This is a hybrid event. Join in person at the LCSB, or tune in remotely via Webex.
An overview of cancer progression and monotonic accumulation models: evolutionary assumptions, casual interpretations, and extensions
Cancer progression is often modeled using monotonic accumulation models, which describe the sequential and irreversible acquisition of mutations or traits. These models are widely applied in computational oncology, virology, and disease progression studies. This talk will examine the evolutionary assumptions underlying these models, highlighting their strengths and limitations in capturing the complexity of cancer dynamics. Special attention will be given to the causal interpretations derived from cross-sectional data and the constraints these impose on inference. The discussion will also explore extensions of monotonic accumulation models, considering alternative frameworks that may better account for the stochastic and selective processes shaping tumor evolution. Applications beyond cancer, including infectious diseases and evolutionary biology, will also be considered, emphasizing the broader relevance of these modeling approaches.
About the speaker
Prof. Ramón Díaz-Uriarte is a Full Professor at the Department of Biochemistry, Universidad Autónoma de Madrid, and the Institute for Biomedical Research “Alberto Sols” (IIBM, CSIC-UAM). He earned his Licentiate in Biology from Universidad Autónoma de Madrid in 1992 and completed his Ph.D. in 2000. His research focuses on bioinformatics and computational biology, with significant contributions to cancer progression modeling and statistical methods for genomic data analysis. His work integrates evolutionary theory, machine learning, and statistical modeling to address key questions in oncology and genomics.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
