Erik De Schutter
Speaker of Workshop 3
Will talk about: Importance of stochasticity and small molecule number in the induction of synaptic plasticity
Erik De Schutter was born in Antwerp, Belgium. He studied medicine and got his MD in 1984 at the University of Antwerp, where he subsequently specialized as a neuropsychiatrist. During his medical residency he started work on computational modeling of central pattern generation in the leech. In 1990 he became a research fellow at the California Institute of Science and Technology where he developed his famous Purkinje cell model. He returned in 1993 to the University of Antwerp to start the Theoretical Neurobiology group, which focused on modeling the cerebellum. He became a senior lecturer at the University of Antwerp in 1999 and was promoted to professor in 2006. His research in Antwerp contributed to understanding synaptic plasticity and oscillations in the cerebellum and on software development for reaction-diffusion modeling and automated parameter searching. The group also contributed experimental studies on cerebellar physiology. Erik De Schutter became in a 2007 a principal investigator and since 2011 a professor at OIST where he leads the Computational Neuroscience Unit. There he continued work on neuronal excitability and molecular modeling of signaling pathways, but also expanded into morphological analysis.
Erik De Schutter is involved with several international organizations promoting computational neuroscience. He is president of the Organization for Computational Neuroscience and member of the governing board of the International Neuroinformatics Coordinating Facility for which he leads a program on model description languages.
It is well known that many aspects of synaptic transmission are highly stochastic, the most obvious being the transmitter release process. The experimental induction of synaptic long-term plasticity important in learning is also highly variable. A likely reason is that the signaling pathways involved with synaptic plasticity induction exist in spines that have very small volumes, resulting in small numbers of molecules (range 10 ~ 100) participating in the reactions.
In a study of the induction of cerebellar long-term depression (LTD) at Purkinje cell synapses, which is evoked by a rise in cytosolic calcium activating PKC and a MAP-kinase based feedback loop, we have demonstrated that the stochasticity of the chemical reactions makes the induction probabilistic (Antunes and De Schutter, J. Neuroscience 2012). Though experimentally a sigmoidal relation between calcium concentration and amount of LTD was measured, the model predicts that this average over the induction in 100s of spines does not imply the existence of a calcium threshold. Instead, in single spines the induction is binary and the calcium concentration only sets the probability of induction as expected from stochastic dithering.
In a follow-up study we have discovered that the properties of this system critically depend on the number of Raf molecules in the spine (Jain et al. 2014). Raf is a proto-oncogene and a principal component of the MAP-kinase based feedback loop activated during cerebellar LTD induction. The predicted number of Raf molecules in a Purkinje cell spine is close to the critical minimal number to allow induction of LTD, a decrease by a few molecules prevents the induction of stable LTD while an increase does not cause much more LTD. Assuming free diffusion of Raf, the expected fluctuations in the number of Raf molecules in a spine will strongly influence its capacity to undergo LTD.
Besides the biological implications, these results also point to the need of detailed stochastic simulations that track integer number of molecules, like the Gillespie method implemented in the STEPS simulator (Hepburn et al., BMC Systems Biology 2012). Stochastic differential equations cannot replicate the dependence on molecule number of this system.
