Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Autocatalytic closure and the evolution of cellular information processing networks

Decraene, James (2009) Autocatalytic closure and the evolution of cellular information processing networks. PhD thesis, Dublin City University.

Cellular Information Processing Networks (CIPNs) are chemical networks of interacting molecules occurring in living cells. Through complex molecular interactions, CIPNs are able to coordinate critical cellular activities in response to internal and external stimuli. We hypothesise that CIPNs may be abstractly regarded as subsets of collectively autocatalytic (i.e., organisationally closed) reaction networks. These closure properties would subsequently interact with the evolution and adaptation of CIPNs capable of distinct information processing abilities. This hypothesis is motivated by the fact that CIPNs may require a mechanism enabling the self-maintenance of core components of the network when subjected to internal and external perturbations and during cellular divisions. Indeed, partially replicated or defective CIPNs may lead to the malfunctioning and premature death of the cell. In this thesis, we evaluate different existing computational approaches to model and evolve chemical reaction networks in silico. Following this literature review, we propose an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is a novel agent-based Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS.bl). The latter is derived from the Holland broadcast language formalism. Our first series of experiments focuses on the emergence and evolution of selfmaintaining molecular organisations in the MCS.bl. Such experiments naturally relate to similar studies conducted in ACs such as Tierra, Alchemy and α-universes. Our results demonstrate some counter-intuitive outcomes, not indicated in previous literature. We examine each of these “unexpected” evolutionary dynamics (including an elongation catastrophe phenomenon) which presented various degenerate evolutionary trajectories. To address these robustness and evolvability issues, we evaluate several model variants of the MCS.bl. This investigation illuminates the key properties required to allow the self-maintenance and stable evolution of closed reaction networks in ACs. We demonstrate how the elongation catastrophe phenomenon can be prevented using a multi-level selectional model of the MCS.bl (which acts both at the molecular and cellular level). Using this multi-level selectional MCS.bl which was implemented as a parallel system, we successfully evolve an artificial CIPN to perform a simple pre-specified information processing task. We also demonstrate how signalling crosstalk may enable the cooperation of distinct closed CIPNs when mixed together in the same reaction space. We finally present the evolution of closed crosstalking and multitasking CIPNs exhibiting a higher level of complexity.
Item Type:Thesis (PhD)
Date of Award:November 2009
Additional Information:Extern Examiner: Prof. Hugues Bersini, Université Libre de Bruxelles Intern Examiner: Dr. Darragh O'Brien, School of Computing
Supervisor(s):McMullin, Barry
Subjects:Biological Sciences > Bioinformatics
Computer Science > Machine learning
Mathematics > Mathematical models
Biological Sciences > Cell biology
Biological Sciences > Molecular biology
Engineering > Artificial life
Computer Science > Algorithms
Computer Science > Computer simulation
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > Research Institute for Networks and Communications Engineering (RINCE)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:EU 6th Framework Programme: Project ESIGNET
ID Code:14810
Deposited On:12 Nov 2009 11:53 by Barry Mcmullin . Last Modified 19 Jul 2018 14:48

Full text available as:

[thumbnail of jd-thesis-09-08-09.pdf]
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
[thumbnail of Source format ZIP archive [latex+bibtex+images]] Other (Source format ZIP archive [latex+bibtex+images])
Creative Commons: Attribution-Share Alike 3.0


Downloads per month over past year

Archive Staff Only: edit this record