Jacob Beal

The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology

The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-driven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.

CRISPR transcriptional repression devices and layered circuits in mammalian cells.

A key obstacle to creating sophisticated genetic circuits has been the lack of scalable device libraries. Here we present a modular transcriptional repression architecture based on clustered regularly interspaced palindromic repeats (CRISPR) system and examine approaches for regulated expression of guide RNAs in human cells. Subsequently we demonstrate that CRISPR regulatory devices can be layered to create functional cascaded circuits, which provide a valuable toolbox for engineering purposes.

Automatic Compilation from High-Level Biologically- Oriented Programming Language to Genetic Regulatory Networks

Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications

We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflows modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.

Composable continuous-space programs for robotic swarms

Programmability is an increasingly important barrier to the deployment of multi-robot systems, as no prior approach allows routine composition and reuse of general aggregate behaviors. The Proto spatial computing language, however, already provides this sort of aggregate behavior programming for non-mobile systems using an abstraction of the network as a continuous-space-filling device. We extend this abstraction to mobile systems and show that Proto can be applied to multi-robot systems with an actuator that turns a vector field into device motion. Proto programs operate on fields of values over an abstract device called the amorphous medium and can be joined together using functional composition. These programs are then automatically transformed for execution by individual devices, producing an approximation of the specified continuous-space behavior. We are thus able to build up a library of simple swarm behaviors, and to compose them together into highly succinct programs that predictably produce the desired complex swarm behaviors, as demonstrated in simulation and on a group of 40 iRobot SwarmBots.

Fast self-healing gradients

We present CRF-Gradient, a self-healing gradient algorithm that provably reconfigures in O(diameter) time. Self-healing gradients are a frequently used building block for distributed self-healing systems, but previous algorithms either have a healing rate limited by the shortest link in the network or must rebuild invalid regions from scratch. We have verified CRF-Gradient in simulation and on a network of Mica2 motes. Our approach can also be generalized and applied to create other self-healing calculations, such as cumulative probability fields.

Aggregate Programming for the Internet of Things

Through field calculus constructs and building-block APIs, aggregate programming could help unlock the IoTs true potential by allowing complex distributed services to be specified succinctly and by enabling such services to be safely encapsulated, modulated, and composed with one another.

Synthetic Biology Open Language (SBOL) Version 2.0.0.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. 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. This document details version 2.0 of SBOL, introducing a standardized format for the electronic exchange of information on the structural and functional aspects of biological designs. The standard has been designed to support the explicit and unambiguous description of biological designs by means of a well defined data model. The standard also includes rules and best practices on how to use this data model and populate it with relevant design details. The publication of this specification is intended to make these capabilities more widely accessible to potential developers and users in the synthetic biology community and beyond.

Protelis: practical aggregate programming

The notion of a computational field has been proposed as a unifying abstraction for developing distributed systems, focusing on the computations and coordination of aggregates of devices instead of individual behavior. Prior field-based languages, however, have suffered from a number of practical limitations that have posed barriers to adoption and use. We address these limitations by introduction of Protelis, a functional language based on computational fields and embedded in Java, thereby enabling the construction of widely reusable components of aggregate systems. We demonstrate the simplicity of Protelis integration and programming through two examples: simulation of a pervasive computing scenario in the Alchemist simulator [24], and coordinated management of a network of services.

Automated Selection of Synthetic Biology Parts for Genetic Regulatory Networks

Raising the level of abstraction for synthetic biology design requires solving several challenging problems, including mapping abstract designs to DNA sequences. In this paper we present the first formalism and algorithms to address this problem. The key steps of this transformation are feature matching, signal matching, and part matching. Feature matching ensures that the mapping satisfies the regulatory relationships in the abstract design. Signal matching ensures that the expression levels of functional units are compatible. Finally, part matching finds a DNA part sequence that can implement the design. Our software tool MatchMaker implements these three steps.