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Register a new userPhosphorylation potential and chemical fluxes govern the biological performance of multiple PdP cycles
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Fission yeast G2/M transition is regulated by a biochemical reaction networks which contains four components: Cdc13, Cdc2, Wee1, and Cdc25. This circuit is characterized by the ultrasensitive responses of Wee1 or Cdc25 to Cdc13/Cdc2 activity, and the bistability of Cdc2 activation. Previous work has shown that this bistability is governed by phosphorylation energy. In this article, we developed the kinetic model of this circuit and conducted further thermodynamic analysis on the role of phosphorylation energy (&[Delta]G). We showed that level &[Delta]G shapes the response curves of Wee1 or Cdc25 to Cdc2 and governs the intrinsic noise level of Cdc2 activity. More importantly, the mutually antagonistic chemical fluxes around the PdP cycles in G2/M circuit were shown to act as a stabilizer of Cdc2 activity against &[Delta]G fluctuations. These results suggests the fundamental role of free energy and chemical fluxes on the sensitivity, bistability and robustness of G2/M transition.
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Protein phosphorylation cycles are important mechanisms of the post translational modification of a protein and as such an integral part of intracellular signaling and control. We consider the sequential phosphorylation and dephosphorylation of a protein at two binding sites. While it is known that proteins where phosphorylation is processive and dephosphorylation is distributive admit oscillations (for some value of the rate constants and total concentrations) it is not known whether or not this is the case if both phosphorylation and dephosphorylation are distributive. We study four simplified mass action models of sequential and distributive phosphorylation and show that for each of those there do not exist rate constants and total concentrations where a Hopf bifurcation occurs. To arrive at this result we use convex parameters to parameterize the steady state and Hurwitz matrices.
Cells store information in DNA and in stable programs of gene expression, which thereby implement forms of long-term cellular memory. Cells must also possess short-term forms of information storage, implemented post-translationally, to transduce and interpret external signals. CaMKII, for instance, is thought to implement a one-bit (bistable) short-term memory required for learning at post-synaptic densities. Here we show by mathematical analysis that multisite protein phosphorylation, which is ubiquitous in all eukaryotic signalling pathways, exhibits multistability for which the maximal number of steady states increases with the number of sites. If there are n sites, the maximal information storage capacity is at least log_2 (n+2)/2 bits when n is even and log_2 (n+1)/2 bits when n is odd. Furthermore, when substrate is in excess, enzyme saturation together with an alternating low/high pattern in the site-specific relative catalytic efficiencies, enriches for multistability. That is, within physiologically plausible ranges for parameters, multistability becomes more likely than monostability. We discuss the experimental challenges in pursuing these predictions and in determining the biological role of short-term information storage.
This work investigates the emergence of oscillations in one of the simplest cellular signaling networks exhibiting oscillations, namely, the dual-site phosphorylation and dephosphorylation network (futile cycle), in which the mechanism for phosphorylation is processive while the one for dephosphorylation is distributive (or vice-versa). The fact that this network yields oscillations was shown recently by Suwanmajo and Krishnan. Our results, which significantly extend their analyses, are as follows. First, in the three-dimensional space of total amounts, the border between systems with a stable versus unstable steady state is a surface defined by the vanishing of a single Hurwitz determinant. Second, this surface consists generically of simple Hopf bifurcations. Next, simulations suggest that when the steady state is unstable, oscillations are the norm. Finally, the emergence of oscillations via a Hopf bifurcation is enabled by the catalytic and association constants of the distributive part of the mechanism: if these rate constants satisfy two inequalities, then the system generically admits a Hopf bifurcation. Our proofs are enabled by the Routh-Hurwitz criterion, a Hopf-bifurcation criterion due to Yang, and a monomial parametrization of steady states.
Biochemical signaling pathways can be insulated from impedance and competition effects through enzymatic futile cycles which consume energy, typically in the form of ATP. We hypothesize that better insulation necessarily requires higher energy consumption, and provide evidence, through the computational analysis of a simplified physical model, to support this hypothesis.
Multisite phosphorylation plays an important role in regulating switchlike protein activity and has been used widely in mathematical models. With the development of new experimental techniques and more molecular data, molecular phosphorylation processes emerge in many systems with increasing complexity and sizes. These developments call for simple yet valid stochastic models to describe various multisite phosphorylation processes, especially in large and complex biochemical networks. To reduce model complexity, this work aims to simplify the multisite phosphorylation mechanism by a stochastic Hill function model. Further, this work optimizes regions of parameter space to match simulation results from the stochastic Hill function with the distributive multisite phosphorylation process. While traditional parameter optimization methods have been focusing on finding the best parameter vector, in most circumstances modelers would like to find a set of parameter vectors that generate similar system dynamics and results. This paper proposes a general $alpha$-$beta$-$gamma$ rule to return an acceptable parameter region of the stochastic Hill function based on a quasi-Newton stochastic optimization (QNSTOP) algorithm. Different objective functions are investigated characterizing different features of the simulation-based empirical data, among which the approximate maximum log-likelihood method is recommended for general applications. Numerical results demonstrate that with an appropriate parameter vector value, the stochastic Hill function model depicts the multisite phosphorylation process well except the initial (transient) period.
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