## Section: Research Program

### Algorithmic Foundations

**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.

#### Modeling Interfaces and Contacts

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 $p$ neighbors of a given atom are represented by $3p-6$ degrees of freedom – the neighborhood being invariant upon rigid motions. The information gathered while modeling contacts can further be integrated into interface models.

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.

#### Modeling Macro-molecular Assemblies

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.

#### Modeling the Flexibility of Macro-molecules

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 [33] .

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
[5] . 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.