Modeling of Cellular Systems: Application in Stem Cell Research and Computational Disease Modeling

The large-scale development of high-throughput sequencing technologies has allowed the generation of reliable omics data at different regulatory levels. Integrative computational models enable the disentangling of a complex interplay between these interconnected levels of regulation by interpreting these large quantities of biomedical information in a systematic way. In the context of human diseases, network modeling of complex gene-gene interactions has been successfully used for understanding disease-related dysregulations and for predicting novel drug targets to revert the diseased phenotype. Furthermore, these computational network models have emerged as a promising tool to dissect the mechanisms of developmental processes such as cellular differentiation, transdifferentiation, and reprogramming. In this chapter, we provide an overview of recent advances in the field of computational modeling of cellular systems and known limitations. A particular attention is paid to highlight the impact of computational modeling on our understanding of stem cell biology and the complex multifactorial nature of human diseases and their treatment.