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Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network
- Academic Editor
- Yuriy Orlov
- Subject Areas
- Bioinformatics, Computational Biology, Mathematical Biology, Molecular Biology, Neuroscience
- Modeling molecular dynamics, Rule-based modelling, Ordinary differential equations, DARPP-32, Dopamine-dependent synaptic plasticity, Kappa language, Molecular signalling, Molecular interactions, Addiction, Context and reward-related learning
- © 2022 Wysocka et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
- Cite this article
- 2022. Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network. PeerJ 10:e14516 https://doi.org/10.7717/peerj.14516
Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.
Computational dynamic modelling probe mechanistic and quantitative aspects of molecular interactions, which can grant the development of mechanism-based therapies with more predictive power on outcomes of therapeutic interventions (Xie et al., 2014). Such interventions often target molecular signalling (Li & Mansmann, 2014; Jia, Piña-Crespo & Li, 2019; Volkow & Boyle, 2018), characterised as a complex system of coupled interacting components, often proteins, forming networks that activity leads to non-additive effects (Kitano, 2002). Defining molecular reactions as a set of coupled ODEs has traditionally enabled dynamic modelling of molecular pathways (Sible & Tyson, 2007). ODE-based modelling is a powerful and acclaimed formalism with a long tradition. It has established standards, and various software tools that support and facilitate the formulation and analysis of ODE models (Dräger et al., 2008; Hoops et al., 2006; Sible & Tyson, 2007). However, the explicit enumeration of molecular species as variables required by the equation-based formulation excludes the representation of molecules that assemble into multivalent protein complexes; typically these have multiple functionally divergent states, a common characteristic of molecules involved in cell signalling (Hlavacek et al., 2003). Computational modelling methods effectively addressed these challenges of expressivity, increasing complexity of the modelled systems. An example of such a method is RB modelling, designed to model interacting proteins. Compared to the ODE-based paradigm, which represents the molecular system as concentrations of molecular species and focuses on their reaction kinetics, RB modelling is an agent-centred method in which the distribution of molecular compositions can be studied along with their abundance (Faeder et al., 2003; Blinov et al., 2004; Danos & Laneve, 2004).
The potential of the RB paradigm has been extensively discussed (Danos, 2007; Chylek et al., 2013, 2014, 2015), exemplified (Danos, 2007) and used to answer novel biological questions across signalling, regulatory and metabolic networks (Wilson-Kanamori et al., 2015; Antunes, Roque & Simoes-de Souza, 2016; Di Camillo et al., 2016; Santibáñez, Garrido & Martin, 2020; Chattaraj, Blinov & Loew, 2021; Nosbisch, Bear & Haugh, 2022, see reviews for earlier models published before 2006—Hlavacek et al., 2006; and from 2007 to 2013—Chylek et al., 2014). These models are often based on chemical reaction networks previously developed for ODEs. Though the RB paradigm was implemented to match the concept of chemical reactions, rules are generators of reaction networks that may lead to diverging results. As the modelling paradigm can affect the underlying model specification, it would be informative to compare simulations of RB and ODE models defined by the same reaction network, originally built to be solved with ODEs. RB and ODE models have been compared on various aspects before. For instance, Blinov et al. (2006) compared network-like model of epidermal growth factor receptor (EGFR) based on reactions first defined in the ODE model of Kholodenko et al. (1999), with simplified pathway-like structure. However, several assumptions underlying the original model were purposefully modified what changed the underlying interaction network, such as the dissociation of EGFR dimers when phosphorylated or bound to other molecules. In addition to contrasting rules to reactions, another intensively studied aspect, finalised with positive conclusions, was whether stochastic simulation can reveal new properties of a system previously modelled as deterministic one (Vlysidis & Kaznessis, 2018; Hahl & Kremling, 2016; Bustos et al., 2018). As simplistic as it may sound, none of the comparisons mentioned were intended to painstakingly disassemble reaction using the rules of a relatively medium-sized model and compare simulation results.
This study compares the existing ODE model with the new RB model, where both are based on the same definition of chemical reactions. We specifically ask: (1) how closely can the dynamics of an ODE-based model be replicated with an RB one? (2) If differences between the two are observed, what is the underlying cause? (3) If the dynamics are indeed replicated, what advantages are there to using an RB model? We present the results of the models’ comparison at the level of notation and model dynamics generated under different conditions. The advantages and disadvantages of the two model representations are discussed, alongside suggestions for future research. We hope that this small-scale attempt may nevertheless bring value to the modelling community to better choose between the two different approaches.
Dynamic computational modelling frameworks consist of model specification and simulation methods. The model specification is a set of equations or instructions written in a machine-interpretable language that define relationships between variables. Models are run as numerical simulations using algorithms that calculate changes in the variables based on the model’s formulation. By adopting a suitable level of abstraction and with the use of a sufficiently expressive language, systems can be modelled such that experimentally-derived evidence can be incorporated to improve the model’s quality. A formal approach to the generation of models is desirable to (i) encode facts in an unambiguous and explicit manner, (ii) facilitate the understanding of models, (iii) allow easy modification of models to accommodate more than one hypothesis, (iv) aid interpretation of the underlying biological phenomena, and (v) provide a standard approach to the integration of novel data sources (Kitano, 2002). These features are especially important because model generation often requires knowledge spanning multiple disciplines; the existence of formal modelling frameworks enforces a common understanding of the explicit meaning of model components.
In the first step of building a kinetic model, transitions from reactants into products are defined as chemical reactions between molecules. Quantitative evaluation of model behaviour over time has been commonly achieved by converting coupled chemical reactions to a set of ODEs that are solved with numerical procedures (Wilkinson, 2006). Each rate equation expresses the change of concentration of a single molecular species over time, formulated with reaction rates that directly take part in the creation and elimination of this species (Sible & Tyson, 2007; Klipp et al., 2005; Hlavacek et al., 2006). Each reaction rate is weighted by a reaction-specific rate constant. Time courses obtained by solving ODE models are continuous and deterministic, characterised by smooth and gradual change of species concentrations over time (Wilkinson, 2009). Although this setup does not reflect the actual characteristics of subcellular events driven by random collisions between discrete molecules (Gillespie, 1976), this approach is correct as long as abundances of reactants are large enough to render random fluctuations as negligible (Chen et al., 2010). If this condition is violated, the current development of machine coding formats for biological models (e.g., SBML) allows deterministic solvers and stochastic simulators to be applied to the same model specification (Gillespie, 1977; Hoops et al., 2006). A more critical shortcoming of ODE-based models lies in the requirement for explicit enumeration of all molecular species in signalling networks (Hlavacek et al., 2003; Danos, 2007). This drawback precludes mechanistic modelling of systems with multi-state promiscuous molecules that can adopt combinatorially complex states (Seshacharyulu et al., 2012; Chen et al., 2016; Mayer, Blinov & Loew, 2009; Suderman & Deeds, 2013), where only a small fraction can be represented with ODEs (Chylek et al., 2014). However, the development of formal methods in computer science has expanded the number of observed properties of biological systems that can be dynamically and quantitatively modelled (Bartocci & Lió, 2016) (reviewed in Machado et al., 2011; Tenazinha & Vinga, 2011; Bartocci & Lió, 2016; Ji et al., 2017; Le Novère, 2015). As this study examines an alternative to ODEs, the focus lies on non-spatial single-scale mechanistic methods. Of those that fit this characterisation (Baeten, 2005; Ciocchetta & Hillston, 2008; Regev, Silverman & Shapiro, 2001; Guerriero, Priami & Romanel, 2007; Dematté et al., 2010) we chose rule-based modelling (RBM) as a suitable framework for representing systems with multistate combinatorial interactions. Among two major RBM implementations (Stefan et al., 2014), the very first being BioNetGen (BNG) (Faeder, Blinov & Hlavacek, 2009; Faeder et al., 2003; Blinov et al., 2004), the Kappa framework (Danos & Laneve, 2004) was chosen for this comparison. Although there are some notational differences between the two frameworks, the reactions are coded as rules in virtually the same way (Suderman & Hlavacek, 2017). An earlier study by Suderman & Deeds (2013) also showed equality of simulation results between both frameworks.
RBM is based on a formal concept of graph rewriting, where molecules are abstracted as structured graph objects referred to as agents with sites that express internal and binding states, together constituting interface of an agent (Feret et al., 2009). Reactions are defined as graph transformations encoded as rules, that are instructions for local and conditioned transformations. The reaction conditions are encoded in the agent interfaces. Depending on the completeness of these conditions, a rule can represent either a set of reactions that match the pattern expressed in a rule, or an exact reaction instance with complete information about the agents’ states (Feret et al., 2009). Thus, a rule can express an infinite number of reactions with a small and finite number of generalised rules (Chylek et al., 2013). During the system simulation agents form mixture of molecular species that contain a complete description of their states and site occupancy, captured by snapshots that provide information on molecular species and their quantities at specified time-points. The trajectories resulting from the model simulation are obtained by declaring variables, called observables. If declared with an incomplete pattern, an observable is the sum of the trajectories of the many molecular species that fit the pattern. The Kappa language also allows to induce perturbations during the simulation, for example by updating rate constants or adding molecules. The Kappa simulator, KaSim (Danos, 2007; Krivine, Danos & Benecke, 2009), is based on Gillespie’s Stochastic Simulation Algorithm (SSA) (Gillespie, 1976, 1977) that produces individual stochastic trajectories of molecules (Gillespie, 1977; Wilkinson, 2009), and whose performance is independent of the size of the reaction network (Yang & Hlavacek, 2011). This RB formulation gives a sufficiently expressive system to capture the principal mechanisms of signalling processes (e.g., biding, dissociation, synthesis, degradation, and state change; Liu & Thiagarajan, 2012), providing site-specific details of molecular interactions (e.g., affinities, dynamics of post-translational modifications, domain availability, competitive binding, causality) and the structure of interaction networks.
