Adriaan Ludl

My current research with Tom Michoel in the Computational Biology Unit focuses on causal inference methods and their applications on genomic data. I am developing methods to distinguish correlation from causation given genomic population data. My goal is to participate in improving our understanding of complex gene regulatory networks involving thousands of genes and their products in order to establish their phenotypic trait outcomes. This work has implications on the study of diseases and healthy phenotypes and finding therapeutic targets.

My research interests and past experience cover diverse areas of biological and physical phenomena, ranging from modeling regulatory networks in the context of quorum sensing in bacteria, simulations of neuronal activity, network inference in neuroscience, to phase transitions and crystallisation of amorphous solids.

In my previous research with Jordi Soriano at the University of Barcelona, Spain, I have worked with inference methods based on information theory to study functional connectivity of living neuronal networks. We used transfer entropy (TE) to infer the connections formed between individual neurons in 2D and 3D in vitro cultures. We worked in closely with colleagues at the Institute of Photonic Sciences (ICFO) near Barcelona on the calcium fluorescence imaging of the samples using light sheet imaging for 3D samples.

Our goal being to contribute to a better understanding of the computational capabilities of nervous systems, I was also working with integrate and fire models of neuronal dynamics and the processes generating connections between neurons.

During my PhD at Université Pierre et Marie Curie in Paris, France, I was working on phase transitions in aqueous electrolyte solutions, mostly salty water, at pressures up to the gigapascal range and temperatures ranging from 80 to 350 K.