Advanced Modeling of Cellular Proliferation: Toward a Multi-scale Framework Coupling Cell Cycle to Metabolism by Integrating Logical and Constraint-Based Models
Biological functions require a coherent cross talk among multiple layers of regulation within the cell. Computational efforts that aim to understand how these layers are integrated across spatial, temporal, and functional scales represent a challenge in Systems Biology. We have developed a computational, multi-scale framework that couples cell cycle and metabolism networks in the budding yeast cell. Here we describe the methodology at the basis of this framework, which integrates on off-the-shelf logical (Boolean) models of a minimal yeast cell cycle with a constraint-based model of metabolism (i.e., the Yeast 7 metabolic network reconstruction). Models are implemented in Python code using the BooleanNet and COBRApy packages, respectively, and are connected through the Boolean logic. The methodology allows for incorporation of interaction data, and validation through –omics data. Furthermore, evolutionary strategies may be incorporated to explore regulatory structures underlying coherent cross talks among regulatory layers.
Key wordsMulti-scale modeling and simulation Systems biology Logical modeling Constraint-based modeling Cell cycle Metabolism
This work was supported by the Systems Biology Grant of the University of Surrey to M.B., and by the SILS Starting Grant of the University of Amsterdam (UvA) and by the UvA-Systems Biology Research Priority Area Grant to M.B.
Author contribution: M.B. conceived the idea and designed the study. L.v.d.Z. and M.B. designed the computational analyses. L.v.d.Z. programmed the source code and performed the simulations. L.v.d.Z. and M.B. analyzed the data. L.v.d.Z. and M.B. wrote the chapter. M.B. provided scientific leadership and supervised the study.
- 7.Fauré A, Naldi A, Lopez F, Chaouiya C, Ciliberto A, Thieffry D (2009) Modular logical modelling of the budding yeast cell cycle. Mol Biosyst 5:1787–1796Google Scholar
- 15.Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D, Weitz KK, Eils R, König R, Smith RD, Palsson BØ (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6:390CrossRefGoogle Scholar
- 22.Choi HS, Su WM, Morgan JM, Han GS, Xu Z, Karanasios E, Siniossoglou S, Carman GM (2011) Phosphorylation of phosphatidate phosphatase regulates its membrane association and physiological functions in Saccharomyces cerevisiae: identification of SER(602), THR(723), AND SER(744) as the sites phosphorylated by CDC28 (CDK1)-encoded cyclin-dependent kinase. J Biol Chem 286:1486–1498CrossRefGoogle Scholar
- 30.Mondeel TDGA, Crémazy F, Barberis M (2018) GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae. Bioinformatics 34:2147–2149Google Scholar
- 31.Birch EW, Udell M, Covert MW (2014) Incorporation of flexible objectives and time-linked simulation with flux balance analysis. J Theor Biol 345:12–21Google Scholar