To directly compare models’ simulations, we selected an ODE model available in a machine-readable format that can be numerically simulated in non-obsolete software, thus satisfying the reproducibility criterion. A model of the immediate interactors of dopamine- and cAMP-regulated neuronal phosphoprotein with molecular weight 32 kDa (DARPP-32) network by Fernandez et al. (2006) satisfies this requirement. The model was also considered as a solid core for the construction of larger and more complex models in the community interested in the modelling of dopamine (DA)-dependent synaptic plasticity (Manninen et al., 2011). Finally, it is a study widely cited not only by modellers (Nair et al., 2016; Nakano et al., 2010; Mattioni & Le Novère, 2013) but also by experimentalists (Bales et al., 2011; Kim et al., 2015; Buesa et al., 2016). It should be noted that the stochastic simulation for this particular system should not show any significant differences from the ODE model, as the number of particle copies is sufficiently large. The reactions underlying the ODE model of Fernandez et al. (2006) (“model B”) were encoded into the Kappa language, version 3.5 (Feret & Krivine, 2012). Then, models were simulated in different variants to obtain time courses of equivalent observables to compare (Fig. 1). First, however, we briefly introduce the DARPP-32 interaction network.
DARPP-32 and its interaction network
DARPP-32, Protein Phosphatase 1 Regulatory Inhibitor subunit 1B isoform 1 (PPP1R1B) (NCBI Resource Coordinators, 2016) is an important multistate and intrinsically disordered protein (Marsh et al., 2010) regulating DA-dependent synaptic plasticity in medium spiny projection neurons (MSPN), the predominant striatal cell type, where signalling cascades are activated simultaneously by glutamatergic and dopaminergic stimuli (Beninger & Gerdjikov, 2005). The malfunction of DARPP-32 relates to a number of neurological disorders, e.g., Alzheimer’s disease (Cho et al., 2015), addiction (Philibin et al., 2011), affective disorders (Kunii et al., 2014), and schizophrenia (Kunii et al., 2014; Wang et al., 2015). Glutamatergic and dopaminergic signals are integrated by DARPP-32 which is involved in a complex network of interactions regulated by its multiple phosphorylation sites, of which four are known to have an impact on DARPP-32 itself (Yger & Girault, 2011): Threonine 34 (Thr34), Threonine 75 (Thr75), Serine 137 (Ser137), and Serine 102 (Ser102). The Threonine sites (Thr34, Thr75) have major regulatory roles in signal processing. Whereas the Serine sites (Ser137, Ser102) reinforce Thr34 signal.
Fernandez et al. (2006) studied the integrative effect and sensitivities of DA- and glutamate (Glu)-mediated signals on the DARPP-32 network (Fig. 2A). Their model examined the particular effect of cyclic adenosine monophosphate (cAMP)-pulse followed by Ca spike trains showing that DARPP-32 is a robust integrator, far more complex than a bistable switch between DA and Glu signals. The authors included two main pathways that mediate these signals, cAMP–PKA–DARPP-32 phosphorylated at Threonine 34 (D34) and Ca –PP2B–DARPP-32 phosphorylated at Threonine 75 (D75). Contrary to the majority of previous models of the Glu and DA signal integration (Lindskog et al., 2006; Gutierrez-Arenas, Eriksson & Kotaleski, 2014; Nair et al., 2016, 2014), DARPP-32 included three phosphorylation sites: Thr34, Thr75 and Ser137. The authors performed two in silico mutagenesis experiments modifying the role of Ser137. The first mutation inhibits site phosphorylation by changing Serine to Alanine at the 137 position (Ser137Ala). The second mutation leads to permanent phosphorylation of the Ser137 site (constSer137).
As a single reaction can be written as a single rule, the translation could have been accomplished in a one-to-one manner. However, to fully benefit from rule patterns, multiple reactions can be condensed into fewer rules by removing irrelevant context, i.e., decontextualised. The context of a reaction in the RB model is defined as the information about the agent’s binding sites, partners and internal states. Based on this definition of reaction context, the following criteria guided decisions about condensing reactions into rules. Given a set of reactions of the same type (forward, backward, or catalytic) between the same reactants (agents), if the difference between reactions lies in agent states (internal or binding) that do not change after the transition from reactants to products, and reaction constants (rates) in all these reactions have the same values, then information about agent states does not define reaction conditions; hence, it can be removed from the reaction notation, i.e., a set of reactions become a rule pattern.
Among the least intuitive cases in encoding reactions into rules are complex substrate activations of PKA and PP2B. PKA is activated by the binding of four cAMP molecules, whereas PP2B activation requires four Ca ions. In other words, multiple molecules of the same type bind substrates on different sites that have to be uniquely named, which requires explicit encoding of all possible binding combinations on four different sides (Danos, Koeppl & Wilson-Kanamori, 2011), called hereafter as combinatorial binding. Translation from deterministic to stochastic rate constants and molecular concentrations to copy-numbers were performed in a standard way (Feret & Krivine, 2012; Sekar & Faeder, 2012). Lastly, the cAMP pulse and the Ca spiking are reproduced by the addition of molecular copy numbers and modification of rate constants during the simulation, respectively.
Approach to comparison of models
The qualitative comparison of the results of two dynamic models required the simulation of both models in a stochastic scheme and the alignment of the trajectory of the corresponding observables under varying conditions to comprehensively compare the frameworks (Fig. 1).
Selecting and pairing observables
The plots in the original publication show aggregated variables that are summed trajectories of multiple molecular species. For instance, “D34” denotes DARPP-32 phosphorylated at Thr34, regardless of its state of binding or other phosphorylation sites. The concept of aggregated variables corresponds to observables in RB modelling, and therefore, we use the term observable hereafter to denote aggregated variables. Observables of the ODE model were aggregated here based on their names matching partial strings representing the observables of interest. To verify this approach, obtained observables of the ODE model with this method were qualitatively compared with the six observables plotted in the original publication. The choice of other observables follows these principles: (1) if an agent has internal states, the activated state is set as its observable form, e.g., “CK1u”; (2) if an agent is created and degraded over the simulation, its observable is set to its least specific form, e.g., “PKA”; (3) if an agent is not created and degraded during the simulation, i.e., its level remains constant throughout the simulation and has no internal states, its observable is set to its bound form, e.g., “_CDK5” (see Table S1 for the complete list of RB and ODE observables with definitions).
We simulate both models in the stochastic scheme but within different simulation environments. The RB model was simulated with KaSim. The SBML format of the ODE model was run with COPASI (version 4.20), a common simulation environment for Systems Biology Markup Language (SBML)-formated models (Hoops et al., 2006), using the deterministic solver (LSODA) and stochastic simulator (direct method Gillespie, 1977). To match units of molecular abundances across all simulation setups, the results of the SBML model were set to “particle numbers”, equivalent to copy numbers in the RBM naming convention.
To induce the first type of site-directed mutations, Ser137Ala and constSer137, we modified rate constants during the simulation. In both cases, the alteration of the model involved the inactivation of four reactions by zeroing their rate constants. In the RB model, these reactions are represented by one rule, thus a change of a single constant induced each mutagenesis.
We additionally tested the RB model with two different binding schemes, applicable only to the RB model, and further called noncompetitive and competitive binding (Fig. 1). In the noncompetitive binding, all interactors of DARPP-32 can bind simultaneously to three different sites. The competitive binding assumes one interaction with DARPP-32 at a time, which reflects the ODE model assumption.
Comparison of models was performed on two levels, model notation and simulation results. The model notation was analysed by dividing the model into components and comparing their sizes. We expect the set of reactions underlying the ODE model to be represented with fewer rules since a single one can constitute a pattern representing several reactions. The comparison of simulation results involved the alignment of equivalent time courses obtained by model simulations. We performed the comparison of time courses between three variants of each model: (1) base-line condition (wild-type) and two site-directed mutations: (2) Ser137Ala and (3) constSer137. Finally, we compared two RB model variants, representing: (1) DARPP-32 with a single binding site; and (2) DARPP-32 with three independent binding sites.
Rule patterns reduce reaction number in a certain type of model components
Table 1 juxtaposes the total counts of model components in each model. The RB model represents 152 reactions with 132 rules, each parameterised by one of 62 unique rate constants. This number is lower than the total number of rate constants used to parameterise the ODE model (152). The final rule set is more than twice as large as the unique number of rate constants, meaning that more than one rule is parameterised by the same rate constant. The number of molecular species in the RB model, obtained with snapshots capturing the state of the molecular mixture over simulation time (every 10,000th event), is 91 for the competitive RB model, and 137 for the non-competitive one. In both cases, the sum of molecular species is higher than in the ODE model (75). As expected, the number of rules corresponding to reactions is lower, and the number of molecular species is much higher, confirming that expression patterns reduce the number of rules needed to represent a reaction system. Nonetheless, the number of rules is only slightly lower than the number of reactions (152 to 131). If we closely compare models by parts representing more general molecular mechanisms, rule representation reduces the reaction number in some components but extends it in others (Table 2). The reduction occurred only in “DARPP-32 phosphorylation” and “PP2A activation by Ca ” components, where combinations of states of DARPP-32 phosphorylation sites do not have to be explicitly named. In contrast, the increase in the number of reactions compared to the reactions occurred in the components “PKA activation” and “PP2B activation”. They both have four sites that bind the same molecules, Ca and cAMP, respectively.
|ODE model||RB model|
|Model component||Total counts||Total counts||Model component|
|Reaction instances||152||132||Reaction rules|
|Concentration-based rate constants||152||62||Stochastic rate constants|
|Initial concentrations||75||8||Initial copy numbers|
|Molecular species||75||91/137||Molecular species|
|Stimuli events||21||21||Stimuli events|
|Model component||Reactions||Rules||Unique rate constants|
|7.||cAMP & Ca degradation||8||8||8|
|8.||PP2A activation by Ca||32||4||4|
RB model recapitulates dynamics of ODE model with minor discrepancies
The RB model recapitulates the principal dynamics of the ODE model, albeit there are some observable differences (compare Figs. 3B and 3C). For instance, during the relaxation phase (after the 600th time point), “D34” in the ODE model needs 100 more time steps to reach the second peak, and it is weaker than its RB counterpart (“D34*”). Worth noting is that the standard deviation in the stochastically simulated ODE model reveals a distinctive variation in abundance of the “D34” observable during the relaxation phase. We further use the stochastic trajectories of the ODE model for comparison with the RB model. For a closer examination, traces of 15 observables (defined in Table S1) obtained from ODE and RB simulations were paired and superimposed (Fig. 4). Next to the clear matches (e.g., Figs. 4B, 4E, 4H and 4N), there are discrepancies between paired curves. Five of these 15 observables (Figs. 4C, 4F, 4I, 4J and 4O) are examples of the largest divergence between models following a similar pattern of behaviour. They are directly connected in a chain of activation reactions that begins with Ca (Fig. 2B). Higher abundance of all Ca ions present in the system of the ODE model (Fig. 4C) could explain differences between the remaining four observables. However, the trajectory of “all_Ca” remains at the 0 level during steady states rising only in the spiking interval, which resembles the abundance of free Ca (Fig. 4B). Ca activates PP2B represented by the trajectory “PP2Bactive”. The higher level of “PP2Bactive” is consistent with the other three observables, suggesting that this is a factor generating the divergences between the models. Based on the curves of the ODE model, we can reason that a stronger activation of PP2B results in proportionally more copies of the unphosphorylated CK1 and phosphorylated D137. This, in turn, increases substrate availability for PP2C; therefore, more copy-numbers of its bound form. This effect is inverted in the trajectories of the RB model.
