David Holcman

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David Holcman is an applied mathematician, biophysicist and computational biologist at École Normale Supérieure in Paris. He is known for his work on the narrow escape problem, the modeling of molecular trafficking in neurobiology and diffusion and electrodiffusion in dendritic, modeling neuronal network dynamics such as Up and down states, developing multiscale methods to analyse large amounts of molecular super-resolution trajectories, and developing polymer physics to study the nucleus organization.

Early life and education

Holcman was born in Paris in 1970, in a Jewish family originating from Hungary (mother) and Poland (father). He graduated from Telecommunications Engineering School and Ecole Normale Superieure de Cachan in 1994. He received a PhD in mathematics in 1998 under the supervision of Prof. Thierry Aubin at Pierre et Marie Curie University... He held postdoctoral positions at the Weizmann Institute of Science with Yakar Kannai, researching subriemmanian geometry, and at Tel Aviv University with Zeev Schuss, researching applied mathematics. He was a fellow of the Keck Center for Computational Neurobiology at UCSF from 2002 to 2004. He worked also with Charles C. Pugh as a postdoctoral fellow at UC Berkeley.

Career

Holcman moved to Israel in 2004 and became a senior lecturer under the Chair of M. Russel Haas, in the department of Mathematics and Computer Science at the Weizmann Institute of Science , Israel. In 2006 he became a CNRS research director at ENS. From 2009 to 2011 he was an adjunct Associate Professor at Tel Aviv University. He was elected French Governmental Fellow of the Churchill college in 2013 and several times overseas fellow. He is also a visiting professor at University of Cambridge, and previously was a visiting professor at the university of University of Oxford in 2015-17, where he was a visiting fellow of the Brasenose College in 2016.. In 2019, he was a visiting Fellow at St John college in Cambridge.

Works

His research interests include mathematical modeling, computational methods, stochastic simulations, data modeling, neuronal networks, computational biology, asymptotic approaches in partial differential equations, predictive medicine, electroencephalography (EEG) analysis, and modeling organelles in cells. Other contributions concern methods of analysis of single particles trajectories and development of polymer models and analysis to study nucleus organization.

Publications

Holcman has more than 300 published journal articles and has registered 2 patents.

He is the co-author of the books:

• David Holcman and Zeev Schuss, Stochastic Narrow Escape in Molecular and Cellular Biology: Analysis and Applications, 2015-09-08, ISBN 978-1493931026 Search this book on .
• David Holcman (editor), Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, 2017-10-04, ISBN 978-3319626260 Search this book on .
• David Holcman and Zeev Schuss, Asymptotics of Elliptic and Parabolic PDEs: and their Applications in Statistical Physics, Computational Neuroscience, and Biophysics, 2018-05-25, ISBN 978-3319768946 Search this book on .

Press coverage

Holcman's research has been frequently covered in the news, for example

• To celebrate the first winners of the European Research Council (ERC) in 2007, an international meeting was organized so that they could discuss their vision and research plan (see Holcman's presentation).
• The narrow escape theory developed by the group of D. Holcman inspired the Fargo TV series in 2017.

• The novel nanoscale molecular organization underlying calcium dynamics in synapses, found by combining multidisciplinary approaches (live cell imaging, modeling, simulation, super-resolution) and published in 2021 was popularized as it brought novel concepts to the basis of memory and memory architecture;

• During the years 2019-2022, the work on Electroencephalogram (EEG) analysis carried in Holcman's group led to novel applications to better monitor and control anesthesia doses, popularized in "Pour La science"
• The work on spermatozoa modeling in the uterus received the Pineapple Science Award (Math Prize), the Chinese equivalent of the Ig Noble Prize.
• D. Holcman proposed to revisit the notion of time for living organisms: time is a random variable and it is controlled by the extreme statistics associated with telomere dynamics. "How cells are counting time?", this work was popularized in a review article: The Life and Death of Cells;
• The discovery that astrocytes could invade the synaptic cleft under some specific conditions was recognized as a key result in "Invasion of Astrocytes: modeling driving experiments"
• In 2011, mathematical modeling was at a turning point and was becoming predictive for molecular and cellular, this moment was summarized in an interview with the CNRS journal: "When biology becomes mathematics...".
• The new algorithm developed by Holcman's group to predict the sensitivity to general anesthesia gained the interest of journalists from the French newspaper "Le Monde" Une équipe française développe des algorithmes d’analyse en temps réel des données de l’EEG, pendant que le patient est sédaté

