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Enlightening the blind spot of the Michaelis–Menten rate law: The role of relaxation dynamics in molecular complex formation Enlightening the blind spot of the Michaelis–Menten rate law: The role of relaxation dynamics in molecular complex formation
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Junghun Chae1,7, Roktaek Lim1,2,7, Thomas L. P. Martin2, Cheol-Min Ghim1,3,4,,and Pan-Jun Kim2,4,5,6,1- Department of Physics, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea2- Department of Biology, Hong Kong Baptist University, Kowloon, Hong Kong 3- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea4- Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 37673, Republic of Korea 5- Center for Quantitative Systems Biology & Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon, Hong Kong6- Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy7- J.C. and R.L. contributed equally to this work.*Author to whom correspondence should be addressed:C.-M.G. (email: cmghim@unist.ac.kr) or P.-J.K. (email: extutor@gmail.com).AbstractThe century-long Michaelis–Menten rate law and its modifications in the modeling of biochemical rate processes stand on the assumption that the concentration of the complex of interacting molecules, at each moment, rapidly approaches an equilibrium (quasi-steady state) compared to the pace of molecular concentration changes. Yet, in the case of actively time-varying molecular concentrations with transient or oscillatory dynamics, the deviation of the complex profile from the quasi-steady state becomes relevant. A recent theoretical approach, known as the effective time-delay scheme (ETS), suggests that the delay by the relaxation time of molecular complex formation contributes to the substantial breakdown of the quasi-steady state assumption. Here, we systematically expand this ETS and inquire into the comprehensive roles of relaxation dynamics in complex formation. Through the modeling of rhythmic protein–protein and protein–DNA interactions and the mammalian circadian clock, our analysis reveals the effect of the relaxation dynamics beyond the time delay, which extends to the dampening of changes in the complex concentration with a reduction in the oscillation amplitude against the quasi-steady state. Interestingly, thecombined effect of the time delay and amplitude reduction shapes both qualitative and quantitative oscillatory patterns such as the emergence and variability of the mammalian circadian rhythms. These findings highlight the drawback of the routine assumption of quasisteady states and enhance the mechanistic understanding of rich time-varying biomolecular activities.

(cid:3047) due to its high similarity with will skip the use of (cid:2889)(cid:2904)(cid:2903)(cid:3119) (cid:3047) (Spearman's (cid:3404) 0.92 and (cid:3407) 10(cid:2879)(cid:2872)). Remarkably, when (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3117) (cid:2930)(cid:2901) parameter conditions (99.0%) have (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3117) . Likewise, we will skip the use of (cid:2929)(cid:2901) (cid:3047) (cid:3047) (cid:3627) is 1 h, most (cid:3627) or (cid:3627)(cid:2930)(cid:2901) (cid:3047) (cid:3047) (cid:3627) [Fig. 1(c) and (cid:3407) 10(cid:2879)(cid:2872)]. Still, (cid:3627) less than (cid:3627)(cid:2930)(cid:2901) (cid:3047) there exist some parameter conditions with (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3117) (cid:3047)
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(cid:3627) 2 h and even 4 h, but such conditions (cid:3627) [Fig. 1(d) and (cid:3407) 10(cid:2879)(cid:2872)]. In other words, both the ETS1 and are >13 times rarer for (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3118) ETS2 with the effect of the relaxation time provide a better estimate of the phase of (cid:4666)(cid:4667) than the quasi-steady state assumption, but the ETS2 exhibits fewer noticeable errors than the ETS1. The latter comes from the fact that the actual phase difference between (cid:4666)(cid:4667) and its quasi-steady state hardly reaches the time average of (cid:2930)(cid:2901) (cid:2879)(cid:2869)(cid:4666)(cid:4667) (cid:2964) thus the ETS1 rather overestimates that phase difference while the ETS2 reduces this error as obvious from Eqs. (18) and (19). These findings demonstrate the importance of relaxation time in complex formation that should be accurately considered to deepen the understanding of the deviation of the
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14 Fig. 1. Proteinprotein interaction. (a) Example time-series of substrate protein levels (cid:4666)(cid:4667) and (cid:3364)(cid:4666)(cid:4667) in Eq. (34) at the top, the full model-based complex level (cid:4666)(cid:4667) and the tQSSA and sQSSA at the center, and (cid:4666)(cid:4667) and the ETS1, ETS2, and ETS3 at the bottom. (cid:3404) (cid:2964) (cid:3047) (cid:2879)(cid:2869) as defined before Eq. (2). (b) (cid:3047) (cid:3047) (cid:3047) (cid:3627) (tQSSA), and
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Probability distributions of (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3117) (cid:3627) (ETS1), (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3118) (cid:3627) (ETS2), (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3119) (cid:3627) (ETS3), (cid:3627)(cid:2930)(cid:2901) (cid:3047) (cid:3627) (sQSSA) over randomly-sampled parameter sets in Table S1. (c,d) Scatter plot of (cid:3627)(cid:2930)(cid:2901) (cid:3627)(cid:2929)(cid:2901) (cid:3047) (cid:3627) and (cid:3047) (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3117) (cid:3047) (cid:3627) (c), or that of (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3117) (cid:3047) (cid:3627) and (cid:3627)(cid:2889)(cid:2904)(cid:2903)(cid:3118) (cid:3627) (d) with randomly-sampled parameter sets in Table S1. A
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