Changes in synaptic efficacy are believed to form the cellular basis for memory. Protein synthesis in dendrites is needed to consolidate long-term synaptic changes. Many signals converge to regulate dendritic protein synthesis, including synaptic and cellular activity, and growth factors. The coordination of these multiple inputs is especially intriguing because the synthetic and control pathways themselves are among the synthesized proteins. We have modeled this system to study its molecular logic and to understand how runaway feedback is avoided. We show that growth factors such as brain-derived neurotrophic factor (BDNF) gate activity-triggered protein synthesis via mammalian target of rapamycin (mTOR). We also show that bistability is unlikely to arise from the major protein synthesis pathways in our model, even though these include several positive feedback loops. We propose that these gating and stability properties may serve to suppress runaway activation of the pathway, while preserving the key role of responsiveness to multiple sources of input.
Protein synthesis is a necessary stage in long-term synaptic plasticity, both for strengthening synapses (long-term potentiation, LTP) and weakening them (long-term depression, LTD) [1], [2]. A substantial part of this synthesis occurs in the dendrites, and possibly close to the modified synapse (s) [3]. mRNAs are transported to the dendrites where local sites of protein synthesis machinery appear to serve a small number of synapses or dendritic spines [4]. There are now several putative signaling pathways that connect synaptic input to protein synthesis [5], [6]. The relationship between synthesis triggered by activity, different receptors, and growth factors is important in defining the logic of memory formation [7], [8]. Dendritic protein synthesis is a particularly obvious example of a positive feedback process, since many of the newly synthesized proteins either provide signal input to the system, or are part of the synthesis machinery itself. From the viewpoint of memory formation, positive feedback is a significant feature: it suggests that self-sustaining activation processes may occur [9]. Such self-sustaining processes frequently take the form of bistable switches, and indeed a protein synthesis switch has been proposed to sustain memory in Aplysia [10]. Many biochemical and signaling switches have been proposed at the mammalian synapse, for example, calcium calmodulin type II kinase (CaMKII) autophosphorylation [11], MAPK-PKC feedback [12], [13], and receptor cycling [14]. Given its known role in synaptic plasticity, it is tempting to consider the possibility that protein synthesis may also form such a switch. Synaptic protein synthesis has attracted considerable recent attention, and several of its key pathways have been identified. Major inputs include neurotrophins such as BDNF [15] and neurotransmitters such as glutamate, which in turn may act through metabotropic [16] and ionotropic [17] pathways. The BDNF pathway has been extensively studied and at least one rather lengthy signaling cascade has been identified that culminates with protein synthesis regulation (Figure 1). In this cascade, BDNF binds to its receptor, tropomyosin-related kinase B (TrkB), which activates phosphatidylinositol-3-kinase (PI3K), then AKT, which elevates Rheb-GTP to activate the mammalian target of rapamycin (TOR). TOR acts on synthesis both through its phosphorylation of the S6 Kinase (S6K), and through its effects on eukaryotic initiation factor 4E (eIF4E). Both of these are involved in translation initiation and hence control protein synthesis. The metabotropic glutamate receptor (mGluR) regulation of synthesis is less understood, but there is evidence that it joins this same cascade at AKT [18], though the upstream steps are yet to be resolved. Ionotropic-glutamate signaling works through the association of postsynaptic depolarization and presynaptic glutamate, leading to opening of the N-methyl-d-aspartate receptor (NMDAR) and calcium (Ca2+) influx. Calcium has multiple effects on protein synthesis. Two of the possible inputs are via Mitogen-activated protein kinase (MAPK, present in neurons as Extracellular signal-regulated kinase II, ERKII) and via calcium-calmodulin type III kinase (CaMKIII). MAPK, like mTOR acts on S6Kinase and 4E-binding protein (4EBP) which blocks eIF4E. CaMKIII phosphorylates eukaryotic elongation factor-2 (eEF2) and inhibiting its elongation activity (Figure 1). These downstream targets (eIF4E and 40S) represent possible control points for selective protein synthesis. This is because 40S phosphorylation has been particularly implicated in the translation of a subset of mRNAs with a 5′ terminal oligopyrimidine sequence (5′TOP). 5′TOP proteins are involved in protein synthesis, and the 40S (S6, a subunit of 40S) protein itself has a 5′TOP [5]. In the absence of such 5′TOP targeting, other dendritic mRNAs are translated, including Arc, αCaMKII, microtubule-associated protein 2 (MAP2), neurogranin [5] and interestingly BDNF itself [19]. In this study we explore this convergence of three inputs: BDNF, MAPK, and Ca2+ onto dendritic protein synthesis. We first parameterize the complex BDNF signaling pathway in stages, using published experimental data. We fold in previously published models of Ca2+ and MAPK signaling, and check that the combined model is consistent with further experiments. With the combined model we explore a range of input combinations and analyze how the positive feedback in the system may influence its behavior.
