Workshop on Bio-Design for Portability (BD4P)

8 July 2019   (co-located with IWBDA 2019)

Room FW11
Department of Computer Science and Technology
University of Cambridge, UK

Synthetic Biology aims at refactoring an organism's genome to add new functionalities, or even designing and synthesising an entirely novel genome. These aims would be achieved my making use of standard engineering practices, and genetic parts, devices and circuits that can be used in different designs and organisms. However, even with 'simple' organisms such as bacteria the reality is very different: “porting” a circuit from, say, Bacillus subtilis to Escherichia coli will most likely require significant redesign and effort. In this workshop we will address some of the challenges relevant for the portability of synthetic designs across species, and will give an overview of possible solutions being developed in a large EPSRC-funded project.

The workshop is sponsored by the EPSRC Portabolomics project.

There is no registration fee, but for catering purposes please register here.


Towards a complete and quantitative view of genetic circuit function
Thomas E. Gorochowski
Royal Society University Research Fellow
School of Biological Sciences, University of Bristol, UK

Synthetic genetic circuits are composed of many interconnected parts that must function together in concert to implement desired biological computations. A major challenge when developing new circuits is that genetic parts often display unexpected changes in their performance when used in new ways. These arise due to contextual effects or unintended interactions with the host cell. In this talk, I will demonstrate how we have been using sequencing technologies and mathematical modelling to tackle this problem. First, I will show how RNA-sequencing can be used to measure the function of every transcriptional part making up large genetic circuits. This enables us to better understand why some designs fail and helps pin point the root cause. Then, I will present some recent work where we combined RNA-sequencing with ribosome profiling and RNA spike-in standards to enable the first large-scale characterisation of transcriptional and translational parts in absolute units. Such capabilities provide a more complete and quantitative view of the inner workings of genetic circuits and improve our understanding of the rules governing the effective construction of larger and more complex biological systems

Paths to Resilient Biological Information Processing
Jacob Beal
Senior Scientist
Raytheon BBN Technologies
Cambridge, MA, USA

Engineered information processing in biological organisms has potential revolutionary implication across many application domains. A major barrier, however, has been the fragility of biological information processing devices to changes in their usage, genetic context, or operating environment. Engineering resilient information processing, however, is not just a biological challenge, but a three-way interplay between device performance, measurement quality, and model accuracy. In this talk, I will discuss how the interplay between these three aspects offer multiple paths for improving the resilience of biological information processing, giving examples of recent progress in reproducible and comparable measurement, the development of high-performance devices and insulators, and precision prediction of genetic circuits.

Reporter systems for metabolic load and stress in Bacillus subtilis
Wendy Smith
Senior Research Associate
Interdisciplinary Computing and Complex Biosystems Research Group
School of Computing, Newcastle University, Newcastle-upon-Tyne, UK

(Joint work with James Knight, Yiming Huagh, Jaume Bacardit and Anil Wipat.)

Hosting synthetic genetic circuits imposes a load on a chassis. This load is manifest in the form of a number of stress responses and can result in detrimental effects on the growth of the host, and function of the introduced system. This is particularly so for heterologous protein and metabolite production where potentially large amounts of the cells available resources are consumed.

This work focuses on building reporter systems for stress, including metabolic load. In order to achieve this we are i) Defining biomarker signatures of gene expression that are indicative of important types of stresses such as metabolic load ii) Developing genetic reporter systems for the variety of different types of stresses which defined metabolic load iii) Developing the regulatory circuits to allow compensatory measures to be implemented

This project is part of a larger project which aims to develop a technology that allows the same synthetic devices and systems to be moved seamlessly between a set of different standardised chassis. Reporter systems developed here will form part of the Portabolomics interface in these strains. Logic circuits will be implemented to monitor the stress gene signature expression profiles and result in the expression of a reporter transcription factor whose presence will flag up the load state and a particular stress type and then control the expression of the genetic circuit and resources dynamics.

Game-theoretic approaches for in silico strain design in metabolic engineering
Shouyong Jiang
College of Science, University of Lincoln, UK

The computational reconstruction of genome-scale metabolic networks for microorganisms enables not only improved understanding of metablism but also valuable guidance in metabolic engineering. Genome-scale metabolic network analysis can provide useful prediction of candidate targets to improve biochemical production, yield, and titer. Particularly, the tradeoff between cell growth and biochemical production is very important in identifying genetic manipulations. We consider this mutally competitive relationship from a game-theoretic point of view, namely, one game player is the host cell and the other is the product one wants to overproduce. We demonstrate our approach through E. coli as a production platform to produce a number of biochemicals. Results show that the game-theoretic approach turns out to be very useful in predicting novel strain design strategies for either single or multiple products.

Heuristic Sequence Selection for Nucleic Acid Origami Scaffolds
Benjamin Shirt-Ediss
Research Associate
Interdisciplinary Computing and Complex Biosystems Research Group
School of Computing, Newcastle University, Newcastle-upon-Tyne, UK

(Joint work with Emanuela Torelli and Natalio Krasnogor.)

Nucleic acid origami exploits predictable molecular self-assembly as a means to engineer nano-sized functional objects from DNA and RNA. Now well-proven in vitro, nucleic acid origami is finding increasing applications in next generation medicine [1].

Computational design methods for nucleic acid origami have tended to focus exclusively on the problem of geometric design - that is, how to line up staple crossovers such that the final nanoshape has the intended geometry, free from unwanted twists or bends [2, 3, 4]. However, in practice, all origami nanoshapes self-assemble into their final geometry from monomer single strands, and the success of this kinetic process is also heavily dependent on the nucleic acid sequences used. The sequence design problem - that is, how to choose a scaffold sequence that folds in high yield - has received relatively little attention as compared to the geometric design problem. This may be explained by the fact that, historically, the only choices for origami scaffolds were a small collection of natural biological sequences (e.g. the M13 virus genome or pUC19 plasmid). However, decreasing costs of nucleic acid polymer synthesis has enabled the production of custom synthetic scaffolds, enabling scaffold sequence to also enter as an important design parameter in the production of origami nano-objects.

Some initial advances on the sequence design problem have started emerging, including the development of (qualitative) sequence-dependent origami folding models [5, 6] and basic heuristic approaches to scaffold sequence design [7, 8]. In this work, we present a new improved heuristic approach for selecting scaffold sequences that are likely to fold into a target DNA or RNA origami object (2D or 3D) in high yield. Given a large initial pool of origami scaffold sequences, our multi-objective design algorithm “OnePot” applies five scoring criteria to calculate which subset of scaffold sequences have the least number of barriers preventing them from folding into the target nanostructure (in a single pot reaction). Further, the initial scaffold sequence pool may be initialised to exclude biologically relevant sub-sequences, making our method applicable for designing origami destined for in-cell (and possibly cross-species) applications.

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