Unbiased modeling of bioadhesion on complex materials

The aim of this project is to establish a junior group at IFG in the area of statistical modeling that can work hand-in-hand with the existing experimental groups to promote novel approaches in materials synthesis and surface engineering. Specifically, the proposal intends to recruit Ramya Kumar from the University of Michigan, Chemical Engineering, to establish unbiased statistical modelling approaches in bioadhesion. During her Ph.D. in the Lahann group, Ramya Kumar has already applied statistical modeling to surface engineering and more recently has developed an unbiased modeling approach to blood-brain-barrier transport of nanoparticles funded through a multi-university grant from the Defense Threat Reduction Agency, USA. The second recruit will be a post-doctoral fellow that will collaborate with Ramya Kumar on machine-learning algorithms to tailor existing theoretical approaches developed for catalysis or as a part of the materials genome activities towards the topics of interest in this effort. Bioadhesion is a major societal challenge with relevance from biotechnology, medicine, membrane research, to agriculture. Because of the inherent complexity, there is no overarching approach towards bioadhesion that simultaneously considers proteins, viruses, and bacteria. We thus propose an unbiased data-driven simulation approach based on statistical models and machine learning, rather than a hypothesis-driven approach using molecular dynamics simulations. Establishment of a statistical modelling capability related to bioadhesion and surfaces effectively aligns with the current experimental efforts at the IFG, the BIFTM program, and biointerfaces activities in a more materials-centered POF4 program.


Name Institute
Members of this project
Stefan Bräse Institute of Biological and Chemical Systems (IBCS)
Matthias Franzreb Institute of Functional Interfaces (IFG)
Joerg Lahann Institute of Functional Interfaces (IFG)
Thomas Schwartz Institute of Functional Interfaces (IFG)
Alexander Welle Institute of Functional Interfaces (IFG)