RB language allows for detailed dissection of observed molecular species
As the “all_Ca” observable trajectory produced by the ODE model is much lower than in the RB model at the steady state, and “PP2Bactive” appears to dictate the higher effect on the other three observables (“D137”, “CK1u”, “_PP2C”), “all_Ca” and “PP2Bactive” are closer analysed in further steps.
According to the reaction system underlying both models, the activated PP2B is a complex of four Ca ions and PP2B. This detail is not explicitly stated in the variable name of the ODE model. Therefore, to obtain the trajectory of all Ca ions, the sum of the copy numbers of the molecular species whose variable names contain “Ca ”, would have to be replaced by a more thorough analysis of the relevant reaction context of the ODE model. This is not the case in the RB model, where an observable of interest is obtained with an automated procedure that sums the trajectories of molecular species containing the specified expression pattern. Since the trajectory of all Ca in the RB model includes the ions bound to PP2B, the comparison of “all_Ca” to “all_Ca*” is inaccurate due to a difference in the molecular species included in these observables. A similar inaccuracy, related to the naming of the observables, explains the discrepancy between the time courses of the total number of cAMP observables (Fig. 3). In contrast to the RB model, the multiple copies of cAMP bound to R2C2 are not included in this ODE model time course (Fig. 4A).
To determine the identity of molecular species whose trajectories were summed up in “all_Ca*”, all species containing Ca in the RB model simulation were isolated from snapshot data ammounting to 24 compared to only 13 in the ODE model (“all_Ca”). These 13 species correspond in molecular composition to 18 of the 24 RB species sampled in total. The six absent species in the “all_Ca” observable are composed of an active form of PP2B containing four Ca ions, either free or bound to phosphorylated CK1, or DARPP-32 in four different combinations of phosphorylation states. The number of species comprising “all_Ca” observable in the RB model is higher by five because the half-active form of PP2B (bound to two Ca ions) in the RB model exists in six variants. Whilst in the ODE model, it is represented as a single species, named “PP2BinactiveCa2”. By rerunning the RB model with a new set of observables matching those in the ODE model and superimposing only one of six trajectories of “PP2BinactiveCa2”, we can obtain a close match between the “all_Ca” observables (Fig. S1C). This demonstrates that the differences between the “all_Ca” observables of the two models can be explained by the difference in the number of representations of the molecular species. As the six trajectories have the same dynamics and average levels of abundances, choosing one of them is arbitrary (Fig. S2). Moreover, the distinction between locations of two Ca ions on the numbered sites of PP2B is irrelevant since all four sites are functionally indistinguishable.
Rate constants of reactions formulated with “combinatorial-binding” notation should be increased to match ODE trajectories
The largest discrepancy between trajectories can be observed in “PP2BinactiveCa2” (Fig. 5M) that for the RB time course was obtained by summing six entities representing a half-active PP2B into one. If divided by six, representing a single variant of half-active PP2B, the trajectory of the RB model becomes lower than the one of the ODE model (Fig. S3M). These six forms of the half-active PP2B suggest that a better fit between the two models can be achieved by decreasing the constant rate of rules that represent the binding of Ca to free PP2B. However, the decrease of this rate constant of PP2B intermediate form would lead to a further decrease of copy numbers of other coupled observables that trajectories are lower than in the ODE model in the current parameter setting, e.g., the fully active form of PP2B (Fig. 4J). To further examine this observation, we can return to the comparison of model specifications (Table 2). The four reactions of PP2B activation are represented by 24 rules, explicit in site-specific detail that includes all combinations of Ca positioning on four sites of PP2B. Moreover, instead of three distinct PP2B species in the reaction representation (inactive, half-active, activated), in the RB model there are eight different molecular species. Thus, the fine-grained representation of species in the molecular mixture slows down the transition from inactive to active PP2B, despite equal presence of the two-step transition encoded in both formalisms. The same can be observed in the second example that required a much larger number of rules, i.e., an activation of PKA. Figure 4D shows that the RB trajectory of the “PKA*” observable also reaches a much lower peak than its ODE counterpart. Accordingly, values of rate constants of rules that the number increased due to the “combinatorial binding” notation in the RB model, i.e., are represented by more variants of species, should be increased to closely match the ones in the ODE model.
RB language facilitates modifications of dynamic models
The ODE model was analysed under two site-directed mutagenesis conditions affecting the Ser137 site. When these perturbations were applied in the RB model, we could observe a close fit in the initial conditions and a general pattern of dynamics based on six key observables (Fig. 6). Thus, RBM allows to emulate experimentally observed perturbations similarly to ODEs.
Competitive and non-competitive site binding
We also compared two variants of the RB model with different binding site specifications to establish whether the dynamics of the model are affected when DARPP-32s binds multiple partners at once. The first specification is a competitive variant of the model with one binding site (oBS). In the second, a non-competitive variant, the partners bind simultaneously (three-binding-sites, tBS). This type of comparison between ODEs and RBs was previously presented by Blinov et al. (2006). In contrast to their results, the superimposed trajectories of two models (Fig. S4) demonstrate no effect on model dynamics. As the direct consequence of this modification is an increase in the size of the complexes to more than two proteins, it seems that larger complexes are rarely formed during the simulation. This interpretation is confirmed in a direct examination of species counts (Fig. S5).
ODE-based modelling is a classical and commonly used method for creating detailed dynamic models of biological systems (Lotka, 1920; Hodgkin & Huxley, 1952; Lisman, 1989). It is frequently a point of reference and comparison to newly proposed modelling methods (Morris et al., 2010; Ciocchetta & Hillston, 2008; Chaouiya, 2007; Chylek et al., 2015; Danos & Laneve, 2004). Nevertheless, modelling of signalling systems with ODE poses difficulties due to complexities underlying molecular interactions (Kholodenko, 2006; Stefan et al., 2014). RBM was proposed as a solution to this problem.
Numerous reviews (Danos, 2007; Chylek et al., 2014; Hlavacek et al., 2006) and studies discussed the advantages of RB modelling over ODE. This article presents results of encoding reactions underlying an ODE model to the RB language and a comparison of their specification and simulation results. The manual translation of this ODE model into any RB language was necessary despite the existence of a method (Tapia & Faeder, 2013) for automated translation of the SBML-format encoding ODE-based models to an RB model format. The translation failed to generate executable model with correctly identified agents (Supplement 1).
Effects of the RB framework on the model notation
Encoding reactions into rules slightly reduced the size of model specification and increased counts of molecular species, which confirmed the well-known advantage of rule representation (Hlavacek et al., 2006; Chylek et al., 2014). Closer analysis of reaction subsets representing more general molecular mechanisms showed that reduction in the reaction number is only true for reactions occurring between the same reactants, describe the same transformation, and are parameterised with the same values of rate constants, but differ only concerning binding or internal state of reactants. In this type of reaction, the number of unique reaction rates was equal to the number of rules. An increase in the number of rules representing reactions occurred where the same partner binds an agent at multiple sites. In such reactions, all possible positions and stages of the binding process had to be explicitly encoded. The “combinatorial binding” notation is not a general property of the RB language but characteristic of the Kappa syntax. In the alternative to Kappa RB framework, BNG, a rule is definable with identical sites’ names. This implies that the rule pattern defined for one applies to the others, effectively shortening the rule description (Sekar & Faeder, 2012).
Discrepancies in dynamics between models
As in other similar study (Blinov et al., 2006), the comparison of trajectories showed agreement between model dynamics, with some discrepancies. Their source is not due to the issue of ambiguous molecularity that can arise in Kappa, which requires control on the part of the modeller, as none of the rules in the DARPP-32 model allow the formation of closed rings between agents. The discrepancies mainly appeared due to a lack of precision in the variable names of the ODE model caused by differences in molecular species comprising tracked observables. When the simulation of the RB model was performed with observables exactly matching the ones in the ODE model, almost all paired trajectories fitted perfectly. The only problematic observable involved the “combinatorial binding” notation. Further analysis suggested that activation of proteins encoded with fine detail was slower than in the ODE model. More specifically, although half-active PP2B was more abundant in the RB model, its active form had lower levels compared to the ODE model. This observation may apply to RBM in general because the molecular mixture when simulated with NFsim (Sneddon, Faeder & Emonet, 2011), a variant of the network-free simulator for BNGL, will implicitly contain the same number of molecular species with different binding variants as it is in KaSim. Further, based on the example of antibodies with identical antigen-binding sites, the BNGL developers demonstrated that the equivalent set of rules in both RBM frameworks are parameterised by the same rate constant (Suderman & Hlavacek, 2017). In the BNG framework, however, this rate is automatically scaled by a factor equivalent to the multiplicity of indistinguishable ways to obtain the reaction product (Faeder et al., 2005; Faeder, Blinov & Hlavacek, 2009). Moreover, comparison of the time traces of Kappa- and BNGL-defined rules shows equivalent results under stochastic realisations (Suderman & Hlavacek, 2017). Thus, we argue that “combinatorial binding” expressed as a single rule in BNGL will not change the dynamics observed with Kappa. These specific discrepancies should be taken into account when the reactions and rate constants of RB models are derived from ODE models, and preferably determined experimentally for these reaction types.
A shift in modelling focus with the use of the RB framework
The process of encoding reactions into rules turns attention to questions such as how many binding partners can simultaneously bind a protein. The translation process has shown that information about interfaces of interacting proteins and their alternative states would considerably ease the process of model development by guiding decisions on agents’ signatures. Therefore, data resources that could support RB modelling, such as a source of protein interaction interfaces, post-translational modifications (PTMs), and protein domains. For instance, proteins containing phosphatase catalytic domains are enzymes of dephosphorylation reactions (Sacco et al., 2012). However, such detailed information is not accessible for most molecular agents.
Facilitation of modification and reuse of dynamic models
