Synthetic biology aims at producing novel biological systems to carry out some desired and well-defined functions. An ultimate dream is to design these systems at a high level of abstraction using engineering-based tools and programming languages, press a button, and have the design translated to DNA sequences that can be synthesized and put to work in living cells. We introduce such a programming language, which allows logical interactions between potentially undetermined proteins and genes to be expressed in a modular manner. Programs can be translated by a compiler into sequences of standard biological parts, a process that relies on logic programming and prototype databases that contain known biological parts and protein interactions. Programs can also be translated to reactions, allowing simulations to be carried out. While current limitations on available data prevent full use of the language in practical applications, the language can be used to develop formal models of synthetic systems, which are otherwise often presented by informal notations. The language can also serve as a concrete proposal on which future language designs can be discussed, and can help to guide the emerging standard of biological parts which so far has focused on biological, rather than logical, properties of parts.
The ability to design and construct synthetic biological systems with predictable behavior could enable significant advances in medical treatment, agricultural sustainability, and bioenergy production. However, to reach a stage where such systems can be reliably designed from biological components, integrated experimental and computational techniques that enable robust component characterization are needed. In this paper we present a computational method for the automated characterization of genetic components. Our method exploits a recently developed multichannel experimental protocol and integrates bacterial growth modeling, Bayesian parameter estimation, and model selection, together with data processing steps that are amenable to automation. We implement the method within the Genetic Engineering of Cells modeling and design environment, which enables both characterization and design to be integrated within a common software framework. To demonstrate the application of the method, we quantitatively characterize a synthetic receiver device that responds to the 3-oxohexanoyl-homoserine lactone signal, across a range of experimental conditions.
Abstract. This paper introduces a Language for Biochemical Systems (LBS) which combines rule-based approaches to modelling with modularity. It is based on the Calculus of Biochemical Systems (CBS) which affords modular descriptions of metabolic, signalling and regulatory networks in terms of reactions between modified complexes, occurring concurrently inside a hierarchy of compartments and with possible crosscompartment interactions and transport. Additional features of LBS, targeted towards practical and large-scale applications, include species expressions for manipulating large complexes in a concise manner, parameterised modules with a notion of subtyping for writing reusable modules, and nondeterminism for handling combinatorial explosion. These features are demonstrated through examples. A formal specification of LBS is then given through an abstract syntax and a general semantics which is parametric on a structure pertaining to the specific choice of target semantical objects. Examples of such structures for the specific cases of Petri nets, coloured Petri nets, ODEs and continuous time Markov chains are also given.
Abstract. CBS is a Calculus of Biochemical Systems intended to allow the modelling of metabolic, signalling and regulatory networks in a natural and modular manner. In this paper we extend CBS with features directed towards practical, large-scale applications, thus yielding LBS: a Language for Biochemical Systems. The two main extensions are expressions for modifying large complexes in a step-wise manner and parameterised modules with a notion of subtyping; LBS also has nested declarations of species and compartments. The extensions are demonstrated with examples from the yeast pheromone pathway. A formal specification of LBS is then given through an abstract syntax, static semantics and a translation to a variant of coloured Petri nets. Translation to other formalisms such as ordinary differential equations and continuous time Markov chains is also possible.
This chapter provides an overview of a programming language for Genetic Engineering of Cells (GEC). A GEC program specifies a genetic circuit at a high level of abstraction through constraints on otherwise unspecified DNA parts. The GEC compiler then selects parts which satisfy the constraints from a given parts database. GEC further provides more conventional programming language constructs for abstraction, e.g., through modularity. The GEC language and compiler is available through a Web tool which also provides functionality, e.g., for simulation of designed circuits.
Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages.
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