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Model-free inference of network structural features from observed dynamics

Marc Timme, TU Dresden
Tuesday October 5, 12-1pm
Zoom Link:聽

础产蝉迟谤补肠迟:听The dynamics of biological networks enables the function of a variety of systems we rely on every day, from gene and protein regulation to metabolic circuits and neural networks in the brain. Understanding and predicting network function relies on suitable models, yet it remains unclear how to extract key features of networks if only time series data from (some) units are available. Here we report on recent progress on model-free inference of network structural features from observed dynamics. First, we demonstrate how to identify the number N of dynamical variables making up a network -- arguably its most fundamental property -- from recorded time series of only a small subset of n<N variables. We eludicate why N may be deducible even if time series from only one variable are available. Second, we present approaches to identify network topological features from observed nodal time series data only, applicable to circadian clocks, metabolic circuits and other networks.

This is work with Jose Casadiego, Mor Nitzan, Hauke Haehne, Georg Boerner and others.

[1] Topical Review: Marc Timme & Jose Casadiego, J. Phys. A 47:343001 (2014).
[2] Casadiego et al., Nature Comm. 8:2192 (2017).
[3] Nitzan et al., Science Adv.:e1600396 (2017).
[4] Haehne et al., Phys. Rev. Lett. 122:158301 (2019).
[5] Boerner et al., in prep. (2021).