**Computational Biology and Computational Structural Biology.**
Understanding the lineage between species and the genetic drift of
genes and genomes, apprehending the control and feed-back loops
governing the behavior of a cell, a tissue, an organ or a body, and
inferring the relationship between the structure of biological
(macro)-molecules and their functions are amongst the major challenges
of modern biology. The investigation of these challenges is
supported by three types of data: genomic data, transcription and
expression data, and structural data.

Genetic data feature sequences of nucleotides on DNA and RNA
molecules, and are symbolic data whose processing falls in the realm
of Theoretical Computer Science: dynamic programming, algorithms on
texts and strings, graph theory dedicated to phylogenetic problems.
Transcription and expression data feature evolving concentrations of
molecules (RNAs, proteins, metabolites) over time, and fit in the formalism of
discrete and continuous dynamical systems, and of graph theory. The
exploration and the modeling of these data are covered by a rapidly
expanding research field termed *systems biology*.
Structural data encode informations about the 3D structures of
molecules (nucleic acids (DNA, RNA), proteins, small molecules) and their
interactions, and come from three main sources: X ray
crystallography, NMR spectroscopy, cryo Electron Microscopy.
Ultimately, structural data should expand our understanding of how the
structure accounts for the function of macro-molecules – one of the
central questions in structural biology. This goal actually subsumes
two equally difficult challenges, which are *folding* – the
process through which a protein adopts its 3D structure, and *docking* – the process through which two or several molecules
assemble. Folding and docking are driven by non covalent interactions,
and for complex systems, are actually inter-twined
.
Apart from the bio-physical interests raised by these processes, two
different application domains are concerned: in fundamental biology,
one is primarily interested in understanding the machinery of the
cell; in medicine, applications to drug design are developed.

**Modeling in Computational Structural Biology.**
Acquiring structural data is not always possible: NMR is restricted to
relatively small molecules; membrane proteins do not crystallize, etc.
As a matter of fact, the order of magnitude of the number of
genomes sequenced is of the order of one thousand, which results in
circa one million of genes recorded in the manually curated
Swiss-Prot database.
On the other hand, the Protein Data Bank contains circa 90,000
structures. Thus, the paucity of structures with respect to the known
number of genes calls for modeling in structural biology, so as to
foster our understanding of the structure-to-function relationship.

Ideally, bio-physical models of macro-molecules should resort to quantum mechanics. While this is possible for small systems, say up to 50 atoms, large systems are investigated within the framework of the Born-Oppenheimer approximation which stipulates the nuclei and the electron cloud can be decoupled. Example force fields developed in this realm are AMBER, CHARMM, OPLS. Of particular importance are Van der Waals models, where each atom is modeled by a sphere whose radius depends on the atom chemical type. From an historical perspective, Richards , and later Connolly , while defining molecular surfaces and developing algorithms to compute them, established the connexions between molecular modeling and geometric constructions. Remarkably, a number of difficult problems (e.g. additively weighted Voronoi diagrams) were touched upon in these early days.

The models developed in this vein are instrumental in investigating
the interactions of molecules for which no structural data is
available. But such models often fall short from providing complete
answers, which we illustrate with the folding problem. On one hand, as
the conformations of side-chains belong to discrete sets (the
so-called rotamers or rotational isomers) ,
the number of distinct conformations of a poly-peptidic chain is
exponential in the number of amino-acids. On the other hand, Nature
folds proteins within time scales ranging from milliseconds to hours,
while time-steps used in molecular dynamics simulations are of the
order of the femto-second, so that biologically relevant time-scales
are out reach for simulations. The fact that Nature avoids the
exponential trap is known as Levinthal's paradox.
The intrinsic difficulty of problems calls for models exploiting
several classes of informations. For small systems, *ab initio*
models can be built from first principles. But for more complex
systems, *homology* or template-based models integrating a variable amount of
knowledge acquired on similar systems are resorted to.

The variety of approaches developed are illustrated by the two
community wide experiments CASP (*Critical Assessment of Techniques
for Protein Structure Prediction*; http://*Critical Assessment of Prediction of Interactions*;
http://

As illustrated by the previous discussion, modeling macro-molecules touches upon biology, physics and chemistry, as well as mathematics and computer science. In the following, we present the topics investigated within ABS.

