Whole yeast model: what and why

Institute of Systems Analysis and Computer Sciences "A. Ruberti" (IASI-CNR), CNR - National Research Council, Rome, Italy
SYSBIO - Centre of Systems Biology
Department of Biotechnology and Biosciences, University of Milan - Bicocca, Milano, Italy
DOI
10.7287/peerj.preprints.27327v1
Subject Areas
Computational Biology
Keywords
Systems Biology, Whole cell models
Copyright
© 2018 Palumbo et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Palumbo P, Vanoni M, Papa F, Busti S, Alberghina L. 2018. Whole yeast model: what and why. PeerJ Preprints 6:e27327v1

Abstract

One of the most challenging fields in Life Science research is to deeply understand how complex cellular functions arise from the interactions of molecules in living cells. Mathematical and computational methods in Systems Biology are fundamental to study the complex molecular interactions within biological systems and to accelerate discoveries. Within this framework, a need exists to integrate different mathematical tools in order to develop quantitative models of entire organisms, i.e. whole-cell models. This note presents a first attempt to show the feasibility of such a task for the budding yeast Saccharomyces cerevisiae, a model organism for eukaryotic cells: the proposed model refers to the main cellular activities like metabolism, growth and cycle in a modular fashion, therefore allowing to treat them separately as single input/output modules, as well as to interconnect them in order to build the backbone of a coarse-grain whole cell model. The model modularity allows to substitute a low granularity module with one with a finer grain, whenever molecular details are required to correctly reproduce specific experiments. Furthermore, by properly setting the cellular division, simulations of cell populations are achieved, able to deal with protein distributions. Whole cell modeling will help understanding logic of cell resilience.

Author Comment

This is an abstract which has been accepted for the BBCC2018 Conference