We incorporated previously published models for CaM, PKC and MAPK (ERKII) signaling inputs to the current model [55] (Figure S2). These models have all been parameterized and matched with experimental data in our earlier work. The main input to these pathways in our model is Ca2+, which stimulates PKC directly. MAPK is downstream of PKC as well as CaM-Ca4 in our model, so MAPK activity is closely tied to synaptic activity leading to Ca2+ influx. Many additional inputs to MAPK are known but were not included in our model. We merged all the sub-models mentioned above and tested our overall model. In contrast to the individual constraint steps for each sub-model, the composite model validation monitored signal flow through the entire cascade. We illustrate the pathway with a block diagram (Figure 5A), where the readout points are in gray. We first modeled a time-course experiment in primary cultures of rat cortical neurons, in which a sustained 2 nM BDNF stimulus was applied in the bath, leading to activation of AKT (Figure 5B) [20]. The simulated time-course was slightly slower than the experimental time-course. We attributed this to the difference in temperatures between the constraining experiments used for our model (∼30 degrees C) and the temperature of this experiment (∼37 degrees C). We then modeled the published experiments which measured the relative increase in several downstream readouts and protein concentration following steady BDNF stimulation at 3. 7 nM (100 ng/ml) [56]. Again, these experiments were carried out using primary cultures of rat cortical neurons at 37 degrees C. Therefore, we ran the simulations for twice as long (TSC1-TSC2*, P-4EBP, eIF4E-4E-BP, P-S6K, P-S6 for 10 minutes and protein for 30 minutes) as in the experiment (i. e. for 5 minutes and 15 minutes respectively) before measuring the readouts (Figure 5C). Finally, we validated our model against experiments carried out in primary hippocampal cultures from control and MAPK knockout mice, again at 37 degrees C [57]. These experiments measured intermediates and protein synthesis responses to BDNF stimulation. Our model was able to closely match the experimental observations (Figure 5D). Overall, our composite model incorporated several individually constrained pathways, and we showed that the combined model was in agreement with more complex experiments that exercised a significant part of the protein synthesis regulatory cascade. Our data sources were diverse, and despite our modular approach to parameter fitting, we had to estimate many rates indirectly. We therefore tested how parameter uncertainty affected model behavior by systematically varying each parameter and comparing responses with those of the original model. This approach reflects the robustness of the in vivo system where a small change in conditions does not lead to much change in its behavior, unless the change impinges on a key cellular control molecule. We systematically varied two enzyme parameters (Michaelis constant (Km) (Figure 6B) and turnover number (kcat) ) (Figure 6C), two reaction parameters (forward rate (Kf) (Figure 6D) and backward rate (Kb) (Figure 6E) ), and the total concentrations of each molecule (CoInit) (Figure 6A). Each parameter was varied over 2 log units, from 0. 1 to 10 times the reference model value. As a readout we monitored the steady-state levels of PIP3_AKT-t308_s473 (active AKT), 4E-BP_tot (sum total of the phosphorylated forms of 4E-BP), MAPK*, S6K_tot (sum total of the phosphorylated forms of S6K) (Figures S3, S4, S5, S6, S7) and protein synthesis (Figure 6). Most parameters introduced only small changes in these readouts. A handful of parameters resulted in a two-fold or greater effect on responses, and these fell into two sets, corresponding to the BDNF and MAPK pathways of the model. For the BDNF pathway, as measured by AKT activation, the major control molecules were PI3K, PTEN, PIP2 and PP2A. Most enzyme and binding reactions that were sensitive were also associated with these molecules. For the MAPK pathway, the control molecules were GEF, Ras, GAP, craf-1, PP2A and MKP-1. In addition to the reactions involving these molecules, the Ca2+ entry and PKC activation steps were sensitive control parameters. A final key control parameter was the background rate of phosphorylation of S6K (S6K*) (Figure 7C). This is a convergence point for many kinases, including MAPK, SAP kinases, p38s and Cdc2 [38]. Of these, only MAPK is included in the present study. As discussed below, this phosphorylation step may act to switch the protein synthesis pathway into two distinct