Molecular species defined in the ODE framework are fixed and definite, whereas, in the RB model, they are a subject of investigation. The determination of created species and their abundances is enabled with snapshots, state vectors of molecular mixture sampled during the simulation. Snapshots have been used as a tool for model exploration in other studies (Sorokina, Sorokin & Armstrong, 2011; Suderman & Deeds, 2013), and their visualisation is included as a standard tool in the Kappa framework (Boutillier et al., 2018). Although, unlike Kappa, BNG allows counting the number of species through network generation, this option is limited by the size of the model. Then, if not sampled, the number of specie types is calculated analytically (Suderman & Deeds, 2013). While this is an approximate approach, by using snapshots, we were able to study the emerging molecular species in detail and precisely track the observables of interest. Furthermore, the automatic identification and merging of time courses into observables makes the RB framework particularly advantageous over the ODE framework, as it avoids errors in the identification of molecules in the modelled system. This is important when the system consists of many molecules and their states are to be analysed in detail. In ODE models, on the other hand, obtaining the details of the molecule composition, hidden in the arbitrary names of the individual molecular species, would require further deconstruction of the reaction system. Though, the precise identification of molecules within species could be performed by parsing the SBML-model encoding, the Fernandez et al. (2006) model web page in the BioModels website (http://www.ebi.ac.uk/biomodels-main/BIOMD0000000153), shows incomplete annotations of molecular species behind variable names, both concerning the actual counts of interactions (e.g., Ca ) and their components.
The Fernandez et al. (2006) study does not discuss if DARPP-32 partners bind to the same or different active sites, though DARPP-32 is an intrinsically disordered protein with an unknown binding interface (Dancheck, Nairn & Peti, 2008; Mollica et al., 2016; Engmann et al., 2015; Choy, Page & Peti, 2012). The ODE model specification demonstrates that DARPP-32 forms at most heterodimers. This type of modification in the ODE model would require the arduous extension to much more complex model by enumeration of additional molecular species, the addition of new equations and updating the existing ones. Contrary to this, a definition of such a binding scenario in the RB notation requires the same number of rules, provided that concurrently bound interactors do not influence each other. The RB model was tested with two types of site-directed modifications, demonstrating the framework’s flexibility to reproduce experimentally conducted perturbations. Though the binding site modification effectively changed the model reaction network, it did not affect the model response. Nevertheless, this intervention demonstrated the ease of performing such alterations within the RB framework. Additionally, the pattern notation improves model clarity and provides an intuitive representation of a model akin to a set of chemical reactions rather than equations, potentially improving the learning curve for a modeller-to-be.
Note on simulation time
The ODE simulation remains incomparably faster than the RB simulation. In both deterministic and stochastic settings, COPASI returns ODE results in seconds, while KaSim 3.5 requires ∼40 min1 to return the RB model results (single core on ThinkPad Core i7-4700MQ; RAM: 16GB). With more recent versions of KaSim (≥4), the execution time could be much shorter thanks to a new solution for the update of observable instances (Boutillier, Ehrhard & Krivine, 2017). Nevertheless, with a sufficiently simple model, network-based simulation can be performed far more efficiently using BNGL, bearing in mind that more complex models will have a higher runtime (Sneddon, Faeder & Emonet, 2011). When generating large-scale models, the type of RB model simulation can be scheduled on a case-by-case basis (Santibáñez, Garrido & Martin, 2020).
Further explorations of the DARPP-32 RB model
There are two main routes for further explore the RB model. The first one is a modification of parameters defining different phases of combinatorially bound Ca ions to PP2B and cAMP to R2C2. A particular task would be to identify factors by which the binding constants could be modified to counteract the many intermediate variants of these complexes and the lower copies of their activated final forms. Next, it would be interesting to identify conditions under which we could observe a difference in model dynamics after the addition of binding sites to DARPP-32. In the current setup, the lack of difference might be caused by the similarity in occupancy between a single site and all three sites together, as they do not counter each other’s binding properties. The probability of a site being connected depends on copy numbers of reactants and the strength of binding affinities. Reactions in the model are classified as weak, with dissociation rates in the range of . Low-affinity bindings generally lead to lower levels of site occupancy. Moreover, the amount of DARPP-32 molecules exceeds the total counts of all its interactors. Thus, with the current proportions of reactants, all three DARPP-32 sites cannot be saturated. To expose the potential differences in dynamics between the two binding scenarios, the site occupancy could be modified by increasing the size of the reactant pools or reducing the level of DARPP-32. As mentioned by Fernandez et al. (2006), levels of DARPP-32 vary considerably, between to tens of , in the striatum. With the greater availability of single-cell techniques for protein quantification (Lo et al., 2015) it would be worth establishing more precisely the range of DARPP-32 even at the resolution of a dendritic spine (Otmakhov & Lisman, 2012). Estimating variability between cells could also be used to compare the varying levels of phosphorylated DARPP-32 at Thr34 that were observed in the stochastic simulations.
Extending the model to match evolving knowledge on the DARPP-32 interaction network
The main advantage of RBM over ODE is the flexibility to extend and manipulate the model, rather than to rewrite it into a new one. Through a single representation, the community interested in DARPP-32-related molecular systems could pool their knowledge. We believe that the refactored model of the DARPP-32 in a rule-based framework will facilitate the creation of such an updated model in future. It should be noted that the “DARPP-32 events” pathway (R-HSA-180024.3) specified in the REACTOME database is in fact the model of Fernandez et al. (2006) used in this study. So far, only early signalling events of DARPP-32 have been modelled, localised mainly in the cytosol. The Ser102 site regulates nuclear transportation of DARPP-32 representing late signalling events (Stipanovich et al., 2008). The site was omitted in the Fernandez et al. (2006) model due to the lack of evidence that it may be affected by DA or Glu signalling (Girault et al., 1989; Svenningsson et al., 2004). However, a recent study by Nishi et al. (2017) suggests that Glu can decrease the effect of DA signalling by dephosphorylating DARPP-32 at Ser102 causing the accumulation of DARPP-32 in the nucleus, the effect known to be promoted by drugs of abuse (Stipanovich et al., 2008). Moreover, the role of the DARPP-32 interaction network in the nucleus appears to be an important future goal of the recently published ODE model incorporating DARPP-32 (Yapo et al., 2018), indicating the need to explicitly demonstrate the contribution of DARPP-32 to the observed switch-like behaviour of PKA in the nucleus. The inclusion of the Ser102 site seems inevitable to achieve this goal, but extending the current model with two phosphorylation sites may prove to be quite complex. Encouragingly, to calibrate this extended model, measurements experimentally obtained by Nishi et al. (2017) for all four phosphorylation sites could be fed into Pleione (Santibáñez, Garrido & Martin, 2019), a recently developed tool for this task in RBM. Lastly, it would be worth to establish if stochastic simulation can demonstrate new behaviour of this particular molecular system that deterministic simulation has so far not revealed.
Need for formal prioritisation methods of emerging molecular species
RB modelling offers tools for the dissection of emerging molecular species during simulations. As the number of such molecular species increases it becomes difficult to identify which model components are of particular importance to the system. There is great potential to exploit commonly used methods for model exploration, such as sensitivity analysis, to identify critical model features including parameters and model output variables. It would be advantageous to support the modeller’s assumptions with automated methods to prioritise model outputs for downstream analysis and to gain greater insight into the underlying biological systems.
Dynamic molecular modelling has become increasingly important in uncovering and integrating our dispersed knowledge of molecular mechanisms. Choosing the best formalism to meet this challenge is a difficult task. In this work, we have presented a detailed and systematic comparison of two major formal approaches to quantitative modelling. We demonstrated the advantages and disadvantages of RB modelling to the prominent ODE approach. We confirm, after other similar studies, that RB modelling is a more detailed and flexible way to represent biological molecular systems, enabling exploration of individual molecular entities, model extension, and future reuse. These conclusions confirm the potential of the RB formalism and hopefully will embolden future exploration and research in this topic.
Comparison of variable compositions of molecular species containing Ca2+ ions tracked in both models.
Comparison of variable compositions of molecular species containing Ca2+ ions tracked in the system in both models with (A) unaltered observables; (B) all molecular species containing Ca2+ ions selected by names to match the original model and summed to obtain a single trace, where 13 molecular species in the ODE model are represented by 18 species in the RB model; (C) 13 molecular species of ODE model matched to 13 of RB model, where only 1 of 6 molecular species of inactive PP2B was selected. In comparison to the unaltered species composition (A), the result shows that discrepancy between the ODE and RB observable trajectories have diminished (B,C).
Trajectories of six molecular species of semi-active PP2B in the RB model.
Half-active PP2B is a complex composed of PP2B and two Ca2+ ions. Simulation of the RB model generates six different molecular species representing this complex due to combinatorial binding of Ca2+ ions to four identical PP2B sites. The graph shows the superimposed trajectories of these six variants of half-active PP2B. None of these six trajectories is distinguished from the others by either the pattern of dynamics or the average abundance level.
Comparison of separated Ca2+ -containing molecular species selected as in the ODE model.
Comparison of separated Ca2+-containing molecular species selected as in the ODE model. The “PP2BinactiveCa2” trajectory in the RB model was obtained by selecting one of 6 entities representing, among others, the inactive form of PP2B in the RB model. There is still a discrepancy between the models, but the trajectory is lower for the RB model.
Comparison of two variants of the RB model.
Comparison of two variants of the RB model in which the agent representing DARPP-32 had one binding site (oBS, red trace) and three binding sites (tBS, black trace). The superimposed trajectories of the respective agents indicate that the model trajectories were not affected by this modification.
Overlay of changes in the number of species over time for two RB model variants.
Overlay of changes in the number of species over time for two models in which DARPP-32 can bind at one site (oBS) and three sites (tBS) (A). The size of the species set is similar in both model variants. The change in the number of unique species is consistent with the stimulus trajectory (B). The dynamics of complex formation are dictated by the pattern of stimulus input, as the largest differences between oBS and tBS occur during stimulus application.
Names of observables in RB and ODE models.
Names of RB observables and corresponding names of ODE observables with definitions. To obtain observable ODEs, the time series of the corresponding molecular species are summed based on their names.

