Kovacs Lab: Complex Systems

We are witnessing an unprecedented boom in biological data availability, inevitably leading to a turning point, where theory can be a guiding force behind experimental design and development, similarly to what happened in physics. Molecules in our cells, genes in our genome or individuals in our societies do not serve their functions in isolation, but in concert with other nodes in their networks, as well as with environmental factors. Pairwise interactions and correlations are an important starting point, captured by network models. Yet, a sufficient understanding of cancer and complex diseases, as well as drug combinations or genetic interactions requires to consider interactions of higher order, between multiple nodes and conditions. Hindered by a combinatorial explosion, limited data availability and quality, going beyond second order in a data-driven way is extremely demanding, with only a handful of examples. The same problem arises not only in systems biology, but in neuroscience, as well as in information and infection spreading. In our work we develop novel methods to fight data incompleteness and biases, leading to experimentally testable, large-scale predictions. Besides bio-physical interactions, our approach can reliably predict a broad spectrum of functional associations, including co-complex membership information, disease associations, pathway membership and (higher order) genetic interactions, toxic and synergistic drug combinations as well as organizing rules in the nervous system.