SynBioNT: A Synthetic Biology Network for Modelling and Programming Cell-Chell Interactions

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

Abstract

The field of synthetic biology holds a great promise for the design, construction and development of artificial (i.e. man-made) biological (sub)systems thus offering viable new routes to 'genetically modified' organisms, smart drugs as well as model systems to examine artificial genomes and proteomes. The informed manipulation of such biological (sub)systems could have an enormous positive impact on our societies, with its effects being felt across a range of activities such as the provision of healthcare, environmental protection and remediation, etc. The basic premise of synthetic biology is that methods commonly used to design and construct non-biological systems, such as those employed in the computational sciences and the engineering disciplines, could also be used to model and program novel synthetic biosystems. Synthetic biology thus lies at the interface of a variety of disciplines ranging from biology through chemistry, physics, computer science, mathematics and engineering. The overarching aim of this network will be to generate new vigorous interactions between the disciplines that impinge (and contribute to) Synthetic Biology by supporting a range of community building activities. These activities will be centred on the specific technical goal of achieving programmable interactions between biological and artificial cells. By focusing on this specific technical challenge we hope to contribute to mending the rift that is appearing in the synthetic biology community between those who argue that synthetic biology should be done in a top-down fashion (i.e. knocking out or modifying functions of existing cells) and those that argue that it should follow a bottom-up approach that starts from first principles. We believe that both approaches are important and will have a role to play in the future of synthetic biology, hence a challenge that calls for the interaction between top-down systems (modified cells) and bottom-up systems (chells, protocells) provides the ideal background against which a new research community can be built and sustained. Rebecca Morell's article 'Creating Life in the Laboratory' that apeared in the BBC NEWS science section on the 19th of October 2007, not only illustrate *perfectly* the growing dichotomy in SB between those who approach it top down and those who do it bottom up but also ends up on a cautionary notice by citing George Attard from Southampton University as saying 'The biggest challenge is not necessarily creating life, but knowing that you have created life - doing the experiment that unambiguously tells you that you've got it..That's because you are going to be looking at a 'soup' that contains several hundred, possibly several thousand, chemical species. How on Earth can you tell that what you have isn't just a chemical waste bottle but something that is exhibiting the signs of life?' SynBioNT will focus *precisely* on shedding light into this question by fostering research on the so call 'cellular imitation game' as we have recently propose in our ground breaking paper 'The imitation game/a computational chemical approach to recognizing life' in Nature Biotechnology, 24:1203-1206, 2006.

Technical Summary

One of the biggest unanswered scientific questions is how what we term as 'life' actually emerged from the 'primordial soup' several billion years ago. Researchers in the molecular sciences have adopted many techniques used by natural systems to assemble structures mimicking those in the natural world, leading to artificial materials with enzyme-like activity, or inorganic structures replicating those of biominerals, etc. Recent efforts are turning this concept around, utilising synthetic principles but using biological building blocks or using biological design principles to select particular structures or assemble meso-scale objects. Increasingly however, the question as to whether it is possible to instil properties considered 'life-like' in wholly synthetic systems, is being posed as molecular synthesis and computational algorithms become ever more sophisticated. Synthetic biologists are attempting to develop 'artificial life', as a proxy for shedding light into the question of the origins of life, and are doing so by following two separate and competing routes: the 'top-down' and 'bottom-up' approaches to minimal cells. In the former, a primordial or minimal cell is generated by systematically reducing a biological cell's genome until it no longer functions. The bottom-up methodology, on the other hand, seeks to assemble from scratch components or information units until an aspect of 'life' emerges. The overall intellectual and experimental challenges of implementing artificial life remain, of course, very much long-term goals. However, along the way, guiding principles, experimental methodologies and theoretical insights from biomimetic chemistry and synthetic biology can be adopted in new ways for practical applications on a much shorter time-scale. We seek *convergence* between biological (minimal) cells and protocells by attempting to implement the imitation game as we proposed recently. Co-funding provided by EPSRC and ESRC under the Networks in Synthetic Biology initiative.
 
