We explore a framework to model the dose response of allosteric multisite phosphorylation proteins using a single auxiliary variable. This reduction can closely replicate the steady state behavior of detailed multisite systems such as the Monod-Wyman-Changeux allosteric model or rule-based models. Optimal ultrasensitivity is obtained when the activation of an allosteric protein by its individual sites is concerted and redundant. The reduction makes this framework useful for modeling and analyzing biochemical systems in practical applications, where several multisite proteins may interact simultaneously. As an application we analyze a newly discovered checkpoint signaling pathway in budding yeast, which has been proposed to measure cell growth by monitoring signals generated at sites of plasma membrane growth. We show that the known components of this pathway can form a robust hysteretic switch. In particular, this system incorporates a signal proportional to bud growth or size, a mechanism to read the signal, and an all-or-none response triggered only when the signal reaches a threshold indicating that sufficient growth has occurred.
Protein phosphorylation is a common form of post-translational modification frequently used in nature to alter protein activity, for instance by changing the electrostatic properties of the protein or its spatial structure. The phosphorylation of the same protein at multiple different aminoacid residues is also very common, and it is found in proteins such as p53 [1], Sic1 [2], EGFR [3], Wee1 [4], Ste5 [5] and many others [6]. The differences in the function of single-site vs. multisite phosphorylation are not completely understood. Many multisite proteins are involved in regulatory processes that can benefit from the presence of bistability, hysteresis, or limit cycles, which require sufficiently nonlinear interactions in addition to the right type of feedback [7], [8]. A reasonable hypothesis is that multisite phosphorylation can give rise to ultrasensitive dose responses, in a way that would not be possible in a comparable single-site system [9]–[13]. Many detailed mechanisms have also been proposed to explain the role of multisite systems in the emergence of bistability (for examples, see [14], [15]). On the other hand, such detailed multisite mechanisms are normally not used as part of actual mathematical models of biochemical interactions. This is because explicitly modeling multiple sites usually involves the introduction of numerous variables, one or more for each phosphorylation state, and realistic models are often too complex already to justify this additional effort. Systems that attempt to model biochemical reactions explicitly often use the assumption that the protein has two states, one active and one inactive, with a simple reaction to transform one into the other, effectively assuming that the protein only has one site. Other models are more phenomenological in nature and include, for example, Hill function terms in the equation that are less clearly tied to the actual biochemical reactions [16], [17]. In this paper we describe a simple mechanistic approach for modeling multisite allosteric proteins. This approach, named modified fraction (MF) modeling, is capable of describing ultrasensitive dynamics without introducing a large number of additional variables. Under this framework one keeps track of the fraction of modified sites in the protein, and the concentration of active protein over time is estimated from this information. The protein is activated in a way that requires the phosphorylation of several but not all of the sites. The approximation becomes increasingly precise as the number of sites increases, with good estimates already for around four or more sites. In a sense, this mechanism can be considered a one-variable, quasi-steady state reduction of a model similar to the Monod-Wyman-Changeux allosteric system [18], although uses of MF modeling outside of MWC are also possible. The MF framework can also be extended to other types of multisite modification such as ligand binding, multisite transcription factor regulation, multisite methylation or acetylation, ubiquitination, etc [19], [20]. Perhaps the best way to test the versatility of a modeling tool such as the one proposed is to implement it in an actual biochemical system. In the current work we describe a detailed mechanistic model of a cell size checkpoint. Cell size checkpoints halt the cell cycle at specific points until sufficient cell growth has occurred [21], [22]. The mechanisms by which cell size checkpoints operate are poorly understood, and it is unclear whether they monitor actual cell size or parameters more closely related to the extent or rate of growth. In budding yeast, growth of a new cell is initiated when a daughter bud is formed on the surface of the cell [23]. The daughter bud initially grows in a polar manner, with all growth directed to the bud tip. Growth of the bud eventually switches to isotropic growth, in which the bud grows over its entire surface (see Figure 1A) [24]. The timing of the switch determines the duration of polar growth, which influences cell size and shape. It has been proposed in recent work by one of the authors that a cell size checkpoint controls the timing of this switch [25]. This checkpoint is the subject of our model. The variables are illustrated in Figure 1B and described in more