Carito Guziolowski

The exponential increase of biological data (genomic, transcriptomic, proteomic) [1] and of biological interaction knowledge in Pathway Databases allows modeling cellular regulatory mechanisms. Modeling biological mechanisms is done, most of the time, using boolean or ordinary differential equation representations. Those approaches have shown their efficiency in cellular phenomena study [2], disease research [3, 4], and bio-production optimization [5].

Only one quarter of Acute Myeloid Leukemia (AML) diagnosed patients survive beyond 5 years. It is therefore worth exploring how mathematical modeling may contribute on a shift towards a more personalized follow up treatment for AML diagnosed patients. On this context, a prediction of the treatment response of AML patients, solely based on proteomic data, may add valuable information and improve clinical decisions.