Artificial Biochemical Networks: Computational Models and Architectures
Lead Research Organisation:
University of York
Department Name: Electronics
Abstract
Previous work by ourselves and others has shown how the structure and organisation of biological organisms can motivate the design of computer hardware and software, with the aim of capturing useful properties such as complex information processing and resistance to environmental perturbation. This proposal focuses upon one of the most complex sets of structures found in biological systems: biochemical networks. These structures are fundamental to the development, function and evolution of biological organisms, and are the main factor underlying the complexity seen within higher organisms. Previous attempts to build hardware and software systems motivated by these structures has led to a group of computer architectures which we collectively refer to as artificial biochemical network models. The best known of these is the artificial genetic network, which has shown itself to be an effective means of expressing complex computational behaviours, particularly within robotic control. Nevertheless, this field of research has received relatively little attention, and little is known about the computational properties of these architectures. The aim of the proposed work is to develop better artificial biochemical network models, which we will do by both bringing together existing work and introducing new understanding from the biological sciences. We will also develop a theoretical framework to better understand what these computational architectures are capable of, and show how how these models can be applied to the difficult problem of controlling a robot in real world environments. It is expected that this work will also produce insights into the function and evolution of the biological systems on which the architectures are modelled.
Organisations
Publications
Turner AP
(2016)
Using epigenetic networks for the analysis of movement associated with levodopa therapy for Parkinson's disease.
in Bio Systems
Fuente LA
(2013)
Computational models of signalling networks for non-linear control.
in Bio Systems
Lones MA
(2013)
Characterising neurological time series data using biologically motivated networks of coupled discrete maps.
in Bio Systems
Turner AP
(2013)
The incorporation of epigenetics in artificial gene regulatory networks.
in Bio Systems
Lones M
(2014)
Evolving Classifiers to Recognize the Movement Characteristics of Parkinson's Disease Patients
in IEEE Transactions on Evolutionary Computation
Lacey G.
(2020)
Improving the transparency of deep neural networks using artificial epigenetic molecules
in IJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence
Lones M
(2013)
Biochemical connectionism
in Natural Computing
Andrew Martin Tyrrell (Author)
(2012)
Using Artificial Epigenetic Regulatory Networks To Control Complex Tasks Within Chaotic Systems
Turner A
(2012)
Information Processign in Cells and Tissues
Lones M
(2012)
Information Processign in Cells and Tissues
Lones M
(2010)
Genetic Programming
Andrew Martin Tyrrell (Author)
(2011)
Controlling Legged Robots with Coupled Artificial Biochemical Networks
Turner A
(2013)
The artificial epigenetic network
Lones M
(2010)
Genetic Programming
Andrew Martin Tyrrell (Author)
(2012)
Evolved Artificial Signaling Networks for the Control of a Conservative Complex Dynamical System
Description | During this project we developed artificial biochemical network models. We also developed a theoretical framework to better understand what these computational architectures are capable of, and showed how how these models can be applied to the difficult problem of controlling a robot in real world environments. During the project we: 1) Investigated and developed novel computational architectures whose form and function are motivated by the genetic, metabolic and signalling networks of biological organisms and their interactions. 2) Improved the expressiveness, evolvability and robustness of existing architectures by applying current biological knowledge about the function and evolution of biochemical networks, and developed new models based upon current understanding of how these networks interact during cellular function and development. 3) Analysed the properties and computational capacities of these architectures using a dynamical systems framework, whilst developing a predictive model to guide the development of the architectures. 4) Demonstrated how these architectures can be used to solve difficult real-world problems by using them in a number of applications including the control of robots in complex indoor environments. |
Exploitation Route | In particular, the algorithms developed in the project are being applied to the assessment of Parkinson's disease in a number of hospitals. |
Sectors | Digital/Communication/Information Technologies (including Software),Electronics,Healthcare |
Description | The main use of our findings have been in the form of conference and journal publications. In addition a Workshop was held in the final year of the project at which 40+ people attended. Work related to this project has been used as part of a new EU project proposal. Algorithms developed during the project are now being applied in the area of Parkinson's assessment and equipment using these algorithms is being trialled in a number of hospitals. |
Sector | Education,Healthcare |
Impact Types | Societal |