When thinking about cell reprogramming strategies, it is important to know what mechanisms act at the molecular level within the cell during the natural differentiation of cells. To illustrate cell differentiation, in 1957 Conrad Waddington introduced the Waddington Landscape metaphor from experimental observations but without numerical measurements. Today, thanks to advances in cell reprogramming, this idea is being revisited. The Waddington Landscape expresses that cell differentiation occurs from one stage to another in the same way as a ball rolls down a slope from a higher to a lower part due to a difference in potential energy. The search for a measure of the ‘potential energy’ of cells would allow us obtain the necessary knowledge to better understand cell differentiation and develop new reprogramming strategies.

Computational biology tools have been developed to exhaustively mathematically model its geometry and dynamics, obtaining a “total” landscape from the integration transcriptomics data obtained through “Big Data” techniques from public databases. They comprise FOntCell, an algorithm for Fusion of Ontologies of Cells, that assist us to automatically annotate differentiation cellular trees from existing ontologies; And a computer parallelized Genetic Algorithm especially designed to optimize the construction of the Waddington Landscape from transcriptomics data fulfilling some mathematical constraints based on the modeling of empirical biological conditions.

In this seminar I will explain the set of algorithms developed and optimized for the construction of a Waddington Landscape based on cell transcriptomics, as well as the results in this regard.