modes of responses to inputs. In a first set of predictions of the composite model, we examined steady-state responses to BDNF and Ca2+ stimulation. The response to BDNF was a typical sigmoid but with a rather high baseline, so that the total increase in protein synthesis rate was just over two-fold (Figure 7A). High Ca2+ lowered the response and nearly flattened the sigmoid (Figure 7B). We then investigated why Ca2+ acted in this unexpected manner. From the reaction scheme, Ca2+ stimuli led to activation of two key kinases with opposing effects (Figure 7C). The MAPK cascade was activated through CaM-Ca4 as well as PKC. BDNF had no effect on MAPK responses. CaMKIII was also activated by CaM-Ca4, but at a higher Ca2+ concentration (Figure 7D). This opposing effect suggests that there may be a narrow window of Ca2+ in which synthesis is maximal. This narrow window gives rise to a bell-shaped curve when protein synthesis is plotted against Ca2+ levels. Interestingly, the peak of this bell curve is at rather low calcium levels, only about 2 or 3 fold basal levels. Mechanistically, this is because the differential region of the two Ca2+ activation curves (Figure 7C) is in this Ca2+ range. Physiologically, this Ca2+ range corresponds to mild rather than intense synaptic input [58]. In our default model, we found that high Ca2+ above 0. 5 µM turned off protein synthesis (Figure 7E). As expected, higher levels of CaMKIII lowered this cutoff point (Figure 7F). Although these responses might suggest that strong Ca2+ influx (as in LTP stimulation) should block synthesis, the CaMKIII response is much faster than that of MAPK, and the system response to a Ca2+ transient may be more nuanced. We test this situation in the section on LTP and LTD stimuli. One of the key regulatory input points to the protein synthesis cascade is S6K phosphorylation. S6K is known to be a target for other kinases, including SAP kinases, p38s and Cdc2 [38], in addition to MAPK and TOR (Figures 3A and 7C). In the model we had a steady basal kinase (BK) activity (rate is equal to 0. 01 /sec) (Figure 7F) to represent the external kinase inputs. We now varied this BK activity to represent regulatory input. We found that the BK rate had a profound effect on the network controlling protein synthesis. At low BK, the network was in a state where protein synthesis was mostly low and gradually increased with an increase in MAPK activity. At high BK, the network was less susceptible to change in MAPK activity (Figure S8). Second, Ca2+ sensitivity switched from an inhibitory response above 0. 5 µM Ca2+ to a BDNF-gated bell-shaped response (Figure 7G and 7H). Thus protein synthesis was high only when BDNF was high, and Ca2+ was in the 0. 15 to 0. 3 µM range. This unusual profile was due to high-sensitivity activation of MAPK by Ca2+, followed by the lower-sensitivity inhibition of protein synthesis via CaMKIII (Figure 7D). Overall, we found that our composite model exhibited two possible kinds of behavior: BDNF gated and MAPK gated. The BDNF-gated behavior seemed more consistent with experiments in neuronal systems, and was contingent upon background phosphorylation of S6K by kinase regulation outside the scope of our study. In this mode BDNF had a strong effect on protein synthesis, and MAPK (stimulated by Ca2+ in our model) acted to further elevate responses. The MAPK-gated behavior may be of interest in other cell types [59], [60] or specific neuro-regulatory contexts [61]. It had a lower basal synthesis rate, a strong dependence on MAPK, and a narrow window of activation by Ca2+. We next tested the model responses to temporal input activity patterns used for inducing LTP and LTD. We combined patterned Ca2+ input with varying levels of simulated BDNF to explore the effects of possible combinations of these inputs at the synapse. Based on the pathways in our model, BDNF should activate synthesis through AKT and TOR, whereas Ca2+ should turn on MAPK to activate synthesis, but also turn on CaMKIII to depress synthesis. We represented the LTP stimulus as three pulses of Ca2+ influx, each 1 second wide, and separated by 300 sec [55]. In addition, we provided a BDNF input of 3. 7 nM for 5 sec for each pulse of Ca2+ (Figure 8A). We monitored MAPK*, AKT* (PIP3_AKT_thr-308), CaMKIII*, and protein synthesis rate levels. The LTP stimulus caused a modest and brief elevation of protein synthesis rate through the combined action of MAPK and BDNF (Figure 8A, 8C, 8E, and 8G). We compared responses with three pulses of 10 µM Ca2+, and at basal Ca2+ (0. 