The research conducted by ABS focuses on three main directions in Computational Structural Biology (CSB), together with the associated methodological developments:

– Modeling interfaces and contacts,

– Modeling macro-molecular assemblies,

– Modeling the flexibility of macro-molecules,

– Algorithmic foundations.

**Keywords:** Docking, interfaces, protein complexes, structural alphabets,
scoring functions, Voronoi diagrams, arrangements of balls.

The Protein Data Bank, http://*interacting* with atoms of the second
one. Understanding the structure of interfaces is central to
understand biological complexes and thus the function of biological
molecules . Yet, in spite of almost three decades
of investigations, the basic principles guiding the formation of
interfaces and accounting for its stability are unknown
. Current investigations follow two routes.
From the experimental perspective , directed
mutagenesis enables one to quantify the energetic importance of
residues, important residues being termed *hot* residues. Such
studies recently evidenced the *modular* architecture of
interfaces
.
From the modeling perspective, the main issue consists of guessing the
hot residues from sequence and/or structural informations
.

The description of interfaces is also of special interest to improve
*scoring functions*. By scoring function, two things are meant:
either a function which assigns to a complex a quantity homogeneous to
a free energy change

Describing interfaces poses problems in two settings: static and dynamic.

In the static setting, one seeks the minimalist geometric model
providing a relevant bio-physical signal. A first step in doing so
consists of identifying interface atoms, so as to relate the geometry and
the bio-chemistry at the interface level .
To elaborate at the atomic level, one seeks a structural alphabet
encoding the spatial structure of proteins. At the side-chain and
backbone level, an example of such alphabet is that of
. At the atomic level and in spite of recent
observations on the local structure of the neighborhood of a given
atom , no such alphabet is known. Specific
important local conformations are known, though. One of them is the
so-called dehydron structure, which is an under-desolvated hydrogen
bond – a property that can be directly inferred from the spatial
configuration of the

In the dynamic setting, one wishes to understand whether selected (hot) residues exhibit specific dynamic properties, so as to serve as anchors in a binding process . More generally, any significant observation raised in the static setting deserves investigations in the dynamic setting, so as to assess its stability. Such questions are also related to the problem of correlated motions, which we discuss next.

**Keywords:** Macro-molecular assembly, reconstruction by data
integration, proteomics, modeling with uncertainties, curved Voronoi
diagrams, topological persistence.

Large protein assemblies such as the Nuclear Pore Complex (NPC),
chaperonin cavities, the proteasome or ATP synthases, to name a few,
are key to numerous biological functions. To improve our
understanding of these functions, one would ideally like to build and
animate atomic models of these molecular machines. However, this task
is especially tough, due to their size and their plasticity, but also
due to the flexibility of the proteins involved.
In a sense, the modeling challenges arising in this context are
different from those faced for binary docking, and also from those
encountered for intermediate size complexes which are often amenable
to a processing mixing (cryo-EM) image analysis and classical docking.
To face these new challenges, an emerging paradigm is that of
reconstruction by data integration . In a
nutshell, the strategy is reminiscent from NMR and consists of mixing
experimental data from a variety of sources, so as to find out the
model(s) best complying with the data.
This strategy has been in particular used to propose plausible models
of the Nuclear Pore Complex , the largest assembly
known to date in the eukaryotic cell, and consisting of 456 protein
*instances* of 30 *types*.

Reconstruction by data integration requires three ingredients. First,
a parametrized model must be adopted, typically a collection of balls
to model a protein with pseudo-atoms. Second, as in NMR, a functional
measuring the agreement between a model and the data must be
chosen. In , this functional is based upon *restraints*, namely penalties associated to the experimental data.
Third, an optimization scheme must be selected.
The design of restraints is notoriously challenging, due to the
ambiguous nature and/or the noise level of the data.
For example, Tandem Affinity Purification (TAP) gives access to a *pullout* i.e. a list of protein types which are known to interact
with one tagged protein type, but no information on the number of
complexes or on the stoichiometry of proteins types within a complex
is provided.
In cryo-EM, the envelope enclosing an assembly is often imprecisely
defined, in particular in regions of low density. For immuno-EM
labelling experiments, positional uncertainties arise from the
microscope resolution.

