCs), which have been widely promised to solve issues with human error and resource waste when used for biochemical experimentation (assays). Despite touted advantages, existing pLoCs are unwieldy to operate, requiring manual translation of assays to sequences of electrode actuations to control their operation. Progress on high-level languages for pLoCs is encouraging, but back-end compiler support is lacking. This part provides solutions to fill in gaps between existing languages and pLoCs, allowing would-be adopters to translate their existing workflows to utilize these devices. Namely, it details (1) abstractions necessary for translating and compiling assays featuring time-constrained reactions, (2) optimizations that reduce waste, decrease latency, and---perhaps most importantly---enable targeting the very small surfaces of existing devices, and (3) a strategy for statically compiling and executing assays featuring pre-compiled functions, showcased on a real-world pLoC. The second part introduces MediSyn, a pharmaceutical research framework providing abstractions for building systems for discovering, synthesizing, and verifying safe drugs. MediSyn implements a superoptimizing search utilizing a Markov chain Monte Carlo strategy over a candidate space of drugs specified as a probabilistic context-free grammar (PCFG). Back-end modules (for candidate synthesis and evaluation) provide abstractions for connecting to remote cloud labs, local execution on pLoC(s), or manual entry when work is carried out on a benchtop. A proof-of-concept is presented as PepSyn, which implements two front-ends: (1) a regex-style domain-specific language (PepSketch) that takes inspiration from sketch-based syntax-guided synthesis, and (2) a user-interface that harnesses techniques used in natural language processing (PepGen) in the programming-by-example paradigm

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

ChemStor: Using Formal Methods To Guarantee Safe Storage and Disposal of Chemicals.

While safe chemical storage and disposal are simple in principle-users should read safety specifications and place chemicals in appropriate cabinets or collection points-high-profile incidents involving improper storage and disposal of chemicals continue to occur. This paper introduces ChemStor, an open-source, automated computational system that can guarantee (mathematically verify a system is correct with respect to its specification), with regard to prescribed constraints, safe storage and disposal of chemicals used in academic, industrial, and domestic settings. ChemStor borrows concepts from formal methods-a branch of computer science capable of mathematically proving a specification or software is correct-to safely store or dispose of chemicals. If two or more chemicals can be combined in the same cabinet without forming possibly dangerous combinations of chemicals (while observing cabinet/shelf space constraints), then ChemStor determines that the storage configuration is safe. Likewise, if chemicals can be added to an existing disposal container without forming possibly dangerous combinations of chemicals (or exceeding the volume of the container), then ChemStor determines that the disposal configuration is safe. ChemStor accomplishes this by first building a chemical interaction graph, a graph that describes which chemicals may interact with each other based on their reactivity groups as determined by the United States Environmental Protection Agency. Next, ChemStor computes the chromatic number of the graph, the smallest number of colors used to color the graph such that no two vertices (chemicals) that share an edge (an interaction) share the same color. ChemStor then assigns all the chemicals of each color to a storage or disposal container after confirming that there is enough space in the container. These steps are encoded into a series of satisfiability modulo theory equations, and ChemStor uses an industry-standard tool to try to find a valid solution to these equations. The result is either a solution which dictates exactly where to store or dispose of each chemical, or an indication that no safe storage or disposal configuration could be found. To demonstrate the feasibility of ChemStor, we used the tool to analyze ten real-world chemical storage and disposal incidents that led to injuries or destruction of property. In each case, ChemStor quickly and successfully identified a proper chemical disposal or storage configuration that would have prevented the incident. In the future, ChemStor may be integrated with electronic laboratory notebooks, voice assistants, and other emerging technology to protect users of chemicals in labs, workplaces, and homes