Andrew Phillips

A programming language for composable DNA circuits

Recently, a range of information-processing circuits have been implemented in DNA by using strand displacement as their main computational mechanism. Examples include digital logic circuits and catalytic signal amplification circuits that function as efficient molecular detectors. As new paradigms for DNA computation emerge, the development of corresponding languages and tools for these paradigms will help to facilitate the design of DNA circuits and their automatic compilation to nucleotide sequences. We present a programming language for designing and simulating DNA circuits in which strand displacement is the main computational mechanism. The language includes basic elements of sequence domains, toeholds and branch migration, and assumes that strands do not possess any secondary structure. The language is used to model and simulate a variety of circuits, including an entropy-driven catalytic gate, a simple gate motif for synthesizing large-scale circuits and a scheme for implementing an arbitrary system of chemical reactions. The language is a first step towards the design of modelling and simulation tools for DNA strand displacement, which complements the emergence of novel implementation strategies for DNA computing.

Efficient, correct simulation of biological processes in the stochastic pi-calculus

This paper presents a simulation algorithm for the stochastic π-calculus, designed for the efficient simulation of biological systems with large numbers of molecules. The cost of a simulation depends on the number of species, rather than the number of molecules, resulting in a significant gain in efficiency. The algorithm is proved correct with respect to the calculus, and then used as a basis for implementing the latest version of the SPiM stochastic simulator. The algorithm is also suitable for generating graphical animations of simulations, in order to visualise system dynamics.

Towards programming languages for genetic engineering of living cells

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.

A compositional approach to the stochastic dynamics of gene networks

We propose a compositional approach to the dynamics of gene regu-latory networks based on the stochastic π-calculus, and develop a representation of gene network elements which can be used to build complex circuits in a transparent and efficient way. To demonstrate the power of the approach we apply it to several artificial networks, such as the repressilator and combinatorial gene circuits first studied in Combinatorial Synthesis of Genetic Networks [1]. For two examples of the latter systems, we point out how the topology of the circuits and the interplay of the stochastic gate interactions influence the circuit behavior. Our approach may be useful for the testing of biological mechanisms proposed to explain the experimentally observed circuit dynamics.

Design and analysis of DNA strand displacement devices using probabilistic model checking

Designing correct, robust DNA devices is difficult because of the many possibilities for unwanted interference between molecules in the system. DNA strand displacement has been proposed as a design paradigm for DNA devices, and the DNA strand displacement (DSD) programming language has been developed as a means of formally programming and analysing these devices to check for unwanted interference. We demonstrate, for the first time, the use of probabilistic verification techniques to analyse the correctness, reliability and performance of DNA devices during the design phase. We use the probabilistic model checker prism, in combination with the DSD language, to design and debug DNA strand displacement components and to investigate their kinetics. We show how our techniques can be used to identify design flaws and to evaluate the merits of contrasting design decisions, even on devices comprising relatively few inputs. We then demonstrate the use of these components to construct a DNA strand displacement device for approximate majority voting. Finally, we discuss some of the challenges and possible directions for applying these methods to more complex designs.

Abstractions for DNA circuit design

DNA strand displacement techniques have been used to implement a broad range of information processing devices, from logic gates, to chemical reaction networks, to architectures for universal computation. Strand displacement techniques enable computational devices to be implemented in DNA without the need for additional components, allowing computation to be programmed solely in terms of nucleotide sequences. A major challenge in the design of strand displacement devices has been to enable rapid analysis of high-level designs while also supporting detailed simulations that include known forms of interference. Another challenge has been to design devices capable of sustaining precise reaction kinetics over long periods, without relying on complex experimental equipment to continually replenish depleted species over time. In this paper, we present a programming language for designing DNA strand displacement devices, which supports progressively increasing levels of molecular detail. The language allows device designs to be programmed using a common syntax and then analysed at varying levels of detail, with or without interference, without needing to modify the program. This allows a trade-off to be made between the level of molecular detail and the computational cost of analysis. We use the language to design a buffered architecture for DNA devices, capable of maintaining precise reaction kinetics for a potentially unbounded period. We test the effectiveness of buffered gates to support long-running computation by designing a DNA strand displacement system capable of sustained oscillations.

