Background and activities
Representation and extraction of causality statements from prior-knowledge.
In systems biology, regulatory process networks are built to depict how components in cell fate decision systems are interconnected and behave. A considerable amount of knowledge provided by different public resources is already available in the form of large biological networks. My project explores this information by breaking down those networks into causality statements: a source entity that influences the activity or the quantity of a target entity given a specific context.
The DrugLogics project is a systems medicine approach to employ computational methods for predicting drug resistance in cancer treatment. The long-term goal is to economize drug screens and to find tailor-made treatments for patients with specific types of cancer. Currently, our pipeline automatically generates boolean models from causal statements to predict drug targets and drug synergies. The extraction of causal statements from existing network resources will feed our model building software pipeline with more input data.
We will design a format to standardize the representation of causal statements by using generally accepted identifiers and ontology terms (i.e. ontology for causal interaction). Using this representation we will design and build software pipelines to extract causal statements from a variety of existing network resources and make them publicly available.
Sep 2015 - Jan 2017 Scientific staff member, Rostock University (Rostock, Germany).
Feb 2015 - Aug 2015 Master internship, European Institute for Systems Biology & Medicine (Lyon, France).
2013 - 2015 MSc in Bioinformatics and Biostatistics, Paris-Sud XI University (Paris, France).
2011 - 2013 BSc in Life Sciences, Pierre et Marie Curie University (Paris, France).
2009 - 2011 Medical Studies, Pierre et Marie Curie University (Paris, France).