Bayesian modeling reveals metabolite‐dependent ultrasensitivity in the cyanobacterial circadian clock. Issue 6 (4th June 2020)

• Record Type: Journal Article
• Authors: Hong, Lu; Lavrentovich, Danylo O; Chavan, Archana; Leypunskiy, Eugene; Li, Eileen; Matthews, Charles; LiWang, Andy; Rust, Michael J; Dinner, Aaron R
• Is Part Of: Molecular systems biology. Volume 16:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-04
• Subjects: Bayes factor -- kinetic modeling -- Markov chain Monte Carlo -- robustness -- substrate competition; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20199355
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: Mathematical models can enable a predictive understanding of mechanism in cell biology by quantitatively describing complex networks of interactions, but such models are often poorly constrained by available data. Owing to its relative biochemical simplicity, the core circadian oscillator in Synechococcus elongatus has become a prototypical system for studying how collective dynamics emerge from molecular interactions. The oscillator consists of only three proteins, KaiA, KaiB, and KaiC, and near‐24‐h cycles of KaiC phosphorylation can be reconstituted in vitro . Here, we formulate a molecularly detailed but mechanistically naive model of the KaiA—KaiC subsystem and fit it directly to experimental data within a Bayesian parameter estimation framework. Analysis of the fits consistently reveals an ultrasensitive response for KaiC phosphorylation as a function of KaiA concentration, which we confirm experimentally. This ultrasensitivity primarily results from the differential affinity of KaiA for competing nucleotide‐bound states of KaiC. We argue that the ultrasensitive stimulus–response relation likely plays an important role in metabolic compensation by suppressing premature phosphorylation at nighttime. Synopsis: This study takes a data‐driven kinetic modeling approach to characterize the interaction between KaiA and KaiC in the cyanobacterial circadian clock to understand how the oscillator responds to changes in cellular metabolic conditions. An extensive dataset of KaiC autophosphorylation measurements is generated and used to constrain a detailed yet mechanistically naive kinetic model within a Bayesian parameter estimation framework. KaiA concentration tunes the sensitivity of KaiC autophosphorylation and the period of the full oscillator to %ATP. The model reveals an ultrasensitive dependence of KaiC phosphorylation on KaiA concentration as a result of differential KaiA binding affinity to ADP‐ vs. ATP‐bound KaiC. Ultrasensitivity in KaiC phosphorylation likely contributes to metabolic compensation by suppressing premature phosphorylation at nighttime. Abstract : This study takes a data‐driven kinetic modeling approach to characterize the interaction between KaiA and KaiC in the cyanobacterial circadian clock to understand how the oscillator responds to changes in cellular metabolic conditions.