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The dynamic behavior of synthetic gene circuits plays a key role in ensuring their correct function. Although there has been substantial work on modeling dynamic behavior after circuit construction, the forward engineering of dynamic behavior remains a major challenge. Previous engineering methods have focused on quantifying average behaviors of circuits over an extended time window, however this provides a static characterization of behavior that is a poor predictor of dynamics. Here we present a method for characterizing the dynamic behavior of synthetic gene circuits, using parameter inference of dynamical system models applied to time-series measurements of cell cultures growing in microtiter plates. We demonstrate that the behaviors of simple devices can be characterized dynamically and used to predict the behaviors of more complex circuits. Specifically, we compose 23 biological parts into 9 devices and use them to design 9 synthetic gene circuits in E. coli that provide core functionality for engineering cell behavior at the population level, including relays, receivers and a degrader. We embody our method in a software package and corresponding programming language. Our method supports the notion of an inference graph for iterative inference of models as new circuits are constructed, without the need to infer all models from scratch, and lays the foundation for characterizing large libraries of synthetic gene circuits in a scalable manner.

Chemical reaction networks (CRNs) have been applied successfully to model a wide range of phenomena and are commonly used for designing molecular computation circuits. Often, CRNs with specific properties (oscillations, Turing patterns, multistability) are sought, which entails searching an exponentially large space of CRNs for those that satisfy a property. As the size of the CRNs being considered grows, so does the frequency of isomorphisms, by up to a factor \(N!\), where \(N\) is the number of species. Accordingly, being able to generate sets of non-isomorphic CRNs within a class can lead to large computational savings when carrying out global searches. Here, we present a bijective encoding of bimolecular CRNs into novel vertex-coloured digraphs called Open image in new window . The problem of enumerating non-isomorphic CRNs can then be tackled by leveraging well-established computational methods from graph theory [20]. In particular, we extend Nauty, the graph isomorphism tool suite by McKay [22]. Our method is highly parallelisable and more efficient than competing approaches, and a software package (genCRN) is freely available for reuse. Non-isomorphs are generated directly by genCRN, alleviating the need to store intermediate results. We provide the first complete count of all 2-species bimolecular CRNs and extend previous known counts for classes of CRNs of special interest, such as mass-conserving and reversible CRNs.

Computational nucleic acid devices show great potential for enabling a broad range of biotechnology applications, including smart probes for molecular biology research, in vitro assembly of complex compounds, high-precision in vitro disease diagnosis and, ultimately, computational theranostics inside living cells. This diversity of applications is supported by a range of implementation strategies, including nucleic acid strand displacement, localization to substrates, and the use of enzymes with polymerase, nickase, and exonuclease functionality. However, existing computational design tools are unable to account for these strategies in a unified manner. This paper presents a logic programming language that allows a broad range of computational nucleic acid systems to be designed and analyzed. The language extends standard logic programming with a novel equational theory to express nucleic acid molecular motifs. It automatically identifies matching motifs present in the full system, in order to apply a spe...

We propose a type-based analysis to infer the session protocols of channels in an ML-like concurrent functional language. Combining and extending well-known techniques, we develop a type-checking system that separates the underlying ML type system from the typing of sessions. Without using linearity, our system guarantees communication safety and partial lock freedom. It also supports provably complete session inference for finite sessions with no programmer annotations. We exhibit the usefulness of our system with interesting examples, including one which is not typable in substructural type systems.

We develop a theory of bisimulations for a simple language containing communicating transactions, obtained by dropping the isolation requirement of standard transactions. Such constructs have emerged as a useful programming abstraction for distributed systems.

Consensus is an often occurring problem in concurrent and distributed programming. We present a programming language with simple semantics and build-in support for consensus in the form of communicating transactions. We motivate the need for such a construct with a characteristic example of generalized consensus which can be naturally encoded in our language. We then focus on the challenges in achieving an implementation that can efficiently run such programs. We setup an architecture to evaluate different implementation alternatives and use it to experimentally evaluate runtime heuristics. This is the basis for a research project on realistic programming language support for consensus.