Lectures & Seminars

Richard Lavery, UCB Lyon 1 & CNRS

  • Lecture 1) DNA fine structure and conformation flexibility
  • Lecture 2) All-atom molecular simulation techniques for nucleic acids
  • Lecture 3) Modeling DNA structure and dynamics
  • Lecture 4) Modeling DNA-ligand and DNA-protein interactions

References

  • Molecular modeling: an interdisciplinary guide. Tamar Schlick, Springer
  • Principles of nucleic acid structure. Wolfram Saenger, Springer-Verlag
  • Nucleic acid structure and recognition. Stephen Neidle, Oxford University Press
  • Protein-Nucleic acid interactions. Phoebe Rice & Carl Correll, RSC Publishing
  • Towards a molecular view of transcriptional control K. Zakrzewska & R. Lavery Curr Opin. Struct. Biol. 22 (2012) 160
  • Conformational analysis of nucleic acids revisited: Curves+. R. Lavery, M. Moakher, J.H. Maddocks, D. Perkevcuite & K. Zakrzewska Nucleic Acids Res. 37 (2009) 5917
  • A systematic molecular dynamics study of nearest-neighbour effects on base pair step conformaitons and fluctuations in B-DNA. R. Lavery, K. Zakrzewska, D. Beveridge et al. Nucleic Acids Res. 38 (2009) 299

Martin Weigt, Université Pierre et Marie Curie, Paris

Statistical modeling of biological sequences: regulatory DNA motifs, phylogeny, and protein co-evolution

Thanks to recent progress in DNA sequencing techniques, by now more than 4000 complete genomes are sequenced. Statistical methods become increasingly important for analyzing such data (in particular the vast number of evolutionarily diverging, but functionally related sequences), and to infer bio-molecular function and structure from sequence information alone. Many of these methods are closely related to methods in statistical mechanics, or even draw their initial inspiration from the statistical physics of disordered systems. The lectures will give an overview going from classical problems in bioinformatics (e.g. aligning sequences) to recent research topics in statistical genomics (e.g. co-evolutionary analysis of proteins).

  • Lecture 1: Regulatory DNA motifs I: From the biophysics of protein-DNA interaction to the bioinformatics of transcription factor binding sites
  • Lecture 2: How similar are two biological sequences: Algorithms for sequence alignment
  • Lecture 3: Regulatory DNA motifs II: Using phylogeny for motif inference
  • Lecture 4: Co-evolution in proteins: From maximum-entropy modeling of protein families to 3D protein structure prediction


Literature:

  • Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids (Cambridge University Press 1998)
  • Erik van Nimwegen, Finding regulatory elements and regulatory motifs: a general probabilistic framework. BMC Bioinformatics. 2007 Sep 27;8 Suppl 6:S4.
  • F. Morcos, A. Pagnani, B. Lunt, A. Bertolino, D. Marks, C. Sander, R. Zecchina, J.N. Onuchic, T. Hwa, M. Weigt, Direct-coupling analysis of residue co-evolution captures native contacts across many protein families, Proc. Natl. Acad. Sci. 108, E1293-E1301 (2011).

Juan de Pablo, University of Wisconsin

  •  Lecture (1) Coarse grain models of DNA, and the role of hydrodynamic interactions in dynamics
  • Lecture (2) Atomistic and mesoscale models of DNA, their advantages and limitations
  • Lecture (3) Coarse-grained models of DNA-histone interactions, and relevance for the study of chromatin

Helmut Schiessel, University of Leiden

Chromatin: a multi-scale jigsaw puzzle in biological physics

Abstract:
This lecture deals with the spatial organization of DNA inside plant- and animal cells. For instance, in each of our cells we have two meters of DNA that need to fit into the micron-sized cell nucleus. This is made possible through the hierarchical complexation of DNA with proteins into the chromatin complex. One of the immediate questions is how the genetic information can be read out as the DNA is so tightly packed.

To answer this and related questions, I shall in this course introduce step by step the necessary theoretical elements and check them against experimental data:

  • Lecture 1: DNA as a wormlike chain (Euler elastica, Kirchhoff analogy, stretching DNA under an external force)
  • Lecture 2: Nucleosome as a DNA spool (nucleosome breathing and force-induced unwrapping)
  • Lecture 3: Chromatin fiber as a problem in geometry (two-angle model, multi-ribbon model)
  • Lecture 4: Chromosome as a fractal globule (internal distances, contact probabilities, molten vs. fractal globule)

Literature:

  • Helmut Schiessel "Biophysics for Beginners: A journey through the cell nucleus" (Pan Stanford Publishing, Feb 28, 2013, 250 pages)

Alain Arneodo, ENS Lyon and CNRS

From DNA sequence to genome structure and function

Understanding how chromatin is spatially and dynamically organized in the nucleus of eukaryotic cells and how this affects genome functions is one of the main challenges of cell biology. Since the different orders of packaging in the hierarchical organization of DNA condition the accessibility of DNA sequence elements to trans-acting factors that control the transcription and replication processes, there is actually a wealth of structural and dynamical information to learn in the primary DNA sequence. In this review, we show that when using concepts, methodologies, numerical and experimental techniques coming from statistical mechanics and nonlinear physics combined with wavelet-based multi-scale signal processing, we are able to decipher the multi-scale sequence encoding of chromatin condensation–decondensation mechanisms that play a fundamental role in regulating many molecular processes involved in nuclear functions.

