Dr. Panayiota Poirazi Foundation for Research and Technology-Hellas (FORTH) – Institute of Molecular Biology and Biotechnology Crete

  • Alumni

Computational Neuroscience

  • Foundation for Research and Technology-Hellas (FORTH)
  • [email protected]
  • +30 2810 391139
  • +30 2810 391101
  • Greece

About Dr. Panayiota Poirazi

Research in the CBL can be divided in two disciplines: (a) computational neuroscience, where we focus on the use of modeling techniques for characterizing the role of dendrites in learning and memory and (b) bioinformatics, where we focus on the development of methods and tools for analyzing and modeling biological data. 

Dendritic Computations
We are particularly interested in understanding how dendrites and their integrative properties contribute to learning and memory functions. Towards this goal, we build abstract mathematical as well as detailed biophysical models of neural cells and circuits across multiple brain regions (hippocampus, amygdala, PFC) and abstraction levels (single neurons, microcircuits, neuronal networks). We then use the models to study how the anatomical, biophysical and plasticity properties of dendrites contribute to memory functions.

We focus on developing computational methods and tools for (a) analyzing large-scale gene expression data related to human cancer in search for gene markers and disease sub-categories, (b) identifying regulatory elements such as miRNA precursors and their targets in whole genomes of plants and mammals, (c) building theoretical models of gene regulatory networks. Our methodological approaches include (a) novel clustering and feature selection algorithms, (b) machine learning algorithms such as artificial neural networks, hidden Markov models etc.

Please see also: http://www.imbb.forth.gr/people/poirazi/drupal/?q=node/2

5 Selected Publications

Oulas A., Karathanasis N., Louloupi A., Iliopoulos I., Kalantidis K., and Poirazi P. (2012) A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2. RNA Biol., 9 (9): 1196-1207.

Sidiropoulou K., and Poirazi P. (2012) Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons. PLoS Comp. Biol., 8 (4): e1002489.

Pissadaki EK., Sidiropoulou K., Reczko M., and Poirazi P. (2010) Encoding of spatio-temporal input characteristics by a single CA1 pyramidal neuron model. PLoS Comp. Biol., 6 (12): e1001038.

Zhou Y., Won J., Karlsson MG., Zhou M., Rogerson T., Balaji J., Neve R., Poirazi P., Silva AJ. (2009) CREB regulates excitability and the allocation of memory to subsets of neurons in the amygdala.” Nat. Neurosci., 12 (11) : 1438-43.

Oulas A. Boutla A., Gkirtzou K. Reczko M., Kalantidis K., and Poirazi P. (2009) Prediction of novel microRNA genes in cancer associated genomic regions: a combined computational and experimental approach. Nucleic Acids Res., 7 (10) : 3276-87.

Awards, Fellowships and Honours

2013            Joined AcademiaNet: a portal for outstanding female scientists after nomination by EMBO
2012            ERC Starting Grant
2004            EMBO Young Investigator Award
2001            IBRO Advanced Course in Comput. Neuroscience, Trieste, Italy
2000            Fred. S. Grodins Graduate Research Award, USC, Los Angeles
1996            Myronis Fellowship for graduate studies at USC, Los Angeles (until 2000)
1996            Levantis Foundation Grant for graduate studies in the U.S. (until 2000)
1996            Highest G.P.A. Award, Dept. of Mathematics, University of Cyprus
1995            Academic Staff Awards, Dept. of Mathematics, University of Cyprus
1994            Aritemi Foundation Scholarship Award, University of Cyprus
1994            Stelios Pichorides Award, Dept. of Mathematics, University of Cyprus

Technical Expertise

  • Computational modeling of neurons and circuits
  • Understanding the role of dendrites in neural arithmetic and memory formation
  • Information coding mechanisms in neurons 
  • Theoretical models of brain function
  • Bioinformatics methods for miRNA gene, mature and target prediction
  • Modeling of gene regulatory networks