A mechanism for bistability in glycosylation

Andrew G. McDonald; Keith F. Tipton; Gavin P. Davey

doi:10.1371/journal.pcbi.1006348

Abstract

Glycosyltransferases are a class of enzymes that catalyse the posttranslational modification of proteins to produce a large number of glycoconjugate acceptors from a limited number of nucleotide-sugar donors. The products of one glycosyltransferase can be the substrates of several other enzymes, causing a combinatorial explosion in the number of possible glycan products. The kinetic behaviour of systems where multiple acceptor substrates compete for a single enzyme is presented, and the case in which high concentrations of an acceptor substrate are inhibitory as a result of abortive complex formation, is shown to result in non-Michaelian kinetics that can lead to bistability in an open system. A kinetic mechanism is proposed that is consistent with the available experimental evidence and provides a possible explanation for conflicting observations on the β-1,4-galactosyltransferases. Abrupt switching between steady states in networks of glycosyltransferase-catalysed reactions may account for the observed changes in glycosyl-epitopes in cancer cells.

Author summary

While enzymes tend to have a narrow substrate specificity, there are a number of enzymes that are promiscuous, acting on a wide range of substrates. In this article we derive expressions for general multi-substrate competitive inhibition for the class of transferases, with particular emphasis on glycosylation. By extending the enzyme reaction mechanism to include inhibition by high substrate concentrations, we show that switching behaviour (bistability) is possible within a thermodynamically open systems of glycosylation enzymes. The biological implication of this finding is that small changes to a predictor variable may induce abrupt changes in the secreted products.

Citation: McDonald AG, Tipton KF, Davey GP (2018) A mechanism for bistability in glycosylation. PLoS Comput Biol 14(8): e1006348. https://doi.org/10.1371/journal.pcbi.1006348

Editor: Nathan E. Lewis, University of California San Diego, UNITED STATES

Received: November 29, 2017; Accepted: July 4, 2018; Published: August 3, 2018

Copyright: © 2018 McDonald et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was part supported by an EU Initial Training Network, Project No. 608381 - Training in Neurodegeneration, Therapeutics Intervention and Neurorepair (TINTIN) awarded to GPD. URL: http://ec.europa.eu/research/fp7/index_en.cfm; and Science Foundation Ireland Grant No. SFI-13/SP SSPC/I2893 URL: http://www.sfi.ie. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

With the ready availability both of computing power and software tools for numerical simulation, the mathematical modelling of metabolic systems has become a core component of cell biology. Models of classical metabolic pathways, such as glycolysis [1–3], the citric-acid cycle [4], the urea cycle [5] and biosynthetic pathways such as N-linked and O-linked glycosylation [6, 7], have been developed as a way to understand how such processes are regulated. Online repositories of such models, such as the BioModels database [8], allow many of these models to be examined without the need for programming ability on the part of the user. Software such as E-Cell [9] have enabled more complex models to be constructed at the cellular or organelle level.

This paper examines a particular class of metabolic model, in which one or more enzymes can act on multiple substrates. To this class belong the cytochrome P450 enzymes that are involved in detoxifying multiple xenobiotics [10], ribonuclease P [11] and also the enzymes of N-linked glycosylation [12–15]. Such enzymes recognise multiple substrates, and the products of the reactions can themselves become substrates, thus introducing a form of competitive inhibition with catalysis. It is known that, in the case of two substrates acted upon by the same enzyme the Michaelis constant of the kinetic rate law will be modified to include the effects of competing substrates upon one another [16, 17]. In the first part of this paper, a general form of the Michaelis-Menten equation for n competing substrates is derived, and extended to an ordered-sequential mechanism involving a donor molecule held in common by all reactions. In the second part, we model galactosyltransferase acting on an initial acceptor glycoprotein to form two products, each of which are substrates for the same enzyme. Here we propose a possible mechanism for such behaviour and apply it to the glycosyltransferase model, demonstrating the switching between stable steady states over a range of parameter values.

Methods

Theoretical development

Consider the case of a general two-substrate enzyme mechanism, in which a donor molecule, Ax, transfers the x moiety to an acceptor, B, a reaction type that is common to the transferases. We consider the situation in which there are n acceptor substrates, B₁ … B_n. For random-order binding of donor and acceptor (Fig 1A), an expression for the initial rate of appearance of the jth acceptor product, Bx_j, is (1) where V_j = k_j[E₀] is the maximal velocity obtained at saturating levels of Ax and B_j, , and . The derivation of this equation under rapid-equilibrium conditions is given in the S1 Appendix. In this model the and are, respectively, the individual dissociation constants of Ax and B_i from the E⋅Ax and E⋅B_i enzyme-substrate complexes, while the Michaelis constants of these species, and are the corresponding dissociation constants of the E⋅Ax⋅B_i complex. The and terms are sums of dimensionless acceptor substrate concentrations representing the degree to which the enzyme is competitively inhibited by substrates other than B_j itself. In the absence of substrate competition, , and Eq (1) reduces to the standard form of a bisubstrate enzyme mechanism. In the limit, as [Ax] → ∞, (1) becomes (2) an equation that is similar in form to that obtained in other studies [14, 18, 19].

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Fig 1. Enzyme mechanisms.

A. Random-order addition of substrates, under reversible, rapid-equilibrium conditions. B. Compulsory-order addition of substrates, with quasi-steady-state assumptions.

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Although the symbolism is a convenience in order to show which terms of the rate law are affected by competitor concentrations, a representation that is more useful in computer simulations is the sum of concentrations of all its substrates, each weighted by its or : (3) Substituting into Eq (1), (4)

Whereas a rapid-equilibrium random-order mechanism is a feature of polypeptide N-acetylgalactosaminyltransferase [20], sulfotransferases [21], fucosyltransferases [22] and sialyltransferases [23], with other glycosyltransferases, such as those of the N-acetylglucosaminyltransferase and galactosyltransferase families, the enzyme must bind the donor first, before catalysis can occur [24]. Under quasi-steady-state conditions (Fig 1B), the rate law for the compulsory order binding is (Eq S3 in S1 Appendix): (5) In such a case, the inhibitory effect of multi-substrate competition will lessen as the concentration of the donor is increased towards saturating levels.

Results

Inhibition at high substrate concentrations

Thus far, the possibility of abortive (dead-end) ternary enzyme complexes has not been considered, which in random-order mechanisms are likely to occur [25]. Experimental evidence for the existence such complexes can be the appearance of inhibition at high substrate concentrations; in the case of glycosyltransferases, the inhibition is usually that of the acceptor [26–29], but can also be that of the donor [30]. If we consider only the acceptor, an examination of the mechanism (Fig 1A) reveals that four additional binding events can occur, with the E⋅Ax, E⋅B_j, E⋅A and E⋅Bx_j complexes. We consider binding of B_j to the second of these complexes, E⋅B_j, to provide a possible explanation for substrate inhibition with increasing acceptor concentration. Not only will B_j bind, but so will any competitive acceptor-substrate B_i, i = 1, …, n.

