(Semi)Formal Artificial Life Through P-systems & Learning Classifier Systems: An Investigation into InfoBiotics

Lead Research Organisation: University of Nottingham
Department Name: School of Computer Science

Abstract

Artificial Life (ALife) has advanced enormously since A. Turing proposed in the early 50s models of pattern formation in living systems. It was Turing who first demonstrated how a simple system of coupled reaction-diffusion equations could give rise to spatial patterns in chemical concentrations through a process of chemical instability. J. von Newman, later, demonstrated that it was possible to build self-replicating abstract machines while A. Lindenmayer introduced L-systems for modelling artificial plants. The bulk of ALife research in the last 20 years has been done with a more ad-hoc bottom-up engineering approach by designing or evolving the rules that govern the local interactions of the entities in the system as to produce certain emergent behaviour. Emergence in this context is interpreted as a process within the system that could not have been predicted from merely inspecting the rules but that it is observed only by running the simulation. Some of the earliest landmarks in ALife were T. Rays' Tierra, J. Holland's Echo and L. Yaeger's Polyword. These early systems were all based on an individual based modelling framework, which were highly abstract and quite limited in the simulated details (i.e. physical and chemical laws) of the environment where the agents performed their interactions. K. Sims's virtual creatures and research like framsticks or swimmers incorporated a more accurate (albeit still arbitrary) physical reality into the ALife system. In turn, this increase in the detail of the environmental interactions allowed richer emergent processes to be observed. More recent work incorporated a more detailed biology through the addition of developmental processes, differential gene expression and genetic regulatory networks endowing ALife simulations with greater realism. Thus, as computing resources became more accessible and our biological knowledge deepened, more and more levels of biological, chemical and physical details were included in a bottom-up fashion into ALife simulations. Recent advances in analytical biotechnology, computational biology, bioinformatics and micro-biology are transforming our views of the complexity of biological systems, particularly the computations they perform (i.e. how information is processed, transmitted and stored) in order to survive, adapt and evolve in dynamic and sometimes hostile environments. We propose to capture some of these more recent biological insights, in particular those related to cell biology, as to develop sophisticated ALife simulations of cellular-like systems. Furthermore, while we propose to stick to the traditional engineering approach of building ALife systems from the bottom-up we would like to extend current research practice towards a more computationally formal and rigorous approach to the design and implementation of ALife research. In this proposal we seek a fundamental rethink on the way bottom-up Artificial Life research is conducted. Until now, much of this research has had a strong ad-hoc component with very little formalisations. We propose a new (semi) formal cellular Artificial Life methodology, which we call InfoBiotics. InfoBiotics proposes that a synergy between formal informatics methods, evolution and learning and biological and biochemical insights are a pre-requisite for a more principled practice of ALife research. The driving research issues behind this proposal are:i. What combinations of formal informatics, evolutionary and learning paradigms and biochemical insights are needed for a successful development of InfoBiotics as a principled approach to Artificial Cellular Life research? ii. What is the balance of each of the former that is needed in order to ask and, be able to, answer scientifically relevant and meaningful ALife questions from an InfoBiotics perspective?

Publications

10 25 50
 
Description Papers: J. Smaldon, F. J. Romero-Campero, F. Fernandez Trillo, M. Gheorghe, C. Alexander, and N. Krasnogor. A computational study of liposome logic: towards cellular computing from the bottom up. Systems and Synthetic Biology (Springer), 4(3):157-179, 2010 H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology (Springer), 4(1):55-84, 2010. have been the top 2 most accessesd papers for Systems and Synthetic Biology since they were first published. These two papers demonstrate the power of in silico specification, design and optimisation of synthetic biology circuits for both top down and bottom up synthetic biology. In top down synthetic biology one takes an existing cell strain and modifies it to carry out a specific task; bottom up synthetic biology starts from simpler chemical entities (e.g. lipid vesicles, polymersomes, etc) and complexifies them so they achieve a specific functionality.
Sector Cultural
Impact Types Cultural

 
Description BBSRC Grouped
Amount £74,587 (GBP)
Funding ID BB/F01855X/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start  
 
Description BBSRC Grouped
Amount £74,587 (GBP)
Funding ID BB/F01855X/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start  
 
Description EPSRC
Amount £729,420 (GBP)
Funding ID EP/G042462/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start  
 
Description EPSRC
Amount £729,420 (GBP)
Funding ID EP/G042462/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start  
 
Title Array Mining 
Description ArrayMining is a server for automating statistical analysis of gene and protein expression microarray data, designed as a supporting tool for investigation of the genetic components of diseases. It performs five common gene expression analysis tasks: cross-study normalisation feature selection sample clustering sample classification network analysis gene set analysis Unlike other microarray-related servers, ArrayMining is using ensemble and consensus techniques (e.g. ensemble feature selection, ensemble prediction, consensus clustering) and performs automatic parameter selection. For a given analysis task it is possible to combine multiple algorithms and data sets in a semi-automatic fashion. This way new exploratory routes become available, e.g. ensemble sample classification can be performed with predictors obtained from a gene set analysis applied to combined data from multiple studies. The analysis is further simplified by the integration with annotation databases. This enables further functional analysis and literature mining. The results are presented as interactive sortable tables and three dimensional VRML visualizations. 
Type Of Technology Webtool/Application 
Year Produced 2009 
Impact -- 
URL http://www.arraymining.net/R-php-1/ASAP/microarrayinfobiotic.php
 
Title The Infobiotics Workbench 
Description The Infobiotics Workbench is a executable biology framework implementing multi-compartmental stochastic and deterministic simulation, formal model analysis and structural/parameter model optimisation for computational systems and synthetic biology. The Infobiotics Workbench is comprised of the following components: a modelling language based on P systems which allows modular and parsimonious multi-cellular model development where the outermost compartments can be positioned in 2-dimensional space to facilitate modelling at either extra-, inter- or intracellular levels of detail deterministic and stochastic simulator using algorithms optimised for large multi-compartmental systems (the simulator also accept a subset of SBML, allowing for visual model specification using tools such as CellDesigner) formal model analysis for the study of temporal and spatial model properties supported the model checkers PRISM and MC2 model structure and parameter optimisation using a variety of evolutionary and population-based algorithms to automatically generate models whose dynamics match specified target timeseries a user-friendly front-end for performing in-silico experiments, plotting and visualisation of simulations with many runs and compartments 
Type Of Technology Software 
Year Produced 2011 
Open Source License? Yes  
Impact -- 
URL http://ico2s.org/software/infobiotics.html
 
Title VRML Generator 
Description VRMLGen is a free software package for 3D data visualisation on the web. It supports VRML and LiveGraphics3D formats. The package runs within the R environment for statistical computing and is available for download from CRAN. It is licensed under the terms of GNU GPL version 2 (or later). VRMLGen can be used to generate 3D line and bar charts, scatter plots with density estimation contour surfaces, visualizations of height maps, parametric functions and 3D object models. The 3D visualisation can be viewed directly in a web browser or a standalone viewer (see e.g. Xj3D, Cortona3D or BS Contact) and studied in detail using zoom, pan and rotate controls. In addition VRMLGen can be combined with POV-Ray using vrml2pov to render high-quality images. 
Type Of Technology Software 
Year Produced 2010 
Open Source License? Yes  
Impact -- 
URL http://ico2s.org/software/vrmlgen.html