Oscillatory stimuli differentiate adapting circuit topologies

Abstract

Adapting pathways consist of negative feedback loops (NFLs) or incoherent feedforward loops (IFFLs), which we show can be differentiated using oscillatory stimulation: NFLs but not IFFLs generically show ‘refractory period stabilization’ or ‘period skipping’. Using these signatures and genetic rewiring we identified the circuit dominating cell cycle timing in yeast. In C. elegans AWA neurons we uncovered a Ca2+-NFL, diffcult to find by other means, especially in wild-type, intact animals. (70 words)

Subharmonics and chaos in simple periodically-forced biomolecular models

“…Our first example, motivated by the paper [14], where similar models appear, is a negative feedback system which consists of two species X and Y such that X enhances the production of Y and Y inhibits the production of X. The concentrations of X and Y at time t are denoted respectively by x = x(t) and y = y(t).…”

“…"dynamic phenotypes") can provide further insight into the structure of biological systems. Recent examples include scale invariance or "fold-change detection" [10,11,12], non-monotonic behavior under monotonic inputs [13], refractory period stabilization [14], and non-entrained solutions or "period skipping" when stimulii are periodic [14].…”

“…The paper [14] found experimentally, in a C. elegans odor sensing neuron, responses whose periods are roughly multiples of the period of an excitation signal (see sample time traces in Section SI-5), thus theoretically implying the presence of negative feedback loops, and went on to propose a circuit architecture that is capable of displaying the observed dynamic phenotype. These findings suggest the following theoretical question: what complicated dynamics can arise in the simplest biochemical systems, more generally, in response to a periodic input?…”

“…Furthermore, and perhaps equally remarkable, our two systems are not rhythmic in the absence of periodic stimulation; quite the contrary, they have unique and globally asymptotically stable steady states when the input is constant. This complexity in ubiquitous systems that constitute components of typical signal sensing and transduction networks suggests a previously unrecognized hidden level of complexity in molecular biology, which is of plausible significance for biological function [14].…”

Diverse cell stimulation kinetics identify predictive signal transduction models

“…However, life evolved to thrive in the presence of gradual changes (Harvey and Smith, 2009;Sorre et al, 2014), and recent studies have demonstrated that many cells respond differently to inputs of equal magnitudes based upon specific variations in input kinetics, such as different temporal frequencies (Albeck et al, 2013;Ashall et al, 2009;Cai et al, 2008;Hao and O'Shea, 2012;Hersen et al, 2008;Mettetal et al, 2008;Wang et al, 2012) or different spatial gradients (Harvey and Smith, 2009). A few pioneering studies have even demonstrated that different kinetic stimulations can dramatically affect intracellular signaling dynamics to create distinct cell phenotypes (Averbukh et al, 2018;Cai et al, 2004;Heltberg et al, 2019;Mettetal et al, 2008;Mitchell et al, 2015;Rahi et al, 2017;Shimizu et al, 2010;Sorre et al, 2014;Thiemicke et al, 2019;Zhang et al, 2019) (Figure 1A). The fact that different kinetics of the same environmental inputs create such different responses offer a new opportunity to generate a wider and richer range of signaling pathway dynamics, while using current experimental assays that measure only a small number of signaling proteins.…”