The oligosaccharides attached to glycoproteins (glycans) can be multivalent, meaning that the same acceptor has more than one recognition domain. By way of illustration, the enzyme β-N-acetylglucosaminylglycopeptide β-1,4-galactosyltransferase (GalT; EC 2.4.1.38), catalyses the transfer of d-galactose (Gal) residue to a terminal N-acetylglucosaminyl (GlcNAc) residue on a glycoprotein, glycopeptide or polysaccharide, with the general reaction: A theoretical system, similar to that studied experimentally by Paquêt and co-workers [31], is shown in Fig 2, in which galactose is incorporated into glycopeptide in four steps, starting with the initial acceptor B₁, to form the final product with two terminal galactoses (B₄). Hence, the products B₂ and B₃ are also substrates of the enzyme, since both contain a terminal GlcNAc on which it can act. All three substrates are therefore competitive inhibitors in the earlier sense, and can form a ternary complex with E⋅B_j, the free terminal β-d-galactose in the acceptor competing with the donor, UDP-Gal [33].

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Fig 2. Model scheme of GalT acting on a diantennary N-glycan, in which species B₁, B₂ and B₃ are competing substrates.

Sugar symbols used, in SNFG notation [32]: blue square, GlcNAc; green circle, Man; yellow circle, Gal.

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General observations on glycosyltransferase networks

Before continuing, we make the parenthetic observation that reaction networks such as those in Fig 2 follow a binomial distribution pattern in the number of acceptors at each step. If the initial substrate has m sites on which an enzyme can act, then the m immediate acceptor-products of that substrate will each have m − 1 available sites. There will be a reaction hierarchy based on the combinatorial filling of available sites until the final product is reached at m = 0, with the number of substrates at the kth step following the familiar ^mC_k pattern, After k steps, a glycan substrate originally with m sites will have m − k sites remaining. The resulting network of all possible reactions, for a single acceptor possessing m sites at which an enzyme can act, will have N(m) nodes and E(m) edges, given by and . Every node, whether substrate or product, will have degree m, with the in-degree of a node at the kth step being k and its out-degree being m − k. The number of possible pathways from initial substrate to final product will be Every glycan will have up to m of each type of dissociation constant, for the enzyme of which it is a substrate, product or inhibitor.

Inhibition at high concentrations of acceptor

Extending the derivation of the rapid-equilibrium random equation in the S1 Appendix, an additional term will be required in the denominator to represent the abortive complex(es). Since there are n substrates, there will be n² ways in which to form E⋅B_k⋅B_i. A double summation over the indices i and k will be required, giving the additional term where is the dissociation constant of the kth acceptor from complex E⋅B_k⋅B_i.

The rate of appearance of the jth product will then be (6) with When n = 1, this reduces to (7) The equation for the compulsory order mechanism will be identical, and the more computationally efficient representation, equivalent to Eq (4), is (8) with two summation terms, and .

A general scheme for the formation of ternary enzyme-acceptor complexes is given in Fig 3A. This scheme does dual service, in illustrating both the formation of n² inhibitory complexes in an n-substrate environment, but also the two catalytic mechanisms involving compulsory-order and random-order binding of substrates, which in the latter case only occurs when j = k, and for substrate inhibition at high concentrations, when j = k = i. The scheme illustrates two aspects of multi-substrate competition: productive, in which catalysis occurs, and non-productive, where there is inhibition as a result of abortive complex formation at higher acceptor concentrations. In the productive case, the n acceptors compete with each other for the E⋅Ax complex, in either random-order or compulsory-order binding mechanisms. In the non-productive case, higher acceptor concentrations compete with the donor for binding to the free enzyme, as well as with each other, for an enzyme-acceptor complex, resulting in non-productive multi-substrate inhibition in compulsory-order mechanisms. In the case of a random-order mechanism, the acceptor may bind to either the free enzyme or to the E⋅Ax complex in pathways leading to the productive ternary (E⋅Ax⋅B_j) complex. Therefore high substrate inhibition may result from the mis-oriented binding of acceptor to the free enzyme or binding of a second B to the E⋅B resulting in an abortive ternary complex. The binding site at which competition occurs may differ, depending on the enzyme mechanism involved. Fig 3B displays three curves of v vs [acceptor], showing the relief of substrate inhibition that occurs as the donor concentration is increased, and in Fig 3C, the velocity-substrate surface defined by two values, for n = 2.

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Fig 3. Substrate inhibition by one or more substrates.

A. Reaction scheme for the formation of ternary enzyme-acceptor complex; the binding of Ax to the E⋅B_j complex (shown in grey) only occurs within the random order model. B. Substrate inhibition of an enzyme with a single acceptor, for three different donor concentrations (0.5, 5, 50) with . C. Total enzyme initial rate as a function of two substrates, B₁ and B₂, exhibiting substrate inhibition through formation of an abortive (dead-end) ternary complex. D. Bistability exhibited by a substrate-inhibited enzyme (Eq (7)) in a system open to substrate, for three different values of the concentration of acceptor available externally: [B]₀ = 0.666 (red), [B]₀ = 2.000 (orange), [B]₀ = 3.333 (green). At [B]₀ = 2.0, the line intercepts the velocity–substrate curve at three points, the two outer points being stable, and the inner an unstable steady-state solution.

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The situation is more complicated when multiple binding sites exist on each molecule of acceptor. According to Fig 2, B₁ is a substrate, but B₄ is not, while B₂ and B₃ can bind as substrate inhibitors, though B₁ cannot because it does not have a terminal GlcNAc. B₄ acts as a competitive (product) inhibitor of UDP-Gal, with two possible inhibition constants, and . The effective value of n is the number of edges, E(m), in the network of a substrate with m recognition sites, as defined in the previous section, which gives 16 summands in s_I. For the network in Fig 2, therefore, (9) in which denotes the ith dissociation constant of the kth acceptor, where X is either s (dissociation from E⋅B_i) or I (dissociation from an abortive ternary complex).

Bistability in an open system

It has been observed that bistability can arise when an enzyme is inhibited by one of its substrates in an open system [34], in which substrate enters at a zero-order rate, and exits at a rate that is first-order in the concentration of that substrate. If the substrate can diffuse into the reaction medium according to (10) where [B]₀ is the concentration of exogenous substrate, then multiple steady-state solutions for the concentration of substrate can coexist for v_enz = v_diff. This is illustrated in Fig 3D, where the number of points of intersection of the line (10) with the curve described by Eq (7) will depend on the values of [B]₀ and the diffusion constant, K.

Bistability can be demonstrated through numerical simulation of the one-dimensional ODE system: (11) where a and b are the concentrations of the donor and acceptor, respectively. It is assumed that the donor concentration is constant, while the external concentration of b is chosen as the parameter to vary. The numerical continuation software AUTO, part of the ODE solver XPPAUT [35], was used to calculate the steady-state level of b for increasing b₀. For the parameters a = 0.6, K = 0.075, K_b = 0.1, K_s = 0.05, V_max = 1 and K_a = 0.6, bistability is obtained for 1.518665 < b₀ < 2.325853 (Fig 4).

