Tiina Manninen

Valmistuin Tampereen teknillisestä yliopistosta diplomi-insinööriksi syyskuussa 2003 ja tekniikan tohtoriksi joulukuussa 2007, kummassakin pääaineenani oli matematiikka. Tällä hetkellä olen yliopistotutkijana Laskennallisen neurotieteen ryhmässä Tampereen yliopistossa ja vierailevana tutkijana Gladstone Instituutissa.

Laskennallisten mallien kehitys hermosolu- ja gliasoluryhmille. Katso tarkempia tietoja Laskennallinen neurotieteen ryhmän sivuilta.

Laskennallisen neurotieteen ryhmä

Laskennallinen neurotiede

Suomen Akatemia

L. Keto and T. Manninen. CellRemorph: A toolkit for transforming, selecting, and slicing 3D cell structures on the road to morphologically detailed astrocyte simulations. Neuroinformatics, 2023. https://doi.org/10.1007/s12021-023-09627-5 

T. Manninen, J. Aćimović, and M.-L. Linne. Analysis of network models with neuron-astrocyte interactions. Neuroinformatics, 2023. https://doi.org/10.1007/s12021-023-09622-w 

M.-L. Linne, J. Aćimović, A. Saudargiene, and T. Manninen. Neuron-glia interactions and brain circuits. Computational Modelling of the Brain, (M. Giugliano, M. Negrello, and D. Linaro eds.), Springer Series in Advances in Experimental Medicine and Biology, vol. 1359, 87-103, 2022. https://doi.org/10.1007/978-3-030-89439-9_4 

T. Manninen, A. Saudargiene, and M.-L. Linne. Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex. PLoS Computational Biology 16(11):e1008360, 2020. https://doi.org/10.1371/journal.pcbi.1008360 

H. L. Payne, R. L. French, C. C. Guo, T. D. B. Nguyen-Vu, T. Manninen, and J. L. Raymond. Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity. eLife 8:e37102, 2019. https://doi.org/10.7554/eLife.37102 

T. Manninen, R. Havela, and M.-L. Linne. Computational models of astrocytes and astrocyte-neuron interactions: Characterization, reproducibility, and future perspectives. Computational Glioscience, (M. De Pittà and H. Berry eds.), Springer Series in Computational Neuroscience, 423 - 454, 2019. https://doi.org/10.1007/978-3-030-00817-8_16 

T. Manninen, J. Aćimović, R. Havela, H. Teppola, and M.-L. Linne. Challenges in reproducibility, replicability, and comparability of computational models and tools for neuronal and glial networks, cells, and subcellular structures. Frontiers in Neuroinformatics (Part of Research Topic: Reproducibility and Rigour in Computational Neuroscience) 12:20, 2018. https://doi.org/10.3389/fninf.2018.00020 

T. Manninen, R. Havela, and M.-L. Linne. Computational models for calcium-mediated astrocyte functions. Frontiers in Computational Neuroscience 12:14, 2018. https://doi.org/10.3389/fncom.2018.00014 

N. P. Rougier, K. Hinsen, F. Alexandre, T. Arildsen, L. A. Barba, F. C. Y. Benureau, C. T. Brown, P. de Buyl, O. Caglayan, A. P. Davison, M.-A. Delsuc, G. Detorakis, A. K. Diem, D. Drix, P. Enel, B. Girard, O. Guest, M. G. Hall, R. N. Henriques, X. Hinaut, K. S. Jaron, M. Khamassi, A. Klein, T. Manninen, P. Marchesi, D. McGlinn, C. Metzner, O. Petchey, H. E. Plesser, T. Poisot, K. Ram, Y. Ram, E. Roesch, C. Rossant, V. Rostami, A. Shifman, J. Stachelek, M. Stimberg, F. Stollmeier, F. Vaggi, G. Viejo, J. Vitay, A. E. Vostinar, R. Yurchak, and T. Zito. Sustainable computational science: the ReScience initiative. PeerJ Computer Science 3:e142, 2017. https://doi.org/10.7717/peerj-cs.142 

T. Manninen, R. Havela, and M.-L. Linne. Reproducibility and comparability of computational models for astrocyte calcium excitability. Frontiers in Neuroinformatics 11:11, 2017. https://doi.org/10.3389/fninf.2017.00011 

J. Intosalmi, T. Manninen, K. Ruohonen, and M.-L. Linne. Computational study of noise in a large signal transduction network. BMC Bioinformatics 12:252, 2011. https://doi.org/10.1186/1471-2105-12-252 

E. Toivari, T. Manninen, A. K. Nahata, T. O. Jalonen, and M.-L. Linne. Effects of transmitters and amyloid-beta peptide on calcium signals in rat cortical astrocytes: Fura-2AM measurements and stochastic model simulations. PLoS ONE 6(3): e17914, 2011. https://doi.org/10.1371/journal.pone.0017914 

T. Manninen, K. Hituri, E. Toivari, and M.-L. Linne. Modeling signal transduction leading to synaptic plasticity: evaluation and comparison of five models. EURASIP Journal on Bioinformatics and Systems Biology 2011: 797250, 2011. https://bsb-eurasipjournals.springeropen.com/articles/10.1155/2011/797250 

T. Manninen, K. Hituri, J. Hellgren Kotaleski, K. T. Blackwell, and M.-L. Linne. Postsynaptic signal transduction models for long-term potentiation and depression. Frontiers in Computational Neuroscience 4:152, 2010. https://doi.org/10.3389/fncom.2010.00152 

T. Manninen, M.-L. Linne, and K. Ruohonen. Developing Itô stochastic differential equation models for neuronal signal transduction pathways. Computational Biology and Chemistry 30(4): 280 – 291, 2006. https://doi.org/10.1016/j.compbiolchem.2006.04.002 

T. Manninen, M.-L. Linne, and K. Ruohonen. A novel approach to model neuronal signal transduction using stochastic differential equations. Neurocomputing 69(10 – 12): 1066 – 1069, 2006. https://doi.org/10.1016/j.neucom.2005.12.047 

A. Pettinen, T. Aho, O.-P. Smolander, T. Manninen, A. Saarinen, K.-L. Taattola, O. Yli-Harja, and M.-L. Linne. Simulation tools for biochemical networks: evaluation of performance and usability. Bioinformatics 21(3): 357 – 363, 2005. https://doi.org/10.1093/bioinformatics/bti018 

M.-L. Linne, T. Manninen, and T. O. Jalonen. A model integrating the cerebellar granule neuron excitability and calcium signaling pathways. Neurocomputing 58 – 60: 569 – 574, 2004. https://doi.org/10.1016/j.neucom.2004.01.096