These uncertainties coupled with the complexity of the functional
being optimized, which in general is non convex, have two
consequences.
First, it is impossible to single out a unique reconstruction, and a
set of plausible reconstructions must be considered. As an example,
1000 plausible models of the NPC were reported in
. Interestingly, averaging the positions of all
balls of a particular protein type across these models resulted in 30
so-called *probability density maps*, each such map encoding the
probability of presence of a particular protein type at a particular
location in the NPC.
Second, the assessment of all models (individual and averaged) is non
trivial. In particular, the lack of straightforward statistical
analysis of the individual models and the absence of assessment for
the averaged models are detrimental to the mechanistic exploitation of
the reconstruction results. At this stage, such models therefore
remain qualitative.

**Keywords:** Folding, docking, energy landscapes, induced fit,
molecular dynamics, conformers, conformer ensembles, point clouds,
reconstruction, shape learning, Morse theory.

Proteins in vivo vibrate at various frequencies: high frequencies
correspond to small amplitude deformations of chemical bonds, while
low frequencies characterize more global deformations. This
flexibility contributes to the entropy thus the *free energy* of
the system *protein - solvent*. From the experimental standpoint,
NMR studies generate ensembles of conformations, called *conformers*, and so do molecular dynamics (MD) simulations.
Of particular interest while investigating flexibility is the notion
of correlated motion. Intuitively, when a protein is folded, all
atomic movements must be correlated, a constraint which gets
alleviated when the protein unfolds since the steric constraints get
relaxed *diffusion - conformer
selection - induced fit* complex formation model.

Parameterizing these correlated motions, describing the corresponding energy landscapes, as well as handling collections of conformations pose challenging algorithmic problems.

At the side-chain level, the question of improving rotamer libraries is still of interest . This question is essentially a clustering problem in the parameter space describing the side-chains conformations.

At the atomic level, flexibility is essentially investigated resorting to methods based on a classical potential energy (molecular dynamics), and (inverse) kinematics. A molecular dynamics simulation provides a point cloud sampling the conformational landscape of the molecular system investigated, as each step in the simulation corresponds to one point in the parameter space describing the system (the conformational space) . The standard methodology to analyze such a point cloud consists of resorting to normal modes. Recently, though, more elaborate methods resorting to more local analysis , to Morse theory and to analysis of meta-stable states of time series have been proposed.

**Keywords:** Computational geometry, computational topology,
optimization, data analysis.

Making a stride towards a better understanding of the biophysical questions discussed in the previous sections requires various methodological developments, which we briefly discuss now.

In modeling interfaces and contacts, one may favor geometric or topological information.

On the geometric side, the problem of modeling contacts at the atomic
level is tantamount to encoding multi-body relations between an atom
and its neighbors. On the one hand, one may use an encoding of
neighborhoods based on geometric constructions such as Voronoi
diagrams (affine or curved) or arrangements of balls. On the other
hand, one may resort to clustering strategies in higher dimensional
spaces, as the

On the topological side, one may favor constructions which remain
stable if each atom in a structure *retains* the same neighbors,
even though the 3D positions of these neighbors change to some
extent. This process is observed in flexible docking cases, and call
for the development of methods to encode and compare shapes undergoing
tame geometric deformations.

In dealing with large assemblies, a number of methodological developments are called for.

On the experimental side, of particular interest is the disambiguation of proteomics signals. For example, TAP and mass spectrometry data call for the development of combinatorial algorithms aiming at unraveling pairwise contacts between proteins within an assembly. Likewise, density maps coming from electron microscopy, which are often of intermediate resolution (5-10Å) call the development of noise resilient segmentation and interpretation algorithms. The results produced by such algorithms can further be used to guide the docking of high resolutions crystal structures into maps.

As for modeling, two classes of developments are particularly stimulating. The first one is concerned with the design of algorithms performing reconstruction by data integration, a process reminiscent from non convex optimization. The second one encompasses assessment methods, in order to single out the reconstructions which best comply with the experimental data. For that endeavor, the development of geometric and topological models accommodating uncertainties is particularly important.

Given a sampling on an energy landscape, a number of fundamental issues actually arise: how does the point cloud describe the topography of the energy landscape (a question reminiscent from Morse theory)? Can one infer the effective number of degrees of freedom of the system over the simulation, and is this number varying? Answers to these questions would be of major interest to refine our understanding of folding and docking, with applications to the prediction of structural properties. It should be noted in passing that such questions are probably related to modeling phase transitions in statistical physics where geometric and topological methods are being used .