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Fig 4. Bistability in an open system for a bisubstrate enzyme reaction exhibiting inhibition at high substrate concentrations.

Shown is a bifurcation diagram of the one-dimensional ODE system given by Eq (11), with the external concentration of acceptor substrate, b₀, as the bifurcation parameter. The donor is assumed to be buffered to a constant concentration. The stable steady state levels of the acceptor, b, are indicated by the red curves, the black curve denoting the unstable steady state. An exchange of stability between the stable and unstable branches occurs at the limit points (LP), b₀ = 1.518665 and b₀ = 2.325853, as indicated by the dashed lines. Other parameters of the model are given in the text.

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Within this range two stable steady states of acceptor concentration can coexist, as shown by upper and lower branches in b–b₀ space. This can be confirmed by solving dv_diff/db = −K for b, using the parameters of Fig 3D, and computing the ordinate-axis intercept for v_diff at these two concentrations, which will be points of tangency of the two lines described by Eq (10) with the velocity–substrate curve. The values of b, computed in Mathematica (version 11.0.1; Wolfram Research, Inc.), are b* = {0.10755, 0.693546}. Substituting into (10), we evaluate b* + v_diff(b*)/K = b₀, obtaining the corresponding solutions b₀ = {1.51866, 2.32585}.

A model of multiple competing substrates with inhibition at high concentrations

The reaction scheme shown in Fig 2 is modelled with five differential equations, (12) (13) (14) (15) (16) where the b_i represent the acceptor concentrations [B_i], i = 1 … 3, a is the concentration of UDP-Gal, and the enzyme velocities v₁ … v₄ are described by Eq (8). As before, the model assumes free diffusion of substrates into the medium in which enzyme is active [36]. There will be additional terms in and s_I, since there will be two sets of constants for the initial oligosaccharide substrate B₁, one set for each recognition site. The total enzymic rate of removal of B₁, for saturating levels of Ax, will be (17) Assuming that the maximal velocities of each of v₁ and v₂ are the same, we can solve for substrate concentration at half-maximal velocity, to obtain apparent K_m as the geometric mean of the individual Michaelis constants, . Under the same assumption, for a substrate with m recognition domains, the apparent K_m will be the solution to (18)

Numerical simulation of the model also displayed bistability (Fig 5). Using a two-parameter continuation, the region of a₀–b₀ space under which bistability exists was determined (Fig 5B). The values of the external concentrations at the point of the cusp were found to be (b₀, a₀) = (0.07094, 0.5959). Bistability was also obtained by varying the diffusion constant, K (Fig 5C); a two-parameter continuation in a₀–K space revealed a closed region of bistability (Fig 5D).

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Fig 5. Bistability in an open reaction system described by the ODE system of Eqs (12)–(16), based on the reactions of Fig 2.

Parameters of the model are given in Table 1. A. Steady-state levels of b₂ as the external concentration of donor substrate, a₀, is varied, with b₀ = 0.3; limit points (LP) separate the branches of stable (red curve) and unstable (black curve) steady states. B. Cusp in a₀–b₀ space, with branches of limit points enclosing a region of bistability. C. Steady-state levels of initial substrate, b₁, as the diffusion constant, K, is varied. D. Region of bistability in a₀–K space, with terminal limit points as indicated.

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Table 1. Parameters used in the model of multi-substrate competition described by Eqs (12)–(16).

Ax and B_i are, respectively, the donor UDP-Gal, and the oligosaccharide acceptors shown in Fig 2. Maximal velocities of all reactions in the model were set to 5.0 and the value of the diffusion constant (K) was 0.075.

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Discussion

In this article a general equation for multi-substrate inhibition is derived, from which are deduced a number of properties of a system of reactions involving rate laws of this kind. Enzymes following a quasi-steady-state compulsory mechanism described by Eq (5) will not show this response with donor concentrations at saturating levels. While the nature of such competitive inhibition had been examined by Schnell and Mendoza [18], and our initial result was presented, without proof, by Umaña and Bailey [12], to our knowledge, this work is the first to present a derivation of a bisubstrate reaction equation with multi-substrate competition, with an extension to include substrate inhibition.

We have extended the treatment of multi-substrate enzymes obeying rapid-equilibrium random-order kinetics to systems exhibiting inhibition at high substrate concentrations. Special note was made of the additional complication of oligosaccharide acceptors, which will have multiple dissociation constants when multivalency is present, and it was shown that, in the bivalent case, an overall Michaelis constant can be predicted from the geometric mean of the individual K_m values. The work of Degn [34] was applied to the transferase enzymes acting in a system held thermodynamically far from equilibrium, and it was shown that two stable solutions can exist over a range of external substrate concentrations.

Bistability was also shown to be possible for a system of reactions catalysed by the enzyme β-N-acetylglucosaminylglycopeptide β-1,4-galactosyltransferase (GalT) evincing both multi-substrate competition and multivalency. Our model provides a possible explanation for both the compulsory-order catalytic mechanism of this enzyme, reported by Qasba et al. [24], in which the donor binds before the acceptor, and the inhibition observed by Freilich et al. at high acceptor concentrations [26]. A derivation of the random-order two-substrate mechanism, under quasi-steady-state assumptions, will lead to a 2:2 rational function that is second order with respect to the concentration of acceptor in both the numerator and denominator [37]. Although this non-Michaelian function would give rise to a velocity-substrate curve similar to that observed with substrate inhibition, it conflicts with the available evidence for the catalytic mechanism. If, as we propose here, the acceptor binds as a substrate analog of the donor, at the donor site, followed by a further acceptor-binding step to form a dead-end ternary complex, the apparent paradox is resolved.

As a biological phenomenon, bistability has previously been identified in apoptosis [38], cancer [39], disease progression [40], cell cycling [41], cell motility [42] and differentiation [43]. It has also been reported in an open reconstituted enzyme system containing phosphofructokinase [44]. Multistability is well known in the context of ultrasensitivity [45], and similar phenomena, such as cooperativity and allostery, where enzymes possess switch-like behaviour [46]. In enzyme-kinetic models, a general condition for multistability is that the rate law be a non-monotonic function of the reactant concentrations. Hence, the competitive inhibition introduced by multi-substrate competition is not a necessary, or sufficient, condition for switching behaviour; rather, it is the formation of ternary enzyme-substrate complexes that can lead to non-monotonicity in the enzyme rate law. The first derivative of such a function, possessing at least one maximum or minimum, must undergo a change of sign, as the substrate or effector concentration is varied. Since the property is shown to be possible for a single enzyme, its origin can be distinguished from that based on network topology [47] or feedback regulation [48].

