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Panayiota Poirazi

Panayiota Poirazi

Research Director
Email address
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Lab web page

My research interests lie in the field of computational biology with a focus on the development and application of in computo modelling techniques for the investigation of neural and gene functions. In my lab we develop 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, and (d) modeling healthy and degenerated brain cells and neural networks in order to relate learning and memory capacity with biophysical and/or morphological properties. Our methodological approaches include (a) novel clustering and feature selection algorithms, (b) machine learning algorithms such as artificial neural networks, hidden Markov models etc, (c) detailed biophysical and/or simplified models of neurons and neural networks and (d) theoretical analysis and abstract mathematical modeling.



Sidiropoulou K, Poirazi P. (2012) Predictive Features of Persistent Activity Emergence in Regular Spiking and Intrinsic Bursting Model Neurons. PLoS Comput Biol 8: e1002489.

Shilyansky C, Karlsgodt KH, Cummings D, Sidiropoulou K, Hardt M, James AS, Ehninger D, Bearden CE, Poirazi P, Jentsch D, Cannon TD, Levine MS, Silva AJ. (2010) Increased corticostriatal inhibition underlies working memory deficits in Neurofibromatosis type I. Proc Natl Acad Sci USA 107:13141-6.

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. Nature Neuroscience 12:1438-43.

Oulas A. Boutla A. Gkirtzou K. Reczko M. Kalantidis K, Poirazi P. (2009) Prediction of novel microRNA genes in cancer associated genomic regions: a combined computational and experimental approach. Nucleic Acid Research 7:3276-3287.

Poirazi P. Brannon T, Mel B.W. (2003) Pyramidal Neuron as 2-Layer Neural Network. Neuron 37:989-999.

Journal Publications



Poirazi, P., Belin, D., Gräff, J., Hanganu‐Opatz, I., and López‐Bendito, G. “Balancing family with a successful career in neuroscience”. European Journal of Neuroscience, 2016. |
[Abstract] [PDF]

Spires-Jones, T.L., Poirazi, P., Grubb, M.S., “Opening Up: open access publishing, data sharing, and how they can influence your neuroscience career”, European Journal of Neuroscience, 04 March 2016 |
[Abstract] [PDF]
Yaksi, E., Poirazi, P., Hanganu-Opatz, I., “The road to independence: how to get funding in neuroscience”, Eur J Neurosci. 10 January 2016 | http://doi: 10.1111/ejn.13169.

Bozelos, P., Stefanou, S.S., Bouloukakis, G., Melachrinos, C., Poirazi, P., “REMOD: A Tool for Analyzing and Remodeling the Dendritic Architecture of Neural Cells:, Front. Neuroanat., 06 January 2016 |



Karathanasis N, Tsamardinos I, Poirazi P, “MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology”. PLoS ONE.
[Abstract] [PDF]

Cutsuridis V, Poirazi P, “A computational study on how theta modulated inhibition can account for the long temporal windows in the entorhinal-hippocampal loop”, Neurobiol Learn Mem.

P.C. Petrantonakis, P.Poirazi, ”Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms”, PLoS ONE
[Abstract] [PDF]
Kastellakis G., Cai D. J., Mednick S. C., Silva A. J., Poirazi P., “Synaptic clustering within dendrites: An emerging theory of memory formation.” Progress in Neurobiology.
[Abstract] [PDF] [Download]



Psarou M., Stefanou – Stamatiadis S., Papoutsi A., Tzilivaki A, Cutsuridis V, Poirazi P. “A Simulation Study on the Effects of Dendritic Morphology on Layer V Prefontal Pyramidal Cell Firing Behavior .” Frontiers in Cellular Neuroscience.
[Abstract] [PDF]
Petrantonakis P.C., Poirazi P.  “A compressed sensing perspective of hippocampal function.” Frontiers in  Systems Neuroscience. doi: 10.3389/fnsys.2014.00141, 2014.
[Abstract] [PDF]
Papoutsi A, Sidiropoulou K, Poirazi P.  “Dendritic Nonlinearities Reduce Network Size Requirements and Mediate ON and OFF States of Persistent Activity in a PFC Microcircuit Model.” PLoS Comput Biol.. doi: 10.1371/journal.pcbi.1003764, 2014.
[Abstract] [PDF]
Ho VM, Dallalzadeh LO, Karathanasis N, Keles MF, Vangala S, Grogan T, Poirazi P, Martin KC.  “GluA2 mRNA distribution and regulation by miR-124 in hippocampal neurons.” Mol Cell Neurosci., pii: S1044-7431(14)00043-8. doi: 10.1016/j.mcn.2014.04.006, 2014.