Awards

Holcman has received several awards, including a Sloan-Keck fellowship award (2002), a Marie-Curie Award (2013), and a Simons Fellowship. He is also recipient of 2 ERCs: a Starting Grant in mathematics (2007) and an Advanced Grant in biology (2019). His work on modeling spermatozoa dynamics during fertility in 2016 won the Pineapple prize, the Chinese equivalent of the Ig Nobel.

Social and higher education improvements and impacts

D. Holcman proposed several improvements of social conditions

-He proposed in 2017 to reshape French higher education and in particular to create a novel cohesion by applying the college system developed in Oxford and Cambridge.

-He alerted in April 2020 about the protecting role of face masks 3 weeks after French lockdown during the first COVID-19 crisis using a statistical comparison with Hong-Kong.

-He proposed in 2016 that the new French economy should be based on AI, a proposition that inspired a national plan.

-He proposed in 2019 to separate bicycle from car network in Paris.

-He alerted in 2019, several times of the heavy French administration in research which he argued has more detrimental impact than the lack of money.

-In 2021, he cofounded a start-up company SignalMed+ SignalMed+ — Developing new ways to predict the brain to apply adaptive algorithm methods to predict the brain, with applications during coma and anesthesia.

Contributions in Mathematics, Biophysics, Cell Biology, Computational Methods and Engineering

1-Modeling molecular trafficking in the cytoplasm and on neuronal membrane (Applied Mathematics, Biophysics and statistical physics)

D. Holcman is at the origin of modeling receptor trafficking on the surface of neurons, developed at UCSF in 2003. With Z. Schuss they derived properties of receptors diffusing in microdomains from a stochastic approach. This analysis has provided the theoretical foundation for estimating the residence time of receptors such AMPA, NMDA, Glycine, etc... at the synapse of neurons. This work allowed extracting biophysical parameters from single particle trajectories recorded in neuronal cells.

2-Narrow escape theory (Applied Mathematics, Partial differential equations, Matched asymptotics, probability, Brownian motion and condensed matter physics)

In collaboration with Z.Schuss and A.Singer, D. Holcman initiated and developed the narrow escape theory and recently with Z. Schuss, the Dire Strait Theory to characterize diffusion in very narrow straits. This theory presents the foundation to obtain asymptotic computation of the time to find a narrow window, in complement with matched asymptotic approach developed by M. Ward and J. Keller. The methods are based on Green's functions, Sneddon's analysis using Abel's integral, asymptotics of PDEs, boundary layer analysis, conformal mapping, matched asymptotics and WKB expansions.

3-Phototransduction in rods and cones (data analysis, modeling and numerical simulations)

D. Holcman developed with J. Reingruber, various mathematical models and data analysis to analyze phototransduction, accounting for the early steps of chemical reactions, the dark noise and the geometrical organization of the photoreceptor outer-segment. The methods were based on homogenization procedure, Markov chain, stochastic analysis, Brownian simulations and allow obtaining a novel understanding of the fast photoresponse. This approach was successfully applied to extract in vivo rate constants for the phosphodiesterase. This model was used to simulate degenerated photoreceptors in Coll. with experimental groups: J. Korenbrot (UCSF) and G. Fain (UCLA).

4-Analysis of dendritic spines (physical modeling, probability and theory of diffusion)

D. Holcman has pioneered the modeling and analysis of diffusion in dendritic spines: he derived the laws of diffusion in dendritic spines in geometry in Coll. Z. Schuss (Tel Aviv U.), E. Kokotian (Weizmann) M. Segal (Weizmann) and A. Biess.

