Poster

Molecular switch architecture determines response properties of signaling pathways

eSMB2020 eSMB2020 Follow 2:30 - 3:30pm EDT, Monday - Wednesday
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Khem Raj Ghusinga

UNC
"Molecular switch architecture determines response properties of signaling pathways"
Many intracellular signaling pathways are composed of molecular switches, proteins that transition between two states—on and off. Typically, signaling is initiated when an external stimulus activates its cognate receptor that in turn causes downstream switches to transition from off to on using one of the following mechanisms: activation, in which the transition rate from the off state to the on state increases; derepression, in which the transition rate from the on state to the off state decreases; and concerted, in which activation and derepression operate simultaneously. We use mathematical modeling to compare these signaling mechanisms in terms of their dose-response curves, response times, and abilities to process upstream fluctuations. Our analysis elucidates several general principles. First, activation increases the sensitivity of the pathway, whereas derepression decreases sensitivity. Second, activation generates response times that decrease with signal strength, whereas derepression causes response times to increase with signal strength. These opposing features allow the concerted mechanism to not only show dose-response alignment, but also to decouple the response time from stimulus strength. However, these potentially beneficial properties come at the expense of increased susceptibility to upstream fluctuations. In addition to above response metrics, we also examine the effect of receptor removal on switches governed by activation and derepression. We find that if inactive (active) receptors are preferentially removed then activation (derepression) exhibits a sustained response whereas derepression (activation) adapts. In total, we show how the architecture of molecular switches govern their response properties. We also discuss the biological implications of our findings.
eSMB2020
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Virtual conference of the Society for Mathematical Biology, 2020.