Overview

Computational Design and Automation is one of our 10 main research themes. Find out about what we do, and which members of staff are involved.

Currently, most design in synthetic biology is done manually by a domain expert on an ad-hoc basis. 

One of the major challenges in biological systems design arises from the fact that molecular interactions on the genome scale are extremely complicated. 

Synthetic biologists can no longer rely on manual effort for designing synthetic systems on a genome-wide scale. 

As the complexity of systems design increases, it is necessary to use automated, computer-aided design processes.

Our research

Computational design

This area of research focuses on the development of computational tools and approaches. Read more about our work in computational design.

Design automation

This area of research looks at ways to automate aspects of the synthetic biology. Read more about our work in design automation.

Find out about our research theme staff members.

Computational Design

This area of our research focuses on the development of computational tools and approaches. These aid the domain expert in designing synthetic constructs, especially for bacterial systems.

Tools

We're developing tools that will be used directly by an end user, such as Computer Aided Design (CAD).

We're developing tools and languages for specifying biological systems to applications. These automatically bring together data from a wide variety of sources. This informs the design process.

Data

We are interested in how data sources that hold biological knowledge can be integrated and mined. This will assist in the design of new biological parts and pathways. 

We have developed tools for building data warehouses. These can be mined to extract information for new synthetic biology parts or design strategies. 

BacillOndex is one such system. It is targeted at the commercially important bacterium Bacillus subtilis.

Modelling

Modelling has an important role to play in synthetic biology. It allows users to test designs in silico before commencing expensive and time-consuming synthesis and laboratory work to actually build the system. 

We are interested in how model-based design supports the synthetic biology lifecycle. We're developing a variety of tools to build:

• designs
• repositories of composable models such as BacilloBricks
• algorithms and approaches for automatically deriving DNA sequence based designs from models (eg MoSeC)

Design Automation

This area of research looks at ways to automate aspects of the synthetic biology.

Design automation starts from programmatic analyses. It requires a model space representation of the design domain. 

Much effort has already gone into devising mathematical models of genetic circuits. These represent the inner workings of biological systems and predict their behaviour in silico. 

Computational intelligence

One promising approach is the use of computational intelligence (CI). This aims to help the design of novel, non-intuitive genetic circuits. 

CI techniques were developed for use in complex areas. In these cases, the desired outcome is known, but how to achieve this outcome is less obvious. 

CI approaches are inspired by biology. They include:

• evolutionary algorithms, based on biological evolution
• artificial neural networks, based loosely on the way in which brains learn
• swarm intelligence, based on the way in which social insects interact to solve problems
• fuzzy systems

Evolutionary algorithms have been widely applied to the design of synthetic genetic circuits. Our research builds on this previous work.

We explore the possibilities of other forms of CI to automate the design of novel, large-scale genetic systems.

Staff

Find out which members of staff are involved in the Computational Design and Automation research theme.