5- Modeling and analysis of synaptic transmission (computational neurobiology and biophysics)

D. Holcman and his PhD students A. Taflia and D. Fresche developed (2006-2011) computational methods and numerical simulations to analyze synaptic transmission. They derived from first principles, expressions for the synaptic current (excitatory) and integrated many sources of noise. Their method allowed to study synaptic transmission in normal and pathological conditions and the role of key parameters such as the geometry, location where vesicles were released as well as receptor trafficking, organization of the post-synaptic density (PSD) in synaptic transmission modulation. In 2011, they predicted the multiple nanocolumn organization of synapses, which was finally found experimentally in 2016 by Blanpied's lab.

These simulations were used to study synaptic transmission occurring during certain pathologies such as epilepsy in collaboration with the group of N. Rouach, College-de-France. These modeling and simulations predicted that glial protrusion could penetrate the synaptic cleft, as confirmed experimentally by N. Rouach in connexin30 deficient, a key protein to organize glia cells in network. This discovery led to a new function of this connexin.

6- Quantifying the early steps of viral infection using stochastic processes and the Fokker-Planck equation (Biophysics and Statistical physics)

D. Holcman with his student T. Lagache was among the pioneers in 2006 to model viral trafficking at the single particle level in cells and the modeling the early steps of viral infection, in Coll. with the experimental groups of O. Danos (Necker) and A. Herrmann (Berlin). Using jump stochastic processes, they quantify the time for several viruses to enter inside the nucleus, following endosomal escape. They proposed that influenza virus buffers the pH in endosomes while trafficking (coll. C.Sieben and A. Herrmann).

7- Development and morphogenetic gradients (Mathematical Biology and Computational Development):

-In collaboration with A. Prochiantz (College-de-France), D. Holcman developed in 2007 a theory to study and predict the formation and the precision of boundaries between morphogenetic regions in the brain based on morphogen interaction and propagation. These boundaries shape the developing tissue.

-With P. Charnay (ENS), D. Holcman and J.Reingruber, they studied the positive feedback loop of Krox20 activation: they developed a Markov model of DNA, mRNA and protein activations: They show that bistability of the mean-field model is actually misleading and Krox20 expression is actually gradual and not bistable.

-With the group of T. Galli, they pioneered a computational approach based on the narrow escape theory to show that dendrite versus axon outgrowth depends on vesicular trafficking and microtubule dynamics.

8-Search process in the nucleus and nuclear organization (Polymer physics, Modeling, Statistical analysis, Data processing, Computational Genetics)

Holcman, with his group (A. Amitai, J. Reingruber, G. Malherbe), were the first in 2007 to report that the search time for a transcription factor in the nucleus is associated with a time in 3 dimensions, different compared to the time spent on the DNA molecule.

- with the experimental work of A. Taddei (Curie), they quantified telomere clustering in nanodomains and provided a framework for studying telomere clusters with a few number of particles (with N. Hoze, PhD student 2009).

-In collaboration with the experimental group of T. Texeira, D. Holcman with K. Daoduc (PhD student 2009) computed the length of the shortest telomere and found new statistical laws underlying senescence onset, showing that time of life could be controlled by extreme statistical events.

-In collaboration with the experimental group of K. Dubrana (CEA), D. Holcman and A. Amitai (PhD student 2009) developed novel statistical methods to analyze the search process of a double stranded DNA break. They introduce a a four parameter analysis and the 4P algorithm in 2017,

-In collaboration with the experimental group of S. Gasser (FMI, Basel), they introduced novel biophysical parameters to quantify single locus trajectories and DNA breaks motions.

-With his student O. Shukron, they developed in 2017 a method to reconstruct chromatin organization from HiC data based on a new polymer theory they developed called random cross-linker polymers. They also studied the mechanism of dsDNA break based on the search process of polymer. They also introduced a two spot analysis based on single particle trajectories, revealing a nanoscale of 100 nms of interaction-fluctuation.