detail below. See Tyson and Novak [26] for an accessible introduction to the systems-wide modeling of cell cycle checkpoints. The MF approximation equation was first developed in [27], in the context of multisite systems with independent modification sites, with an emphasis on the estimation of the Hill exponents of sequential and nonsequential systems and on the comparison of their qualitative behavior. A major advance of the current paper, beyond the application to the cell cycle checkpoint, is to extend this work to cooperative and allosteric systems. Such systems are by definition non-independent, since the modification of one site accelerates the rate of modification of its neighbors. Cooperative systems are also more common and much better characterized than independent ones. The validation of the approximation in cooperative systems is ultimately based on a computational comparison of the MF reduction with detailed cooperative models having or variables. In the first two Results sections we carry out a description of the modified fraction method to model multisite systems, and we compare simulations of the reduced model with those of a detailed mechanistic model. In the remaining two Results sections we carry out a mathematical analysis of the proposed checkpoint signaling pathway. We hypothesize that this interaction pathway has the capacity to produce a bistable signal responsible for a sudden switch from polar to isotropic growth, once the bud has undergone sufficient polar growth. The model presents several desirable qualities for a checkpoint, in particular a clear downstream signal when a sufficient polar bud growth has occurred.
We start by describing the assumptions on our model in the context of multisite phosphorylation (although it could also be applied to other irreversible covalent modifications as well as noncovalent ligand binding). Suppose that a protein substrate is phosphorylated by a kinase at possible sites and dephosphorylated by a phosphatase. The system is assumed to be nonsequential, so that there are different phosphoforms of, and the number of sites is thought to be relatively large e. g. . The system is cooperative in the sense that site phosphorylation accelerates the phosphorylation of neighboring sites. Since the number of sites is relatively large, the activation is thought to be cumulative and the effect of any individual site is assumed to be small. The sites are assumed to be equivalent to each other, in the sense that the rate of phosphorylation and dephosphorylation is similar across all sites and that no site has a stronger effect on substrate activation than other sites. The activation of the substrate may be due to binding to another molecule or body, such as the cell membrane. It could also be due to an internal structural change that allows the substrate to interact differently with other proteins. Thus the active protein concentration can be defined as the concentration of the protein bound to a particular molecule or in a particular molecular state. Suppose that the phosphorylations lead to the concerted, redundant activation of the protein. That is, multiple phosphorylations are necessary for activation (concerted), and not all sites need to be phosphorylated for full activation (redundant). We define the activity function such that the fraction of active protein with phosphorylated sites is given by. There are many systems that likely fall within this general framework. For instance, Ste5 is a scaffold protein in budding yeast with phosphorylation sites, which relays a pheromone response only when it is bound to the membrane [28], [29]. When phosphorylated by Cdk1, it tends to unbind from the membrane, shutting down its activity. The sites are predicted to lie on an unstructured region of the protein and appear to act by changing the protein' s bulk electrostatic properties. In the paper [29], it was shown through site-directed mutagenesis that around five or more phosphorylations are necessary and sufficient for deactivation. Another recent example is the multisite phosphorylation of Cdc25 by Cdk1 in fission yeast, which was similarly studied in detail by mutating individual sites [30]. According to the modified fraction framework, we estimate the concentration of a particular protein state from the overall fraction of modified sites. For instance, if the protein has sites and the fraction of phosphorylated sites is, then the fraction of protein with only the first and last sites phosphorylated is roughlyHere is the total protein concentration. This is not an equality since cooperative effects introduce correlations among the sites, i. e. the sites are not independent of each other, but it is an approximation assuming cooperative effects are sufficiently weak. Multiplying on both sides by and adding over all possible phosphoforms with phosphorylations, the concentration of proteins with exactly phosphorylated sites out of a total of sites isOne can estimate the overall concentration of active protein asA key aspect of this formula is that if we denote the right hand side by, it can be shown that converges to as increases [31]. This gives the approximation (1) which becomes increasingly precise for large. Notice that the different quantities in this formula can potentially be measured in the lab - the active protein concentration via an activity assay, the total protein concentration via Western