Our main result is consistent with the prediction by Neelamegham and Liu [49] that bistability could arise under circumstances where Michaelis-Menten kinetics, with nonlinearities caused by large numbers of possible substrates and products, were combined with feedback/feedforward regulation. We have considered only initial rate kinetics in this study, ignoring the effects of product concentration in the derivations in the S1 Appendix, although product inhibition effects will also play a role, as can be seen at the early stages of the proof. In neglecting the product concentrations, we have constructed the system in such a way that the primary cause of the bistability is more readily apparent. The models of Shen and Larter [50], who studied the membrane-bound enzyme acetylcholinesterase, not only displayed bistability, but also oscillatory behaviour when either autocatalysis or product inhibition were included. Higher order dynamic behaviour might therefore arise if our model was expanded to incorporate the effects of product concentrations.

The conventional approach to modelling metabolism has involved the construction of systems of ordinary differential equations using kinetic rate laws appropriate to the enzymes and transporters involved, as has been the case for most models of glycosylation to date [12, 14, 51], and in the present work. Such models assume an underlying deterministic process and a detailed knowledge of the parameters, which may not be available. Another approach is to model the transitions between the reactants in a network by a Markov chain, an application of which to glycosylation has recently appeared [52]. It is known that bistability can arise within noise-driven biochemical systems operating at the level of cellular volume [53], even where it is not predicted in the deterministic limit. Multistability as a general principle, therefore, and outside of the specific application to glycosylation, can exist within either modelling framework. As the volume size of the system decreases, the rate of switching between biologically realisable steady states increases exponentially [54], which has implications for several of the phenomena cited above, such as cellular differentiation and cancer. The transitions between steady states, perturbed by stochastic fluctuations, may additionally require that the system be close to the boundaries of the basins of attraction [55].

Conclusion

These results demonstrate that the complex interplay of enzyme and substrate can give rise to nonlinear behaviour in systems of reactions held far from thermodynamic equilibrium. The significance of the present study is that small changes in one condition, such as the amount of available sugar-nucleotide donor [56], might incur large and abrupt changes in the amount of product formed. Since GalT action influences the number of sites available for sialylation, such changes should have important implications for cancer progression and metastasis, which have been shown to be related to these processes [57], and for biotechnology, such as in the production of therapeutic antibodies [58], which can be influenced through control of metabolic flux [59]. More generally, the occurrence of bistability in metabolism could provide the basis for cellular long-term memory [60]. The commonly occurring pattern of substrate inhibition in transferases should complement the already known behaviours of models based on sigmoidal functions. For instance, it is known that different glycosylation enzymes associate, and co-locate with the Golgi, according to the ‘kin recognition’ model [61], and may therefore display cooperativity. Whether a combination of cooperativity and substrate inhibition could lead to higher order dynamic behaviour, such as oscillations in acceptor concentration, is an open question that deserves further study.

Supporting information

S1 Appendix.

https://doi.org/10.1371/journal.pcbi.1006348.s001

(PDF)

