This project aims to develop a rigorous design-modeling web-based tool for synthetic biology using a novel design methodology that has been recently developed in the Control Group (Engineering Department).
Synthetic biology aspires to design and construct novel biological systems to reprogramme the cell for biomedical or biotechnological purposes. Despite many achievements in the last decade, the sheer complexity of interactions in biological systems has presented insurmountable challenges. The field currently lacks systematic principles for modelling, designing and constructing synthetic circuits that are robust, tunable and scalable. Current design approaches do not take advantage of key links between experimental and theoretical knowledge, and instead rely either on trial and error experimental redesigns of systems (with a limited number of available biological parts and components), or make experimentalists responsible for working with advanced mathematical modeling.
This project aims to develop a rigorous design-modeling web-based tool for synthetic biology using a novel design methodology that has been recently developed in the Control Group (Engineering Department) as part of Peyman Gifani’s PhD project. This design method hides the mathematical complexity of design and modelling and makes it easily understandable for non-technical users. We intend for the method to be accessible and practical for both experimentalists and designers, to help researchers from a variety of backgrounds to tackle the nonlinear dynamics of biological systems. The SynBioFund will help our team to develop essential interactive web-based software that will guide users in designing target circuits, therefore ensuring that the method is freely accessible to the synthetic biology research community.
Using the method, biologists will be able to translate their experiential knowledge into meaningful mathematical representations that will enable them to both predict and control synthetic systems’ behaviours in silico. The software developed with SynBio funding guides the user to gradually translate a qualitative input-output relation to a quantitative representation (nonlinear feedback loops required for the target circuit) via a graphical user interface (GUI). This GUI hide the complexity of underlying dynamical systems theory and makes the required theoretical knowledge accessible to experimentalists. The design tool can be used in two ways: to design new circuits with increased complexity, robustness and tunability and to analyze and explore the capability of currently available circuits to redesign them for better functionality.
The design framework engages nonlinear dynamical systems theory via a novel graphical method in which the user first develops a detailed qualitative description of a target circuit, specifying design criteria or goals for particular cellular input-output relations. The method then guides the user in transforming these design criteria into a specific representational form via a unique graphical palette. This graphical representation is converted into a qualitative mathematical representation by modeling the system’s trajectories in a phase space. The design framework then specifies building blocks with meaningful biological interpretations, which with the user can translate the qualitative mathematical representation into a mathematical formula representing the circuit structure and its parameters. This formula can then be interpreted as the system’s ordinary differential equation (ODE). This allows the construction of ordinary differential equation-based models, and provides a potential blueprint for implementation of a biological device based on standardised parts. The framework is intended to be used as an iterative process, whereby the qualitative and quantitative representations are gradually refined in order to best fulfil the design criteria.
The proposed project will be a multidisciplinary collaboration between the Engineering Department (Control Group) and the Computer Lab (Rainbow Group), capitalising on the unique intersection between control theory, machine learning, artificial intelligence and synthetic biology. We will consult researchers from the Plant Science and Genetics departments to optimise the user interface design process.
This project provides a valuable contribution to the current field. Designing a synthetic circuit from the interconnection of parts or devices can be significantly facilitated by using systematic in silico modeling and design relying on the separation of the design from the actual implementation. In this approach, various designs are first optimised and their properties are assessed using mathematical analysis and model-based computer simulations.
This project also can be used as an educational tool for teaching nonlinear dynamical systems theory to student with biological science background. Biologists can understand better the role of nonlinear feedback loops in genetic regulatory systems by designing system using the proposed tool.