08 µM). We found that the MAPK response was entirely Ca2+ dependent. At basal Ca2+ CaMKIII and protein responses were indeed present, and were about half as large as for the 10 µM Ca2+ stimulus. This suggested that the contributions of the BDNF and Ca2+/MAPK inputs to protein synthesis were about equal. We modeled LTD input as a single 900 sec Ca2+ pulse [62] along with a BDNF elevation to 3. 7 nM for the same period. We again monitored MAPK*, AKT* (PIP3_AKT_thr-308), CaMKIII* and protein synthesis rate (Figure 8B, 8D, 8F, and 8H). Interestingly, the MAPK response was much stronger for LTD than for LTP stimulus, and the protein synthesis levels were nearly twice as large. BDNF had exhibited gating behavior for steady-state responses, so we tested if it also did so for these transient stimuli. We ran additional simulations with zero BDNF, and found that though there were transients above and below baseline; neither the LTP nor LTD Ca2+ stimulus resulted in any net synthesis (Figure S9). Thus BDNF ‘gates’ LTP/LTD stimulated protein synthesis. While we did not model mGluR explicitly, it is known to converge onto AKT [18] which is in the BDNF pathway as well, and upstream of TOR in our model. Activation of TOR strongly enhanced the ability of both LTP and LTD stimuli to cause protein synthesis. Thus mGluR input might also act as a gate. In contrast to the steady-state results, the large but brief Ca2+ transients in HFS stimuli caused an increase in protein synthesis provided BDNF was present. Our simulations showed that the transient stimulus did cause a brief CaMKIII-mediated reduction in synthesis, but this inhibition was relieved as soon as Ca2+ returned to baseline (Figure 8E and 8F). The same Ca2+ transient also acted through the slower MAPK-eIF2E pathway, leading to an elevation of synthesis after the inhibition had subsided. Thus the protein synthesis cascade acted like a transient detector for Ca2+ inputs. There are also CaMKII-mediated inputs to protein synthesis which may provide further timing interactions, but we were unable to parameterize these mechanisms on the basis of current data [58]. The duration of protein synthesis for LTP and LTD stimuli was short: of the order of 30 minutes. This is much less than the time-scale of protein synthesis dependent plasticity responses. We consider this interesting discrepancy in the discussion. How might LTP and LTD stimuli differentially affect protein synthesis? One possibility is the differential phosphorylation of S6 leading to the activation of the 40S subunit, which has been implicated in 5′TOP mRNA translation [5], [63]. Another selective pathway is the eIF4E phosphorylation, which is elevated for CAP protein production [64], [65]. It is believed that MAPK phosphorylates MAPK-interacting kinase (MNK) which in turn phosphorylates eIF4E [5], but our model did not include MNK. There was no direct estimate for eIF4E phosphorylation so we took active MAPK (which phosphorylates eIF4E) as readout for CAP-protein production. We monitored peak levels of 40S and active MAPK complex (as an estimate of CAP-protein production) for LTP and LTD simulations, while varying the BDNF stimulation over the range 0 to 3. 7 nM in each case. This exploration was necessary because we did not have direct data for BDNF release levels during different synaptic stimulus protocols. We found that the MAPK response was independent of BDNF (Figure 8J), but the 40S levels were strongly BDNF dependent (Figure 8I). Except for very low levels of BDNF, the LTD stimulus elicited a nearly 2-fold larger 40S response than the LTP stimulus. Overall, we find that TOR activation by BDNF is a prerequisite for both LTP- and LTD-triggered protein synthesis in our model. Different activity patterns, and different levels of co-regulatory inputs such as BDNF, may lead to a spectrum of differential activation of 5′TOP mRNA translation. There are at least two forms of feedback possible in this system. First, protein synthesis may increase the production of the protein synthesis machinery itself. The ribosomal 40S subunit and the eEF2 protein are examples of such feedback molecules in our model (Figure 9A). Second, protein synthesis may increase the production of molecules, including BDNF, that contribute to activating synthesis (Figure 9A). BDNF is released from neurons in an activity-dependent manner, and recent studies show that some of this release may occur through postsynaptic mechanisms [66], [67]. Furthermore, there is evidence that BDNF has a postsynaptic site of action, in the induction of LTP [68]. Several studies have suggested that this release, coupled with the role of BDNF