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David Holcman


Group of Data Modeling, Computational NeuroBiology and Predictive Medicine, Applied Mathematics

Our main interest is to

  • Investigate brain coding principles, functions at multiple scales : from cellular to the network level, based on biophysical and cellular mechanisms.
  • Identify and implement computational principles of molecular, cellular and neuronal network organization, cell migration, communication and interactions.
  • Develop mathematical models and Asymptotics Data driven analysis, Stochastic modeling and computational, PDEs expansion, design and implement algorithms and classification methods.
  • Quantify and predict function of nano-, micro- domains in cell biology and neurobiology based on local structures, biochemistry networks as well as brain responses in real-time for medical applications.

Methods and Expertise : are data modeling, stochastic processes, mathematical computations, signal processing, machine-Learning, numerical simulations, statistical biology, algorithms and softwares to extract features from large datasets (super-resolution single particle trajectories, Hi-C analysis, calcium time series, EEG databases).

Medical predictions :
We developed various codes and methods to study neuronal network activity and predict the brain states during anesthesia and coma. We studied time series, EEG recorded during coma, or anesthesia. We predicted brain responses for neurobiology and medical applications.

Biography and history

The lab has initiated in 2006 at ENS as a relocation from the Weizmann Institute. The group is also affiliated with the University of Cambridge and Churchill College (UK).
In the past, we developed several computational and biophysical theories, such as the Narrow Escape Theory (2004), polymer physics of chromain organization (2010), neural network short-term plasticity responses (2006), neuron-glia interaction (2010), Extreme Statistics in biology (2017) providing a framework to study molecular signaling in cells, neurons, trafficking in dendritic spines (2007) and cellular microdomains in general.
The lab has also developed high throughput single particle trajectory analysis since 2012 and predicted potential well organization of dense nanodomains (2012), as well as multiple nano-column organization of synapses (2011).
We expanded our computational methods (hybrid simulations, asymptotic of PDE) to study trafficking on membranes and in organelles such as the endoplasmic reticulum and nucleus organization (>2018). Recently the lab developed hybrid simulations to study cell navigation in the brain (>2020), but also stochastic simulations to study calcium signaling in spines (2022) and during neuron-glia network interactions.
We are now currently using our expertise to identify neuronal and glial networks (> 2022) and also to predict brain states during coma and anesthesia (>2018) using EEG and hemodynamics data.

Past and present Collaborations involved : A. Singer (Princeton university), D. Menassa (Oxford), N. Kubis and D. Longrois (APHP), C. and G. Kaminskii (U. of Cambridge) E. Korkotian (Weizmann), M. Heine (Mainz), K. Tsaneva (Exceter), N. Rouach (College-de-France), J. Paz (UCSF), R. Yuste (Columbia), T. Lagache (Pasteur) and many more.

Breaking news of the lab :

  • Dec 20 2023 : our new State-chart representation to map the brain in stable states during anesthesia in collaboration with the group of J. Paz (UCSF) : see V. Loison et al Neuroimage
  • Sept 29 2023 : our new algorithm on SPT classification has just appeared in Nature Struc. and molecular Biology, in Collaboration with the group of E. Laue, S. Basu. D. Klenerman and D.Heinrich from Cambridge.
  • P. Parutto, previous PhD student of the lab, was elected in 2023 Byfellow of the Churchill college, Cambridge, UK.
  • The project on neurophiology and anesthesia was awarded an ANR grant 2023.
  • Our novel method to analyse single particle trajectory in Cell Reports Methods (October 2022) *Cover page
  • French Newspaper "le Monde" (April 2022) popularize the concept of predictive anesthesia and the real-time algorithm to predict brain sensitivity developed by C. Sun in the lab.
  • Cover page on exteme statistics with switching European J. of physics B

Striking recent peer reviewed publications of the lab :

S. Basu, O. Shukron et al, Live-cell 3D single-molecule tracking reveals how NuRD modulates enhancer dynamics, Nature Struct. Mol. Biology, 2023

T. Perochon, Z. Krsnik, T. Lagache, D.A. Menassa D. Holcman, Spatiotemporal mapping of human microglia during brain development with advanced spatial statistics assisted by deep-learning, 2023.

