Introducing a Fresh Mathematical Framework for Biological Networks

 Introducing a Fresh Mathematical Framework for Biological Networks

The research led by Professor Michael Joswig and his team introduces a novel mathematical modeling concept for understanding genetic interactions in biological systems. The study, published in the "Proceedings of the National Academy of Sciences" (PNAS), focuses on identifying master regulators within entire genetic networks, providing a theoretical framework for analyzing biological systems.

Traditionally, studies have concentrated on pairwise interactions within genetic systems, often influenced by genetic background and biological context. The team's innovative approach considers higher-order interactions, addressing the challenge of high-dimensional biological networks. Professor Joswig, a mathematics professor at Technische Universität Berlin and a group leader at the Max Planck Institute for Mathematics in the Sciences, emphasizes the importance of investigating context-dependent effects in biology.

The researchers applied high-dimensional geometry to real datasets from biologists studying the life expectancy of fruit flies based on bacterial combinations in the gut. They reinterpreted the biological concept of epistasis, which involves interactions between genes, to mathematically describe these processes. Epistatic interactions are essential for understanding genetic inheritance and the diversity of traits.

The study involved analyzing the microbiome of fruit flies with different bacterial species, mapping relevant biological information using fitness landscapes. By quantifying epistasis, the researchers identified master regulators in the biological network, representing a significant contribution to the field. The method allows for the interpretation of concrete experiments and the identification of relevant signals in higher dimensions.

The interdisciplinary study at the intersection of biology and mathematics incorporates real experiments, demonstrating the proposed method's capability to detect biologically relevant information and reliable signals. The researchers hope that their geometric-statistical analysis method will serve as a powerful tool for exploring biological networks in higher dimensions.

The study's implications extend to the potential application of the quantification method in humans, particularly concerning the significant influence of microbiotic composition on life expectancy. While applying the method to the complex human gut microbiome remains challenging, the researchers anticipate future developments in simpler methods and classic transformation processes that could facilitate applications such as the development of customized drugs.