blot, and the activity function through site-directed mutagenesis. A timescale decomposition argument can be made to use this approximation away from steady state. If is the fraction of active sites over time, and the timescale of protein activation is much faster than the rate at which changes, then one can approximate at any given time using the same formula. This produces a convenient method for modeling multisite systems under the given assumptions by keeping track of the variable, without creating, let alone variables. On the other hand, if, or any other process affecting protein activation, is at least as fast as protein activation itself, then nontrivial dynamics might take place such as limit cycle oscillations, and the approximation can introduce errors. It is necessary to calculate the fraction of phosphorylation itself. Assuming linear rates of phosphorylation and dephosphorylation for, one obtains the system That is, , where is the fraction of inactive sites. This is the default for the model, although any other rate equation for can be used, including Michaelis-Menten complex formation at the level of the individual sites. As for the activity function, any sigmoidal function can be used, including functions measured directly by experiments. By default we assume the following form, which we will derive in the next section: See also [5], [9], [27] for other uses and derivations of this formula in the literature. The function can actually be highly switch-like for large, which illustrates how small increases in the kinase can result in large activity changes in the protein, unlike linear rate models with only one site. Other forms for the activity function have effectively been considered by by Kapuy et al [32], and also by Wang et al [10]. In Figure 2A we show the relationship between the fraction of phosphorylated sites and the active protein concentration for a particular choice of the MF parameters using the approximation formula (and chosen to fit the detailed model described in the next section for). Notice that the activation is concerted and redundant, in that a minimal threshold of phosphorylation is required for activation, and activation is reached for less than full phosphorylation. A validation of the performance of this model for is now shown in Figure 2 in the context of a system similar to the classical and widely used Monod-Wyman-Changeux model of an allosteric multisite protein [18]. The original MWC model describes the binding of oxygen to the different sites of hemoglobin and the allosteric transitions of this protein between two different states. Rather than modeling oxygen binding, a protein with phosphorylations is assumed to change between an active conformation and an inactive conformation. Each of these forms can also be phosphorylated or dephosphorylated at the rates given in the diagram in Figure 2B. Although the model is interpreted in a different way from MWC, from a mathematical point of view it is almost identical. The coefficient accounts for an assumption that the protein is phosphorylated at a faster rate when it is active than when it is inactive. The coefficients etc represent the fact that this is still a nonsequential model: for instance, can be phosphorylated at different sites, so the phosphorylation rate is multiplied by. See the derivation of this model from more basic principles in Text S1, and a recent review on multisite systems by one of the authors [28]. Using this multisite model, we now derive parameters for a corresponding MF model. For instance, one can define the ‘average’ phosphorylation rate at a given phosphorylation site, regardless of whether the protein is active or inactive, , and the dephosphorylation rate. By way of derivation of the activity function, suppose that a protein with phosphorylations is switching between active and inactive form, At steady state, we assume that this exchange is balanced and calculate. Then the fraction of active sites with phosphorylations at steady state isIn other words, where and. See Text S1. 3 for more details. In particular, the ultrasensitive behavior of the function generally increases with the number of sites. At any given time, the active protein concentration of the full model is defined as. In Figure 2C we compare the full 12-variable MWC model for with the corresponding MF approximation. For every value of the input kinase concentration, the resulting concentration of active protein is plotted at steady state. Notice the close similarity between the two graphs, which is even more surprising since MF is essentially a one-variable model. For comparison, we also plot the behavior of an overly simplified but all too commonly used model, in which the substrate is assumed to have a single phosphorylation site instead of sites, and it is modeled according to the reactionusing linear reaction rates. Notice that the behavior of this single-site model in Figure 2C is very different from that of the MWC model, and that any switch-like behavior in the response is lost. This can have important consequences regarding the existence of multiple steady states, hysteresis, oscillations etc in the context of larger systems, which will be illustrated below. It is easy to show that, i. e. it corresponds to when. It should be noted that if the single site system is modeled using Michaelis-Menten reactions rather than linear rates, it could have strongly