There has been recent interest in the application of nonlinear dynamical and probabilistic approaches to model the interaction between crucial genes involved in an experimental paradigm. More importantly that of progenitor and stem cell differentiation which exhibit heterogeneity, plasticity, de-differentiation, coexpression of multiple lineages and multiple developmental routes to an end phenotype. Techniques such as clonal analysis have been useful to obtain insight into these complex biological processes. However, there is a need for rigorous and sophisticated quantitative techniques to interpret such experimental data sets. We propose to develop an interactive and mathematically rigorous toolbox (CloNET) for modeling the dependencies and network structure from gene expression data obtained from clonal analysis. The toolbox would be developed in the language R. The objectives of the proposed project are multifaceted and include
(1) Mathematical Aspects: Develop generic, existing and novel algorithms to model the functional dependencies and network structure of progenitor and stem cell differentiation from gene expression obtained using clonal analysis. Test the robustness of the techniques on synthetic and experimental data sets. (2) Biological Aspects: Apply the techniques developed to obtain insight into the delicate balance between crucial myogenic and adipogenic markers myogenic progenitor cell differentiation as a function of age. (3) Educational Aspects: Develop a graphic user interface with accompanying tutorial, that can be used by the biologist and as a teaching tool in graduate curriculum on systems biology, to be developed in conjunction with Arkansas Biomedical Research Infrastructure Network (BRIN) (4) Software Aspects: The package would be developed in R which is a open source software (GNU) and disseminated through the prestigious CRAN (Comprehensive R Archive Network). Promote data and code sharing that would help researcher to plan new and similar experimental paradigms.