Sensitivity analysis of population based model.

<p>(A) Cellular growth sensitivity to each of the parameters, perturbed by 10%. Parameter definitions listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032975#pone.0032975.s003" target="_blank">Table S1</a>. (inset) Comparison of cellular growth output histogram from nominal (red) and perturbed (blue) parameters. (B) Number of sensitive parameters determined for each mechanistic model. Proliferation induced: A, all phenotypes; E&U, endoderm and uncommitted (hESC and mesendoderm); U, uncommitted only.</p

Validation of model with experimental gene expression data.

<p>Simulated dynamics of the undifferentiated (A (Condition A), B (Condition B)) and mesendoderm (D (Condition A), E (Condition B)) phenotypes were compared to experimental data of their respective genes, measured by qPCR (markers: experimental measurements; lines: linear connections between data points): Oct4 (Undifferentiated; C) and Brachyury (Mesendoderm; F). The simulated dynamics bands represent 4000 stochastic simulations using the optimized parameters of Mechanism B. mRNA levels were measured with time using qPCR. Data was first normalized to the housekeeping gene Gapdh then to undifferentiated cells. Fold change levels, determined by the 2^−ΔΔCt method, were then normalized to the maximum level for each respective gene (data reported as percent of maximum fold change).</p

Convergence study of simulated cell population over various initial cell populations and total stochastic runs.

<p>Output is percent of the simulated population positive for CXCR4, averaged over all stochastic runs at Day 5.</p

Ensemble parameter estimation and model errors.

<p>(A) Parameter values for Mechanism B ensemble yielding errors of less than 0.025. Each parameter is compared to the most sensitive parameter, ‘d’. Color bar denotes the ensemble error for that particular parameter value. (B) Minimum ensemble error generated for each mechanistic model. Proliferation induced: A, all phenotypes; E&U, endoderm and uncommitted (hESC and mesendoderm); U, uncommitted only. Blue, Condition A; Red, Condition B.</p

Proposed differentiation scheme of hESC during endoderm induction as generated by the population-based model.

<p>Shown is the presence of mesendoderm, the lack of CXCR4 in mesoderm, and selective phenotype proliferation. ME: mesendoderm, VE: visceral endoderm.</p

Significant K-means clusters.

<p>Clusters obtained for each induction condition. <b>(A)</b> WNT3A <b>(B)</b> PI3KI <b>(C)</b> FGF2 and <b>(D)</b> BMP4. The k-means clusters show close similarity of our induction conditions WNT3A and FGF2 with pancreatic organogenesis and PI3KI with definitive endoderm commitment. The markers <i>SOX17, FOXA2, HLXB9</i> are closely regulated under all the induction conditions.</p

Multi-stage Differentiation System.

<p><b>(A)</b> Schematic representation of multi-stage differentiation system. Detailed media formulation found in Supp table 1. DE was induced by modulation of nodal pathway simultaneously with one of four alternate pathways. PP was achieved by SHH inhibition along with retinol signaling. Maturation was induced by notch inhibition. Differentiation using WNT3A <b>(B)</b>, BMP4 <b>(C)</b>, PI3KI <b>(D)</b> or FGF2 (E) at DE stage. IF pictures show nuclear staining of SOX17 (green) and Flow cytometry shows yield of FOXA2 after DE induction, followed by nuclear PDX1 IF pictures (purple) after PP induction and cytoplasmic C-Peptide IF (red) expression yield as measured by flow cytometry after maturation.</p

Transcription factor dynamics.

<p><b>(A)</b> Heat map for the entire data set of genes and conditions illustrating marker progression throughout differentiation stages. The genes are organized according to the expression clusters found through hierarchical clustering. The treatments are denoted on the right hand side as prefixes to the gene names. BMP4 induction condition typically was found to cluster separately from the rest. Hierarchical clustering was performed on the mean centered and variance scaled data of transcription factor dynamics across all the four DE induction conditions. <b>(B)</b> Biplot of transcription factor dynamics assessed by principal component analysis on the mean data-set. The first component shows a demarcation of the undifferentiated and differentiated states. The second component divides the markers according to their expected appearance during <i>in vivo</i> differentiation. The PI3KI curve moves closer to the DE markers, BMP4 curve does not perform well and the WNT3A and FGF2 curves show successful pancreatic maturation.</p

Marker Progression.

<p>A representative sample (based on <i>INS</i> expression) for each group was analyzed and compared to in-vivo <b>(A)</b> pancreatic development <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094307#pone.0094307-OliverKrasinski1" target="_blank">[24]</a> in order to identify which DE pathway modulation(s) lead to better resemblance to pancreatic organogenesis. Similarities can be observed when DE induction is achieved by modulation of <b>(B)</b> FGF2, <b>(C)</b> BMP4, <b>(D)</b> WNT3A and <b>(E)</b> PI3KI while we observed that marker progression greatly differs under BMP4 induction. The different stages of pancreatic development were grouped to represent the 3 stages of the differentiation protocol. Primitive gut endoderm (PGE) and prospective pancreatic endoderm (PPE) represent definitive endoderm induction (light green) pancreatic progenitor (PP) and early endocrine progenitors (EEP) represent pancreatic progenitor induction (medium green) and endocrine progenitors (EP), immature β- cells, mature β- cells (MC) represent the maturation stage (dark green).</p

Predictors of <i>INS</i> expression.

<p>Partial least squares regression performed on the mean expression. Most PP markers show high degree of correlation to <i>INS</i> expression while there is no significant dependence on the DE markers. WNT3A and FGF2 conditions gave positive coefficients with most of the PP and mature markers indicating that these conditions are optimal for <i>INS</i> expression. R² values were above 0.995.</p