Synaptic Computation

Synaptic computation
Synapses are the basic units of computation in the brain. They transmit information from one cell to the next in a highly nonlinear manner, adapting their strength on a millisecond to hours or longer timescale. This allows them to perform critical computations in the neural circuit and to be a key aspect of information processing and cognitive processes like learning and memory. In the synapse a relative small number (~2000) of genes is expressed, which are responsible for the complex processes involved in neurotransmission. We apply a systems biology approach, combining bioinformatics, functional analysis, and mathematical modeling to resolve the gene networks underlying synaptic transmission.

Functional analysis
Our experimental model to study synaptic transmission is the hippocampal autapse, a single neuron that makes synaptic contacts on its own dendritic tree. This reduced system allows sampling of a large and well defined population of synapses using patch-clamp measurements and can be easy genetically manipulated using cDNA or mRNI constructs. In this way we can experimentally test the function of important synaptic genes identified in the interactome or with our model simulations.

Mathematical modeling
Our goal is to construct a dynamical model of the synapse which is able to simulate synaptic transmission under various conditions. The model will include protein interactions and calcium dynamics and fit the experimental data obtained in our lab and published in the literature. We have started with a dynamical model for fast calcium dynamics in dendritic spines and are currently focusing on a model for vesicle release from the presynaptic terminal.

Synaptic gene networks and disease
Impaired synaptic transmission might be implicated in several brain disorders. We help to find synaptic gene networks which can be tested in genome wide association studies in large patient cohorts to identify synaptic genes involved in brain disease. Disease genes will be functionally tested in the autapse system and their role at the systems level will be simulated in the mathematical model.

Groffen AJ, Martens S, Diez Arazola R, Cornelisse LN, Lozovaya N, de Jong AP, Goriounova NA, Habets RL, Takai Y, Borst JG, Brose N, McMahon HT & Verhage M. (2010) Doc2b is a high-affinity Ca²⁺ sensor for spontaneous neurotransmitter release. Science 327, 1614-1618.

Schotten S, Meijer M, Walter AM, Huson V, Mamer L, Kalogreades L, ter Veer M, Ruiter M, Brose N, Rosenmund C, Sorensen JB, Verhage M, Cornelisse LN (2015) Additive effects on the energy barrier for synaptic vesicle fusion cause supralinear effects on the vesicle fusion rate. eLife 4: e05531

Cornelisse LN, Tsivtsivadze E, Meijer M, Dijkstra TM, Heskes T, Verhage M (2012) Molecular machines in the synapse: overlapping protein sets control distinct steps in neurosecretion. PLoS computational biology 8: e1002450

de Jong AP, Meijer M, Saarloos I, Cornelisse LN, Toonen RF, Sorensen JB, Verhage M (2016) Phosphorylation of synaptotagmin-1 controls a post-priming step in PKC-dependent presynaptic plasticity. Proceedings of the National Academy of Sciences of the United States of America 113: 5095-5100

Ruano D, Abecasis GR, Glaser B, Lips ES, Cornelisse LN, de Jong AP, Evans DM, Davey Smith G, Timpson NJ, Smit AB, Heutink P, Verhage M & Posthuma D. (2010) Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability. Am J Hum Genet 86, 113-125.

Cornelisse LN, van Elburg RA, Meredith RM, Yuste R & Mansvelder HD. (2007). High speed two-photon imaging of calcium dynamics in dendritic spines: consequences for spine calcium kinetics and buffer capacity. PLoS One 2, e1073.