From an algorithmic standpoint, such questions are reminiscent of
*shape learning*. Given a collection of samples on an (unknown) *model*, *learning* consists of guessing the model from the samples
– the result of this process may be called the *reconstruction*. In doing so, two types of guarantees are sought:
topologically speaking, the reconstruction and the model should
(ideally!) be isotopic; geometrically speaking, their Hausdorff
distance should be small.
Motivated by applications in Computer Aided Geometric Design, surface
reconstruction triggered a major activity in the Computational
Geometry community over the past ten years.
Aside from applications, reconstruction
raises a number of deep issues:
the study of distance functions to the model and to the samples,
and their comparison; the study of Morse-like constructions stemming from distance
functions to points; the analysis of topological invariants of the model and the samples,
and their comparison.

*Structural Bioinformatics Library*

Keywords: Structural Biology - Biophysics - Software architecture

Functional Description: The SBL is a generic C++/python cross-platform software library targeting complex problems in structural bioinformatics. Its tenet is based on a modular design offering a rich and versatile framework allowing the development of novel applications requiring well specified complex operations, without compromising robustness and performances.

More specifically, the SBL involves four software components (1-4 thereafter). For end-users, the SBL provides ready to use, state-of-the-art (1) applications to handle molecular models defined by unions of balls, to deal with molecular flexibility, to model macro-molecular assemblies. These applications can also be combined to tackle integrated analysis problems. For developers, the SBL provides a broad C++ toolbox with modular design, involving core (2) algorithms, (3) biophysical models, and (4) modules, the latter being especially suited to develop novel applications. The SBL comes with a thorough documentation consisting of user and reference manuals, and a bugzilla platform to handle community feedback.

Release Functional Description: In 2018, major efforts targeted two points. First, the simplification of installation procedures – now possible with conda/python. Second, the development of packages revolving on molecular flexibility at large: representations in internal and Cartesian coordinates, generic representation of molecular mechanics force fields (and computation of gradients), exploration algorithms for conformational spaces.

Contact: Frédéric Cazals

Publication: The Structural Bioinformatics Library: modeling in biomolecular science and beyond

**Keywords:** docking, scoring, interfaces, protein complexes, Voronoi diagrams,
arrangements of balls.

In collaboration with S. Magadan, L. Jouneau, S. Marillet, P. Boudinot (INRA, Virologie et Immunologie Moléculaires, Université Paris-Saclay, Jouy-en-Josas, France); M. Puelma Touzel, T. Mora, A. Walczak (Laboratoire de Physique Théorique, CNRS, Sorbonne Université, and Ecole Normale Supérieure (PSL), Paris, France); W. Chaara, A. Six (Sorbonne Université, INSERM, UMR S 959, Immunology-Immunopathology -Immunotherapy (I3), Paris, France); E. Quillet (INRA, Génétique Animale et Biologie Intégrative, Université Paris-Saclay, Jouy-en-Josas, France); O. Sunyer (Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, United States); S. Fillatreau (INEM, INSERM U1151/CNRS UMR8253, Institut Necker-Enfants Malades, Faculté de Médecine Paris Descartes, Paris, France; Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France; Assistance Publique des Hopitaux de Paris (AP-HP), Hopital Necker Enfants Malades, Paris, France).

Vaccination induces *publi*c antibody clonotypes common to all
individuals of a species, that may mediate universal protection
against pathogens. Only few studies tried to trace back the origin of
these public B-cell clones. Here
we used Illumina sequencing and
computational modeling to unveil the mechanisms shaping the structure
of the fish memory antibody response against an attenuated Viral
Hemorrhagic Septicemia rhabdovirus. After vaccination, a persistent
memory response with a public VH5JH5 IgM component was composed of
dominant antibodies shared among all individuals. The rearrangement
model showed that these public junctions occurred with high
probability indicating that they were already favored before
vaccination due to the recombination process, as shown in mammals. In
addition, these clonotypes were in the naive repertoire associated
with larger similarity classes, composed of junctions differing only
at one or two positions by amino acids with comparable properties. The
model showed that this property was due to selective processes exerted
between the recombination and the naive repertoire. Finally, our
results showed that public clonotypes greatly expanded after
vaccination displayed several VDJ junctions differing only by one or
two amino acids with similar properties, highlighting a convergent
response. The fish public memory antibody response to a virus is
therefore shaped at three levels: by recombination biases, by
selection acting on the formation of the pre-vaccination repertoire,
and by convergent selection of functionally similar clonotypes during
the response. We also show that naive repertoires of IgM and IgT have
different structures and sharing between individuals, due to selection
biases. In sum, our comparative approach identifies three conserved
features of the antibody repertoire associated with public memory
responses. These features were already present in the last common
ancestors of fish and mammals, while other characteristics may
represent species-specific solutions.