References

1. Garfinkel D, Hess B. Metabolic control mechanisms. VII. A detailed computer model of the glycolytic pathway in ascites cells. J Biol Chem. 1964;239:971–983. pmid:14165947
- View Article
- PubMed/NCBI
- Google Scholar
2. Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der Weijden CC, Schepper M, et al. Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem. 2000;267(17):5313–5329. pmid:10951190
- View Article
- PubMed/NCBI
- Google Scholar
3. Hynne F, Danø S, Sørensen PG. Full-scale model of glycolysis in Saccharomyces cerevisiae. Biophys Chem. 2001;94:121–163. pmid:11744196
- View Article
- PubMed/NCBI
- Google Scholar
4. Garfinkel D. Simulation of the Krebs cycle and closely related metabolism in perfused rat liver. I. Construction of a model. Comput Biomed Res. 1971;4(1-2):1–17. pmid:4397233
- View Article
- PubMed/NCBI
- Google Scholar
5. Maher AD, Kuchel PW, Ortega F, de Atauri P, Centelles J, Cascante M. Mathematical modelling of the urea cycle. A numerical investigation into substrate channeling. Eur J Biochem. 2003;270:3953–3961. pmid:14511377
- View Article
- PubMed/NCBI
- Google Scholar
6. McDonald AG, Hayes JM, Bezak T, Głuchowska SA, Cosgrave EFJ, Struwe WB, et al. Galactosyltransferase 4 is a major control point for glycan branching in N-linked glycosylation. J Cell Sci. 2014;127:5014–5026. pmid:25271059
- View Article
- PubMed/NCBI
- Google Scholar
7. McDonald AG, Tipton KF, Davey GP. A knowledge-based system for display and prediction of O-glycosylation network behaviour in response to enzyme knockouts. PLoS Comput Biol. 2016;12(4):e1004844. pmid:27054587
- View Article
- PubMed/NCBI
- Google Scholar
8. Le Novère N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, et al. BioModels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucl Acids Res. 2006;34:D689–D691. pmid:16381960
- View Article
- PubMed/NCBI
- Google Scholar
9. Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, Miyoshi F, et al. E-CELL: software environment for whole-cell simulation. Bioinformatics. 1999;15(1):72–84. pmid:10068694
- View Article
- PubMed/NCBI
- Google Scholar
10. Wang RW, Newton DJ, Scheri TD, Lu AYH. Human cytochrome P450 3A4-catalyzed testosterone 6β-hydroxylation and erythromycin N-demethylation. Competition during catalysis. Drug Metab Dispos. 1997;25(4):502–507. pmid:9107550
- View Article
- PubMed/NCBI
- Google Scholar
11. Yandek LE, Lin HC, Harris ME. Alternative substrate kinetics of Escherichia coli ribonuclease P: determination of relative rate constants by internal competition. J Biol Chem. 2013;288(12):8342–8354. pmid:23362254
- View Article
- PubMed/NCBI
- Google Scholar
12. Umaña P, Bailey JE. A mathematical model of N-linked glycoform biosynthesis. Biotech Bioeng. 1997;55(6):890–908.
- View Article
- Google Scholar
13. Dennis JW, Nabi IR, Demetriou M. Metabolism, cell surface organization, and disease. Cell. 2009;139(7):1229–1241. pmid:20064370
- View Article
- PubMed/NCBI
- Google Scholar
14. del Val IJ, Nagy JM, Kontoravdi C. A dynamic mathematical model for monoclonal antibody N-linked glycosylation and nucleotide sugar donor transport within a maturing Golgi apparatus. Biotechnol Prog. 2011;27(6):1729–1743.
- View Article
- Google Scholar
15. Beheshti Zavareh R, Sukhai MA, Hurren R, Gronda M, Wang X, Simpson CD, et al. Suppression of cancer progression by MGAT1 shRNA knockdown. PloS ONE. 2012;7(9):e43721. pmid:22957033
- View Article
- PubMed/NCBI
- Google Scholar
16. Dixon M, Webb EC IV. In: Enzymes. 2nd ed. New York: Academic Press; 1964. p. 84.
17. Cha S. Kinetics of enzyme reactions with competing alternative substrates. Mol Pharmacol. 1968;4(6):621–629. pmid:5725576
- View Article
- PubMed/NCBI
- Google Scholar
18. Schnell S, Mendoza C. Enzyme kinetics of multiple alternative substrates. J Math Chem. 2000;27:155–170.
- View Article
- Google Scholar
19. Schäuble S, Stavrum AK, Puntervoll P, Schuster S, Heiland I. Effect of substrate competition in kinetic models of metabolic networks. FEBS Lett. 2013;587:2818–2824. pmid:23811082
- View Article
- PubMed/NCBI
- Google Scholar
20. Wragg S, Hagen FK, Tabak LA. Kinetic analysis of a recombinant UDP-N-acetyl-d-galactosamine:polypeptideN-acetylgalactosaminyltransferase. J Biol Chem. 1995;270(28):16947–16954. pmid:7622513
- View Article
- PubMed/NCBI
- Google Scholar
21. Wang T, Cook I, Falany CN, Leyh TS. Paradigms of sulfotransferase catalysis: the mechanism of SULT2A1. J Biol Chem. 2014;289(38):26474–26480. pmid:25056952
- View Article
- PubMed/NCBI
- Google Scholar
22. Ihara H, Ikeda Y, Taniguchi N. Reaction mechanism and substrate specificity for nucleotide sugar of mammalian α1,6-fucosyltransferasea large-scale preparation and characterization of recombinant human FUT8. Glycobiol. 2006;16(4):333–342.
- View Article
- Google Scholar
23. Gupta R, Matta KL, Neelamegham S. A systematic analysis of acceptor specificity and reaction kinetics of five human α(2,3)sialyltransferases: Product inhibition studies illustrate reaction mechanism for ST3Gal-I. Biochem Biophys Res Comm. 2016;469:606–612. pmid:26692484
- View Article
- PubMed/NCBI
- Google Scholar
24. Qasba PK, Ramakrishnan B, Boeggeman E. Substrate-induced conformational changes in glycosyltransferases. TIBS. 2005;30(1):53–62. pmid:15653326
- View Article
- PubMed/NCBI
- Google Scholar
25. Segel IH. In: Enzyme kinetics: behavior and analysis of rapid equilibrium and steady-state enzyme systems. New York, NY: Wiley Interscience; 1975. p. 308.
26. Freilich LS, Lewis RG, Reppucci AC Jr, Silbert JE. Galactosyl transferase of a Golgi fraction from cultured neoplastic mast cells. J Cell Biol. 1977;72:655–666. pmid:14165
- View Article
- PubMed/NCBI
- Google Scholar
27. Takeya A, Hosomi O, Ishiura M. Complete purification and characterization of α-3-N-acetylgalactosaminyltransferase encoded by the human blood group A gene. J Biochem. 1990;107:360–368. pmid:2341371
- View Article
- PubMed/NCBI
- Google Scholar
28. Chandrasekaran EV, Rhodes JM, Jain RK, Bernacki RJ, Matta KL. A biosynthetic control on structures serving as ligands for selectins: the precursor structures, 3-sialyl/sulfo Galβ1,3/4GlcNAcβ-0-R, which are high affinity substrates for α1,3/4-L-fucosyltransferases, exhibit the phenomenon of substrate inhibition. Biochem Biophys Res Comm. 1994;201(1):78–89. pmid:7515235
- View Article
- PubMed/NCBI
- Google Scholar
29. Brockhausen I, Benn M, Bhat S, Marone S, Rile JG, Montoya-Peleaz P, et al. UDP-Gal: GlcNAc-R β1,4-galactosyltransferase—a target enzyme for drug design. Acceptor specificity and inhibition of the enzyme. Glycoconj J. 2006;23:525–541. pmid:17006644
- View Article
- PubMed/NCBI
- Google Scholar
30. Ichikawa Y, Lin YC, Dumas DP, Shen GJ, Garcia-Junceda E, Williams MA, et al. Chemical-enzymatic synthesis and conformational analysis of Sialyl lewis x and derivatives. J Am Chem Soc. 1992;114:9283–9298.
- View Article
- Google Scholar
31. Paquet MR, Narasimhan S, Schachter H, Moscarello MA. Branch specificity of purified rat liver Golgi UDP-galactose:N-acetylglucosamine β-1,4-galactosyltransferase. Preferential transfer of galactose on the GlcNAcβ1,2-Manα1,3-branch of a complex biantennary Asn-linked oligosaccharide. J Biol Chem. 1984;259(8):4716–21. pmid:6425277
- View Article
- PubMed/NCBI
- Google Scholar
32. Varki A, Cummings RD, Aebi M, Packer NH, Seeberger PH, Esko JD, et al. Symbol nomenclature for graphical representations of glycans. Glycobiology. 2015;25(12):1323–1324. pmid:26543186
- View Article
- PubMed/NCBI
- Google Scholar
33. Ujita M, McAuliffe J, Suzuki M, Hindsgaul O, Clausen H, Fukuda MN, et al. Regulation of I-branched poly-N-acetyllactosamine synthesis. Concerted actions by i-extension enzyme, I-branching enzyme, and β1,4-galactosyltransferase I. J Biol Chem. 1999;274(14):9296–9304. pmid:10092606
- View Article
- PubMed/NCBI
- Google Scholar
34. Degn H. Bistability caused by substrate inhibition of peroxidase in an open reaction system. Nature. 1968;217:1047–1050. pmid:4296138
- View Article
- PubMed/NCBI
- Google Scholar
35. Ermentrout B. Simulating, Analyzing, and Animating Dynamical Systems: A Guide to XPPAUT for Researchers and Students. Philadelphia, USA: SIAM; 2002.