Description Bar-Coded Biomaterials - Designing Self-Authenticating Medicines
Amount £1,020,960 (GBP)
Funding ID EP/H005625/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 01/2009 
End 01/2014
 
Description Paving the Way for Future Emerging DNA-based Technologies: Computer-Aided DNA Processing Utilizing DNA Reuse (CADMAD)
Amount £3,999,000 (GBP)
Funding ID 265505 
Organisation European Commission (EC) 
Sector Public
Country European Union (EU)
Start 02/2011 
End 01/2014
 
Description Paving the Way for Future Emerging DNA-based Technologies: Computer-Aided DNA Processing Utilizing DNA Reuse (CADMAD)
Amount £3,999,000 (GBP)
Funding ID 265505 
Organisation European Commission (EC) 
Sector Public
Country European Union (EU)
Start 02/2011 
End 01/2014
 
Description ROADBLOCK: Towards Programmable Defensive Bacterial Coatings & Skins
Amount £791,388 (GBP)
Funding ID EP/I031642/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 02/2012 
End 07/2015
 
Description Self-assembling Liposome Nano-transducers
Amount £733,385 (GBP)
Funding ID EP/J001953/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 10/2011 
End 09/2016
 
Description Synthetic Cognitive Systems
Amount £1,068,621 (GBP)
Funding ID EP/H022112/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 03/2010 
End 02/2015
 
Description Towards a Universal Biological-Cell Operating System (AUdACiOuS).
Amount £1,026,408 (GBP)
Funding ID EP/J004111/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 01/2012 
End 12/2016
 
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 EnrichNET 
Description EnrichNet is a network-based enrichment analysis method to identify functional associations between user-defined gene or protein sets and cellular pathways. The datasets are mapped onto a protein interaction network (or other user-defined molecular network) and their pairwise associations are assessed by computing a graph-based statistic, i.e. distances between the network nodes are mapped against a background model. In contrast to the classical overlap-based enrichment analysis, associations can also be identified for non-overlapping gene/protein sets and the user can investigate them in detail by visualizing corresponding sub-graphs. 
Type Of Technology Webtool/Application 
Year Produced 2012 
Impact -- 
URL http://www.enrichnet.org/
 
Title PathExpand 
Description Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have challenged the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks is therefore a promising strategy to obtain more robust pathway and process representations. PathExpand is a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and expanding them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new pathway components, and/or as regulators of the communication between different cellular processes. 
Type Of Technology Webtool/Application 
Year Produced 2010 
Impact -- 
URL http://www.pathexpand.org/
 
Title Protein Model Comparator 
Description Protein Models Comparator (pm-cmp) is a scalable web application for a fast distributed comparison of protein models (3D protein structures based on the same sequence of amino acids) with a number of measures (currently RMSD, GDT TS, TM-score and Q-score). It can be used to evaluate structural models against a target (e.g. the native structure) or to compare the models against each other (e.g. for further analysis with clustering algorithms). It runs on the Google App Engine cloud platform and is a showcase of how the emerging PaaS (Platform as a Service) technology could be used to simplify the development of scalable bioinformatics services. The functionality of pm-cmp is accessible through API which allows a full automation of the experiment submission and results retrieval (example Python script is provided). Pm-cmp is a free software released under the Affero GNU Public Licence and its source code is available for download. 
Type Of Technology Webtool/Application 
Year Produced 2011 
Impact -- 
URL http://pm-cmp.appspot.com/
 
Title TopoGSA 
Description TopoGSA (Topology-based Gene Set Analysis) computes and visualise the topological properties of a set of genes/proteins mapped onto a molecular interaction network. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, are computed and compared against those of known cellular pathways and processes (KEGG, BioCarta, GO, etc.). TopoGSA is updated twice per year to integrate newly available protein interaction data and reference gene sets. Alternatively, a user-defined networks can be uploaded and analysed. The pathways and processes similar to the uploaded set can be identified based on the 2D and 3D interactive visualisations or using an aggregated similarity measure that combines the topological properties into a single score. 
Type Of Technology Webtool/Application 
Year Produced 2010 
Impact -- 
URL http://www.topogsa.org/
 
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