**Keywords:** macro-molecular assembly, reconstruction by data integration,
proteomics, mass spectrometry, modeling with uncertainties, connectivity inference.

In collaboration with N. Cohen (CNRS, Laboratoire de Recherche en Informatique) and F. Havet (CNRS, Inria/I3S project-team Coati) and I. Sau (CNRS, Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier) and R. Watrigant (University Lyon I, Laboratoire de l'Informatique du Parallélisme).

**Keywords:** protein, flexibility, collective coordinate,
conformational sampling dimensionality reduction.

The root mean square deviation (RMSD) and the least RMSD are two widely used similarity measures in structural bioinformatics. Yet, they stem from global comparisons, possibly obliterating locally conserved motifs. We correct these limitations with the so-called combined RMSD , which mixes independent lRMSD measures, each computed with its own rigid motion. The combined RMSD is relevant in two main scenarios, namely to compare (quaternary) structures based on motifs defined from the sequence (domains, SSE), and to compare structures based on structural motifs yielded by local structural alignment methods. We illustrate the benefits of combined RMSD over the usual RMSD on three problems, namely (i) the assignment of quaternary structures for hemoglobin (scenario #1), (ii) the calculation of structural phylogenies (case study: class II fusion proteins; scenario #1), and (iii) the analysis of conformational changes based on combined RMSD of rigid structural motifs (case study: one class II fusion protein; scenario #2). Using these, we argue that the combined RMSD is a tool a choice to perform positive and negative discrimination of degree of freedom, with applications to the design of move sets and collective coordinates. Combined RMSD are available within the Structural Bioinformatics Library (http: //sbl.inria.fr).

In collaboration with P. Guardado-Calvo, J. Fedry, and F. Rey (Inst. Pasteur Paris, France).

In collaboration with S. Pion (Auctus, Inria Bordeaux).

The Wang-Landau (WL) algorithm is a recently developed stochastic algorithm computing densities of states of a physical system. Since its inception, it has been used on a variety of (bio-)physical systems, and in selected cases, its convergence has been proved. The convergence speed of the algorithm is tightly tied to the connectivity properties of the underlying random walk. As such, we propose in an efficient random walk that uses geometrical information to circumvent the following inherent difficulties: avoiding overstepping strata, toning down concentration phenomena in high-dimensional spaces, and accommodating multidimensional distribution. Experiments on various models stress the importance of these improvements to make WL effective in challenging cases. Altogether, these improvements make it possible to compute density of states for regions of the phase space of small biomolecules.

**Keywords:** Computational geometry, computational topology,
optimization, data analysis.

Making a stride towards a better understanding of the biophysical questions discussed in the previous sections requires various methodological developments discussed below.

In collaboration with A. Lhéritier (Amadeus, France).

Given samples from two distributions, a nonparametric two-sample test aims at determining whether the two distributions are equal or not, based on a test statistic. Classically, this statistic is computed on the whole dataset, or is computed on a subset of the dataset by a function trained on its complement. We consider methods in a third tier , so as to deal with large (possibly infinite) datasets, and to automatically determine the most relevant scales to work at, making two contributions. First, we develop a generic sequential nonparametric testing framework, in which the sample size need not be fixed in advance. This makes our test a truly sequential nonparametric multivariate two-sample test. Under information theoretic conditions qualifying the difference between the tested distributions, consistency of the two-sample test is established. Second, we instantiate our framework using nearest neighbor regressors, and show how the power of the resulting two-sample test can be improved using Bayesian mixtures and switch distributions. This combination of techniques yields automatic scale selection, and experiments performed on challenging datasets show that our sequential tests exhibit comparable performances to those of state-of-the-art nonsequential tests.

