Mathematical Biocomplexity

Mathematical Biocomplexity

Bringing mathematics closer to biology

Harnessing algebraic topology and the information complexes hidden in massive data-sets, we uncover relationships among the building blocks of life and new insights into evolution dynamics and molecular interactions. Our work tackles the mysteries of "junk DNA" - the 98% that is non-coding - and of RNAs in general, leading to innovations in bioinformatics that help from demystifying how viruses mutate to advancing personalized cancer medicine.

Use Our Tools

As we progress in our work we develop software tools that aid in our research. Some turn out to have a much wider scale applicability than expected and could thus prove useful to others. We package our software and make them freely accessible to the global research community at large.

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Our Team

From homological algebra and combinatorics, across molecular evolution and RNA structure design all the way to computer programming and software development, the transdisciplinary range of expertise in our team allows us to investigate biological systems in radical new ways. Together we're setting the foundations of and helping to advance the science of biocomplexity.

Our Focus Areas

Pick any key component of life, and within it you'll find a complex system, whether it's a snippet of RNA or a network of interacting cells. Our work centers on a trio of challenging and understudied areas in biology.

Our research aims to answer some of the most important questions in biology, including the role genetic entropy plays in virus dynamics, the impact of structure on RNA molecular interaction, the development of a topological language that can describe the hierarchical complexity we observe in biological systems.

We explore the information content in structured sequence data to reveal how changes in one influence the time evolution of the other. This contributes unique insights into viral dynamics at the host population level.

We investigate the structure of large bio-networks such as long noncoding RNA molecules. This contributes to the understanding of cancer development.

We study the abstraction to shape of a biological system - using it as a mathematical language to measure its complexity of interaction and provide ways of decomposing it into more fundamental blocks. This contributes to the understanding of ncRNA and structure switching.