Postdoctoral Fellows


Ana Quiroga Campano

Research Interests

Mathematical modelling of biological systems and experimental validation
Model-based biomanufacturing optimization
Leukemia therapies: Model-based outcome predictions and treatment optimization



BSc Double Major Chemical Engineering and Bioengineering, Universidad de Chile

MSc Chemical Engineering, Universidad de Chile

PhD Chemical Engineering, University College London, 2017


Ana Quiroga received her PhD (2017) in Chemical Engineering from Imperial College London. Her research project was focused on the development of a mathematical model based on the energy metabolism of mammalian cells producing monoclonal antibodies. The model was used to develop a computational platform for the optimization and acceleration of upstream process development, in specific for the development of low-cost and tailor-made supplemental media and optimized feeding strategies for fed-batch cultures.


During her MSc in Chemical Engineering and Double major BSc (Eng) in Chemical Engineering and Bioengineering at Universidad de Chile, she optimized the production of enzymes for detergents, in E. coli, using Design-of-Experiments (DoE) and mathematical modelling, and discovered the advantage of mathematical models over DoE to optimize biological systems. Currently at BSEL, Ana works on the development of a computational application based on a dynamic mathematical modelling framework, using patient-, leukemia- and treatment-specific data to predict outcomes and optimize chemotherapy regimens for patients with Acute Myeloid Leukemia (AML).

The model consists of a pharmacokinetic and pharmacodynamic (PK/PD) module and a population balance models (PBMs) module that describes the concertation and effect of different chemotherapy drugs in the bone marrow (BM) populations: both normal cell populations (stem cells, progenitors and precursors) and abnormal cell populations (leukemic sensitive blasts (LSB) and leukemic resistant blasts (LRB)). The precision therapy tool has the potential to personalize optimal standard and novel treatments for AML in real time.