ultrasensitive behavior in the saturation regime via zero-order ultrasensitivity [33]; see the Discussion section for more details. We carried out a calculation of the distance between and for many different combinations of the number of sites and the allosteric parameter. For every such set of parameters, the two graphs were plotted at steady state as a function of, and the error was calculated in Figure 2D. Notice that the approximation is within 1% precision for arbitrary and. On the other hand, in order to obtain high ultrasensitivity it is required that be relatively large and/or be small (Figure 2E). See also Figure S2, where additional parameter variations are explored over four orders of magnitude using the same type of graphs, with similar results. It is worth comparing this methodology with the approach known in the literature as rule-based modeling, where a series of chemical reactions is defined using a streamlined algorithm, and high-powered computing is used to handle the resulting large number of variables; see e. g. BioNetGen [34]. The advantage of this method is that a large number of reactions can be defined and handled this way, including complex parameter optimizations. One disadvantage is that the combinatorial explosion resulting from combining reactions can sometimes exceed the computational power. Another is that the large number of equations makes any mathematical analysis difficult, if at all possible. It is interesting that the MWC model can actually be described in terms of rule-based modeling. In Figure S1A we describe a series of chemical reactions, over all possible phosphoform states, and we show in Text S1 that this system is in fact equivalent to the MWC model. Thus MF can also be seen as the 1-variable reduction of a system with variables and a much larger number of reactions. In this section, we will embed the MF system within increasingly complex systems of equations. We consistently use upper case for proteins and lower case for modified fractions of sites. However, we will first provide some technical experimental background regarding this specific pathway. In order to find the steady states of both subsystems together, recall that each one can be reduced to a single equation, so that the steady states correspond to the joint solution of the two equations. For fixed, the solutions of the full model form the intersection of the graphs for the equations (2), (3). This is illustrated in Figure 3F, where the graphs in Figure 3C and Figure 3E are superimposed on the same plane. From a control perspective, the upstream and downstream systems have each an input and an ouptut, and they feed back into each other (see the two dotted boxes in Figure 1B). The active Zds1/PP2A dimer also acts as the overall output of the system, since it triggers the downstream response to cell cycle regulatory proteins. Although it is natural that an increase in the Rho1 flow can eventually trigger the activation of the pathway, the main focus here is not in the flow but in the overall Rho1 concentration at the bud tip. Given that Rho1 has a rate of growth proportional to and a linear rate of degradation, at steady state one can show that and are proportional, . This follows from adding the ODE rate equations at steady state, . In this way one can use the total Rho1 concentration as a bifurcation parameter at steady state even though it is simultaneously a variable in the system. Alternatively, since enzymatic reactions and dimer formation are fast processes compared with Rho1 flux and Rho1 degradation, one can let be the slow variable in the system and carry out a timescale decomposition analysis using as a constant [39]. Let' s look at how the system has a hysteretic response for increasing values of the flow signal and the corresponding total Rho1 concentration at steady state. An increase in these values has the effect of raising the graph associated with the upstream system, as shown in Figure 4A. For smaller values of, the intersection of both graphs includes three positive steady states (notice the two graphs don' t quite intersect at the origin). But when increases over a certain threshold, the intersection of the two graphs contains a single positive steady state, with a large value of. This can cause an abrupt change in the qualitative behavior of the system, triggering a sudden increase in the Zds1/PP2A output. Once this change has taken place, the concentration of the output stays high even if the input decreases. Figure 4B shows a sample timecourse of the system for a time-variable vesicle flow (dotted line). The total Rho1 concentration increases over time with the inflow of vesicles. At a certain timepoint the active Rho1 concentration abruptly increases, due to the switch at the Pkc1/Rho1 upstream level. An increase in Rho1/Pkc1 concentration some time before this can be seen in Figure 4C. At a later time the switch between Zds1 and PP2A is also triggered, leading to a sudden increase in PP2A/Zds1 concentration. Even under variable flow, the total Rho1 concentration roughly corresponds to the membrane accumulated at the bud, except for a certain amount of variability due to Rho1 degradation. Lowering the Rho1 degradation rate can decrease this difference. Notice that the vesicle flow oscillations do not correspond to cell division, but to oscillations in the rate of growth, for instance due to varying food availability. In