A graphical representation for biological processes in the stochastic pi-calculus

This paper presents a graphical representation for the stochastic π-calculus, which is formalised by defining a corresponding graphical calculus. The graphical calculus is shown to be reduction equivalent to stochastic π, ensuring that the two calculi have the same expressive power. The graphical representation is used to model a couple of example biological systems, namely a bistable gene network and a mapk signalling cascade. One of the benefits of the representation is its ability to highlight the existence of cycles, which are a key feature of biological systems. Another benefit is its ability to animate interactions between system components, in order to visualise system dynamics. The graphical representation can also be used as a front end to a simulator for the stochastic π-calculus, to help make modelling and simulation of biological systems more accessible to non computer scientists.

Computational modeling of synthetic microbial biofilms.

Microbial biofilms are complex, self-organized communities of bacteria, which employ physiological cooperation and spatial organization to increase both their metabolic efficiency and their resistance to changes in their local environment. These properties make biofilms an attractive target for engineering, particularly for the production of chemicals such as pharmaceutical ingredients or biofuels, with the potential to significantly improve yields and lower maintenance costs. Biofilms are also a major cause of persistent infection, and a better understanding of their organization could lead to new strategies for their disruption. Despite this potential, the design of synthetic biofilms remains a major challenge, due to the complex interplay between transcriptional regulation, intercellular signaling, and cell biophysics. Computational modeling could help to address this challenge by predicting the behavior of synthetic biofilms prior to their construction; however, multiscale modeling has so far not been achieved for realistic cell numbers. This paper presents a computational method for modeling synthetic microbial biofilms, which combines three-dimensional biophysical models of individual cells with models of genetic regulation and intercellular signaling. The method is implemented as a software tool (CellModeller), which uses parallel Graphics Processing Unit architectures to scale to more than 30,000 cells, typical of a 100 μm diameter colony, in 30 min of computation time.

Localized hybridization circuits

Molecular computing executed via local interactions of spatially contiguous sets of molecules has potential advantages of (i) speed due to increased local concentration of reacting species, (ii) generally sharper switching behavior and higher precision due to single molecule interactions, (iii) parallelism since each circuit operates independently of the others and (iv) modularity and scalability due to the ability to reuse DNA sequences in spatially separated regions. We propose detailed designs for local molecular computations that involve spatially contiguous molecules arranged on addressable substrates. The circuits act via enzyme-free DNA hybridization reaction cascades. Our designs include composable OR, AND and propagation Boolean gates, and techniques to achieve higher degree fan-in and fan-out. A biophysical model of localized hybridization reactions is used to estimate the effect of locality on reaction rates. We also use the Visual DSD simulation software in conjunction with localized reaction rates to simulate a localized circuit for computing the square root of a four bit number.

The cell biology of major histocompatibility complex class I assembly: towards a molecular understanding

Major histocompatibility complex class I (MHC I) proteins protect the host from intracellular pathogens and cellular abnormalities through the binding of peptide fragments derived primarily from intracellular proteins. These peptide-MHC complexes are displayed at the cell surface for inspection by cytotoxic T lymphocytes. Here we reveal how MHC I molecules achieve this feat in the face of numerous levels of quality control. Among these is the chaperone tapasin, which governs peptide selection in the endoplasmic reticulum as part of the peptide-loading complex, and we propose key amino acid interactions central to the peptide selection mechanism. We discuss how the aminopeptidase ERAAP fine-tunes the peptide repertoire available to assembling MHC I molecules, before focusing on the journey of MHC I molecules through the secretory pathway, where calreticulin provides additional regulation of MHC I expression. Lastly we discuss how these processes culminate to influence immune responses.