domain: biology


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Inner PADS ALFA 01 pads - provide protection against mechanical impacts, when jumping and various types of combat activities.
Absorbs and reduces kinetic energy generated by small arms projectiles, reflected projectiles and shrapnel. A set of internal removable pads allows the ballistic helmet to be optimally adjusted to the exact disposition and needs of each individual.
The PADS ALFA 01 material is a functional material that wicks away sweat and provides thermoregulation. The inner part of the cushions is a special foam that is guaranteed to be used in the extreme temperature range of -32°C to 44°C, for short periods of 4 hours up to 80°C. Functionality of the properties and declared parameters verified by tests in an accredited testing facility and load tests by members of the Czech Armed Forces.
The internal removable PADS ALFA 01 pads are attached to the inside of the shell with velcro discs. The ARGUN ballistic helmet is already equipped with these velcro discs glued in the optimal and recommended positioning when initially delivered.
Basic kit assembly:
9 pads in total
Front cushion - 1 piece (variant size)
Rear cushion -1 pc (size variant)
Temporal cushion assembly - 3 pcs (size variant)
Front cushion - 1 piece (UNI)
Rear cushion - 1 piece (UNI)
Right side demarcation pad - 1 piece (UNI)
Left side demarcation pad - 1 piece (UNI)
Type of UNI pad:
Right side demarcation pad, left side demarcation pad
Front boundary pad
Rear demarcation pad
Supplied in three thicknesses under the label: "10", "12" and "14" Always one selected variant size in the whole set, no combinations possible.
Front cushion, back cushion, temple cushion assembly: Thickness (cushions) "10" : cushion height 12.5 mm Thickness (cushions) "12" : cushion height 14,5 mm Thickness (cushions) "14" : cushion height 17.0 mm
UNI pads: Side demarcation pad right, left : pad height 17.0 mm Front demarcation cushion: cushion height 6.0 mm Rear cushion: cushion height 6.0 mm

domain: design


text:
A Memorandum of Understanding (MoU) was signed by the European Commission and the EOSC Association, marking the start of the Co-programmed European Partnership on European Open Science Cloud (EOSC) under the Horizon Europe Framework Programme.
In support of the Internationalization of EOSC, an online meeting between the two regional projects of NI4OS-Europe for SouthEast Europe and LA Referencia (Federated Network of Institutional Repositories of Scientific Publications) for Latin America, was held on Friday 4/06/2021.
EOSC Symposium 2021 starts tomorrow, June 15th and we are happy to share the presence of the NI4OS-Europe project is quite significant. The full program is available here.
A workshop on priorities and international alignment for the European Open Science Cloud
We are happy to announce that Digital skills for FAIR and Open Science is published!
First resources have been on-boarded to NI4OS-Europe service catalogue

