A generalised enzyme kinetic model for predicting the behaviour of complex biochemical systems

Wong, Martin Kin Lok; Krycer, James R.; Burchfield, James G.; James, David E.; Kuncic, Zdenka

doi:10.1016/j.fob.2015.03.002

Cited by 14 publications

(10 citation statements)

References 49 publications

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“…Once several kinases of interest have been identified, their regulation can be subsequently studied at a mechanistic level. For instance, there are discrepancies between kinase and substrate phosphorylation in the Insulin Dataset; such intricacies may be studied using ordinary differential equation modeling [ 40 , 41 ] with targeted experiments containing focused time-points and inhibitors tailored to the kinases of interest. Thus, KSR-LIVE offers the means to identify patterns in kinase activity that can be subject to further investigation.…”

Section: Discussionmentioning

confidence: 99%

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

et al. 2016

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In response to stimuli, biological processes are tightly controlled by dynamic cellular signaling mechanisms. Reversible protein phosphorylation occurs on rapid time-scales (milliseconds to seconds), making it an ideal carrier of these signals. Advances in mass spectrometry-based proteomics have led to the identification of many tens of thousands of phosphorylation sites, yet for the majority of these the kinase is unknown and the underlying network topology of signaling networks therefore remains obscured. Identifying kinase substrate relationships (KSRs) is therefore an important goal in cell signaling research. Existing consensus sequence motif based prediction algorithms do not consider the biological context of KSRs, and are therefore insensitive to many other mechanisms guiding kinase-substrate recognition in cellular contexts. Here, we use temporal information to identify biologically relevant KSRs from Large-scale In Vivo Experiments (KSR-LIVE) in a data-dependent and automated fashion. First, we used available phosphorylation databases to construct a repository of existing experimentally-predicted KSRs. For each kinase in this database, we used time-resolved phosphoproteomics data to examine how its substrates changed in phosphorylation over time. Although substrates for a particular kinase clustered together, they often exhibited a different temporal pattern to the phosphorylation of the kinase. Therefore, although phosphorylation regulates kinase activity, our findings imply that substrate phosphorylation likely serve as a better proxy for kinase activity than kinase phosphorylation. KSR-LIVE can thereby infer which kinases are regulated within a biological context. Moreover, KSR-LIVE can also be used to automatically generate positive training sets for the subsequent prediction of novel KSRs using machine learning approaches. We demonstrate that this approach can distinguish between Akt and Rps6kb1, two kinases that share the same linear consensus motif, and provide evidence suggesting IRS-1 S265 as a novel Akt site. KSR-LIVE is an open-access algorithm that allows users to dissect phosphorylation signaling within a specific biological context, with the potential to be included in the standard analysis workflow for studying temporal high-throughput signal transduction data.

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Section: Discussionmentioning

confidence: 99%

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

et al. 2016

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“…The issue of substrate mass balance or conservation abounds in literature [12][13][14][15]24]. There was a need to introduce the concept of total substrate concentration.…”

Section: Resultsmentioning

confidence: 99%

Total Enzyme-substrate Complex Which Includes Product-destined Enzyme-substrate Complex

Udema¹

2019

AJRB

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Background: There is no much interest in the determination of total enzyme-substrate complex concentration ([ES]T) which includes undissociated ES that is unaccounted for unlike the usual ES destined for transformation into free enzyme and product or substrate. The reason is speculatively as a result of the lack of awareness of such possibility via sequestration. Objectives: 1) To derive on the basis of both reverse – and standard – quasi-steady – state assumptions equations for the determination of [ES]T which is not restricted to the complex which dissociates to product/substrate and free enzyme and 2) quantitate the value of [ES]T. Methods: A theoretical research and experimentation using Bernfeld method to determine velocities of amylolysis with which to calculate relevant parameters. Results: The [EST] is < [E] ( i. e. [ET] - [ES]); [EST] decreased with increasing [ST] and increased with increasing concentration of enzyme [ET] while the velocity of amylolysis, v and maximum velocity of amylolysis, vmax expectedly increased with increasing [ET] and [ST]. Conclusion: The equations for the determination of the total enzyme-substrate complex, free enzyme without any complex formation before and after dissociation of enzyme-complex into product and/or substrate and free enzyme were derived. The difference, [ET] - [ES] is a heterogeneous mixture of undissociated ES and free enzyme without any complex formation. This is the case because [ES] which dissociates into product is only a part of the total enzyme-substrate complex. There is a continuous formation of ES during and at the expiry of the duration of assay as long as there is no total substrate depletion.

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“…Complex systems of enzyme reactions can be modeled using networks of coupled ordinary differential equations (ODEs) ( Chen et al, 2010;Wong et al, 2015 ). When spatial distribution of enzymes is a relevant factor, e.g.…”

Section: Discussionmentioning

confidence: 99%

Reverse allostasis in biological systems: Minimal conditions and implications

Rezaei‐Ghaleh

Bakhtiari

Rashidi

2017

Journal of Theoretical Biology

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Biological control systems regulate the behavior of biological systems in a constantly changing environment. Homeostasis is the most widely studied outcome of biological control systems. Homeostatic systems maintain the system in its desired state despite variations in system parameters or the externally-determined input rates of their constituents, i.e. they have zero or near zero steady state error. On the other hand, allostatic systems are not resistant against environmental changes and the steady state level of their controlled variables responds positively to the changes in their input rates. Little is known, however, on the existence and frequency of reverse allostatic systems, where the steady state value of the controlled variable correlates negatively with the input rate of that variable. In the present study, we derive the minimal conditions for the existence and local stability of reverse allostatic systems, and demonstrate in examples of metabolic, pharmacological, pathophysiological and ecological systems that the reverse allostasis requirements are relatively non-stringent and may be satisfied in biological systems more commonly than usually thought. The possible existence of reverse allostatic systems in nature and their counter-intuitive implications in physiological systems, drug treatment, ecosystem management, and biological control are explored and testable predictions are made.

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A generalised enzyme kinetic model for predicting the behaviour of complex biochemical systems

Abstract: Graphical abstract

Cited by 14 publications

References 49 publications

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

Total Enzyme-substrate Complex Which Includes Product-destined Enzyme-substrate Complex

Reverse allostasis in biological systems: Minimal conditions and implications

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