in synaptic plasticity, may constitute a feedback loop, and that at least part of this loop may be postsynaptic (reviewed in [69]). While it is likely that there is a combination of pre- and postsynaptic BDNF feedback in its overall action, for the purposes of our analysis we have considered only the postsynaptic component. Both the protein synthesis machinery feedback, and the BDNF feedback have a positive sign, and are therefore suggestive of bistability [70], [71]. While 40 to 400 species of synaptic molecules have been reported to be synthesized at the dendrite [72], our model only included these three putative feedback molecules: BDNF, eEF2, and 40S. We first considered synthesis control of each of these individually, then in combination. A simple graphical way to identify bistable systems arising from chemical feedback is to use overlaid dose-response curves [12], [73]. Intersection points on such curves indicate stable points of the system. If there are three intersection points (e. g. , Figure 9B between the sigmoid and middle straight line) then the system is bistable. The lower intersection point is a stable state of low activity, and the upper intersection point a stable state of high activity. The intermediate intersection point has properties of a threshold or transition point between the two stable points. Note that these are steady-state curves, and the time taken to actually change state depends on reaction kinetics, the initial conditions, and on the kind of stimulus. In our study the concentration of the synthesized protein (BDNF, eEF2 or 40S) is directly proportional to our readout of protein synthesis rate, assuming that molecular degradation is first order in concentration. In the following analysis, we therefore used the protein synthesis rate on the y axis as a surrogate for concentration. This has the advantage that two unknowns can be folded together into a single scaling factor: the degradation rate and the fraction of the specific protein out of the total protein synthesized. In the case of protein synthesis feedback, the simplest assumption was that a fixed fraction F of the synthesized protein could feedback to increase synthesis. Thus, protein concentration [X] may be represented as a straight line of slope 1/F through the origin (dashed lines in Figure 9B and 9C). It is clear from the geometries that a sigmoid dose-response curve may exhibit protein-synthesis bistability for some values of F (Figure 9B), but a logarithmic or saturating curve will never be bistable if it has a linear dependence on synthesis rate (dashed line in Figure 9C). The only situation in which a molecule with a saturating response curve might exhibit feedback bistability is if the level of protein is a steep sigmoid function of the synthesis rate (smooth line in Figure 9C). We ran dose response curves for BDNF, the eEF2 protein and the ribosomal 40S subunit to look for bistability in this system (Figure 9D–F). None of the curves could support bistability with the linear feedback assumption (straight lines). In each case, there was just one intersection point between the dose-response curves, for any value of F. This was the case even if the system was co-stimulated with 3. 7 nM BDNF (Figure 9E and 9F). Having shown that individual synthetic pathways were unlikely to lead to bistability, we then asked if combinations might do so. We first considered higher-order activation processes, using the BDNF response as a typical dose-response curve. A hypothetical mechanism for this might be if the molecule acted in a higher-order manner to stimulate protein synthesis. We found that a fourth-order reaction of the form of the BDNF curve was sufficiently sigmoidal to just support bistability, but a third-order reaction was not (Figure 9G). As a more biologically grounded test, we considered if the known dendritically synthesized molecules BDNF, eEF2, and 40S might act synergistically to support bistability. We took an ‘optimum’ condition, where the protein synthesis scaling factor F for each was such that their half-maximal levels (Khalf) coincided. Using this scaling we re-ran the dose-response simulation, and found that the resultant curve was indeed sigmoidal and could support bistability for a narrow range of synthesis scaling factors (Figure 9H). As we discuss below, this bistability requires a finely tuned set of conditions to occur, but on the other hand may be strengthened by other, as yet unknown feedback reactions. Overall, we conclude that a protein-synthesis switch at the dendrite is unlikely with currently known feedback mechanisms.
