Tiina Manninen

I received MSc(Eng) degree in mathematics in September 2003 and DSc(Tech) degree in mathematics in December 2007 from Tampere University of Technology. At the moment I am a Senior Research Fellow in the Computational Neuroscience Group at Tampere University and a Visiting Scientist in Gladstone Institutes.

Development of computational models for neuronal and glial cells. See our website Computational Neuroscience Group for model details.

Computational Neuroscience Group

Computational Neuroscience

Research Council of Finland

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