Multi-scale modeling of biological systems, for instance of tissues composed of millions of cells, are extremely demanding to simulate, even resorting to High Performance Computing (HPC) facilities, particularly when each cell is described by a detailed model of some intra-cellular pathways and cells are coupled and interacting at the tissue level. Model abstraction can play a crucial role in this setting, by providing simpler models of intra-cellular dynamics that are much faster to simulate so to scale better the analysis at the tissue level. Abstractions themselves can be very challenging to build ab-initio. A more viable strategy is to learn them from single cell simulation data. In this paper, we explore this direction, constructing abstract models of chemical reaction networks in terms of Discrete Time Markov Chains on a continuous space, learning transition kernels using deep neural networks. This allows us to obtain accurate simulations, greatly reducing the computational burden.

Deep Abstractions of Chemical Reaction Networks

Bortolussi, Luca
Membro del Collaboration Group
;
PALMIERI, LUCA
Membro del Collaboration Group
2018-01-01

Abstract

Multi-scale modeling of biological systems, for instance of tissues composed of millions of cells, are extremely demanding to simulate, even resorting to High Performance Computing (HPC) facilities, particularly when each cell is described by a detailed model of some intra-cellular pathways and cells are coupled and interacting at the tissue level. Model abstraction can play a crucial role in this setting, by providing simpler models of intra-cellular dynamics that are much faster to simulate so to scale better the analysis at the tissue level. Abstractions themselves can be very challenging to build ab-initio. A more viable strategy is to learn them from single cell simulation data. In this paper, we explore this direction, constructing abstract models of chemical reaction networks in terms of Discrete Time Markov Chains on a continuous space, learning transition kernels using deep neural networks. This allows us to obtain accurate simulations, greatly reducing the computational burden.
9783319994284
https://www.springer.com/series/558
File in questo prodotto:
File Dimensione Formato  
CMSB2018.pdf

Open Access dal 25/08/2019

Descrizione: Authors may self-archive the author’s accepted manuscript of their articles on their own websites. Authors may also deposit this version of the article in any repository, provided it is only made publicly available 12 months after official publication or later. He/ she may not use the publisher's version (the final article), which is posted on SpringerLink and other Springer websites, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be provided by inserting the DOI number of the article in the following sentence: “The final publication is available at Springer via http://dx.doi.org/0.1007/978-3-319-99429-1 _ 2
Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Copyright Editore
Dimensione 1.13 MB
Formato Adobe PDF
1.13 MB Adobe PDF Visualizza/Apri
front matter and Bortolussi.pdf

Accesso chiuso

Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 1.2 MB
Formato Adobe PDF
1.2 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2931410
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 4
social impact