domain: library


Eleven important Acadia National Park events shaped the decade at the Maine national park and left some lasting changes including new records in visits, a generous donation of land and projects in the Schoodic section, the park’s 100th anniversary, a new superintendent, a presidential visit and a heightened awareness of climate change.
Here are some key moments, happenings and trends that dominated Acadia National Park during the 2010s:
Legacy of President Barack Obama
A presidential visit may have been the most memorable of Acadia National Park events. On the heels of his biggest political victory – passage of a national health insurance plan – Barack Obama became the first sitting president to visit Acadia National Park. The president’s family vacation in July 2010 drew crowds and created a lot of excitement in Bar Harbor and the park. Obama, his wife, Michelle, and daughters Malia and Sasha spent three days in the park including hiking the summit loop on Cadillac Mountain and Ship Harbor and visiting Bass Harbor Head Light. While the short vacation put the national spotlight on Acadia, possibly Obama’s most important legacy in Maine occurred in August 2016 when he used the Antiquities Act to unilaterally approve a new national monument – the Katahdin Woods and Waters National Monument. Both Acadia and the new Maine monument were created with private land donations and both overcame political hurdles. Obama also started the Every Kid in a Park initiative in 2015, renewed every year since, in which the National Park Service gives every fourth grader and family free admission to national parks. President Donald J. Trump has affirmed Katahdin Woods and Waters National Monument, and Every Kid in a Park (although the Trump administration now calls it Every Kid Outdoors).
Climate change consequences in Acadia
During the decade, climate change emerged as possibly the biggest environmental threat facing Acadia National Park, partly fueled by a Portland-based Gulf of Maine Research Institute study in 2014 that found the Gulf of Maine warmed faster than 99 percent of the world’s oceans. The effects of climate change in Acadia were underscored with the opening of an exhibit on the topic at an upgraded Sieur de Monts Nature Center in 2016 and efforts by the park to study the issue. Seeking to educate the public on the current and future impacts of climate change, the provocative Nature Center exhibit examines the crisis out to 2050 including on the park’s summits, where certain subalpine species like alpine azalea may perish with warmer temperatures and lower elevation plants may move up to supplant them, on salt marshes possibly flooded by rising sea levels, on lobsters, which are moving north to colder waters. Park staff is attempting to plan for any future sea level rise on infrastructure such as at Thunder Hole, certain roads and the hiking trails close to the coast. Rising global temperatures may also have contributed to a boom in visits during October, which rose to a record 507,595 in 2017, up 75 percent from 289,114 in 2010. The park is also aiming to increase use of the fare-free Island Explorer partly to reduce the number of private vehicles and carbon emissions, which contribute to trapping heat on the planet.
Dealing with a record number of visitors, Acadia National Park approved a sweeping transportation plan intended to reduce traffic at popular destinations and better control parking from about mid-June to mid-October. The most disputed part of the plan, released in March of 2019, calls for a timed reservation system at a fee of likely less than $10 for cars on Cadillac Mountain, the north lot of Jordan Pond and the Ocean Drive corridor. The reservation system is tentatively planned to start in 2021. Other parts of the transportation plan include expanded use of the fare-free Island Explorer, parking expansion at the Hulls Cove Visitors Center, eventually abolishing right lane parking now allowed on sections of the Park Loop Road and removal of an existing parking lot and restroom on the north side of Route 233 at Eagle Lake to be replaced with a new, 125-space parking lot constructed south of the highway at an NPS maintenance storage yard known as Liscomb Pit. Acadia National Park events and meetings were held to air a draft transportation plan and receive public comment in the spring of 2018.
New superintendent, key retirements
Kevin Schneider was named as superintendent of Acadia National Park and St. Croix Island International Historic Site in December 2015, replacing Sheridan Steele, who began as superintendent in 2003. Schneider oversaw release of the transportation plan for the park, helped celebrate the park’s centennial during his first year and served during the busiest visitation years in the park’s history. Before starting at Acadia, Schneider was most recently deputy superintendent of Grand Teton National Park in Wyoming. The park lost some key institutional knowledge and skills, when longtime Acadia Ranger and natural resource specialist Charlie Jacobi retired at the end of 2017, park wildlife biologist Bruce Connery retired about a year later, and Chief Ranger Stuart West, a 16-year Acadia veteran, left at the end of September 2019 to become the new superintendent for the High Plains Group of parks in Colorado and New Mexico.
Expansion of national park on the Schoodic Peninsula
In the early part of the decade, some 3,200 acres of open space on the Schoodic peninsula abutting Acadia National Park was targeted for a huge resort development, including a hotel and golf course, but in 2011 an anonymous buyer purchased the property and donated 1,400 acres south of Route 186. The donor paid for planning and design, construction and furnishing of the beautiful Schoodic Woods Campground, 100-seat amphitheater, ranger station and visitor center, maintenance building, 8.5 miles of multipurpose paths styled after the park’s carriage roads, 4.5 miles of new hiking trails, an underground utility line along the main road and a causeway bike lane and bridge. The causeway was a separate project because it was inside the park, but everything else was done by a contractor hired by the donor, said Sheridan Steele, former superintendent of Acadia who represented the park in planning and design and provided a park engineer to help oversee the actual construction work. “Saving this extraordinary property, including two islands and about a mile of shoreline south of 186, by itself was a spectacular victory for those who wished to see the amazing natural values and high quality visitor experience in Acadia NP forever protected,” Steele wrote in an email. “The end result has become a dramatic example of the incredible generosity and conservation leadership of Acadia’s donors, volunteers and partners. Working together with Acadia’s dedicated professionals, the larger team effort sets Acadia apart from many other national parks.”
The Schoodic Woods Campground, which opened in September 2015, includes 33 RV sites with water and power, including 20 pull-through; 50 car tent sites with electric, including 26 listed as for tents only and 9 private hike-in sites.
The decade also saw the official naming of the Schoodic Institute in 2013 and further improvements to the institute’s campus, built on a former Navy base, which closed in 2002. The campus offers housing and meals for individual researchers, groups and conferences, classrooms, laboratories, and a modern 124-seat auditorium.
Loss of a giant environmentalist
Of all Acadia National Park events, the death of David Rockefeller in March 2017 was among the saddest, marking a huge loss for the park as well as the rest of Maine. Rockefeller was noted for donating coastal properties and conservation easements in Maine and providing major contributions to conservation organizations. He was the youngest child of John D. Rockefeller Jr., a friend of park co-founder George B. Dorr who donated about 11,000 acres to the park and financed construction of 45 miles of carriage roads. David Rockefeller’s wife, Peggy, who died in 1996, was a founder of the Maine Coast Heritage Trust in 1970 and also an important advocate for preserving open space in Maine. When he turned 100 in 2015, David Rockefeller gave a 1,000-acre reserve of pristine woodlands with views over the Great Harbor to the Land & Garden Preserve. The Maine Coast Heritage Trust worked with him to create public access at a number of sites on Mount Desert Island including areas at Lower Day Mountain and Hunter’s Beach. He also donated Little Long Pond and guaranteed public access to its extensive trails and carriage roads.
Acadia National Park and the National Park Service both celebrated centennials in 2016. The park was established as the Sieur de Monts National Monument on July 8, 1916 by President Woodrow Wilson and Wilson signed a law to create the National Park Service on Aug. 25, 1916. The centennial celebration was organized by the Acadia Centennial Task Force, co-chaired by Jack Russell and Cookie Horner. More than 70 Acadia National Park events and other experiences filled up the official centennial calendar, featuring programs, occasions and celebrations during every month, and involving some of the more than 250 Centennial partners.
New and improved hiking trails, other facilities
Acadia inaugurated the Quarry and Otter Cove Trails in 2014 connecting Gorham Mountain, Sand Beach and Otter Cove to the Blackwoods Campground. The new trail and rehabilitations on many other trails received federal dollars and funds from Acadia Trails Forever, a special endowment started in 1999 for the park. The $13 million fund to benefit the trails includes $9 million in private donations raised by the Friends of Acadia and $4 million in federal funds, mostly from the park’s entry fees.
An extensive rehabilitation of the historic Valley Cove Trail was completed by the Acadia trails crew and the trail opened in October of 2019. The trail, which had been closed for about three years, is located on the east side of St. Sauveur Mountain and runs along the west shore of Somes Sound.
The trails crew, led by foreman Gary Stellpflug, finished countless important trail rehabilitations during the 2010s including Acadia Mountain, Flying Mountain, Ship Harbor, Gorge Path, the Asticou Trail, Jordan Cliffs, Jordan Stream Path, the Cadillac Cliffs Trail, the Deer Brook Trail, Ocean Path and the Valley Trail.
Another major upgrade at Acadia included the the Hulls Cove Visitor Center, the main visitor center for Acadia National Park, which received $1.2 million in renovations during a project completed in 2019.
New “wayside exhibits,” funded by a $450,000 grant from the National Park Service, were installed at the park including on Mount Desert Island in the fall of 2014. The exhibits feature colorful and succinct explainers on the geology, oceanography, wildlife, botany, scenic views and history of the park and orientation around the park.
Federal government shutdowns rattle Acadia
Acadia National Park events and work were interrupted by two major US government shutdowns during the decade including one that came during the peak of the fall tourist season and then a winter shutdown that was the longest in history. A 16-day shutdown that started Oct. 1, 2013 dramatically cut visits to the park during a traditionally busy month and hurt sales at hotels, restaurants and other businesses in Bar Harbor. The National Park Service closed all national park units during the shutdown, which was caused by a lapse in appropriations. In a report, the National Park Service estimated that visits to Acadia fell by 192,000, or 67 percent, from the average for the month for the three prior years and visitor spending plummeted to $8 million from an average $24.1 million, even accounting for a possible rescheduling of visits to the final two weeks of October or a shifting of spending to other non-NPS sites. During the 2013 shutdown, all park roads — including the Park Loop and the road to the summit of Cadillac Mountain — were shut to traffic. Hiking trails and carriage roads were also are closed during 2013. Hikers and bike riders entered the park around barricades but at one point rangers fined people who used scooters or mopeds.
A 35-day shutdown, starting on Dec. 22, 2018, had little economic impact on surrounding communities because it came during the slowest time of year for Acadia and Bar Harbor. During the partial shutdown, certain gates at Acadia and many other national parks were kept open for visitors, but most staff was put on furlough. Fortunately, Acadia escaped the vandalism that occurred at some big parks in the West. The shutdown did postpone the start of a vehicle reservation system at Acadia for a year, because almost all of the park’s 80-90 full-time staff, including Superintendent Kevin Schneider, were put on furlough and finalization of the transportation plan was delayed. When staff returned to work, the priority was to compensate for lost time by hiring seasonal staff for 2019 and to prepare the park for visitors.
Acadia sets records for visits
With the nation emerging from the Great Recession at the turn of the decade, it seemed people were looking for an escape and Acadia National Park benefited. The country’s eighth most visited National Park, Acadia set an attendance record in 2017 with more than 3.509 million visits, up 40 percent from 2.5 million in 2010. Visits were down slightly in 2018 and 2019 but only by about 1 percent from the peak. National polls showed the park was growing in popularity as the economy improved. In 2014, Acadia was selected America’s favorite place by viewers of “Good Morning America” and the No. 1 National Park by readers of USA Today. Some might say the park became too popular at certain times. Traffic congestion forced the closure of the summit road to Cadillac Mountain 58 times in 2019 and the road to Bass Harbor Head Light 32 times, demonstrating the need for a reservation system to park at certain popular attractions. During 2019, July 5 was the busiest day in the history of Acadia with visits exceeding 35,000 for the day and also a hectic one for rangers and rescue personnel responding to a string of hiking mishaps.
Backlog of maintenance and fee hike
Advocates for national parks, including the Friends of Acadia and the National Park Service, underscored the need for reducing a logjam of maintenance work. Of the nearly $12 billion of deferred maintenance in national parks across the country, Acadia weighed in with nearly $60 million. The work includes upgrades to old buildings, waste water systems, roads, dams and campgrounds.
A Senate committee in November 2019 approved the Restore Our Parks Act to reduce the maintenance backlog, but the future of the bill is unclear. A lead sponsor of the bill, Sen. Angus King, said Acadia is being “loved to death,” with annual visitation equal to almost three times the 1.3 million population of the state of Maine. To raise money for deferred maintenance, the Department of Interior in 2017 proposed a new five-month peak season annual fee of $70 for 7 days at Acadia and 16 other parks, but after an outcry, the annual fee at Acadia saw a modest increase from $50 to $55 in 2018 and the one-week pass per vehicle rose from $25 to $30.