36. Patterson GH, Hirschberg K, Polishchuk RS, Gerlich D, Phair RD, Lippincott-Schwartz J. Transport through the Golgi apparatus by rapid partitioning within a two-phase membrane system. Cell. 2008;133(6):1055–1067. pmid:18555781
- View Article
- PubMed/NCBI
- Google Scholar
37. McDonald AG, Tipton KF. Kinetics of Catalyzed Reactions—Biological. In: Encyclopedia of Catalysis, Horváth I (Ed.); 2002. p. 424.
38. Eissing T, Conzelmann H, Gilles ED, Allgower F, Bullinger E, Scheurich P. Bistability analyses of a caspase activation model for receptor-induced apoptosis. J Biol Chem. 2004;279(35):36892–7. pmid:15208304
- View Article
- PubMed/NCBI
- Google Scholar
39. Kim D, Rath O, Kolch W, Cho KH. A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways. Oncogene. 2007;26(31):4571–9. pmid:17237813
- View Article
- PubMed/NCBI
- Google Scholar
40. Kellershohn N, Laurent M. Prion diseases: dynamics of the infection and properties of the bistable transition. Biophys J. 2001;81(5):2517–29. pmid:11606267
- View Article
- PubMed/NCBI
- Google Scholar
41. Yao G, Lee TJ, Mori S, Nevins JR, You L. A bistable Rb-E2F switch underlies the restriction point. Nat Cell Biol. 2008;10(4):476–82. pmid:18364697
- View Article
- PubMed/NCBI
- Google Scholar
42. Byrne KM, Monsefi N, Dawson JC, Degasperi A, Bukowski-Wills JC, Volinsky N, et al. Bistability in the Rac1, PAK, and RhoA signaling network drives actin cytoskeleton dynamics and cell motility switches. Cell Systems. 2016;2(1):38–48. pmid:27136688
- View Article
- PubMed/NCBI
- Google Scholar
43. Xiong W, Ferrell JE Jr. A positive-feedback-based bistable ‘memory module’ that governs a cell fate decision. Nature. 2003;426(6965):460–5. pmid:14647386
- View Article
- PubMed/NCBI
- Google Scholar
44. Eschrich K, Schellenberger W, Hofmann E. In vitro demonstration of alternate stationary states in a open enzyme system containing phosphofructokinase. Arch Biochem Biophys. 1980;205(1):114–121. pmid:6449908
- View Article
- PubMed/NCBI
- Google Scholar
45. Goldbeter A, Koshland DG Jr. An amplified sensitivity arising from covalent modification in biological systems. Proc Natl Acad Sci USA. 1981;78(11):6840–6844. pmid:6947258
- View Article
- PubMed/NCBI
- Google Scholar
46. Ferrell JE Jr, Ha SH. Ultrasensitivity part III: cascades, bistable switches, and oscillators. TIBS. 2014;39(12):612–618. pmid:25456048
- View Article
- PubMed/NCBI
- Google Scholar
47. Craciun G, Tang Y, Feinberg M. Understanding bistability in complex enzyme-driven reaction networks. Proc Natl Acad Sci USA. 2006;103(23):8697–8702. pmid:16735474
- View Article
- PubMed/NCBI
- Google Scholar
48. Angeli D, Ferrell JE Jr, Sontag ED. Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci USA. 2004;101(7):1822–1827. pmid:14766974
- View Article
- PubMed/NCBI
- Google Scholar
49. Neelamegham S, Liu G. Systems glycobiology: biochemical reaction networks regulating glycan structure and function. Glycobiology. 2011;21:1541–1553. pmid:21436236
- View Article
- PubMed/NCBI
- Google Scholar
50. Shen P, Larter R. Role of substrate inhibition kinetics in enzymatic chemical oscillations. Biophys J. 1994;67:1414–1428. pmid:7819481
- View Article
- PubMed/NCBI
- Google Scholar
51. Krambeck FJ, Bennun SV, Narang S, Choi S, Yarema KJ, Betenbaugh MJ. A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data. Glycobiol. 2009;19(11):1163–1175.
- View Article
- Google Scholar
52. Spahn PN, Hansen AH, Hansen HG, Arnsdorf J, Kildegaard HF, Lewis NE. A Markov chain model for N-linked protein glycosylation—towards a low-parameter tool for model-driven glycoengineering. Metabol Eng. 2016;33:52–66.
- View Article
- Google Scholar
53. Bishop LM, Qian H. Stochastic bistability and bifurcation in a mesoscopic signaling system with autocatalytic kinase. Biophys J. 2010;98:1–11. pmid:20074511
- View Article
- PubMed/NCBI
- Google Scholar
54. Vellela M, Qian H. Stochastic dynamics and non-equilibrium thermodynamics of a bistable chemical system: the Schlögl model revisited. J R Soc Interface. 2009;6:925–940. pmid:19095615
- View Article
- PubMed/NCBI
- Google Scholar
55. Wu M, Su RQ, Li X, Ellis T, Lai YC, Wang X. Engineering of regulated stochastic cell fate determination. Proc Natl Acad Sci USA. 2013;110(26):10610–10615. pmid:23754391
- View Article
- PubMed/NCBI
- Google Scholar
56. Lau KS, Partridge EA, Grigorian A, Silvescu CI, Reinhold VN, Demetriou M, et al. Complex N-glycan number and degree of branching cooperate to regulate cell proliferation and differentiation. Cell. 2007;129:123–134. pmid:17418791
- View Article
- PubMed/NCBI
- Google Scholar
57. Varki A. Biological roles of glycans. Glycobiology. 2017;27(1):3–49. pmid:27558841
- View Article
- PubMed/NCBI
- Google Scholar
58. Hayes JM, Wormald MR, Rudd PM, Davey GP. Fc gamma receptors: glycobiology and therapeutic prospects. J Inflamm Res. 2016;9:209–219. pmid:27895507
- View Article
- PubMed/NCBI
- Google Scholar
59. McDonald AG, Hayes JM, Davey GP. Metabolic flux control in glycosylation. Curr Opin Struct Biol. 2016;40:97–103. pmid:27620650
- View Article
- PubMed/NCBI
- Google Scholar
60. Padirac A, Fujii T, Rondelez Y. Bottom-up construction of in vitro switchable memories. Proc Natl Acad Sci USA. 2012;109(47):E3212–E3220. pmid:23112180
- View Article
- PubMed/NCBI
- Google Scholar
61. Nilsson T, Slusarewiez P, Hoe MH, Warren G. Kin recognition. A model for the retention of Golgi enzymes. FEBS Lett. 1993;330(1):1–4. pmid:8370450
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Garfinkel D, Hess B. Metabolic control mechanisms. VII. A detailed computer model of the glycolytic pathway in ascites cells. J Biol Chem. 1964;239:971–983. pmid:14165947
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der Weijden CC, Schepper M, et al. Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem. 2000;267(17):5313–5329. pmid:10951190
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Hynne F, Danø S, Sørensen PG. Full-scale model of glycolysis in Saccharomyces cerevisiae. Biophys Chem. 2001;94:121–163. pmid:11744196
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Garfinkel D. Simulation of the Krebs cycle and closely related metabolism in perfused rat liver. I. Construction of a model. Comput Biomed Res. 1971;4(1-2):1–17. pmid:4397233
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Maher AD, Kuchel PW, Ortega F, de Atauri P, Centelles J, Cascante M. Mathematical modelling of the urea cycle. A numerical investigation into substrate channeling. Eur J Biochem. 2003;270:3953–3961. pmid:14511377
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. McDonald AG, Hayes JM, Bezak T, Głuchowska SA, Cosgrave EFJ, Struwe WB, et al. Galactosyltransferase 4 is a major control point for glycan branching in N-linked glycosylation. J Cell Sci. 2014;127:5014–5026. pmid:25271059
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. McDonald AG, Tipton KF, Davey GP. A knowledge-based system for display and prediction of O-glycosylation network behaviour in response to enzyme knockouts. PLoS Comput Biol. 2016;12(4):e1004844. pmid:27054587
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Le Novère N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, et al. BioModels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucl Acids Res. 2006;34:D689–D691. pmid:16381960
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, Miyoshi F, et al. E-CELL: software environment for whole-cell simulation. Bioinformatics. 1999;15(1):72–84. pmid:10068694
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Wang RW, Newton DJ, Scheri TD, Lu AYH. Human cytochrome P450 3A4-catalyzed testosterone 6β-hydroxylation and erythromycin N-demethylation. Competition during catalysis. Drug Metab Dispos. 1997;25(4):502–507. pmid:9107550
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Yandek LE, Lin HC, Harris ME. Alternative substrate kinetics of Escherichia coli ribonuclease P: determination of relative rate constants by internal competition. J Biol Chem. 2013;288(12):8342–8354. pmid:23362254
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Umaña P, Bailey JE. A mathematical model of N-linked glycoform biosynthesis. Biotech Bioeng. 1997;55(6):890–908.
View Article
Google Scholar