[Abstract] [PDF]

Karathanasis, N., Tsamardinos, I., & Poirazi, P. (2014). Don’t use a cannon to kill the … miRNA mosquito.
Bioinformatics (Oxford, England), (x), 1–2. doi:10.1093/bioinformatics/btu100
[Abstract] [PDF]
Xanthippi Konstantoudaki, Athanasia Papoutsi, Kleanthi Chalkiadaki, Panayiota Poirazi and Kyriaki Sidiropoulou
Modulatory effects of inhibition on persistent activity in a cortical microcircuit model
Frontiers in  Neural Circuits doi: 10.3389/fncir.2014.00007
[Abstract] [PDF]



Papoutsi A, Sidiropoulou K, Cutsuridis V and Poirazi P.
Induction and modulation of persistent activity in a layer V PFC microcircuit model
Frontiers in  Neural Circuits 7:161. doi: 10.3389/fncir.2013.00161

Manioudaki ME and Poirazi P
Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.
Front. Genet. 4:110. doi: 10.3389/fgene.2013.00110
[Abstract] [PDF]

A. Papoutsi, G. Kastellakis, M. Psarrou, S. Anastasakis, P. Poirazi.
Coding and Decoding with Dendrites
Journal of Physiology-Paris doi:10.1016/j.jphysparis.2013.05.003.
[Abstract]  [PDF]

Romani, A., Marchetti, C., Bianchi, D., Leinekugel, X., Poirazi, P., Migliore, M., Marie, H.
Computational modeling of the effects of amyloid-beta on release probability at hippocampal synapses.
Frontiers in Computational Neuroscience, January 2013; doi: 10.3389/fncom.2013.00001
[Abstract] [PDF]



Anastasis Oulas, Nestoras Karathanasis, Annita Louloupi, Ioannis Iliopoulos, Kriton Kalantidis and Panayiota Poirazi
A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2
RNA Biology, September 2012; 9 (9), 1196-1207.

[Abstract] [PDF]

Kyriaki Sidiropoulou, Panayiota Poirazi
Predictive Features of Persistent Activity Emergence in Regular Spiking and Intrinsic Bursting Model Neurons
PLoS Comput Biol. 2012 April; 8(4): e1002489. doi: 10.1371/journal.pcbi.1002489

[Abstract] [PDF]



Gómez González JF, Mel BW, Poirazi P.
Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It’s about Time.
Frontiers in Computational Neuroscience. 2011 November 14 ;5:44.
[Abstract] [PDF]

Tzamali E, Poirazi P, Tollis IG, Reczko M.
A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities.
BMC Syst Biol. 2011 Oct 18;5:167.
[Abstract] [PDF]

Oulas, A, Karathanassis N., Luloupi, A. and Poirazi, P.
Finding cancer-associated miRNAs: methods and tools
Molecular Biotechnology, DOI: 10.1007/s12033-011-9416-4, June 2011.
[Abstract] [PDF]



Pissadaki E.K., Sidiropoulou K., Reczko M., and Poirazi P.
Encoding of Spatio-temporal Input Characteristics by a CA1 Pyramidal Neuron Model.
PLoS Computational Biology 2010 Dec;6(12): e1001038.
[Abstract] [PDF]

Gkirtzou K, Tsamardinos I, Tsakalides P, Poirazi P.
MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors.
PLoS One 2010 Aug;5(8). pii: e11843
[Abstract] [PDF]

Shilyansky C, Karlsgodt KH, Cummings DM, Sidiropoulou K, Hardt M, James AS, Ehninger D, Bearden CE, Poirazi P, Jentsch JD, Cannon TD, Levine MS, Silva AJ.
Neurofibromin regulates corticostriatal inhibitory networks during working memory performance.
Proc Natl Acad Sci U S A. 2010 Jul;107(29):13141-6.
[Abstract] [PDF]



Zhou Y, Won J, Karlsson MG, Zhou M, Rogerson T, Balaji J, Neve R, Poirazi P, Silva AJ.
CREB regulates excitability and the allocation of memory to subsets of neurons in the amygdala.
Nature Neuroscience 2009 Nov;12(11):1438-43.
[Abstract] [PDF]