Figure 4D we plot the output signal PP2A/Zds1 as a function of total Rho1 at steady state and overlay the solution of the timecourse simulation (red stars). This graph also illustrates the hysteretic behavior of the system, in that once a critical threshold of Rho1 concentration is reached, the output signal is dramatically increased. This change would constitute a clear signal that the bud has reached a large enough size for crossing the polar/isotropic growth checkpoint. Since both the downstream (PP2A, Zds1) switch and the upstream (Rho1, Pkc1) switch are driven by positive feedback loops, it is valid to ask which of the two loops is more relevant for the overall system behavior. We argue that it is the downstream loop that is more essential, using the bifurcation analysis in Figure 3 and Figure 4A. If the upstream system is not bistable but has a single steady state for every input, then the graph in Figure 3E is replaced by a single-valued decreasing function. Nevertheless this (green) line can still have one or three intersections with the (blue) downstream dose response in Figure 4A, indicating hysteresis for the overall system. On the other hand if the downstream system is not bistable, then the blue curve in Figure 4A is replaced by a single-valued, increasing function, which would be unlikely to have three intersection points with the (green) upstream dose response. Thus the downstream switch is essential, while the upstream switch is not. Notice that this system contains the standard elements of a signal transduction pathway, including an initiating signal (vesicles), a sensor (Rho1), a series of transducers (Pkc1, PP2A, etc), and an effector (active Zds1/PP2A). Total Rho1 is a proxy for the membrane concentration, even if bud growth slows for a period of time, and the cascade of reactions allows the signal to be transduced from the membrane to Zds1/PP2A and ultimately the cyclin dependent kinase. To ensure the high fidelity of the signal transmission [40], the downstream signal is sent abruptly after total Rho1 concentration reaches a particular size. Notice that longer periods of inactivity can potentially reduce the Rho1 concentration significantly – one possible prediction is that after such a period the bud grows longer than expected.
In this paper we have introduced a simple and compact framework to describe the dynamics of allosteric multisite phosphorylation systems, and we have applied this tool to a new molecular model of a size checkpoint in budding yeast. Multisite phosphorylation modeling can be problematic because ignoring the multiple sites can have significant effects in the dynamics, while introducing many auxiliary phosphoform variables can be cumbersome in more realistic models. The modified fraction approach is intuitive and flexible (model the sites instead of the protein), and it only introduces one additional variable per protein. The components of the MF model, namely the function and the rates of phosphorylation and dephosphorylation of individual sites, can potentially be subject to direct experimental measurement, unlike the use of more abstract Hill function terms. This can allow to carry out ‘raw-data modeling’ e. g. to use an experimentally measured activity function directly in the model rather than using it to derive parameters. This methodology is also useful out of equilibrium when the timescale of phosphorylation is sufficiently fast compared with other timescales in the system. There are several reasons why the MF method might be particularly suitable for modeling many multisite phosphorylation systems. Nonsequential phosphorylation is likely more common in nature than the more often modeled sequential systems, since enforcing sequential phosphorylations would require an additional mechanistic effort. Bioinformatic data suggests that most phosphorylation sites in multisite proteins are located in unstructured and unconserved protein regions [6], suggesting that often it is the collective effect that matters rather than the individual sites. There is also experimental evidence in yeast signal transduction that certain proteins, such as Ste5, are activated in a concerted and redundant manner, although this type of information is still unknown for most proteins. Notice that the approximation formula would still hold if the protein activation is not concerted or redundant. In that case the formula will just approximate a dose response that may not be ultrasensitive. One of the best known mechanisms for ultrasensitive dose responses is zero-order ultrasensitivity, as suggested by Goldbeter and Koshland [33], [41]. Its main assumption is that substrate concentration needs to be in the saturation regime i. e. large compared to the value of the enzymes. The MF method does not pose any constraint on, in fact the linear regime we used can be found when substrate concentrations are small compared to values. Moreover, MF also applies when the enzymatic reactions involve complex formation, by writing a Michaelis-Menten equation for. Therefore zero-order ultrasensitivity can be used in synergy with MF in the saturation regime, and MF can be used regardless of value. A zero-order dose response could likely replicate the behavior of the MWC model as shown in Figure 2C, however it could not be considered a short hand notation for MWC since the two mechanisms are fundamentally different. In the case of the checkpoint pathway, the active proteins Pkc1 and PP2A have been found to have an approximate of 0. 