» Articles » PMID: 19329576

Toward Stem Cell Systems Biology: from Molecules to Networks and Landscapes

Overview
Specialty Biology
Date 2009 Mar 31
PMID 19329576
Citations 13
Authors
Affiliations
Soon will be listed here.
Abstract

The last few years have seen significant advances in our understanding of the molecular mechanisms of stem-cell-fate specification. New and emerging high-throughput techniques, as well as increasingly accurate loss-of-function perturbation techniques, are allowing us to dissect the interplay among genetic, epigenetic, proteomic, and signaling mechanisms in stem-cell-fate determination with ever-increasing fidelity (Boyer et al. 2005, 2006; Ivanova et al. 2006; Loh et al. 2006; Cole et al. 2008; Jiang et al. 2008; Johnson et al. 2008; Kim et al. 2008; Liu et al. 2008; Marson et al. 2008; Mathur et al. 2008). Taken together, recent reports using these new techniques demonstrate that stem-cell-fate specification is an extremely complex process, regulated by multiple mutually interacting molecular mechanisms involving multiple regulatory feedback loops. Given this complexity and the sensitive dependence of stem cell differentiation on signaling cues from the extracellular environment, how are we best to develop a coherent quantitative understanding of stem cell fate at the systems level? One approach that we and other researchers have begun to investigate is the application of techniques derived in the computational disciplines (mathematics, physics, computer science, etc.) to problems in stem cell biology. Here, we briefly sketch a few pertinent results from the literature in this area and discuss future potential applications of computational techniques to stem cell systems biology.

Citing Articles

Self-organization of whole-gene expression through coordinated chromatin structural transition.

Zimatore G, Tsuchiya M, Hashimoto M, Kasperski A, Giuliani A Biophys Rev (Melville). 2024; 2(3):031303.

PMID: 38505632 PMC: 10903504. DOI: 10.1063/5.0058511.


Charting cellular differentiation trajectories with Ricci flow.

Baptista A, MacArthur B, Banerji C Nat Commun. 2024; 15(1):2258.

PMID: 38480714 PMC: 10937996. DOI: 10.1038/s41467-024-45889-6.


A practical way to prepare primer human chondrocyte culture.

Isyar M, Yilmaz I, Sirin D, Yalcin S, Guler O, Mahirogullari M J Orthop. 2016; 13(3):162-7.

PMID: 27408489 PMC: 4919283. DOI: 10.1016/j.jor.2016.03.008.


Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development.

Davila-Velderrain J, Martinez-Garcia J, Alvarez-Buylla E Front Genet. 2015; 6:160.

PMID: 25954305 PMC: 4407578. DOI: 10.3389/fgene.2015.00160.


Network based meta-analysis prediction of microenvironmental relays involved in stemness of human embryonic stem cells.

Mournetas V, Nunes Q, Murray P, Sanderson C, Fernig D PeerJ. 2014; 2:e618.

PMID: 25374775 PMC: 4217173. DOI: 10.7717/peerj.618.


References
1.
Maayan A . Network integration and graph analysis in mammalian molecular systems biology. IET Syst Biol. 2008; 2(5):206-21. PMC: 2745214. DOI: 10.1049/iet-syb:20070075. View

2.
Chang H, Hemberg M, Barahona M, Ingber D, Huang S . Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature. 2008; 453(7194):544-7. PMC: 5546414. DOI: 10.1038/nature06965. View

3.
Goldberg A, Allis C, Bernstein E . Epigenetics: a landscape takes shape. Cell. 2007; 128(4):635-8. DOI: 10.1016/j.cell.2007.02.006. View

4.
Mathur D, Danford T, Boyer L, Young R, Gifford D, Jaenisch R . Analysis of the mouse embryonic stem cell regulatory networks obtained by ChIP-chip and ChIP-PET. Genome Biol. 2008; 9(8):R126. PMC: 2575516. DOI: 10.1186/gb-2008-9-8-r126. View

5.
Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-50. PMC: 1239896. DOI: 10.1073/pnas.0506580102. View