[46] View Article

[47] Google Scholar

[ref13] 13. Dennis JW, Nabi IR, Demetriou M. Metabolism, cell surface organization, and disease. Cell. 2009;139(7):1229–1241. pmid:20064370
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. del Val IJ, Nagy JM, Kontoravdi C. A dynamic mathematical model for monoclonal antibody N-linked glycosylation and nucleotide sugar donor transport within a maturing Golgi apparatus. Biotechnol Prog. 2011;27(6):1729–1743.
View Article
Google Scholar

[53] View Article

[54] Google Scholar

[ref15] 15. Beheshti Zavareh R, Sukhai MA, Hurren R, Gronda M, Wang X, Simpson CD, et al. Suppression of cancer progression by MGAT1 shRNA knockdown. PloS ONE. 2012;7(9):e43721. pmid:22957033
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Dixon M, Webb EC IV. In: Enzymes. 2nd ed. New York: Academic Press; 1964. p. 84.

[ref17] 17. Cha S. Kinetics of enzyme reactions with competing alternative substrates. Mol Pharmacol. 1968;4(6):621–629. pmid:5725576
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref18] 18. Schnell S, Mendoza C. Enzyme kinetics of multiple alternative substrates. J Math Chem. 2000;27:155–170.
View Article
Google Scholar

[65] View Article

[66] Google Scholar

[ref19] 19. Schäuble S, Stavrum AK, Puntervoll P, Schuster S, Heiland I. Effect of substrate competition in kinetic models of metabolic networks. FEBS Lett. 2013;587:2818–2824. pmid:23811082
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref20] 20. Wragg S, Hagen FK, Tabak LA. Kinetic analysis of a recombinant UDP-N-acetyl-d-galactosamine:polypeptideN-acetylgalactosaminyltransferase. J Biol Chem. 1995;270(28):16947–16954. pmid:7622513
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref21] 21. Wang T, Cook I, Falany CN, Leyh TS. Paradigms of sulfotransferase catalysis: the mechanism of SULT2A1. J Biol Chem. 2014;289(38):26474–26480. pmid:25056952
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref22] 22. Ihara H, Ikeda Y, Taniguchi N. Reaction mechanism and substrate specificity for nucleotide sugar of mammalian α1,6-fucosyltransferasea large-scale preparation and characterization of recombinant human FUT8. Glycobiol. 2006;16(4):333–342.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref23] 23. Gupta R, Matta KL, Neelamegham S. A systematic analysis of acceptor specificity and reaction kinetics of five human α(2,3)sialyltransferases: Product inhibition studies illustrate reaction mechanism for ST3Gal-I. Biochem Biophys Res Comm. 2016;469:606–612. pmid:26692484
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref24] 24. Qasba PK, Ramakrishnan B, Boeggeman E. Substrate-induced conformational changes in glycosyltransferases. TIBS. 2005;30(1):53–62. pmid:15653326
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref25] 25. Segel IH. In: Enzyme kinetics: behavior and analysis of rapid equilibrium and steady-state enzyme systems. New York, NY: Wiley Interscience; 1975. p. 308.

[ref26] 26. Freilich LS, Lewis RG, Reppucci AC Jr, Silbert JE. Galactosyl transferase of a Golgi fraction from cultured neoplastic mast cells. J Cell Biol. 1977;72:655–666. pmid:14165
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref27] 27. Takeya A, Hosomi O, Ishiura M. Complete purification and characterization of α-3-N-acetylgalactosaminyltransferase encoded by the human blood group A gene. J Biochem. 1990;107:360–368. pmid:2341371
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref28] 28. Chandrasekaran EV, Rhodes JM, Jain RK, Bernacki RJ, Matta KL. A biosynthetic control on structures serving as ligands for selectins: the precursor structures, 3-sialyl/sulfo Galβ1,3/4GlcNAcβ-0-R, which are high affinity substrates for α1,3/4-L-fucosyltransferases, exhibit the phenomenon of substrate inhibition. Biochem Biophys Res Comm. 1994;201(1):78–89. pmid:7515235
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref29] 29. Brockhausen I, Benn M, Bhat S, Marone S, Rile JG, Montoya-Peleaz P, et al. UDP-Gal: GlcNAc-R β1,4-galactosyltransferase—a target enzyme for drug design. Acceptor specificity and inhibition of the enzyme. Glycoconj J. 2006;23:525–541. pmid:17006644
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref30] 30. Ichikawa Y, Lin YC, Dumas DP, Shen GJ, Garcia-Junceda E, Williams MA, et al. Chemical-enzymatic synthesis and conformational analysis of Sialyl lewis x and derivatives. J Am Chem Soc. 1992;114:9283–9298.
View Article
Google Scholar

[108] View Article

[109] Google Scholar

[ref31] 31. Paquet MR, Narasimhan S, Schachter H, Moscarello MA. Branch specificity of purified rat liver Golgi UDP-galactose:N-acetylglucosamine β-1,4-galactosyltransferase. Preferential transfer of galactose on the GlcNAcβ1,2-Manα1,3-branch of a complex biantennary Asn-linked oligosaccharide. J Biol Chem. 1984;259(8):4716–21. pmid:6425277
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref32] 32. Varki A, Cummings RD, Aebi M, Packer NH, Seeberger PH, Esko JD, et al. Symbol nomenclature for graphical representations of glycans. Glycobiology. 2015;25(12):1323–1324. pmid:26543186
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref33] 33. Ujita M, McAuliffe J, Suzuki M, Hindsgaul O, Clausen H, Fukuda MN, et al. Regulation of I-branched poly-N-acetyllactosamine synthesis. Concerted actions by i-extension enzyme, I-branching enzyme, and β1,4-galactosyltransferase I. J Biol Chem. 1999;274(14):9296–9304. pmid:10092606
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref34] 34. Degn H. Bistability caused by substrate inhibition of peroxidase in an open reaction system. Nature. 1968;217:1047–1050. pmid:4296138
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref35] 35. Ermentrout B. Simulating, Analyzing, and Animating Dynamical Systems: A Guide to XPPAUT for Researchers and Students. Philadelphia, USA: SIAM; 2002.

[ref36] 36. Patterson GH, Hirschberg K, Polishchuk RS, Gerlich D, Phair RD, Lippincott-Schwartz J. Transport through the Golgi apparatus by rapid partitioning within a two-phase membrane system. Cell. 2008;133(6):1055–1067. pmid:18555781
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref37] 37. McDonald AG, Tipton KF. Kinetics of Catalyzed Reactions—Biological. In: Encyclopedia of Catalysis, Horváth I (Ed.); 2002. p. 424.