Oulas A, Boutla A, Gkirtzou K, Reczko M, Kalantidis K and Poirazi P
Prediction of novel microRNA genes in cancer-associated genomic regions–a combined computational and experimental approach.
Nucleic Acids Research 2009 Jun;37(10):3276-87.
[Abstract] [PDF]

Oulas A, Reczko M and Poirazi P.
MicroRNAs and Cancer-the search begins!
IEEE Trans Inf Technol Biomed. vol. 13, No 1, pg. 67-77, Jan 2009.
[Abstract] [PDF]



Petalidis LP, Oulas A, Backlund M, Wayland MT, Liu L, Plant K, Happerfield L, Freeman TC, Poirazi P, Collins VP.
Improved grading and survival prediction of human astrocytic brain tumors by artificial
neural network analysis of gene expression microarray data.
Molecular Cancer Therapeutics (on the cover) vol. 7, No 5, pg. 1013-1024, May 2008.
[Abstract] [PDF]

Liebmann L, Karst H, Sidiropoulou K, van Gemert N, Meijer OC, Poirazi P, Joels M.
Differential effects of corticosterone on the slow afterhyperpolarization in the
basolateral amygdala and CA1 region: possible role of calcium channel subunits.
Journal of Neurophysiology vol. 99, No 2, pg. 958-968, Feb. 2008.
[Abstract] [PDF]



Poirazi, P., Leroy, F., Georgalaki, M.D., Aktypis, A., De Vuyst, L., and Tsakalidou, E.
Use of artificial neural networks and a gamma-concept-based approach to model growth of and bacteriocin production
by Streptococcus macedonicus ACA-DC 198 under conditions simulating Kasseri cheese technology.
Applied Environmental Microbiology vol. 73, No 3, pg. 768-776, Feb. 2007.
[Abstract] [PDF]

Pissadaki, E.K. and Poirazi P.
Modulation of excitability in CA1 pyramidal neurons via the interplay of EC and CA3 inputs.
Neurocomputing, Special Issue for CNS 2006, vol. 70, No 11-12, pg. 1735-1740, June 2007.
[Abstract] [PDF]

Sidiropoulou K., Joels M., and Poirazi P.
Modeling stress-induced adaptations in Ca++ dynamics.
Neurocomputing, Special Issue for CNS 2006, vol. 70, No 11-12, pg. 1640-1644, June 2007.
[Abstract] [PDF]



Pavlidis P. and Poirazi P.
Individualized markers optimize class prediction of microarray data.
BMC Bioinformatics, vol. 7, pg. 345-359, July 2006.
[Abstract] [PDF]

Sidiropoulou K., Pissadaki E.K., and Poirazi P.
Inside the brain of a neuron.
EMBO Reports, vol. 7, pg. 886-892, September 2006.
[Abstract] [PDF]



Markaki M., Orphanoudakis S. and Poirazi P.
Modelling reduced excitability in aged CA1 neurons as a calcium-dependent process.
Neurocomputing, vol. 65-66, pg. 305-314, June 2005.
[Abstract] [PDF]



Poirazi, P. Neocleous, C., Pattichis, C. and Schizas, C.
Classification Capacity of a Modular Neural Network Implementing Neurally Inspired Architecture and Training.
IEEE Transactions in Neural Networks, vol 15, No. 3, pg. 597-612, May 2004.
[Abstract] [PDF]



Poirazi, P. Brannon, T. and Mel, B.W.
Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell.
Neuron, vol 37, pg. 977-987, March 2003.
[Abstract] [PDF]

Poirazi, P. Brannon, T. and Mel, B.W.
Pyramidal Neuron as 2-Layer Neural Network.
Neuron, vol 37, pg. 989-999, March 2003.
[Abstract] [PDF]

Poirazi, P. Brannon, T. and Mel, B.W.
About the Model (online supplement)
Neuron, vol 37, pg. 988, March 2003.



Poirazi, P. and Mel, B.W.
Impact of Active Dendritic Processing and Structural Plasticity on Learning and Memory.
Neuron, vol 29, pg. 779-796, March 2001.
[Abstract] [PDF]

Poirazi, P. and Mel, B.W.
Choice and value flexibility jointly contribute to the capacity of a subsampled quadratic classifier.
Neural Computation, vol. 12, num. 5, pg. 1189-1207, 2000.
[Abstract] [PDF]

Poirazi, P. and Mel, B.W.
Towards the Memory Capacity of Neurons with Active Dendrites.
Neurocomputing, vol. 26-27, pg. 237-245, 1999.
[Abstract] [PDF]