Bistable switches are plausible mediators of long-term storage of information at a signaling level. Several such switches have been proposed to be involved in synaptic plasticity: CaMKII autophosphorylation [11], MAPK feedback loops [12], [13], [74], and alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptor (AMPAR) trafficking cycles [75], Our expectation for this pathway was that it might exhibit switch-like behavior of its own, resulting from positive feedback from synthesized proteins back into the synthesis machinery. However, we found that bistability could not occur if we used the simple assumption that the each feedback molecule was present at a fixed fraction of the total protein synthesized. We were able to achieve a fragile bistability when we assumed coordinated feedback effects, but only with considerable tuning of feedback processes. At face value this is extremely unlikely situation. Furthermore, an elevation of basal synthesis rates, or influx of some of the 5′TOP proteins from non-local synthesis, would shift the synthesis curves upward along the y-axis, and thus eliminate the bistability. As a counterpoint, however, it is possible that synergistic effects may occur due to further feedback effects of additional dendritically-synthesized proteins. Overall, we feel that protein-synthesis-feedback bistability is unlikely, but it will take considerable additional data to resolve this point. While it seems reasonable that synaptic plasticity should draw upon new proteins and hence require elevated synthesis, recent studies suggest that the relationship between synthesis and plasticity is complex. Even though new synthesis is needed for plasticity, the total level of protein synthesis does not appear to change much following L-LTP stimuli [2]. Furthermore, background activity may actually act to suppress synthesis [76]. These observations further complicate the idea of protein-synthesis bistability in the dendrite. Our model of dendritic protein synthesis involves some 130 molecular species, including phosphorylation states and complexes of the key proteins. We have chosen this size model as a compromise between completeness and parameter unknowns. These concerns are present in all but the most exhaustively characterized signaling pathways [77], [78]. We have selected a subset of implicated pathways for this study based on their reported roles, and on the degree to which the pathways have been characterized. For example, we were not able to include the mGluR input to the pathway as it is still lacking key functional details [18]. Nevertheless, our integrated model validation results suggest that we have succeeded in capturing many of the key interactions for the processes we wished to study. The resulting model was rather insensitive to parameter variations. Even the ‘sensitive’ parameters, when scaled up or down tenfold, affected model responses by barely a factor of two. Key targets for future refinement of the model, besides mGluR input, would be a more complete analysis of differential protein synthesis, and incorporation of the kinases that phosphorylate S6K on its p70 carboxy-terminal tail [38]. A much more challenging goal is to model the turnover of every protein in the model, so that all of the molecular concentrations are the outcome of regulatory and homeostatic controls. This awaits substantial advances in our understanding of all stages of neuronal protein synthesis control. The synapse is an unusual computational entity: it modifies its own hardware. Most computer languages manipulate data, but scrupulously avoid modifying program instructions. By these engineering standards, the synapse appears peculiar and unstable. In this system molecular signals (data) compute through chemical interactions (program instructions), which among other things also structurally modify the synapse and thereby change its computational properties. The protein synthesis cascade is an instance of this self-modification. How does the cell avoid runaway processes? Our study has some possible hints about how the system activity remains bounded. First, the synthetic increase is quite limited and the only way to get large fold activation over baseline is to provide synergistic BDNF and activity input. Thus, gating restricts the conditions where self-modification may occur. Second, our model suggests that the molecular logic of synthesis control may be somewhat insulated from the potentially dangerous process of positive feedback. Instead, potential feedback molecules seem to have saturating response curves with a high baseline that are effective at damping runaway buildup. With this interpretation, activity triggers leading to plasticity may initially activate biochemical switches rather than a protein synthesis loop. Candidate biochemical switches include CaMKII for fast switching, and MAPK or trafficking switches for slower phases of plasticity [12], [75], [79]. These switches, in turn, may regulate the protein synthesis pathway. Thus the short time-course of responses of the current model to synaptic input may simply reflect the role of synthesis as an effector of an upstream switch, rather than a switch itself. We speculate that very long-term changes may involve a shift in what is synthesized, and where it is made, rather than in how much synthesis occurs. To use our computational metaphor, this would be more like the machine loading different programs, rather than rebuilding and redesigning itself. This view would predict that very long-term plasticity involves decisions between a set of possible ‘programs’ executed by dendritic synthesis machinery to influence local synaptic function.