-In Collaboration with the group of E. Laue (U. of Cambridge), D. Holcman with his student O. Shukron developed a classification algorithm of many SPT trajectories, that allowed to discover two novel scales of chromatin folding associated with the NuRD remodeler complex.

9-Stochastic approach to analyze super-resolution single particle trajectories (Biophysics, Data analysis, Statistics)

-D. Holcman and his group introduced the diffusion, drift and potential maps, reconstructed from overlapping super-resolution single particle trajectories modeled by the Langevin’s equation. The map were generated using optimal two-step estimators based on the first and second statistical moment of the displacement $X(t+\mathrm{\Delta }t)-X(t)$. This was in contrast with the mean square displacement analysis computed along trajectories. With his student N. Hoze, they demonstrated that AMPAR could be retained at synapses by potential wells. The data were collected by the group of D. Choquet (Bordeaux).

-With the group of M. Heine, they found in 2020, that high density regions of calcium channels are also characterized by potential wells, the energies of which are reduced compared to the ones of the post-synaptic terminal.

-D. Holcman developed with his PhD student P. Parutto automated analysis of SPT potential wells and to reconstruct biological network such as endoplasmic reticulum from single particle trajectories. -These results suggested that excitatory synapses are characterized by a faster molecular dynamics in the pre-synaptic terminal versus the post-synaptic one.

10-Semi-classical limit and Partial Differential Equations (Pure and Applied Mathematics)

In the field of asymptotics of PDE and analysis on manifolds, Holcman and Kupka have described in 2001 the semi-classical limit associated with a general non-gradient drift term and solved first order PDE on Riemannian manifolds, a research published from 2001 to 2011.

11-Spectrum of the non-self-adjoint Fokker-Planck operator and escape probability (Pure and Applied Mathematics, Probability)

-D. Holcman in coll. with Z. Schuss obtained in 2013 an exact expression for the spectrum of the Fokker-Planck operator associated with a randomly perturbed dynamical system in dimension 2 (with non-conservative drift). This question remained unsolved since the introduction of the Fokker-Planck equation more than 100 years ago.

- With his K. Dao Duc (PhD student), they discovered a new resonance-oscillation in the exit time density function. This phenomenon allows quantifying the exit time in Up states, observed in certain cortical neuronal dynamics as described by the physiology groups D. Ferster, A. Konnerth and B. Sakmann.

12- Statistical physics and asymptotic analysis of transient polymer dynamics in confined domains (Modeling, Laplace's equation, PDEs, Differential Geometry)

D. Holcman with his student A. Amitai introduced novel approaches to estimate the mean time for a polymer to loop in free and confined domains, summarized in an extended review. This approach is based on asymptotic analysis of the first eigenvalue of the Laplace operator in high dimensional space on flat manifolds. They applied their analysis to reveal for the first time the degree of confinement of a locus in the cell nucleus from Chromosomal Capture data.

13- Theory of stochastic chemical and mean time to threshold (Physical chemistry, Biophysics, Statistical physics)

D. Holcman and Z. Schuss initiated a theory of stochastic chemical reactions in microdomains (in 2005) based on the narrow escape theory. The theory was extended to the mean time that the number of bound molecules reaches a given threshold, called mean time to threshold developed with his PhD K. Dao Duc. The theory was applied to estimate the probability of Long-Term Potentiation induction in neurobiology, mRNAs modulation by siRNAs in the nucleus (coll. K. Burrage, Oxford) or computing the first time for the first TRP channel to open in fly photoreceptor. The method is based on two-dimensional Markov chains with zero absorbing boundary conditions. They recently extended with his postdoc A. Papale, this approach to define the memory of chromatin organization

14- Asymptotics of PNP in bounded domain with cusp geometry for non electroneutrality (Biophysics, Applied mathematics, Computational biology, Simulations)

D. Holcman with his student Cartailler developed a novel approach to solve the Poisson-Nernst-Planck equation in bounded domains and computed the difference of potential between any two points, when there is no electro-neutrality. This analysis is based on a new de-singularization method and matched asymptotic analysis.

