Publications of Tiina Manninen

Articles in refereed scientific journals:

• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• E. Mäkiraatikka, T. Manninen, A. Saarinen, A. Ylipää, H. Teppola, K. Hituri, A. Pettinen, and M.-L. Linne. Modeling and simulation of chemical reactions (in Finnish, Kemiallisten reaktioiden mallintaminen ja tietokonesimulaatiot). Solubiologi 26(1): 21 - 28, 2008.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• 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.
  
  
• M. Honkiniemi and T. Manninen. Small group improves mathematical skills (in Finnish, Pienryhmä parantaa matematiikan taitoja). Dimensio 68(1): 24 - 26, 2004.
  

DSc(Tech) Dissertation:

• T. Manninen. Stochastic methods for modeling intracellular signaling. DSc(Tech) thesis, Mathematics, Department of Science and Engineering, Tampere University of Technology, 152 p., 2007. [Abstract] Ask a copy of the thesis by emailing me.

Articles in refereed scientific conference proceedings:

• T. Manninen and D. X. Keller. Comparison of discrete- and continuous-state stochastic methods to model neuronal signal transduction. Proceedings of the ACM International Conference on Bioinformatics and Computational Biology (ACM-BCB 2010), pp. 394 - 397, invited talk, Niagara Falls, New York, USA, 2.-4.8.2010.
  
  
• J. Intosalmi, T. Manninen, K. Ruohonen, and M.-L. Linne. Modeling biochemical systems: analysis in time and frequency domain. Proceedings of the 3rd Conference on Foundations of Systems Biology in Engineering (FOSBE 2009), pp. 121 - 124, Denver, USA, 9.-12.8.2009.
  
  
• J. Intosalmi, T. Manninen, K. Ruohonen, and M.-L. Linne. Modeling protein kinase C activation in the presence of calcium ion fluctuations. Proceedings of the 6th International Workshop on Computational Systems Biology (WCSB 2009), pp. 75 - 78, Aarhus, Denmark, 10.-12.6.2009.
  
  
• J. Intosalmi, T. Manninen, K. Hituri, K. Ruohonen, and M.-L. Linne. Modeling of IP3 receptor function using stochastic approaches. Proceedings of the 5th International Workshop on Computational Systems Biology (WCSB 2008), pp. 69 - 72, Leipzig, Germany, 11.-13.6.2008.
  
  
• T. Manninen and M.-L. Linne. Stochastic kinetic simulations of activity-dependent plastic modifications in neurons. Proceedings of the 5th International Workshop on Computational Systems Biology (WCSB 2008), pp. 101 - 104, invited talk, Leipzig, Germany, 11.-13.6.2008.
  
  
• T. Manninen, A. Saarinen, A. Ylipää, M.-L. Linne, O. Yli-Harja, and K. Ruohonen. Sequential Monte Carlo based maximum likelihood estimation for calcium binding reactions. Proceedings of the 2nd Conference on Foundations of Systems Biology in Engineering (FOSBE 2007), (F. Allgöwer & M. Reuss, eds), pp. 189 - 194, Stuttgart, Germany, 9.-12.9.2007.
  
  
• E. Mäkiraatikka, T. Manninen, A. Saarinen, A. Ylipää, H. Teppola, K. Hituri, A. Pettinen, O. Yli-Harja, M.-L. Linne. Stochastic simulation tools for cell signaling: survey, evaluation and quantitative analysis. Proceedings of the 2nd Conference on Foundations of Systems Biology in Engineering (FOSBE 2007), (F. Allgöwer & M. Reuss, eds), pp. 171 - 176, Stuttgart, Germany, 9.-12.9.2007.
  
  
• T. Manninen, A. Ribeiro, J. Lloyd-Price, M.-L. Linne, K. Ruohonen, O. Yli-Harja, and S. A. Kauffman. Parameter estimation and tuning of firefly luciferase pathway model. Proceedings of the 5th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS'07), 4 p., invited talk, Tuusula, Finland, 10.-12.6.2007.
  
  
• A. Pettinen, T. Manninen, O. Yli-Harja, K. Ruohonen, and M.-L. Linne. Estimation of neuronal signaling model parameters using deterministic and stochastic in silico training data: evaluation of four parameter estimation methods. Proceedings of the 5th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS'07), 4 p., Tuusula, Finland, 10.-12.6.2007.
  
  
• T. Manninen, M.-L. Linne, and K. Ruohonen. Modeling neuronal signal transduction using Itô stochastic differential equations and the Gillespie stochastic simulation algorithm. Proceedings of the IASTED International Conference on Computational and Systems Biology (CASB 2006), (D.-Z. Du, ed.), vol. 540, pp. 89 - 94, invited talk, Dallas, Texas, USA, 13.-14.11.2006.
  
  
• T. Manninen, E. Mäkiraatikka, A. Ylipää, A. Pettinen, K. Leinonen, M.-L. Linne. Discrete stochastic simulation of cell signaling: comparison of computational tools. Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2006), pp. 2013 - 2016, New York, USA, 29.8.-3.9.2006.
  
  
• T. Manninen, A. Saarinen, K. Ruohonen, and M.-L. Linne. Simulation study of deterministic differential equation model for protein kinase C signaling: sensitivity of stimuli, parameter values, and initial concentrations. Proceedings of the 4th TICSP Workshop on Computational Systems Biology (WCSB 2006), (P. Ruusuvuori, T. Manninen, H. Huttunen, M.-L. Linne & O. Yli-Harja (eds), pp. 61 - 64, Tampere, Finland, 12.-13.6.2006.
  
  
• T. Manninen, M.-L. Linne, and K. Ruohonen. A novel approach to model neuronal signal transduction using stochastic differential equations. Proceedings of the 14th Annual Computational Neuroscience Meeting (CNS*2005), Madison, Wisconsin, USA, 17.-21.7.2005.
  
  
• K. Hituri, T. Manninen, and M.-L. Linne. A program to obtain signal transduction models from a database. Proceedings of the 3rd TICSP Workshop on Computational Systems Biology (WCSB 2005), (T. Manninen, M.-L. Linne, & O. Yli-Harja, eds), pp. 47 - 48, Tampere, Finland, 13.-14.6.2005.
  
  
• T. Manninen, M.-L. Linne, and K. Ruohonen. Incorporating stochasticity into deterministic differential equation models for neuronal signal transduction. Proceedings of the 3rd TICSP Workshop on Computational Systems Biology (WCSB 2005), (T. Manninen, M.-L. Linne, & O. Yli-Harja, eds), pp. 59 - 60, Tampere, Finland, 13.-14.6.2005.
  
  
• T. Manninen. Varying stimulus and parameter values in the PKC pathway model. Proceedings of the 2nd TICSP Workshop on Computational Systems Biology (WCSB 2004), (T. Aho, H. Lähdesmäki, & O. Yli-Harja, eds), pp. 41 - 42, Silja Opera, Helsinki, Finland - St. Petersburg, Russia, 14.-16.6.2004.
  
  
• M.-L. Linne, T. Manninen, and T.O. Jalonen. A model integrating the cerebellar granule neuron excitability and calcium signaling pathways. Proceedings of the 12th Annual Computational Neuroscience Meeting (CNS*2003), Alicante, Spain, 5.-9.7.2003.
  

Publications in university and department series:

• T. Manninen, A. Saarinen, and M.-L. Linne. Simulation study of differential equation model for protein kinase C signaling. Report 3, 20 p., Institute of Signal Processing and Institute of Mathematics, Tampere University of Technology, Tampere, Finland, 2004. [PDF] [PS]