Open Positions - 2 PhDs in semantic web and systems biology

[Apologies for cross-posting]

 

The Digital Enterprise Research Institute (DERI), the Systems Biology
Ireland (SBI) Institute and the Regenerative Medicine Institute (REMEDI)
are looking for applicants for two PhD scholarships. The Structured PhD
Programme in Simulation Science is a new multi-institutional
collaborative Ph.D. programme involving University College Dublin,
Trinity College Dublin, Queen's University Belfast, National University
of Ireland Galway and is supported by the Irish Centre for High End
Computing.

 

Applications are now being accepted for these Fellowships.   Please send
application, including full CV and names of referees by email to Ms
Helena Deus (helena.deus@deri.org <mailto:helena.deus@deri.org> ).
Applications will be accepted up to 5 pm (GMT) on Monday October 31,
2011. Please also indicate to which of the projects you are applying. 

 


Project SSG -001: A Semantic Laboratory Information Management System
for Stem Cell Research


 

Supervisors: Prof. Frank Barry and Dr. Helena F. Deus (NUIG)


One of the most pressing needs in understanding stem cell
differentiation and accelerating its application to improving health
care is the ability to reuse and integrate experimental results with
existing biomedical data sources. This project will focus on the
development of a semantic web based computational methodology for the
integration of experimental results and public genomics, proteomics and
metabolomics datasets.

 

Description

Most modern laboratories engaged in stem cell research rely on several
methodologies for data collection and correlation. More often than not,
experimental results are stored in different, proprietary systems which
complicate its integration for the design of comprehensive biological
models. Even when there are attempts to create Laboratory Information
Management Systems (LIMS) as integration tools, the high heterogeneity
and frequent update of experimental data challenge automated
integration. The state of the art in the development of LIMS has relied
on relational or object databases with fixed schemas [1]. Such systems,
however, rarely enable the changes to the data model which would be
required to support capturing and recording novel experimental variables
[2].

Efforts to standardize "omics" databases have resulted in document
models such as e.g. the Minimum Information About a Microarray
Experiment (MIAME). Such standards facilitate reusing genomics data
across laboratories and experiments but they have become victims of
their own success - the challenge of reusing experimental data has been
complicated by the existence of too many standards to choose from [3].
Semantic web and Linked Data technologies can provide a solution for
this problem. By relying on a high level abstraction for representing
data, it becomes possible to cross-reference and disambiguate the
multitude of standards and data models to represent, e.g. proteomics
data [4].

This project will rely on devising and applying a solution to solve the
data integration problem in Stem Cell research through the development
of a Semantic Laboratory Information Management System (SLIMS). The
methodologies used will rely on semantic web and linked data
technologies to integrate and align proprietary data sources (where
experimental results are collected) with public proteomics, genomics and
metabolomics data sources available on the Web.

 


References


[1] Troshin,P.V. et al. (2008) Laboratory information management system
for membrane protein structure initiative--from gene to crystal.
Molecular Membrane Biology, 25, 639-652.

[2] Almeida,J.S. et al. (2006) Data integration gets "Sloppy". Nature
biotechnology, 24, 1070-1.

[3] Quackenbush,J. (2006) Standardizing the standards. Molecular Systems
Biology, 2, 2006.0010.

[4] Wang,X. et al. (2005) From XML to RDF: how semantic web technologies
will change the design of "omic" standards. Nature biotechnology, 23,
1099-103.

 


Project SSG -004: Understating Cell Signalling through Linked Data


Supervisors: Prof. Frank Barry and Dr. Helena F. Deus (NUIG), Prof.
Walter Kolch and Prof Boris Kholodenko (UCD)

 

Understanding and predicting protein-protein interactions (PPI) can have
a direct effect on our ability to therapeutically target the signalling
networks that ultimately regulate carcinogenic processes. This project
will focus on making use of Linked Data technologies to create multiple,
non-overlapping layers of information that can be used as inputs for
devising encompassing PPI prediction models.

 

 Description

Many cell processes such as proliferation and differentiation are
controlled by signalling cascades, i.e. chains of proteins responsible
for communicating signals from the surface of the cell to the nucleus,
effectively affecting protein transcription. One of the most important
signalling cascades in carcinogenesis is the ERK/MAPK pathway - it is
believed that mutations in the genes responsible for the proteins
involved in this pathway may lead normal cells to become cancer cells.
Recent research has revealed that such signal transduction pathways
appear to be organised as communication networks where information is
processed and integrated through relay stations formed by multi-protein
complexes [1]. Identifying the proteins involved in these signalling
cascades and understanding how they interact to produce a chain of
events is therefore a crucial step towards our ability to devise drugs
that restore normal activity in the cell.

 

Computational simulation methods have become a popular method for
predicting potential protein-protein interactions based on 3D protein
docking, domain-domain interactions or the co-evolution model. The
accuracy and predictive power of such computer models relies heavily on
the amount and quality of integrated information used as input [2]. The
current state of the art in devising such models relies on ad hoc
integration of the relevant information e.g. sequence and structure
information, to build a useful predictive model. Every additional layer
of information must be extracted, transformed and integrated separately
before it can be used as input. Alternatively, Linked Data can be used
as an integrative technology as it relies on the simple concept that
existing relationships between entities, such as proteins, can be
represented as a network where each individual entity is represented by
a node and its relationships to other entities in the graph, e.g. drugs
or other proteins, are represented by an arc. Moreover, both the
entities and the links established between them can be deferenced, i.e.
their description and associated properties can be automatically
retrieved from the Web to be used in the creation of new layers of
integrated information. Multiple studies have shown that these
technologies are suitable for integrating proteomics and genomics
experimental results [3-5]. 

 

In this project, Linked Data technologies will be weaved to represent
protein-protein interactions. The research focus will be on identifying
the type of relationships that are best used to represent both the
provenance of the interaction information (e.g. mass spectrometry,
co-upregulation, etc) and its probabilistic value in order to create
non-overlapping layers of information. Representing protein-protein
interaction data in this format will enable the creation of mathematical
constructs, e.g. adjacency matrixes that can be algebraically
manipulated to identify the topology of the protein-protein interaction
network. The advance beyond the state of the art will be the possibility
to enrich the predictive models with ad hoc layers of information such
as drug interactions and its effect on the network topology.



References


1. Kolch W: Coordinating ERK/MAPK signalling through scaffolds and
inhibitors. Nature Reviews Molecular Cell Biology 2005, 6:827-837.

2. Wierling C, Herwig R, Lehrach H: Resources, standards and tools for
systems biology. Briefings in functional genomics proteomics 2007,
6:240-251.

3. Anwar N, Hunt E: Francisella tularensis novicida proteomic and
transcriptomic data integration and annotation based on semantic web
technologies. BMC Bioinformatics 2009, 10:S3.

4. Deus HF, Prud E, Zhao J, Marshall MS, Samwald M: Provenance of
Microarray Experiments for a Better Understanding of Experiment Results.
In ISWC 2010 SWPM. 2010.

5. Deus HF, Veiga DF, Freire PR, et al. Exposing The Cancer Genome Atlas
as a SPARQL endpoint. Journal of Biomedical Informatics 2010,
43:998-1008.

 

 

 

 

 

Best Regards,

Helena F. Deus

Post-doctoral Researcher
Digital Enterprise Research Institute

National University of Ireland, Galway

http://lenadeus.info