Simulations were developed in GENESIS, the General Neuronal Simulation System, using the Kinetikit interface [80] and solved using the exponential Euler method. Later parameter sensitivity, dose-response, and time-course runs were done using MOOSE, the Multiscale Object-Oriented Simulation Environment (http: //moose. sourceforge. net/) and solved using an adaptive Runge-Kutta method (GNU scientific library). Simulations were carried out on PC workstations and on a SUN/Opteron cluster running Linux. Complete model reaction schemes and parameters are presented in Dataset S2. To further check the model calculations, and to facilitate community access to our simulations, we converted our model to SBML Level 2 Version 1 (Dataset S3). We had to remove a tabulated BDNF-dependent calcium stimulus which is not supported by most simulators. We compared this slightly modified model with other simulators (e. g, COPASI 4. 4 [81] and CellDesigner 4. 0 [82]) and obtained the same results as with GENESIS and MOOSE. To validate our composite model, we ran the simulation for 6000 sec without any stimulus and then noted the concentration of TSC1-TSC2*, 4E-BP_tot, S6K_tot and total synthesized protein. We then applied a steady stimulus of 3. 7 nM BDNF. In the experiments, the levels of TSC1-TSC2*, 4E-BP_tot and S6K_tot were noted at 5 min and the level of protein at 15 min. As our simulations used room-temperature rates, and these experiments were done at physiological temperatures, we doubled the runtimes for our simulations when carrying out these validations. The activated values were divided by the basal values to obtain fold activation. Sensitivity analysis was done by scaling each parameter one at a time in the range of 0. 1 to 10-fold of the original parameter values. The parameters were: initial concentration (for molecules with non-zero initial concentrations); Km and kcat (for enzymes); and Kf and Kb (for binding/conversion reactions). To measure sensitivity, we ran the scaled model for 6000s without any stimulus. At 6000s the value of BDNF was set at 3. 7 nM and then the concentration of readouts was recorded at 6600 sec and 9600 sec. The concentration of the readouts (PIP3_AKT-t308_s473 (active AKT), 4E-BP_tot, MAPK*, S6K_tot and protein) were normalized by dividing the obtained concentration by the value obtained from the original parameter model. These normalized fold change were plotted against logarithmic value of the parameter scale factor to obtain the sensitivity plots.
Memory formation involves the controlled production of new proteins close to the site of input stimuli on nerve cells. Strong inputs, in combination with growth factors, stimulate the synthesis of several kinds of synaptic proteins. These new proteins are believed to participate in remodeling the contacts between cells. This gives rise to a potentially unstable situation of a self-modifying cellular machine, because the new proteins rebuild their own inputs and their own production machinery. We have analyzed these interactions by modeling multiple inputs and the process of self-modifying feedback. We find that runaway modifications are prevented in two ways: first, a molecule called mTOR acts as a gate to suppress synthesis except under very tightly regulated conditions. Second, the feedback processes operate in a range where it is very unlikely that they can give rise to runaway buildup. Thus, the system avoids instability even though it is capable of modifying itself in response to many kinds of inputs.
computational biology/computational neuroscience
neuroscience/neural homeostasis
neuroscience/neuronal signaling mechanisms
computational biology/signaling networks
computational biology/systems biology
Signaling Logic of Activity-Triggered Dendritic Protein Synthesis: An mTOR Gate But Not a Feedback Switch