Dr. Olga Papadodima, Functional Research Scientist Ioannis Valavanis, Postdoctoral researcher Eleftherios Pilalis, MSc, Bioinformatician, PhD student Panagiotis Moulos, MSc, Applied Mathematician, PhD student
Metabolic engineering principles have produced great advances for bioprocess applications and for increasing the general understanding of biological systems. The DNA microarray technologies creates a new environment for metabolic engineering, although its focus and central components remain the same, new tools are required to take advantage of the opportunities arising from the availability of whole-genome sequence information, and the gene profiling experiments, which allow to derive abundant information concerning the interactions between the classes of biological molecules (genes, proteins, metabolites, transcription factors, cofactors, etc) underlying the cellular phenotype.
The group of metabolic engineering and bioinformatics of IBRB is based on relevant activities initiated by the Laboratory of Biotechnology (Prof. F.N. Kolisis) of the School of Chemical Engineering NTUA. The group focuses its research interest in the study of biological mechanisms through the use of a wide range of computational tools in order to overcome the issues of significant measurement variation and the overwhelming complexity which is inherent in the biological systems. Using the chain of causal relations between the gene, the protein and the metabolic pathway level as a hypothesis bedrock, it aims to expand the boundaries of biological knowledge in the respective fields (genomic, proteomic, metabolic) by perceiving these fields as supplementary mechanisms of the same phenomenon. To this end, it uses systemic quantitative approaches to describe, analyze and create simulation models to study functional characteristics of specific physiological/ pathological mechanisms.
1. Metabolic Flux Analysis as a tool for the elucidation of the metabolism of neurotransmitter glutamate. A.Á. Chatziioannou, G. Palaiologos & F. N. Kolisis Metabolic Engineering 5, 2003, pp. 201-210.
2. Operational criteria for selecting a cDNA microarray data normalization algorithm C. Argyropoulos, A.A. Chatziioannou G. Nikiforidis, A. Moustakas, G. Kollias and V. Aidinis. Oncology reports. 15 Spec no.4, 2006 pp. 983-996.
3. Microarray analysis of survival pathways in human PC-3 prostate cancer cells. Tenta, R., Katopodis, H., Chatziioannou, A., Pilalis, E., Calvo, E., Van Luu-The, Labrie, F., Kolisis, Koutsilieris, M. Cancer Genomics and Proteomics 4 (4), 2007, pp. 309-317.
5. Radial Basis Function Neural Networks Classification for the Recognition of Idiopathic Pulmonary Fibrosis in Microscopic Images. I. Maglogiannis, H. Sarimveis, C. T. Kiranoudis, A. Chatziioannou, N. Oikonomou, and V. Aidinis. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 12, NO. 1, JANUARY 2008.
6. An in-silico model of the biosynthesis of neurotransmitter glutamate, elucidates the complex regulatory role of glucocorticoids in neurotransmitter glutamate release. A.Chatziioannou, G. Palaiologos and F.N. Kolisis. Computers in Biology and Medicine 39 (6), 2009, pp. 501-511.
7. A transcriptomic computational analysis of mastic oil-treated Lewis lung carcinomas reveals molecular mechanisms targeting tumor cell growth and survival. P. Moulos, O. Papadodima, A. Chatziioannou, H. Loutrari, C. Roussos, F. N Kolisis (BMC Medical Genomics2009, 2:68), doi:10.1186/1755-8794-2-68.
8. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB. A. Chatziioannou, P. Moulos, F. N Kolisis (BMC Bioinformatics 10:354), 2009, (characterized as Highly Accessed).
9. 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).
10. 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.
11. An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism. E. Pilalis, A. Chatziioannou, B. Thomasset, F. Kolisis Biotechnol Bioeng. 2011 Jul;108(7):1673-82. doi: 10.1002/bit.23107. Epub 2011 Mar 11.
12. Exploiting statistical methodologies and controlled vocabularies for prioritized functional analysis of genomic experiments: The StRAnGER web application Chatziioannou A. A., Moulos P. 2011, Front Neurosci. 2011 Jan 26;5:8.
13. Escherichia coli Genome-Wide Promoter Analysis: Identification of Additional AtoC Binding Target Elements. Pilalis E, Chatziioannou AA, Grigoroudis AI, Panagiotidis CA, Kolisis FN, Kyriakidis DA. BMC Genomics. 2011 May 13;12(1):238. [Epub ahead of print]