In collaboration with R. Watrigant (University Lyon I, Laboratoire de l'Informatique du Parallélisme, France).

Clustering is a fundamental problem in data science, yet, the variety of clustering methods and their sensitivity to parameters make clustering hard. To analyze the stability of a given clustering algorithm while varying its parameters, and to compare clusters yielded by different algorithms, several comparison schemes based on matchings, information theory and various indices (Rand, Jaccard) have been developed. We go beyond these by providing a novel class of methods computing meta-clusters within each clustering–a meta-cluster is a group of clusters, together with a matching between these. Let the intersection graph of two clusterings be the edge-weighted bipartite graph in which the nodes represent the clusters, the edges represent the non empty intersection between two clusters, and the weight of an edge is the number of common items. We introduce the so-called D-family-matching problem on intersection graphs, with D the upper-bound on the diameter of the graph induced by the clusters of any meta-cluster. First we prove NP-completeness and APX-hardness results, and unbounded approximation ratio of simple strategies. Second, we design exact polynomial time dynamic programming algorithms for some classes of graphs (in particular trees). Then, we prove spanning-tree based efficient algorithms for general graphs. Our experiments illustrate the role of D as a scale parameter providing information on the relationship between clusters within a clustering and in-between two clusterings. They also show the advantages of our built-in mapping over classical cluster comparison measures such as the variation of information (VI).

In collaboration with J.-C. Bermond (Inria/I3S project-team Coati) and A. Chaintreau (Columbia University in the city of New York) and G. Ducoffe (National Institute for Research and Development in Informatics and Research Institute of the University of Bucharest).

We consider a community formation problem in social networks, where the users are either friends or enemies. The users are partitioned into conflict-free groups (*i.e.*, independent sets in the *conflict graph* *simultaneously*, in such a way that they all strictly increase their utilities (number of friends *i.e.*, the cardinality of their respective groups minus one).
Previously, the best-known upper-bounds on the maximum time of
convergence were

See for details.

In collaboration with J. Bensmail (I3S, Inria/I3S project-team Coati) and F. Mc Inerney (Inria/I3S project-team Coati) and N. Nisse (Inria, Inria/I3S project-team Coati) and S. Pérennes (CNRS, Inria/I3S project-team Coati).

In the localization game, introduced by Seager in 2013, an invisible
and immobile target is hidden at some vertex of a graph

We address the generalization of this game where *metric dimension* of a graph. Precisely, given a graph *localization* problem asks
whether there exists a strategy to locate a target hidden in *i.e.*, an algorithm that computes in time

We also consider some of these questions in the context where, upon
probing the vertices, the relative distances to the target are
retrieved. This variant of the problem generalizes the notion of the
*centroidal dimension* of a graph.

Internship of Maria Guramare, Harvard University, Cambridge,
Massachusetts. Supervision: Frédéric Cazals and Dorian
Mazauric. *Shortest Paths under Constraints Problem with
Application for Structural Alignments.*

Internship of Xuchun Zhang, École Polytechnique de
l'Université Nice Sophia Antipolis, filière Mathématiques
Appliquées et Modélisation, year 4 (Master 1). Supervision:
Jean-Baptiste Caillau (Inria project-team McTao), Enzo Giusti
(startup Oui!Greens), Dorian Mazauric, and Joanna Moulierac
(Inria/I3S project-team Coati). *Problèmes d'affectations
d'annonces dans un réseau anti gaspillage !*

Project of Ruiqing Chang and Xuchun Zhang, École
Polytechnique de l'Université Nice Sophia Antipolis, Filière
Mathématiques Appliquées et Modélisation, year 4 (Master
1). Supervision: Jean-Baptiste Caillau (Inria project-team
McTao), Enzo Giusti (startup Oui!Greens), Dorian Mazauric, and
Joanna Moulierac (Inria/I3S project-team Coati).
*Problèmes d'affectations d'annonces dans un réseau anti
gaspillage !*

Internship of Nguyen Thi Viet Ha, Master 2 Fundamental Computer
Science, École Normale Supérieure de Lyon. Supervision:
Frédéric Havet (Inria/I3S project-team Coati), Dorian Mazauric,
and Rémi Watrigant (École Normale Supérieure de Lyon and
Université Claude Bernard Lyon 1). *Graph Algorithms for low
resolution model of large protein assemblies.*

Internship of Timothée O'Donnell, Master 2 University Paris Saclay,
Master bioinformatique. *Structural modeling of FMRP dimers in solution*.
Supervision: F. Cazals.

