Date and time: Tuesday 9 June 2026, 13:00-14:00 CEST
Speaker: Konstantin Mischaikow, Rutgers University
Title: Dynamics of Regulatory Networks
Where: Digital Futures hub, Osquars Backe 5, floor 2 at KTH main campus OR Zoom
Directions: https://www.digitalfutures.kth.se/contact/how-to-get-here/
OR
Zoom: https://kth-se.zoom.us/j/69560887455
Host: Martina Scolamiero scola@kth.se
Bio: Konstantin Mischaikow is a Professor at the Department Mathematics and BioMaPS Institute at Rutgers University whose work bridges mathematics, computer science, and the life sciences. His research focuses on dynamical systems, applied and computational topology, artificial intelligence, and systems biology, with applications ranging from gene regulatory networks and bioinformatics to neuroimaging and synthetic biology.
Mischaikow has published widely in leading journals including PLoS Computational Biology, SIAM Journal on Applied Dynamical Systems, and the Journal of Applied and Computational Topology. A Fellow of the American Mathematical Society since 2015, he is recognized for his contributions to dynamical systems and applied topology, as well as for collaborative work advancing computational approaches to complex biological systems.
Abstract: Models in systems biology are ofter presented in the form of a regulatory network, a diagram that purports to identify how genes/proteins/biochemical units interact. However, dynamics is parameter dependent and thus to extract dynamics requires an additional level of modeling. Two popular choices are Boolean models and Ordinary Differential Equations (ODEs).
The advantage of Boolean models is that they are easy to compute and reduce the need for parameter identification. The disadvantage is that the resulting dynamics is highly simplified, potentially misleading, and difficult to associate with parameters. The advantage of ODEs is that they provide precision, but at high experimental cost (nonlinearities and parameters need to be identified) and at high computational cost (solving the ODE over multiple initial conditions).
In this talk I will discuss a third approach: Dynamic Signatures Generated by Regulatory Networks (DSGRN). It is based on characterizing dynamics via order theory and algebraic topology. The major points I wish to convey about this novel approach to dynamics are the following:
- The regulatory network defines a finite combinatorial parameterization of the DSGRN dynamics.
- DSGRN dynamics is efficiently computable and can be used to readily detect not only dynamics, but also global bifurcations of biological interest.
- Boolean dynamics is a subset of DSGRN dynamics and thus DSGRN dynamics provides a natural parameterization for Boolean dynamics.
- DSGRN dynamics can be identified with dynamics of Ordinary Differential Equations.
- DSGRN dynamics can be readily compared against data.
Time permitting I will provide biological examples.
