This project is a collaborative effort between scientists with complementary expertise in mathematics and biomedical sciences. The project has three aims: (i) Develop a mathematical signaling model able to reproduce blood lineage differentiation, using associative memories to represent single cell states. The model will be able to make predictions on the effect on differentiation of specific combinations of receptor ligands and drugs. (ii) Develop a mathematical model for an ensemble of different hematological cells, under co-culture conditions. The model will describe the dynamics of cells as interacting attractors. (iii) Verify the predictions of the mathematical modeling using in vitro experiments to detect markers of differentiation, to assess cellular differentiation by flow cytometry, and by performing RNA-seq on pools of cells and on single cells. Cells will be studied as pure populations and in co-culture conditions. The rapidly increasing availability of gene expression data of different types of cells has created new opportunities for integrating these datasets into mathematical models to make experimentally verifiable predictions. The proposed model will capture the multistable nonlinear dynamics in complex cell signaling networks regulating cell differentiation. This will be realized by using RNA-seq data on pooled cell samples and single cells. The model will make predictions on combinations of transcription factors or receptor ligands that could induce a specific cell lineage. By comparison with our planned experiments, the model will clarify the role of specific receptor ligands in cell fate decision of single cells or a population of cells. The proposed methodology will enhance our general understanding of biological processes and diseases where cell differentiation plays a key role. In particular, this project could provide new biomedical insight in stem cell biology, immunology, hematology, and human development.
This project will provide new biomedical insight relevant to biological systems and diseases where crosstalk among cells and cell differentiation play a key role. The project could increases our chances of finding new cures for many pathologies, including immunological, hematological, and developmental diseases.
|Szedlak, Anthony; Sims, Spencer; Smith, Nicholas et al. (2017) Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems. PLoS Comput Biol 13:e1005849|