  • Lecture 1: Long-range correlations in eukaryotic DNA: a footprint of nucleosome packaging
  • Lecture 2: DNA sequence effect on the nucleosomal organization of the eukaryotic chromatin fiber
  • Lecture 3: Large-scale analysis of genomic sequences: from the detection of replication origins to the modeling of replication in higher eukaryotes
  • Lecture 4: Spatio-temporal organization of replication: On genome evolution and large-scale chromatin folding


Review article

  • A. Arneodo, C. Vaillant, B. Audit, F. Argoul, Y. d’Aubenton-Carafa,, C. Thermes, Multi-scale coding of genomic information: From DNA sequence analysis to genome structure and function, Phys. Rep. 498 (2011) 45- 188.

Peter Rein ten Wolde, AMOLF Amsterdam

Information transmission in biochemical networks

Biochemical networks are the information processing devices of living cells. They allow the cell to perform a large number of computational tasks analogous to electronic circuits. Yet, their design principles are markedly different. In a biochemical network, the computations are performed by biomolecules such as proteins and DNA, which chemically and physically interact with one another. These interactions are stochastic in nature, which becomes particularly important when the concentrations are low, as is typically the case inside the living cell. Indeed, experiments in recent years have vividly demonstrated that biochemical networks can be highly stochastic, raising the question how reliably living cells can process information. In this set of lectures, I will give an overview of recent studies on noise in biochemical networks, with an emphasis on the importance of spatio-temporal correlations at the molecular level. I will then describe how these networks can be modeled; how these models can be solved analytically using the chemical master equation and linear-noise approximation; and how they can be simulated using recently developed algorithms such as GFRD. I will end by discussing how concepts of information theory can be used to quantify information transmission in biochemical networks.

  • Lecture 1: Overview of noise in biochemical networks
  • Lecture 2: Modeling biochemical networks
  • Lecture 3: Simulating biochemical networks
  • Lecture 4: Quantifying information transmission in biochemical networks using measures from information theory

Literature:

  • Van Kampen NG (1992) Stochastic processes in physics and chemistry.
  • Gillespie DT (2000) The chemical Langevin equation. J Chem Phys 113:297–306.
  • Shannon CE (1948) A mathematical theory of communication. Bell SystTechJ 27:379–423.
  • Takahashi K, Tanase-Nicola S, Wolde ten PR (2010) Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proc Natl Acad Sci U S A 107:2473–2478.
  • Tostevin F, Wolde ten PR (2009) Mutual Information between Input and Output Trajectories of Biochemical Networks. Phys Rev Lett 102:218101.
  • Tostevin F, Wolde ten P (2010) Mutual information in time-varying biochemical systems. Phys Rev E 81:061917.

Olivier Rivoire, Université Joseph Fourier & CNRS

Seminar: Synteny in bacterial genomes

Synteny, the conservation of the positions of genes in genomes of different species, is a primary source of information when studying the factors that constrain the evolution of genomes. I will present an approach to infer from the sequences of multiple genomes the pairs of genes whose relative distance is significantly conserved. Applied to bacterial genomes, this approach reveals a network of co-localized genes with remarkable statistical properties. I will describe this network, and show how dynamical models can be built and tested against the data to infer the evolutionary mechanisms responsible for its properties. 
 
(Work in collaboration with Ivan Junier, Barcelona).

Ralf Everaers, Ecole normale supérieure de Lyon

Seminar: Universal Aspects of Chromosome Folding

During cell division (mitosis) chromosomes adopt a compact form that is suitable for transport. During periods of normal cell activity (interphase), they decondense inside the cell nucleus. Being long-chain molecules (in the case of human chromosomes the contour length of the chromatin fiber is on the order of 1 mm), the random thermal motion of interphase chromatin fibers is hindered by entanglements, similar to those restricting the manipulation of a knotted ball of wool. In the first part of the talk, I will show that entanglement effects cause sufficiently long chromosomes to remain segregated during interphase and to form “territories.” In particular, our model reproduces with zero adjustable parameters currently avaliable experimental results for the internal chromosome structure and dynamics in interphase nuclei as measured in Fluorescence in-situ hybridization (FISH) and chromosome conformation capture (3C, HiC) experiments. In the second part of the talk, I will explore the subtle physics of solutions of non-concatenated ring polymers as a model for interphase nuclei. We develop a multi-scale approach combining massive Molecular Dynamics simulations on the fiber level with Monte Carlo simulations of a lattice model of interacting, randomly branched polymers for the fractal, large scale crumpled loop structure. We show that not only the territorial confinement but also other characteristic features of chromosome folding such as the loop-on-loop structure of internal contacts arise as a generic consequence of the polymeric nature of chromosomes.
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