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Here’s a conference for librarians who find themselves both in the NYC metropolitan area and interested in the state of civil liberties: “Personal Liberties in the Age of Terrorism”, which being hosted by the New York Metropolitan Library Council (METRO to you Visigoths) on May 13 from 9:00am – 2:30pm at the Brooklyn College Library. They’re advertising attendances by civil liberties attorney Norman Siegel, Daniel Lyons from the FBI, and Marcia Hoffman from the Electronic Privacy Information Center, so this is likely to be a lot of fun. I already signed up for it, as this is a field of intense concern for me (if you couldn’t guess by my letter to the NRO last week).
The conference description is here.
The METRO (all right, so I’m a Visigoth) registration link is here.
And I’ll post a bit about what I learn there next week.


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An amplification-free electrochemical detection of exosomal miRNA-21 in serum samples
MetadataShow full item record
Recent evidence suggests that small non-coding RNAs or microRNA (miRNA)s encapsulated in exosomes represent an important mechanism of communication between the cells. Exosomal miRNAs play an important role in oncogenesis via stimulating cell to cell communication and facilitating metastasis in cancers. Despite progressive advances, current methods for exosomal miRNA detection most rely on labor intensive sequencing approaches which are often prone to amplification bias and require costly and bulky equipment. Herein, we report an electrochemical approach for detection of cancer-derived exosomal miRNAs in human serum samples by selectively isolating the target miRNA using magnetic beads pre-functionalized with capture probes and then directly absorbing the targets onto the gold electrode surface. The level of adsorbed miRNA is detected electrochemically in the presence of the [Fe(CN)6]4-/3- redox system. This method enabled an excellent detection sensitivity of 1.0 pM with a relative standard deviation (%RSD) of <5.5% in cancer cells and serum samples (n = 8) collected from patients with colorectal adenocarcinoma. We believe that our approach could be useful for the quantification of exosomal miRNA in clinical samples.
This publication has been entered into Griffith Research Online as an Advanced Online Version.
Analytical Chemistry not elsewhere classified

domain: literature


5 things to watch: Ohio State
On Saturday, the Spartans face off against the Buckeyes on the road in Columbus, with the winner taking the lone spot at the top of the Big Ten East Division standings.
Here are five things to watch for in Saturday’s game:
How MSU’s youth handles pressure: This weekend brings a championship-game vibe; it’s essentially a winner-take-all contest. As long as the winner of Saturday’s game doesn’t allow its remaining two opponents to come away with an upset victory, the victor this weekend will be headed to the Big Ten title game.
Both teams sit at 5-1 in the conference standings, with Michigan and Penn State right behind at 4-2.
MSU exhibits one of the youngest teams in the nation. They have handled pressure and played well in big-time games. Their season relies on this game, and how the youth on this team are able to handle their nerves Saturday will be a deciding factor in who wins this game.
Continuing history: Based off recent history, Spartan fans have some hope entering Saturday as the home team hasn’t won in the series since 2007.
While it doesn’t necessarily correlate with performance, the Spartans will head to Columbus hoping to continue this part of history.
Stopping OSU’s prolific offense: The Buckeyes rank first in the conference in total offense, scoring and passing offense.
While Penn State’s Saquon Barkley brought a tough matchup against MSU, the Spartans were able to contain him and the rest of the Nittany Lions’ offense, however, PSU isn't as high powered of an offense as Ohio State. The Buckeyes are fifth in the nation in total offense, the Nittany Lions are 36th.
Headed by quarterback J.T. Barrett and running back J.K. Dobbins, OSU’s offense will likely bring the biggest challenge this season to the Spartans’ defense.
Lewerke’s dominance: Sophomore quarterback Brian Lewerke is coming off two straight 400 yard passing games.
Over the last two games, Lewerke has completed 72 of 113 passes for 845 yards with six touchdowns and two interceptions.
He has shown he can be clutch when he needs to orchestrate a late-game scoring drive, but will his luck run out against the Buckeyes?
Improving the rushing attack: MSU is currently eighth in the conference in rushing offense, while the Buckeyes are second in the conference.
The Spartans haven’t recorded over 100 yards rushing in a game since their road win against Minnesota on Oct. 14. If they can turn around this aspect of their game, the Spartans will have a viable shot at leaving Columbus with a win.


Stan Lee (born Stanley Martin Lieber, December 28, 1922) is an American comic book writer, editor, publisher, media producer, television host, actor, voice actor and former president and chairman of Marvel Comics.
Here’s The Real Story Behind Stan Lee’s Deleted ‘Guardians Of The Galaxy’ Cameo
MTV News, 2014-09-04 13:31:58
The story you might have heard about Stan Lee's original "Guardians of the Galaxy" cameo isn't true.
The Stan Lee cameo that never made it into 'Guardians'
USA Today Movies, 2014-09-02 16:28:32
An obscene gesture was originally in the works for Marvel's elder statesman. Related StoriesThe absolute best part of 'Guardians of the Galaxy' is now onlineFeel-good Friday: Watch Pixar's 'Lava' previewJoan Rivers would be 'touched' by outpouring, Melissa says
Stan Lee Confirms ‘Black Panther’ Movie, Teases ‘Black Widow’ Spin-Off
MTV News, 2014-09-01 17:51:30
Stan Lee confirms "Black Panther" movie is happening, teases that "Black Widow" is probably next, makes everybody freak out.


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Explore Gallery of 2 Piece Panel Wood Wall Decor Sets (set Of 2)
Showing Photo About Vivaterra with Regard to Well-liked 2 Piece Panel Wood Wall Decor Sets (Set Of 2)
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text: You can get by cheap! I purchased 150 rag decoys about 7 years ago and they work great when you don't have the budget for alot of shells. When my one buddy doesn't bring all his decoys along, we'll just use my rags and we get our share of birds. Of course we have a good location/field so that helps alot, but I've used the rags in other locations and they work quite well everywhere we've tried. The first time my buddy brought out the 50 rags that he bought, I laughed at him, thinking these geese are too smart to fall for a bunch of brown plastic bags. We got a pair to come in shortly after we set up, I thought "just got lucky with a couple of dumb geese". After the second flock of 100+ set their wings and dropped in, I changed my mind!

domain: hobby


Edward II (ballet)
| Edward II (ballet) ： ウィキペディア英語版|
''Edward II'' is a ballet in two acts, with music by the English composer John McCabe.
Commissioned by Stuttgart Ballet, with a narrative conceived by choreographer David Bintley, the ballet largely follows the plot of Christopher Marlowe's play of the same name, rather than the life of the real Edward. Bintley incorporates elements of the 14th century satire ''Le Roman de Fauvel'', with the character of Edward replacing that of the ass Fauvel, Piers Gaveston as the Fool, and Queen Isabella as the Virgin. As such, the narrative depicts the self-obsessed sexual relationships of the three main characters with the increasing civil unrest in the country as a backdrop.
Although McCabe's score uses motifs associated with music of medieval times, the treatment is modern, with the orchestra including a large battery of percussion as well as a celesta and electric guitar.〔Rickards, Guy (1999) Liner notes to CDA67135/6, Hyperion Records〕
The ballet was premiered in Stuttgart on 15 April 1995. The UK première was given by the Birmingham Royal Ballet at the Birmingham Hippodrome on 9 October 1997. The score was recorded by Hyperion Records in 1999, with the Royal Ballet Sinfonia conducted by Barry Wordsworth.
* List of historical ballet characters
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
■ウィキペディアで「Edward II (ballet)」の詳細全文を読む
| 翻訳と辞書 : 翻訳のためのインターネットリソース|
Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.