5 [42] and 1. 2 [43], respectively, for specific targets. The overall concentrations of their substrates in the cell are much lower - however these proteins tend to localize at the bud, so that the resulting local concentrations are unknown and it is unclear whether a zero-order approach would apply. A recent paper by Martins and Swain [44] points out that zero-order ultrasensitivity often results from low enzyme to substrate ratios, and localized proteins that act as enzymes and substrates for each other would likely not satisfy such ratios. That paper proposes instead a mechanism involving an allosteric model analogous to MWC, using enzyme sequestration to obtain ultrasensitivity. The paper by Kapuy et al [32] also proposes a mechanism for bistability through ultrasensitive effects, and this mechanism is applied to a detailed model of the budding yeast G1-S transition in Barik et al [45]. Other mechanisms for ultrasensitivity involve competition among substrates for the same enzyme [46] and protein localization [12], among others [28], [47]. More generally, in cell regulatory networks there is a need to implement nontrivial dynamics such as bistable switches and hysteresis, which requires some form of nonlinear response in addition to the right feedback interconnections. It has been observed that several regulatory proteins have multiple phosphorylation sites, and there are many open questions regarding their intended function. Together with the onerous nature of modeling several multisite proteins using sequential networks and multiple variables each, it can be seen why a one-variable reduction such as MF can allow for much-needed simplicity. The actual mechanisms regulating the interactions between cell size and cell division remain largely unanswered in many cases. This has left few alternative options apart from somewhat heuristic approaches in otherwise very detailed models, see e. g. [48]. The present model is an attempt, based on recent experiments, to construct a detailed mechanistic model in the context of the polar to isotropic bud transition in yeast. Notice that if the proteins PP2A, Pkc1, Zds1 had only one site each, then according to the argument in the first Results section, and the downstream and upstream models could never be bistable (see equations (2) and (3) ). The multiple sites are providing the underlying nonlinearity so that the models can have interesting dynamical behaviors. This is consistent with the work by Yang et al [49], which reached the same conclusion through randomized parameter searches in multisite cell cycle models. Also, the equations (2), (3), which represent the steady states of the downstream and upstream systems, have the same qualitative behavior for a wide range of parameters. In this sense one can say that the switch-like nature of the checkpoint is robust to many parameter changes, provided that a few key qualities are satisfied. The bistability in each subsystem is due in part to positive feedback loops in each subsystem, one between Pkc1 and the Rho1/Pkc1 dimer, and another between Zds1 and the PP2A/Zds1 dimer. Notice that while Rho1/Pkc1 activates PP2A, the downstream PP2A/Zds1 inactivates Pkc1, forming a negative feedback loop. This feedback could serve to reduce the activity of the pathway before a sufficient Rho1 signal has accumulated. Notice that the switch-like activation of Cdk1 is a complex process that may well be regulated by other mechanisms in conjunction with the switch discussed, and that this overall regulation also depends on the organism studied. The MF framework eliminates several parameters such as the number of phosphorylation sites (as long as it is sufficiently large), the transition rates and the cooperativity coefficient. The remaining parameters, such as the shape of the activity function, can potentially be measured in the lab using site-directed mutagenesis and activity assays. However this is still a formidable task and one that is yet to be done for most proteins involved in cell cycle regulation. Since it is assumed in the derivation of the formula (1) that the sites are roughly independent from each other, one might think that the MF framework doesn' t work for allosteric or cooperative systems. However the detailed model in Figure 2 is allosteric, and yet the model closely describes its dynamics. In simulations we find that the accuracy of the representation is increased when is large (e. g.) and/or the cooperativity is weak. The use of a MWC-type model for multisite phosphorylation has been pointed out in the past, see for instance [5] and the more recent [44]. Questions for future work include the following: if phosphorylation and dephosphorylation of the multisite protein is not faster than other processes in the system, can one still approximate away from equilibrium? This might be possible by defining a simple differential equation for instead of the algebraic equation (1). Also, the linear dynamics used to calculate the fraction can be replaced by more complex models such as a Michaelis-Menten reaction, which may be explored in detail, including the interaction with zero-order mechanisms. This might lead to bistable behavior in the full multisite model, which raises the question of how the corresponding model reduction might be, possibly involving multivalued functions.
