Robust dependencies and structures in stem cell differentiation

Radhakrishnan Nagarajan, Jane E. Aubin, Charlotte A. Peterson

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Cell differentiation is a complex process governed by the timely activation of genes resulting in a specific phenotype or observable physical change. Recent reports have indicated heterogeneity in gene expression even amongst identical colonies (clones). While some genes are always expressed, others are expressed with a finite probability. In this report, a mathematical framework is provided to understand the mechanism of osteoblast (bone forming cell) differentiation. A systematic approach using a combination of entropy, pair-wise dependency and Bayesian approach is used to gain insight into the dependencies and underlying network structure. Pair-wise dependencies are estimated using linear correlation and mutual information. An algorithm is proposed to identify statistically significant mutual information estimates. The robustness of the dependencies and the network structure to decreasing number of colonies (colony size) and perturbation is investigated. Perturbation is achieved by generating bootstrap samples. The methods discussed are generic in nature and can be extended to similar experimental paradigms.

Original languageEnglish
Pages (from-to)1503-1514
Number of pages12
JournalInternational Journal of Bifurcation and Chaos in Applied Sciences and Engineering
Issue number4
StatePublished - Apr 2005

Bibliographical note

Funding Information:
We would like to thank Kevin Murphy, Philip Leray for making available their Matlab routines and the Arkansas Cancer Research Center (ACRC) differentiation group for useful discussions. This research was supported in part by funds provided to the UAMS Microarray Facility through Act 1, The Arkansas Tobacco Settlement Proceeds Act of 2000 and by NIH Grant #P20 RR-16460 from the BRIN Program of the National Center for Research Resources and by NIH AG20941.


  • Bayesian networks
  • Clonal analysis
  • Genetic networks
  • Pairwise dependencies
  • Stem cell

ASJC Scopus subject areas

  • Modeling and Simulation
  • Engineering (miscellaneous)
  • General
  • Applied Mathematics


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