High Performance airMAX ac Bridge
UniFi Network Set up a UniFi Access Point in Standalone mode Use the UniFi Network app to set up and configure your UniFi Access Point in Standalone mode. The UniFi Security Gateway extends the reach of the UniFi Enterprise System to route, secure, and manage your network for optimal performance and reliability. Managed by the UniFi Controller software, the UniFi Switch delivers powerful performance, intelligent switching, and PoE+ support for your enterprise networks. Topology views are created automatically for Ubiquiti devices, and can be easily adjusted for 3rd party devices. Data to blow your mind and improve your network UNMS stores up to a year of data for all your devices. The Ubiquiti Networks® PBE-RAD-400 is a protective radome that shields the radio from the elements; it is available as an optional accessory for the Ubiquiti Networks® PowerBeam PBE-M5-400. Ubiquiti 5GHz airMAX MIMO Rocket WiFi Radio RocketM5 Available at: www.switchcom.co.za The Ubiquiti ROCKETM5 is a rugged, high-power, linear 2x2 MIMO.
airMAX ac technology provides performance improvements in latency, throughput and scalability compared to other outdoor systems in its class. airMAX ac products offer improved latency, noise immunity, scalability and significantly increased throughput performance.
Revolutionary MIMO TDMA Protocol System
Unlike standard WiFi protocol, airMAX Time Division Multiple Access (TDMA) protocol allows each client to send and receive data using pre-designated time slots managed by an intelligent AP controller.
High-Performance Wireless Backhaul
airFiber ushers in a new era in price-disruptive wireless technology, delivering amazing wireless gigabit+ performance, low latency and long range, making it ideal for carrier backhaul, building-to-building enterprise use or public safety applications.
Scalable and Unified Enterprise WiFi Management
UniFi is the revolutionary Wi-Fi system that combines Enterprise performance, unlimited scalability, a central management controller and disruptive pricing.
Advanced Network Routing and Switching
Carrier-class reliability and performance are combined with enterprise-level features in a compact and affordable unit. EdgeMAX software integrates with EdgeMAX hardware for the routing, switching, security and management features required to efficiently run your network.
High-Performance Routing and Advanced Security & Network Switching
The UniFi Security Gateway extends the reach of the UniFi Enterprise System to route, secure, and manage your network for optimal performance and reliability. Managed by the UniFi Controller software, the UniFi Switch delivers powerful performance, intelligent switching, and PoE+ support for your enterprise networks.
Unified Video Surveillance Management
Easily scale IP surveillance camera networks to hundreds of plug and play, high-performance devices across multiple locations. Manage and control your system with intuitive software packed with powerful features and analytic capabilities — all without licensing fees or support costs.
Scalable Enterprise Telephony
Powered by Android, the UniFi VoIP Phones are enterprise desktop smartphones designed to seamlessly integrate into the UniFi Enterprise System.
Ubiquiti UniFi UAP-AC-PRO, 3dBi, 22dBm, 450Mbps, 3x3 @ 2. 4GHz & 3dBi, 22dBm
- Up to 5X Faster with Dual-Radio 3×3 11AC MIMO Technology
- Unifi AP AC PRO UAP-AC-PRO-US 802.11ac PRO Wireless Access Point – Enterprise Wi-Fi System
- The UniFi AC Pro AP supports simultaneous dual-band, 3×3 MIMO technology in the 5 and 2.4 GHz radio bands.
- The UniFi AC Pro AP is a gigabit, 802.3af-powered access point suitable for indoor or outdoor use.
- Install, configure, and manage all of the UniFi APs with the intuitive and user-friendly UniFi controller user interface.
Ubiquiti UniFi nanoHD Compact 802.11ac Wave2 MU-MIMO Enterprise Access Point
- Four stream 802.11AC Wave2 technology
- Supports 200+ concurrent users
- 802.3af PoE compatibility
- Optional covers (sold separately) allow the Unifi nanohd AP TO discreetyly blend into its setting
- 3 Gigabit Ethernet ports, CLI management for advanced users
- 1 million packets per second for 64-byte packets
- 3 Gbps total line rate for packets 512 bytes or larger
- Integrated and managed with UniFi Controller v4.x
- Secure off-site management and monitoring, Silent, fanless operation
Ubiquiti UniFi Switch Lite 8 PoE 8-Port Gigabit Switch with 4 PoE+ 802.3at Ports (USW-Lite-8-PoE)
- (8) Gigabit RJ45 ports including (4) 802.3at PoE+ ports
- Managed by UniFi Controller (5.13.10 or later)
- External 60W power adapter
- Wall-mountable (Kit included)
Ubiquiti Networks Networks UniFi Switch 8-Port 150 Watts, White
- Country of origin : China
- Model Number : US-8-150W
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- High‐Performance Dual Band 802. 11ac 4×4 Wave 2 AP
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- The UniFi Cloud Key Gen2 from Ubiquiti Networks allows you to configure your network quickly with a Bluetooth setup from the UniFi mobile applications.
- The UniFi Cloud Key Gen2 features a 10/100/1000 Mb/s Gigabit Ethernet port, which supports 802.3af PoE.
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- Up to 50 Devices
Ubiquiti Networks 2,4/5Ghz 450/1300Mbps 122m 5Pk Indoor/Outdoor, UAP-AC-PRO_5
- Ubiquiti 5-PACK UAP-AC-PRO (P/N UAP-AC-PRO-5-US)
- Important -5-Packs Do Not Include PoE Power Injectors. Assumes use of PoE Switch or Must Purchase 802.3af PoE Separate
- 3×3 11AC MIMO Dual Band 5 and 2.4GHz
- (2) 10/100/1000 Ethernet with 5 GHz Speed: 1300 Mbps and 2.4GHz Speed: 450 Mbps
- Indoor / Outdoor Use (See Outdoor Specifications for Details)


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Irina Dumitrescu is professor of English medieval studies at the University of Bonn. Her writing has appeared in The London Review of Books, The Times Literary Supplement, The New York Times, The Atlantic and the LA Review of Books, among others. She is the author of The Experience of Education in Anglo-Saxon Literature (2018). She lives in Bonn in Germany.
Stories and literature
Get medieval on your haters: lessons from Beowulf and Chaucer
When your reputation is at stake, get medieval on your haters: lessons from Beowulf, Margery Kempe and Geoffrey Chaucer
by Irina Dumitrescu


Performance counters are modeled using the PerfCounterInfo data object type. Each instance of a PerfCounterInfo data object contains metadata about a specific metric available from the system. The disk group of counters support metrics for I/O (input/output) performance (such as latency and read- and write-speeds), and utilization metrics for storage as a finite resource. This page includes documentation for storage-utilization counters only. See the Disk I/O Counters page for information about disk I/O counters.
A datastore is a logical construct, conceptually similar to a volume. A datastore provides an abstraction of the underlying LUNs (logical unit numbers), which provide the actual physical storage. The storage-utilization counters measure various aspects of datastore space.
Some key points about storage utilization:
The instance property of the metric ID identifies the device associated with a metric returned from the system. Conversely, device information can be set in the instance property, in the PerfQuerySpecs optional metricId and passed to a PerformanceManager query operation to return storage utilization information for specific virtual machines or filetypes. The descriptions below include this information.
These counters are available from vCenter Server only (not directly from ESX).
Values are populated every 30 minutes.
|Configured size of the datastore. Available from a datastore entity only.|
|provisioned||absolute||kiloBytes||1||latest||Storage Space Potentially Used|
|Amount of storage set-aside for use by a datastore or virtual machine. Files on the datastore and the virtual machine can expand to this size, but not beyond it. Available from datastore and virtual machine target entities.|
|unshared||absolute||kiloBytes||1||latest||Storage Space Not Shared|
|Amount of space associated exclusively with a virtual machine. Available from datastore and virtual machine target entities.
|used||absolute||kiloBytes||1||latest||Storage Space Actually Used|
|Amount of space actually used by the virtual machine or the datastore. May be less than the amount provisioned at any given time, depending on whether the virtual machine is powered-off, whether snapshots have been created or not, and other such factors. Available from datastore and virtual machine target entities.
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