– Frédéric Cazals was member of the advisory board of:

*Algorithms in Structural Bio-informatics*. The 2018/2019 edition (January 2019, CIRM, Marseille) focuses on RNA bioinformatics. See https://

– Frédéric Cazals was member of the following program committees:

Symposium On Geometry Processing

Symposium on Solid and Physical Modeling

Intelligent Systems for Molecular Biology (ISMB) / Protein Interactions & Molecular Networks

IEEE International Conference on BioInformatics and BioEngineering

– Frédéric Cazals reviewed for the following journals:

Journal of computational geometry

PLOS Computational Biology

– Dorian Mazauric reviewed for the following journal and conference:

Theoretical Computer Science

16th Workshop on Approximation and Online Algorithms (WAOA 2018)

– Frédéric Cazals gave the following invited talks:

*Energy landscapes: sampling, analysis, comparison*, RNA Kinetics days, Ecole polytechnique, October 2018.

*Randomized algorithms for volume/density of states
calculations in high-dimensional spaces*:
Energy landscapes, Kalamata, Greece, September 2018;

*Randomized algorithms for volume/density of states
calculations in high-dimensional spaces*:
Advances in Computational Statistical Physics, CIRM, France, September 2018.

*Understanding scoring/energy landscapes: a tale of local minima and density of states*, Meet-U: when proteins meet each other, January 2018, Paris.

– Frédéric Cazals:

2010-.... Member of the steering committee of the *GDR
Bioinformatique Moléculaire*, for the *Structure and
macro-molecular interactions* theme.

2017-.... Co-chair, with Yann Ponty, of the working group /
groupe de travail *(GT MASIM - Méthodes Algorithmiques pour les
Structures et Interactions Macromoléculaires*, within the *GDR
de BIoinformatique Moléculaire* (GDR BIM,
http://

– Frédéric Cazals:

2017-.... President of the *Comité de suivi doctoral*
(CSD), Inria Sophia Antipolis - Méditerranée. The CSD supervises all aspects of PhD
student's life within Inria Sophia Antipolis - Méditerranée.

2018-.... Member of the *bureau du comité des équipes projets*.

– Dorian Mazauric:

2016-2019. Member of the *Comité de Centre*, Inria Sophia Antipolis - Méditerranée.

2018-.... Member of the *Commission de Développement Technologique*, Inria Sophia Antipolis - Méditerranée.

Master: Frédéric Cazals (Inria ABS) and Frédéric Chazal (Inria Saclay),
*Foundations of Geometric Methods in Data Analysis*, Data Sciences
Program, Department of Applied Mathematics, Ecole Centrale
Paris. (http://

Master : Dorian Mazauric, Algorithmique et Complexité, 36 h TD, niveau M1, École Polytechnique de l'Université Nice Sophia Antipolis, filière Sciences Informatiques, France.

**PhD:** Romain Tetley,
*Mixed sequence-structure based analysis of proteins, with applications to functional annotations*,
defended on the 21/11/2018. Université Côte d'Azur.

**PhD in progress, 4th year:** Augustin Chevallier,
*Random walks for estimating the volume of convex bodies and densities of states in high dimensional spaces*, defense scheduled in February 2019. Université Côte d'Azur.

**PhD in progress, 2nd year:** Denys Bulavka,
*Modeling macro-molecular motions*. Université Côte d'Azur.
Under the supervision of Frédéric Cazals.

**PhD in progress, 2nd year:** Méliné Simsir, *Modeling drug efflux by Patched*. Université Côte d'Azur.
Thesis co-supervised by Frédéric Cazals and Isabelle Mus-Veteau, IPMC/CNRS.

**PhD in progress, 1st year:** Timothée O'Donnel, *Modeling the influenza polymerase*. Université Côte d'Azur.
Thesis co-supervised by Frédéric Cazals and Bernard Delmas, INRA Jouy-en-Josas.

**PhD in progress, 1st year** : Thi Viet Ha Nguyen, Graph Algorithms techniques
for (low and high) resolution models of large protein assemblies, Frédéric Havet (Inria/I3S project-team Coati) and Dorian Mazauric.

