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Efficient computational implementation of polymer physics models to explore chromatin structure.

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Title: Efficient computational implementation of polymer physics models to explore chromatin structure.
Authors: Conte, Mattia1 (AUTHOR), Esposito, Andrea1,2 (AUTHOR), Fiorillo, Luca1 (AUTHOR), Campanile, Raffaele1 (AUTHOR), Annunziatella, Carlo1 (AUTHOR), Corrado, Alfonso1 (AUTHOR), Chiariello, Maria Gabriella3 (AUTHOR), Bianco, Simona1 (AUTHOR) biancos@na.infn.it, Chiariello, Andrea M.1 (AUTHOR) chiariello@na.infn.it
Source: International Journal of Parallel, Emergent & Distributed Systems. Jan2022, Vol. 37 Issue 1, p91-102. 12p.
Subjects: Cell nuclei, Chromosome structure, Molecular dynamics, Physics, Polymers, Chromatin
Abstract: The development of novel experimental technologies able to map genome-wide chromatin contacts, as Hi-C, GAM or SPRITE, allowed to derive detailed information about the spatial structure of chromosomes in the cell nucleus. They revealed that the genome has a complex spatial organisation, which is highly connected with its activity. In the last years, such an abundance of experimental data prompted the development of quantitative models based on Polymer Physics to describe the chromatin architecture, clarifying many aspects about the molecular mechanisms underlying genome folding. Efficient algorithms are thus fundamental to perform massive numerical simulations for testing the accuracy of these models and provide a good description for small genomic regions or for whole chromosomes. Here, we consider the performances of Molecular Dynamics (MD) implementation of commonly used polymer physics models. Such models can be combined with Machine Learning approaches informed with experimental data to produce more accurate descriptions of real genomic regions. However, the execution times increase as a power-law with the size of the input data, which ultimately reflects the complexity of the investigated system. The best strategy is therefore a convenient trade-off between the accuracy in the description and the availability of computational resources. The combination of innovative experimental data and polymer physics theories allow to reconstruct the 3D genome structure. This is achieved by the use of machine learning approaches and massive parallel computing. Efficient algorithms and computational resources are then fundamental to produce models of increasingly high accuracy. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 154225228
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Efficient computational implementation of polymer physics models to explore chromatin structure.
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  Data: <searchLink fieldCode="AR" term="%22Conte%2C+Mattia%22">Conte, Mattia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Esposito%2C+Andrea%22">Esposito, Andrea</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fiorillo%2C+Luca%22">Fiorillo, Luca</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Campanile%2C+Raffaele%22">Campanile, Raffaele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Annunziatella%2C+Carlo%22">Annunziatella, Carlo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Corrado%2C+Alfonso%22">Corrado, Alfonso</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chiariello%2C+Maria+Gabriella%22">Chiariello, Maria Gabriella</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bianco%2C+Simona%22">Bianco, Simona</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> biancos@na.infn.it</i><br /><searchLink fieldCode="AR" term="%22Chiariello%2C+Andrea+M%2E%22">Chiariello, Andrea M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chiariello@na.infn.it</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Parallel%2C+Emergent+%26+Distributed+Systems%22">International Journal of Parallel, Emergent & Distributed Systems</searchLink>. Jan2022, Vol. 37 Issue 1, p91-102. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Cell+nuclei%22">Cell nuclei</searchLink><br /><searchLink fieldCode="DE" term="%22Chromosome+structure%22">Chromosome structure</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+dynamics%22">Molecular dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22Polymers%22">Polymers</searchLink><br /><searchLink fieldCode="DE" term="%22Chromatin%22">Chromatin</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The development of novel experimental technologies able to map genome-wide chromatin contacts, as Hi-C, GAM or SPRITE, allowed to derive detailed information about the spatial structure of chromosomes in the cell nucleus. They revealed that the genome has a complex spatial organisation, which is highly connected with its activity. In the last years, such an abundance of experimental data prompted the development of quantitative models based on Polymer Physics to describe the chromatin architecture, clarifying many aspects about the molecular mechanisms underlying genome folding. Efficient algorithms are thus fundamental to perform massive numerical simulations for testing the accuracy of these models and provide a good description for small genomic regions or for whole chromosomes. Here, we consider the performances of Molecular Dynamics (MD) implementation of commonly used polymer physics models. Such models can be combined with Machine Learning approaches informed with experimental data to produce more accurate descriptions of real genomic regions. However, the execution times increase as a power-law with the size of the input data, which ultimately reflects the complexity of the investigated system. The best strategy is therefore a convenient trade-off between the accuracy in the description and the availability of computational resources. The combination of innovative experimental data and polymer physics theories allow to reconstruct the 3D genome structure. This is achieved by the use of machine learning approaches and massive parallel computing. Efficient algorithms and computational resources are then fundamental to produce models of increasingly high accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/17445760.2019.1643020
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 91
    Subjects:
      – SubjectFull: Cell nuclei
        Type: general
      – SubjectFull: Chromosome structure
        Type: general
      – SubjectFull: Molecular dynamics
        Type: general
      – SubjectFull: Physics
        Type: general
      – SubjectFull: Polymers
        Type: general
      – SubjectFull: Chromatin
        Type: general
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      – TitleFull: Efficient computational implementation of polymer physics models to explore chromatin structure.
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            – D: 01
              M: 01
              Text: Jan2022
              Type: published
              Y: 2022
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