Mathematics applied to pediatric oncohematology (MATHPOH)


Leukemias and lymphomas are the most common hematological cancers in childhood and the leading non-traumatic cause of death in children.  New strategies are necessary to identify and select patients who do not respond to standard chemotherapy and who are at higher risk of relapse. The main objective of this project is to develop and use mathematical tools to improve the prognosis of pediatric patients with hematological cancers. The scientific knowledge generated by the project will focus on mathematical models that simulate the dynamics of hematological diseases, and on mathematical tools that predict response to treatments.

Why Mathematics?

Mathematical models can describe the appearance of the leukemic clones and their response to treatments. Model validation with flow cytometry follow-up data allows the creation of in-silico patient populations that complement the picture provided by in-vitro and animal models. Computational flow cytometry algorithms have the potential to speed up, automate, and reduce bias in conventional assays, complementing the work of the cytometrists and extracting, by more sophisticated mathematical methods and algorithms, relevant information related to the disease.

The Team

The research team includes María Rosa Durán (MOLAB-UCA, PI), Salvador Chulián García (MOLAB-UCA), Cristina Blázquez (Hospital Virgen del Rocío), Eva Gálvez (Hospital de Jerez), Tamara Garrido (UCA), Elena Recio (UCA), Rafael de la Rosa (UCA), Juan Francisco Gutiérrez (Hospital de Jerez), Álvaro Martínez Rubio (MOLAB-UCA), Almudena Márquez (UCA), Ana del Rosario Niño (MOLAB-UCA).


We intend to improve diagnosis and treatment of oncohematological patients. We aim to discover biomarkers of response that could be incorporated into the clinical practice with the collaboration of the team medical doctors. This will improve the resources available for clinical decision-making, together with the creation of software for automated analysis, that could be integrated in the diagnosis routine. In this way, clinicians would be able to use the in-silico simulations based on patient’s real data through mathematical models when making decisions regarding optimal and personalized treatments.

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