---
_id: '84'
abstract:
- lang: eng
text: The advent of high-throughput technologies and the concurrent advances in
information sciences have led to a data revolution in biology. This revolution
is most significant in molecular biology, with an increase in the number and scale
of the “omics” projects over the last decade. Genomics projects, for example,
have produced impressive advances in our knowledge of the information concealed
into genomes, from the many genes that encode for the proteins that are responsible
for most if not all cellular functions, to the noncoding regions that are now
known to provide regulatory functions. Proteomics initiatives help to decipher
the role of post-translation modifications on the protein structures and provide
maps of protein-protein interactions, while functional genomics is the field that
attempts to make use of the data produced by these projects to understand protein
functions. The biggest challenge today is to assimilate the wealth of information
provided by these initiatives into a conceptual framework that will help us decipher
life. For example, the current views of the relationship between protein structure
and function remain fragmented. We know of their sequences, more and more about
their structures, we have information on their biological activities, but we have
difficulties connecting this dotted line into an informed whole. We lack the experimental
and computational tools for directly studying protein structure, function, and
dynamics at the molecular and supra-molecular levels. In this chapter, we review
some of the current developments in building the computational tools that are
needed, focusing on the role that geometry and topology play in these efforts.
One of our goals is to raise the general awareness about the importance of geometric
methods in elucidating the mysterious foundations of our very existence. Another
goal is the broadening of what we consider a geometric algorithm. There is plenty
of valuable no-man’s-land between combinatorial and numerical algorithms, and
it seems opportune to explore this land with a computational-geometric frame of
mind.
article_processing_charge: No
author:
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Patrice
full_name: Koehl, Patrice
last_name: Koehl
citation:
ama: 'Edelsbrunner H, Koehl P. Computational topology for structural molecular biology.
In: Toth C, O’Rourke J, Goodman J, eds. Handbook of Discrete and Computational
Geometry, Third Edition. Handbook of Discrete and Computational Geometry.
Taylor & Francis; 2017:1709-1735. doi:10.1201/9781315119601'
apa: Edelsbrunner, H., & Koehl, P. (2017). Computational topology for structural
molecular biology. In C. Toth, J. O’Rourke, & J. Goodman (Eds.), Handbook
of Discrete and Computational Geometry, Third Edition (pp. 1709–1735). Taylor
& Francis. https://doi.org/10.1201/9781315119601
chicago: Edelsbrunner, Herbert, and Patrice Koehl. “Computational Topology for Structural
Molecular Biology.” In Handbook of Discrete and Computational Geometry, Third
Edition, edited by Csaba Toth, Joseph O’Rourke, and Jacob Goodman, 1709–35.
Handbook of Discrete and Computational Geometry. Taylor & Francis, 2017. https://doi.org/10.1201/9781315119601.
ieee: H. Edelsbrunner and P. Koehl, “Computational topology for structural molecular
biology,” in Handbook of Discrete and Computational Geometry, Third Edition,
C. Toth, J. O’Rourke, and J. Goodman, Eds. Taylor & Francis, 2017, pp. 1709–1735.
ista: 'Edelsbrunner H, Koehl P. 2017.Computational topology for structural molecular
biology. In: Handbook of Discrete and Computational Geometry, Third Edition. ,
1709–1735.'
mla: Edelsbrunner, Herbert, and Patrice Koehl. “Computational Topology for Structural
Molecular Biology.” Handbook of Discrete and Computational Geometry, Third
Edition, edited by Csaba Toth et al., Taylor & Francis, 2017, pp. 1709–35,
doi:10.1201/9781315119601.
short: H. Edelsbrunner, P. Koehl, in:, C. Toth, J. O’Rourke, J. Goodman (Eds.),
Handbook of Discrete and Computational Geometry, Third Edition, Taylor & Francis,
2017, pp. 1709–1735.
date_created: 2018-12-11T11:44:32Z
date_published: 2017-11-09T00:00:00Z
date_updated: 2023-10-16T11:15:22Z
day: '09'
department:
- _id: HeEd
doi: 10.1201/9781315119601
editor:
- first_name: Csaba
full_name: Toth, Csaba
last_name: Toth
- first_name: Joseph
full_name: O'Rourke, Joseph
last_name: O'Rourke
- first_name: Jacob
full_name: Goodman, Jacob
last_name: Goodman
language:
- iso: eng
month: '11'
oa_version: None
page: 1709 - 1735
publication: Handbook of Discrete and Computational Geometry, Third Edition
publication_identifier:
eisbn:
- '9781498711425'
publication_status: published
publisher: Taylor & Francis
publist_id: '7970'
quality_controlled: '1'
scopus_import: '1'
series_title: Handbook of Discrete and Computational Geometry
status: public
title: Computational topology for structural molecular biology
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...