– Frédéric Cazals:

Hugo Schweke, Paris-Saclay University, December 2018.
Rapporteur on the PhD thesis
*Développement d'une méthode *in silico* pour
caractériser le potentiel
d'interaction des surfaces
protéiques dans un environnement encombré*.
Advisors: Marie-Hélène Mucchielli-Giorgi and Anne Lopes.

Julien Ogor, ENSTA Bretagne, May 2018.
Rapporteur on the PhD thesis
*Design of algorithms for the automatic characterization
of marine dune morphology and dynamics*.
Advisor: B. Zerr.

Rodrigo Dorantes-Gilardi, University of Lyon, April 2018.
Rapporteur on the PhD thesis
*Bio-Mathematical aspects of the plasticity of proteins*.
Advisors: L. Vuillon and C. Lesieur.

– Dorian Mazauric:

Romain Tetley, Université Côte d'Azur, Novembre 2018.
PhD thesis
*Mixed sequence-structure based analysis of proteins, with applications to functional annotations*.
Advisor: Frédéric Cazals.

This part mainly concerns Dorian Mazauric.

Member of Mastic Commission (Médiation et Animation scientifiques Inria Sophia Antipolis - Méditerranée).

Coordinator of the popularization project GALEJADE (Graphes et ALgorithmes : Ensemble de Jeux À Destination des Écoliers (mais pas que)) founded by Inria, Fondation Blaise Pascal, and Université Côte d'Azur. See https://

Coordinator of the internships for undergraduates of middle school (niveau collège, troisième) at Inria Sophia Antipolis - Méditerranée (12 interns during one week).

Frédéric Cazals published the following opinion article:

*Recherche et développement : les entreprises françaises n’ont pas de vision*, Le Monde, April 2018. See https://

Dorian Mazauric published online contents and posters. See https://

Trainings for 100 future teachers at ÉSPÉ (École SupÉrieure du Professorat et de l'Éducation) of Académie de Nice.

Two trainings for 60 teachers of Cycle 3 (Le Cannet).

Trainings for 20 teachers at numeric culture week-end organised by Class'Code MED.

National events:

Fête de la Science : Village des Sciences de Vinon-sur-Verdon, Juan-les-Pins, Villeneuve-Loubet et Mouans Sartoux : *La magie des graphes et du binaire, Algorithmes grandeur nature et jeux combinatoires*.

Semaine des maths : Conferences and activities at Centre International de Valbonne. With Christophe Godin. *Réfléchir pour Calculer ou Calculer pour Réfléchir.*

Conferences and activities at salon Code & Play 2018. *Graphes et algorithmes ? Jeux grandeur nature : algorithme de plus court chemin, algorithme de tri avec des cerceaux et des lattes en plastique – La magie des graphes et du binaire : tours de magie.*

In educational institutions:

Two trainings for 60 teachers of Cycle 3 (Le Cannet).

Trainings for 100 future teachers at ÉSPÉ (École SupÉrieure du Professorat et de l'Éducation) of Académie de Nice.

High school: Conferences at Centre International de Valbonne. *Pas besoin de réfléchir, les ordinateurs calculent tellement vite ? Théorie des graphes et algorithmique.*

Middle school: Conferences at collège Alphonse Daudet of Nice and conferences at collège Jules Verne of Cagnes-sur-Mer. *La magie des graphes et du binaire.*

Primary: Conferences at École élémentaire of Tourrettes-sur-Loup. With Florence Barbara.

Welcoming of schoolchildren or the general public in an Inria center:

MathC2+ internship: Activity for 40 students (high school). With Maria Guramare. *Algorithmes grandeur nature pour le calcul du plus court chemin et pour trier.*

Open days of Inria Sophia Antipolis - Méditerranée: *La magie des graphes, des algorithmes et du binaire*.

Presentation for twelve interns of middle school (niveau collège, troisième) by Frédéric Cazals.

Creation of the website of the popularization project GALEJADE (Graphes et ALgorithmes : Ensemble de Jeux À Destination des Écoliers (mais pas que)) founded by Inria, Fondation Blaise Pascal, and Université Côte d'Azur. See https://

Development of wooden objects for the dissemination of the scientific culture: wooden plateau for graph algorithms and convex hull, chocolate bar game made by 3D printers, kakemonos... See https://