Chang LH, Ghosh S, Papale A, Luppino JM, Miranda M, Piras V, Degrouard J, Edouard J, Poncelet M, Lecouvreur N, Bloyer S, Leforestier A, Joyce EF, Holcman D, Noordermeer D. Multi-feature clustering of CTCF binding creates robustness for loop extrusion blocking and Topologically Associating Domain boundaries.Nat Commun. 2023

Mc Hugh J, Makarchuk S, Mozheiko D, Fernandez-Villegas A, Kaminski Schierle GS, Kaminski CF, Keyser UF, Holcman* D, Rouach* N. Diversity of dynamic voltage patterns in neuronal dendrites revealed by nanopipette electrophysiology. Nanoscale. 2023 Jul 27 ;15(29):12245-12254. doi : 10.1039/d2nr03475a.

P Parutto, J Heck, M Lu, C Kaminski, E Avezov, M Heine, D Holcman, High-throughput super-resolution single-particle trajectory analysis reconstructs organelle dynamics and membrane reorganization, Cell reports methods 2 (8), 100277

U Dobramysl, D Holcman, Computational methods and diffusion theory in triangulation sensing to model neuronal navigation, Reports on Progress in Physics 2022.

Basnayake, K., Mazaud, D., Kushnireva, L., Bemelmans, A., Rouach, N., Korkotian, E., & Holcman, D. (2021). Nanoscale molecular architecture controls calcium diffusion and ER replenishment in dendritic spines, Science Advances 7(38), eabh1376. >https://www.science.org/doi/10.1126/sciadv.abh1376.

U Dobramysl, D Holcman, Triangulation Sensing to Determine the Gradient Source from Diffusing Particles to Small Cell Receptors, Physical Review Letters 125 (14), 148102. 2020

Youtube presentation of the group :

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How to join the lab ?

  1. at the master level : enroll in our class that belongs to Master 2 of Paris VI (Applied mathematics) or interdisciplinary Master at ENS (Imalys)
  2. at a PhD level : you must have spent 6 months of training period in the lab.
  3. at a postdoc level : physicists, mathematicians, computer scientists are welcome to apply.
  4. at a senior level : we are 3 senior researchers. Please contact D. Holcman

Some projects

 Predicting Brain state from EEG during Anesthesia. We are developing methods to analyse EEG and predict the Brain states.

 Applied mathematics and probability, Mathematical Modeling and analysis.
* We are developing asymptotic methods and Brownian simulations to compute mean first passage time formulas, with applications to chemical reactions in microdomains.

* We are developing asymptotic and simulations methods to compute the voltage in nanodomains.

 Synaptic transmission, trafficking and voltage dynamics in dendrites : we are developing models of synaptic transmission and tools to extract features from superresolution data. We use the Poisson-Nernst-Planck equations to model the voltage dynamics at excitatory synapses and investigate the role of the local geometry.

Other projects in integrative biology concern sensor cells, such as photoreceptors, where we build model of the single photonresponse including dark noise in rods and cones with Juergen Reingruber.

In the past, by using asymptotic analysis, we computed the expansion of the mean time for a Brownian molecule to escape through a small hole located on a piece of a cell membrane (Narrow escape problem (See WIKI)). This computation defines the forward binding rate of chemical reactions occurring in microdomains.

Keywords

Fields : Computational Methods, Data analysis, Neuroscience, Real-time algorithms, Brain simulations, Mathematical Biology, Statistical Biophysics, Predictive Medicine, Applied Mathematics, Model-Machine-Learning, Asymptotic Analysis, Applied Probability, EEG analysis, Deconvolution methods, Electrophysiological time series, Coma, Anesthesia, Brain analysis, Polymer Modeling, Neuron-glia interactions, microdomains, Nuclear Organization.

Sub-Fields :
Diffusion, stochastic modeling, Data Geometry, Partial Differential Equations, Brownian simulations, Brownian Motion, Narrow Escape Time, Dire Strait Time, Asymptotic methods, Mean First Passage Times, Markov chains, Extreme statistics, Hybrid simulations, Statistical methods, Wavelet decomposition, Analysis of single particle trajectory, Stochatic simulations, Aggregation-Dissociation model, Conformal methods, WKB expansion, boundary layer analysis, polymer looping, modeling telomere organization, Molecular and Vesicular Trafficking, Synaptic Transmission, Numerical methods, Early Steps of Viral Infection, Neurite outgrowth, Super-resolution data analysis, boundary layer methods, dsDNA break, dendritic spines, modeling calcium dynamics, looping time, synaptic transmission.

More about our research :
More about our past research in french :

copyrights@David Holcman, free to use with appropriate reference.