The result was applied to interpret data collected in the Yuste's lab about voltage in dendritic spine. A deconvolution of time series was also used. This research was disseminated in high profile journals of several disciplines (neuroscience, applied math, statistical physics, chemical physics, etc...).

Recently in coll. with I.M. Sokolov, they study global but non-local electroneutrality of electrolyte to study how far an electrical field can penetrate inside the ionic solution.

14- EEG analysis and applications to predict coma outcome and depth of anesthesia (signal Processing, Classification, Predictive medicine)

D. Holcman with several students of his group developed novel methods mixing signal processing and Machine-Learning to obtain a predictive analysis of EEG, extracting also novel transient features. The results are used to predict the outcome of anoxic coma and the depth of anesthesia (Patent with Pr. N. Kubis).

-With J. Cartailler they developed a novel method to predict the sensitivity of anesthesia after 10 minutes.

-They also developed a statistical analysis of EEG parameter to predict post-anesthetic complications.

-With M. Dora, PhD in the lab, they developed a novel feedback control approach to guarantee an optimal anesthesia.

15- Extreme statistics for narrow escape and applications to cell biology and neuroscience (Statistical physics, Applied mathematics)

D. Holcman with his student K. Basnayake developed the theory of extreme narrow escape in 2016-2017, allowing to compute the mean time for the first among many random particles to find a small target. They obtained asymptotic formulas in dim 1,2 and 3. They further developed with Z. Schuss the theory of redundancy (redundancy principle) expressing the role of many copies of the same particles to make rare events likely to happen. This is well examplified for the case of fertility where many sperms are present to compensate for the random and small location of the egg in the uterus.

The framework they developed allowed to explain various fast signaling time scales, such as the fast ms activation of the endoplasmic reticulum located in dendritic spines in few ms, also the classical property of diffusion would predict tens to hundreds of ms (coll. with the group E. Korkotian). Finally, this theory applies and predict many laws of chemical signaling, well described by extreme statistics.

D. Holcman further extended with his PhD student S. Toste, the extreme diffusion theory to dynamics driven by switching, with various initial distributions, or when a killing field is added. They study the case of various dimensions, leading to novel asymptotic formulas for the extreme statistics. Finally, they further explored how to define the number n of copies: how to give an estimation of n large? (work with S. Toste and F. Pacquin-Lefevre, postdoc).

16-Reconstruction of the flow in the Endoplasmic reticulum and predicting the mode of propagation (Cell biology, inverse problems, Biophysics)

With his PhD student P. Parutto, D. Holcman has developed a novel analysis to reconstruct the ER flow from the analysis of many redundant SPTs. They discovered a novel model of propagation that correlated with the ER organization in tubule and nodes. With his PhD M. Dora, D. Holcman, they developed a new model of the ER based on graph theory showing that the material propagates in packets, due to the alternating flow inside the tubules.

17-How the Spine-apparatus is refilled and depleted in calcium during learning and memory (Stochastic simulations, Applied mathematics, Cell Biology)

With his PhD student K. Basnayake, D. Holcman proposed a novel mechanism of ER refilling based on store operated calcium entry and ER depletion based on extreme statistics of the first ions to reach the base of a dendritic spine. The results were found in collaboration with the group of E. Korkotian from the Weizmann Institute of Science.

18-Triangulation sensing: How navigating cells can sense morphological gradient and triangulate the position of a source releasing Brownian particles (Applied mathematics, mathematical biology, Theory of stochastic simulation)

In coll with U. Dubrasmyl postdoc at Cambridge, D. Holcman developed a theory called triangulation sensing, based on narrow escape theory to explain how a navigating cells with several receptors on its surface can localize the position of a source releasing Brownian particles. This theory can be used to analyze how neurons migrate toward their target in the developing brain.

This development lead also to a fast hybrid stochastic simulations to study the flow of Brownian particles to a target in an open space, without the need of simulating single trajectories except in a small neighborhood of the domain. The theory was developed in dimensions 2 and 3.

External links

• David Holcman's page, Ecole Normale Superieure
• David Holcman's channel on YouTube

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