Geometric and statistical methods in systems biology: the case of metabolic networks
Sampling the solution space of metabolic networks is a powerful, however computationally challenging, approach for interpreting the underlying mechanisms that govern the biological processes. Volestipy is a Python library that addresses this challenge by providing a variety of computational tools that scale to high dimensions.
I am a Bioinformatician and a PhD candidate in University of Crete focusing on deciphering the underlying mechanisms of ecosystem functioning. In the framework of my dissertation, two main approaches are held:
- data integration methods: to extract associations between different organisms, environments and environmental types, from ‘omics data and metadata.
- metabolic networks analysis: in an attempt to infer microbial interactions
As a biologist, my research interests focus on microbial ecology and ecosystem functioning at the microbial dimension. However, as computer science and mathematics could not be just a means to an end, I am also an ethousiast of the geometrical features of convex bodies.
Apostolos is a PhD student in Computer Science at the Department of Telecommunications and Informatics in National Kapodistrian University of Athens. His scientific and research interests are:
- Markov chains for sampling from high dimensional multivariate distributions.
- Volume estimation of convex and non-convex bodies in high dimensions.
- Crises detection and portfolio performance evaluation in big stock markets.
- Randomized methods for convex optimization.
- Optimized implementation of state-of-the-art geometric algorithms.