Using all the factors together does not improve upon the endoderm derived Y27632 by PI3KI treatment. The second group of conditions also contains FGF2 as a major factor along with WNT3A. It is found that both pluripotency and the endoderm factors are relatively favored by conditions involving FGF2 and WNT3A as the major contributor. In fact, FGF2 has been found to be suffi cient to maintain the hESCs in the pluripotent state and has also been Inhibitors,Modulators,Libraries used for endoderm induction in several differentiation protocols. Thus, FGF2 can potentially favor both pluripotency as well as endoderm differentiation depending on associated conditions. Identification of co regulated transcription factors by biclustering While hierarchical clustering enables a fast and simplis tic analysis of the Inhibitors,Modulators,Libraries experimental data sets, it does not provide information on which subsets of TFs are co regulated across subsets of conditions.

Identifying such co clusters will be beneficial, since the governing signal ing pathways change with the induction condition and the same TFs may not be co regulated. The technique of biclustering serves to mine subgroups of such TFs exhi biting similar trends in their expression level under sub sets of conditions. Hence TFs appearing in the same bicluster Inhibitors,Modulators,Libraries can be inferred to be co regulated and constitu ents of a similar network architecture. The experimental data matrix, X, constituting the mean expression data across all the growth factor conditions is analyzed using the algorithm elaborated in Methods section.

Here, the biclustering approach is formulated as an optimization problem solved using genetic algorithm Inhibitors,Modulators,Libraries and the quality of every candidate bicluster is assessed by a fit ness function. The fitness function has a number of free parameters associated with it which can be tuned in order to identify certain desired trends. The detailed pro cedure on the selection of the optimum parameters Inhibitors,Modulators,Libraries is outlined in the Additional file 2. The developed optimization based bicluster identifica tion algorithm was applied to the mean expression data with the above mentioned parameters, which resulted in a 3 gene 5 condition bicluster as illustrated in Figure dilution calculator 4. However, to identify additional biclusters, possibly with overlaps, the SEBI algorithm was subsequently run by penalizing the identified biclusters. One such biclus ter is presented in Figure 4. Although, the SEBI algo rithm allows some degree of overlapping amongst the subsequent biclusters, the current mean dataset did not result in any overlaps. Recently, a new method was proposed by Banka et al. called as Fuzzy Possibilistic Biclustering which assigns a membership value to each gene condition pair in the expression matrix and therefore, allows varying degree of overlapping amongst the biclusters.