[ref38] 38. Eissing T, Conzelmann H, Gilles ED, Allgower F, Bullinger E, Scheurich P. Bistability analyses of a caspase activation model for receptor-induced apoptosis. J Biol Chem. 2004;279(35):36892–7. pmid:15208304
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref39] 39. Kim D, Rath O, Kolch W, Cho KH. A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways. Oncogene. 2007;26(31):4571–9. pmid:17237813
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref40] 40. Kellershohn N, Laurent M. Prion diseases: dynamics of the infection and properties of the bistable transition. Biophys J. 2001;81(5):2517–29. pmid:11606267
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref41] 41. Yao G, Lee TJ, Mori S, Nevins JR, You L. A bistable Rb-E2F switch underlies the restriction point. Nat Cell Biol. 2008;10(4):476–82. pmid:18364697
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref42] 42. Byrne KM, Monsefi N, Dawson JC, Degasperi A, Bukowski-Wills JC, Volinsky N, et al. Bistability in the Rac1, PAK, and RhoA signaling network drives actin cytoskeleton dynamics and cell motility switches. Cell Systems. 2016;2(1):38–48. pmid:27136688
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref43] 43. Xiong W, Ferrell JE Jr. A positive-feedback-based bistable ‘memory module’ that governs a cell fate decision. Nature. 2003;426(6965):460–5. pmid:14647386
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref44] 44. Eschrich K, Schellenberger W, Hofmann E. In vitro demonstration of alternate stationary states in a open enzyme system containing phosphofructokinase. Arch Biochem Biophys. 1980;205(1):114–121. pmid:6449908
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref45] 45. Goldbeter A, Koshland DG Jr. An amplified sensitivity arising from covalent modification in biological systems. Proc Natl Acad Sci USA. 1981;78(11):6840–6844. pmid:6947258
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref46] 46. Ferrell JE Jr, Ha SH. Ultrasensitivity part III: cascades, bistable switches, and oscillators. TIBS. 2014;39(12):612–618. pmid:25456048
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref47] 47. Craciun G, Tang Y, Feinberg M. Understanding bistability in complex enzyme-driven reaction networks. Proc Natl Acad Sci USA. 2006;103(23):8697–8702. pmid:16735474
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref48] 48. Angeli D, Ferrell JE Jr, Sontag ED. Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci USA. 2004;101(7):1822–1827. pmid:14766974
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref49] 49. Neelamegham S, Liu G. Systems glycobiology: biochemical reaction networks regulating glycan structure and function. Glycobiology. 2011;21:1541–1553. pmid:21436236
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref50] 50. Shen P, Larter R. Role of substrate inhibition kinetics in enzymatic chemical oscillations. Biophys J. 1994;67:1414–1428. pmid:7819481
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref51] 51. Krambeck FJ, Bennun SV, Narang S, Choi S, Yarema KJ, Betenbaugh MJ. A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data. Glycobiol. 2009;19(11):1163–1175.
View Article
Google Scholar

[185] View Article

[186] Google Scholar

[ref52] 52. Spahn PN, Hansen AH, Hansen HG, Arnsdorf J, Kildegaard HF, Lewis NE. A Markov chain model for N-linked protein glycosylation—towards a low-parameter tool for model-driven glycoengineering. Metabol Eng. 2016;33:52–66.
View Article
Google Scholar

[188] View Article

[189] Google Scholar

[ref53] 53. Bishop LM, Qian H. Stochastic bistability and bifurcation in a mesoscopic signaling system with autocatalytic kinase. Biophys J. 2010;98:1–11. pmid:20074511
View Article
PubMed/NCBI
Google Scholar

[191] View Article

[192] PubMed/NCBI

[193] Google Scholar

[ref54] 54. Vellela M, Qian H. Stochastic dynamics and non-equilibrium thermodynamics of a bistable chemical system: the Schlögl model revisited. J R Soc Interface. 2009;6:925–940. pmid:19095615
View Article
PubMed/NCBI
Google Scholar

[195] View Article

[196] PubMed/NCBI

[197] Google Scholar

[ref55] 55. Wu M, Su RQ, Li X, Ellis T, Lai YC, Wang X. Engineering of regulated stochastic cell fate determination. Proc Natl Acad Sci USA. 2013;110(26):10610–10615. pmid:23754391
View Article
PubMed/NCBI
Google Scholar

[199] View Article

[200] PubMed/NCBI

[201] Google Scholar

[ref56] 56. Lau KS, Partridge EA, Grigorian A, Silvescu CI, Reinhold VN, Demetriou M, et al. Complex N-glycan number and degree of branching cooperate to regulate cell proliferation and differentiation. Cell. 2007;129:123–134. pmid:17418791
View Article
PubMed/NCBI
Google Scholar

[203] View Article

[204] PubMed/NCBI

[205] Google Scholar

[ref57] 57. Varki A. Biological roles of glycans. Glycobiology. 2017;27(1):3–49. pmid:27558841
View Article
PubMed/NCBI
Google Scholar

[207] View Article

[208] PubMed/NCBI

[209] Google Scholar

[ref58] 58. Hayes JM, Wormald MR, Rudd PM, Davey GP. Fc gamma receptors: glycobiology and therapeutic prospects. J Inflamm Res. 2016;9:209–219. pmid:27895507
View Article
PubMed/NCBI
Google Scholar

[211] View Article

[212] PubMed/NCBI

[213] Google Scholar

[ref59] 59. McDonald AG, Hayes JM, Davey GP. Metabolic flux control in glycosylation. Curr Opin Struct Biol. 2016;40:97–103. pmid:27620650
View Article
PubMed/NCBI
Google Scholar

[215] View Article

[216] PubMed/NCBI

[217] Google Scholar

[ref60] 60. Padirac A, Fujii T, Rondelez Y. Bottom-up construction of in vitro switchable memories. Proc Natl Acad Sci USA. 2012;109(47):E3212–E3220. pmid:23112180
View Article
PubMed/NCBI
Google Scholar

[219] View Article

[220] PubMed/NCBI

[221] Google Scholar

[ref61] 61. Nilsson T, Slusarewiez P, Hoe MH, Warren G. Kin recognition. A model for the retention of Golgi enzymes. FEBS Lett. 1993;330(1):1–4. pmid:8370450
View Article
PubMed/NCBI
Google Scholar

[223] View Article

[224] PubMed/NCBI

[225] Google Scholar

Figures

Abstract

Author summary

Introduction

Methods

Theoretical development

Results

Inhibition at high substrate concentrations

General observations on glycosyltransferase networks

Inhibition at high concentrations of acceptor

Bistability in an open system

A model of multiple competing substrates with inhibition at high concentrations

Discussion

Conclusion

Supporting information

S1 Appendix.

References