Since most quantitative information about the pathway is unknown, we make educated estimates on the order of magnitude of the parameters. Since parameters are clustered in equations (2) and (3), dependence on the parameters is more limited. Protein concentrations usually range from 0. 001 to 10 in the cell. The concentrations of total PP2A (), Zds1 () and Pkc1 () are set between 0. 01 and 0. 1 as indicated in Table 1. Define for all. The dissociation rate has been observed to be quite low in experiments since most Zds1 has been found bound to PP2A. We set it as 0. 001, which is in the range of drugs binding to their target proteins. is set higher at 0. 1. The unit-less parameters are set to 0. 1 and 1 respectively, indicating the steady state ratio of inactive to active substrate when the two antagonistic enzymes are in similar concentration. The rates are set to 0. 01, indicating that when e. g. there are equal amounts of active and inactive substrate at steady state. is set at 0. 0002. Very little is known about the values of the individual rates. Fortunately as it is shown in the analysis in Text S1, most of the dynamic rate constants appear only in the form, instead of individually. These steady state ratios are generally easier to estimate experimentally than the individual parameters [50]. However the actual rates determine the transient behavior of the system and to some extent determine also its steady state values. Since a majority of the reverse rates share the same units of, we set the values of these parameters and then find the corresponding to fit the given ratio. For simplicity we set [14], [51]. We set for maximal protein concentration, that is, . The Rho1 degradation rate is set to 0. 0001; it can be further decreased in order to stabilize the Rho1 protein. Regarding the activity functions, we assume that the ultrasensitive behavior of these graphs increases with the number of phosphorylation sites; see the derivation of in the Results and also [5]. Since PP2A, Zds1, and Pkc1 have been found to have around 3,5, and 8 sites respectively, we implement this with parameters that produce the graph observed in Figure 3A. See Table 1 for a list of parameter values. The initial conditions used in the model correspond to the system in the off state. They are equal to zero for all variables, except and.
A large number of proteins in the cell are modified post-translationally by phosphorylation at multiple specific locations. This can help bring about interesting dynamical behaviors such as bistability or all-or-none responses to stimuli. Such behaviors are in turn important for cellular decision-making, differentiation, or the regulation of cellular processes. In this paper we propose the use of a specific technique for modeling allosteric multisite proteins, which can be thought of as the reduced version of a more detailed set of reactions. After validating this technique computationally by comparison to more detailed systems, we apply it to a new model of a signal transduction pathway with several multisite proteins. The model is concerned with a mechanism in budding yeast that is thought to measure the extent of daughter bud growth and send a signal to initiate mitosis when sufficient growth has occurred. Using the given framework we derive an analytically tractable model that creates the desired all-or-none signal. Overall, we give quantitative support to a newly discovered biochemical pathway, and we show the utility of the new modeling framework in the context of a realistic biological problem.
nonlinear dynamics
systems biology
biochemical simulations
signal transduction
mathematics
mechanisms of signal transduction
regulatory networks
signaling networks
biology
molecular cell biology
computational biology
Compact Modeling of Allosteric Multisite Proteins: Application to a Cell Size Checkpoint