GRISSOM Platform: Grids for In Silico Systems Biology and Medicine

Scientists from University of Central GreeceNational Hellenic Research Foundation and University of Aegean have used the HellasGrid Infrastructure and the EGI Grid infrastructure in order to solve problems coming from the area of  bioinformatics.

Transcriptomic experiments perform global gene expression monitoring, enabling thus, thorough probing of the in-vivo cellular state and its regulation, in healthy and disease state, in response to numerous environmental stimuli, across different species, etc. DNA microarrays have become a mainstay for a vast range of genomic applications, helping to identify significant alterations in transcriptomic expression of the system investigated, and map them to specific phenotypic outcomes.

There is a pressing request for computationally intelligent solutions, which manage to provide versatile, powerful and user-friendly data mining functionalities, in order to tackle the enormous underlying complexity of gene profiling experiments. On the other hand, there is an ever growing need for computational power as the size of the experimental datasets keeps increasing.

In Figure 1, an overview of the workflow structure of GRISSOM [1] is illustrated. The platform has been designed in order to effectively accommodate the needs of a wide range of users with different levels of expertise, aspiring to perform versatile and varying series of operations. The core of the developed web application, namely the quantitative signal processing and statistical analysis of the microarrays, which represent the computationally expensive part of the analysis pipeline, but also the storage of the datasets as well as of the annotation files, are exploiting the HellasGrid infrastructure. Overall, the DNA microarray experimental data analysis tasks implemented within the platform, encompass diversified processing steps, entailing versatile, heterogeneous in nature of processing, data type and complexity tasks. These can be basically partitioned into the categories of data import, gene selection, gene annotation tasks (gene, platform, and experiment), integrative interpretation capabilities, secure database storage and maintenance, and support of various output formats.

Figure 1- Workflow structure of GRISSOM

With respect to the efficient interpretation of DNA microarray experiments, GRISSOM supports gene classification based on clustering algorithms or cellular pathway analysis, through the integration of statistical ranking of annotated genomic experimental results. In this way, statistical enrichment analysis is performed, which exploits controlled biological vocabularies like the GO or the KEGG Ontology. Another capability of GRISSOM is the reconstruction of cellular network super-pathway models, which are SBML-compliant by exploiting KEGGConverter that is based on the KEGG pathway IDs derived from the analysis performed by StRAnGER.

Grids represent extremely heterogeneous, in terms of resources, tasks, policies and time demands, environments, posing sheer challenges regarding the effective accommodation and routing of all these striving requests. In order for GRISSOM being able to use as much processing power as possible, and keep manageable the queuing time too, the application has been designed to enable distributed computing methodologies, for two different grid configurations: a) through job schedulers that perform supervised job management in the Grid, as derived by a special directed acyclic graph (DAG), written in Python and Octave Forge mathematical language and b) by utilizing MPI computing workflows. DAG management renders the system resilient even for huge datasets, which can be executed even when the grid infrastructure is extremely loaded and has minimal resource availability. An Overview of how the web application resides between users and the HellesGRID infrastructure is shown in Figure 2.

Figure 2 - Overview of how the web application resides between users and the HellasGrid infrastructure


  • I. Maglogiannis, University of Central Greece, Lamia, Greece, imaglo (at)
  • A. Chatziioannou, National Hellenic Research Foundation, Greece, achatzi (at)
  • I. Kanaris, University of the Aegean, Mytilene, Greece, kanaris.i (at)
  • C. Doukas, University of the Aegean, Mytilene, Greece, doukas (at)
  • P. Moulos, National Hellenic Research Foundation, Greece, p.moulos (at)
  • F. Kolisis, National Hellenic Research Foundation, Greece, kolisis (at)


  1. GRISSOM Platform: Enabling distributed Processing and Management of Biological Data through fusion of Grid and Web Technologies. A. Chatziioannou, I. Kanaris, C. Doukas, P. Moulos, F.N. Kolisis and I. Maglogiannis (IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2011 15 (1), art. no. 5638146, pp. 83-92.
  2. A. Chatziioannou, I. Kanaris, I. Maglogiannis, C. Doukas, P. Moulos, E. Pilalis and F. Kolisis : “GRISSOM web based Grid portal: Exploiting the power of Grid infrastructure for the interpretation and storage of DNA microarray experiments” In Proc of  9th IEEE International Special Topic Conference on Information Technology in Biomedicine (ITAB 2009) Larnaka Cyprus
  3. KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database. K.Moutselos, I.Kanaris, A.Chatziioannou, I. Maglogiannis, F.N. Kolisis (BMC Bioinformatics 10:324), 2009. (featured article of the volume, characterized as Highly Accessed).
  4. C. Doukas, I. Maglogiannis, A. Chatziioannou, “Certification and Security Issues in Biomedical Grid Portals: The GRISSOM Case Study”, “Certification and Security in Health-Related web applications: Concepts and Solutions” IGI Press (to appear)
  5. A. Chatziioanou, I. Maglogiannis, I. Kanaris, C. Doukas, E. Pilalis, P. Moulos, F. Kolisis, “GRISSOM: A Web based portal and repository for interpretation and storage of DNA microarray experiments”, presented at 4th EGEE User Forum/OGF 25 and OGF Europe’s 2nd International Event, 2-6 March 2009, Catania, Italy.



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