since 2015: Edinburgh-Zhejiang lecturer, University of Edinburgh
2013 - 2014: Lecturer and curriculum fellow for quantitative biology, Harvard Medical School (US)
2010 - 2013: Postdoctoral fellow, Kennedy lab, California Institute of Technology (US)
2010: Visiting fellow, Kuroda lab, University of Tokyo (JP)
2005 - 2009: Pre- and postdoctoral research fellow, Le Novère lab, EMBL-European Bioinformatics Institute, Cambridge
PhD, Molecular Biology/Bioinformatics, University of Cambridge and EMBL
MSc, Mathematics, The Open University
MSc, Genetics, University of Salzburg (AT)
Awards and fellowships
since 2014: Fellowship in Medical Education Research, Harvard Medical School Academy
since 2013: NeXXt fellow, New York Academy of Sciences
2011 - 2013: Fast Track fellow, Robert Bosch Stiftung
2010 - 2012: Long-term post-doctoral fellowship, European Molecular Biology Organization
2010: Short-term post-doctoral fellowship, Japan Society for the Promotion of Science
2009: Christian Doppler Prize for Biology, State of Salzburg (AT)
2009 - 2010: Short-term post-doctoral fellowship, European Molecular Biology Laboratory
2005 - 2009: Pre-doctoral fellowship, European Molecular Biology Laboratory
2002: Excellence award for mathematics, University of Salzburg (AT)
Biochemical basis of learning and memory
When we learn something, connections between neurons in our brain grow stronger. This is mediated by a complex machinery of molecules inside the neuron that can receive and transmit signals and set in motion a series of functional and structural changes. We work on understanding how those molecules work together and regulate each other. In order to do this, we build computer models of neuronal compartments and of the molecules in them and run computer simulations of how they react to specific events, such as the activation of a neuronal connection. These simulations help us understand how these systems work, how they are affected by neurological disorders, and what effect specific drugs are likely to have. We are part of the Patrick Wild Centre for Research into Autism, Fragile X Syndrome and Intellectual Disabilities.
We are also interested in developing both the conceptual frameworks and the computational tools necessary for understanding and computationally representing living biochemical systems such as those underlying learning and memory.
Using online data to understand student learning
Another part of our research looks at questions around learning and memory at the level of individual learners. Topics of interest include the use of online learning tools, and the learning of computational and quantitative concepts.
Richard Fitzpatrick (PhD student)
- Prof. Upinder S. Bhalla (NCBS, Bangalore)
- Dr. Kevin Bonham (Harvard University)
- Ranjita Dutta Roy (Karolinska Institutet)
- Prof. Dragan Gasevic (University of Edinburgh)
- Prof. Tamara Kinzer-Ursem (Purdue University)
- Dr. Noboru Komiyama (University of Edinburgh)
- Dr. David Sterratt (University of Edinburgh)
- Dr. Sergiy Sylantyev (University of Edinburgh)
1. R. Dutta Roy, Christian Rosenmund, M.I. Stefan. Cooperative Binding Mitigates the High-Dose Hook Effect. Accepted, BMC Systems Biology
2. K.S. Bonham, M.I. Stefan. Gender disparity in computational biology research publications. Accepted, PLoS Computational Biology.
3. M.I. Stefan (2017). Cooperativity: A competition of definitions . J Math Biol 74(7): 1679–1681.
4. N. Rodriguez, J.-B. Pettit, P. Dalle Pezze, L. Li, A. Henry, M.P. van Iersel, G. Jalowicki, M. Kutmon, K.N. Natarajan, D. Tolnay, M.I. Stefan, C.T Evelo, and N. Le Novère (2016). The Systems Biology Format Converter. BMC Bioinformatics, 17:154.
5. M.I. Stefan, J.L. Gutlerner, R.T. Born, M. Springer (2015). The quantitative methods boot camp: teaching quantitative thinking and computing skills to graduate students in the life sciences. PLoS Comput Biol, 11(4): e1004208.
6. M.I. Stefan, T.M. Bartol, T.J. Sejnowski, M.B. Kennedy (2014). Multi-state Modeling of Biomolecules. PLoS Comput Biol, 10(9): e1003844.
7. L. Li, M.I. Stefan, N. Le Novère (2012). Calcium Input Frequency, Duration and Amplitude Differentially Modulate the Relative Activation of Calcineurin and CaMKII. PLoS ONE, 7(9): e43810.
8. M.I. Stefan, D.P. Marshall, N. Le Novère (2012). Structural Analysis and Stochastic Modelling Suggest Mechanism for Calmodulin Trapping by CaMKII. PLoS ONE, 7(1): e29406.
9. C. Li, M. Donizelli, N. Rodriguez, H. Dharuri, L. Endler, V. Chelliah, L. Li, E. He, A. Henry, M.I. Stefan, J.L. Snoep, M. Hucka, N. Le Novère, C. Laibe (2010). BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol, 4:92.
10. M.I. Stefan, S.J. Edelstein, N. Le Novère (2008). An allosteric model of calmodulin explains differential activation of PP2B and CaMKII. PNAS, 105(31):10768-73.
Information for students:
Willingness to discuss research projects with undergraduate and postgraduate students: YES - please click here