English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Understanding the cell: Future views of structural biology

Beck, M., Covino, R., Hänelt, I., & Müller-McNicoll, M. (2024). Understanding the cell: Future views of structural biology. Cell, 187(3), 545-562. doi:10.1016/j.cell.2023.12.017.

Item is

Files

show Files
hide Files
:
1-s2.0-S0092867423013491-main.pdf (Any fulltext), 5MB
Name:
1-s2.0-S0092867423013491-main.pdf
Description:
-
OA-Status:
Not specified
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Beck, Martin1, 2, Author                 
Covino, Roberto3, Author
Hänelt, Inga2, Author
Müller-McNicoll, Michaela2, Author
Affiliations:
1Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society, ou_3040395              
2Goethe University Frankfurt, Frankfurt, Germany, ou_persistent22              
3Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany, ou_persistent22              

Content

show
hide
Free keywords: cellular self-organization, computational modeling, digital twin, structural biology
 Abstract: Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.

Details

show
hide
Language(s): eng - English
 Dates: 2024-02-01
 Publication Status: Issued
 Pages: 18
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cell.2023.12.017
BibTex Citekey: beck_understanding_2024
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Cell
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, Mass. : Cell Press
Pages: - Volume / Issue: 187 (3) Sequence Number: - Start / End Page: 545 - 562 Identifier: ISSN: 0092-8674
CoNE: https://pure.mpg.de/cone/journals/resource/954925463183