Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model.

Author summary Adenylyl cyclases (ACs) are enzymes that can translate extracellular signals into the intracellular molecule cAMP, which is thus a 2nd messenger of extracellular events. The brain expresses nine membrane-bound AC variants, and AC5 is the dominant form in the striatum. The striatum is the input stage of the basal ganglia, a brain structure involved in reward learning, i.e. the learning of behaviors that lead to rewarding stimuli (such as food, water, sugar, etc). During reward learning, cAMP production is crucial for strengthening the synapses from cortical neurons onto the striatal principal neurons, and its formation is dependent on several neuromodulatory systems such as dopamine and acetylcholine. It is, however, not understood how AC5 is activated by transient (subsecond) changes in the neuromodulatory signals. Here we combine several computational tools, from molecular dynamics and Brownian dynamics simulations to bioinformatics approaches, to inform and constrain a kinetic model of the AC5-dependent signaling system. We use this model to show how the specific molecular properties of AC5 can detect particular combinations of co-occuring transient changes in the neuromodulatory signals which thus result in a supralinear/synergistic cAMP production. Our results also provide insights into the computational capabilities of the different AC isoforms. Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory G alpha(olf) and inhibitory G alpha(i) proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if G alpha(olf) and G alpha(i) can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5's ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.

The opposing action of dopamine and acetylcholine has long been known to play an important role in basal ganglia physiology. However, the quantitative analysis of dopamine and acetylcholine signal interaction has been difficult to perform in the native context because the striatum comprises mainly two subtypes of medium-sized spiny neurons (MSNs) on which these neuromodulators exert different actions. We used biosensor imaging in live brain slices of dorsomedial striatum to monitor changes in intracellular cAMP at the level of individual MSNs. We observed that the muscarinic agonist oxotremorine decreases cAMP selectively in the MSN sub population that also expresses D-1 dopamine receptors, an action mediated by the M-4 muscarinic receptor. This receptor has a high efficacy on cAMP signaling and can shut down the positive cAMP response induced by dopamine, at acetylcholine concentrations which are consistent with physiological levels. This supports our prediction based on theoretical modeling that acetylcholine could exert a tonic inhibition on striatal cAMP signaling, thus supporting the possibility that a pause in acetylcholine release is required for phasic dopamine to transduce a cAMP signal in D1 MSNs. In vivo experiments with acetylcholinesterase inhibitors donepezil and tacrine, as well as with the positive allosteric modulators of M-4 receptor VU0152100 and VU0010010 show that this effect is sufficient to reverse the increased locomotor activity of DAT-knockout mice. This suggests that M-4 receptors could be a novel therapeutic target to treat hyperactivity disorders.

Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours. Results: We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions. This approach is useful both for experimental design as well as model building.

The basal ganglia are involved in the motivational and habitual control of motor and cognitive behaviors. Striatum, the largest basal ganglia input stage, integrates cortical and thalamic inputs in functionally segregated cortico-basal ganglia-thalamic loops, and in addition the basal ganglia output nuclei control targets in the brainstem. Striatal function depends on the balance between the direct pathway medium spiny neurons (D1-MSNs) that express D1 dopamine receptors and the indirect pathway MSNs that express D2 dopamine receptors. The striatal microstructure is also divided into striosomes and matrix compartments, based on the differential expression of several proteins. Dopaminergic afferents from the midbrain and local cholinergic interneurons play crucial roles for basal ganglia function, and striatal signaling via the striosomes in turn regulates the midbrain dopaminergic system directly and via the lateral habenula. Consequently, abnormal functions of the basal ganglia neuromodulatory system underlie many neurological and psychiatric disorders. Neuromodulation acts on multiple structural levels, ranging from the subcellular level to behavior, both in health and disease. For example, neuromodulation affects membrane excitability and controls synaptic plasticity and thus learning in the basal ganglia. However, it is not clear on what time scales these different effects are implemented. Phosphorylation of ion channels and the resulting membrane effects are typically studied over minutes while it has been shown that neuromodulation can affect behavior within a few hundred milliseconds. So how do these seemingly contradictory effects fit together? Here we first briefly review neuromodulation of the basal ganglia, with a focus on dopamine. We furthermore use biophysically detailed multi-compartmental models to integrate experimental data regarding dopaminergic effects on individual membrane conductances with the aim to explain the resulting cellular level dopaminergic effects. In particular we predict dopaminergic effects on Kv4.2 in D1-MSNs. Finally, we also explore dynamical aspects of the onset of neuromodulation effects in multi-scale computational models combining biochemical signaling cascades and multi-compartmental neuron models.

Although it is known that protein kinase A (PKA) in the nucleus regulates gene expression, the specificities of nuclear PKA signaling remain poorly understood. Here, we combined computational modeling and live-cell imaging of PKA-dependent phosphorylation in mouse brain slices to investigate how transient dopamine signals are translated into nuclear PKA activity in cortical pyramidal neurons and striatal medium spiny neurons. We observed that the nuclear PKA signal in striatal neurons featured an ultrasensitive responsiveness, associated with fast all-or-none responses, which is not consistent with the commonly accepted theory of a slow and passive diffusion of catalytic PKA in the nucleus. Our numerical model suggests that a positive feed-forward mechanism inhibiting nuclear phosphatase activity - possibly mediated by DARPP-32 (also known as PPP1R1B) - could be responsible for this non-linear pattern of nuclear PKA response, allowing for a better detection of the transient dopamine signals that are often associated with reward-mediated learning.

Key points

• Brief dopamine events are critical actors of reward-mediated learning in the striatum; the intracellular cAMP–protein kinase A (PKA) response of striatal medium spiny neurons to such events was studied dynamically using a combination of biosensor imaging in mouse brain slices and in silico simulations.
• Both D1 and D2 medium spiny neurons can sense brief dopamine transients in the sub-micromolar range.
• While dopamine transients profoundly change cAMP levels in both types of medium spiny neurons, the PKA-dependent phosphorylation level remains unaffected in D2 neurons.
• At the level of PKA-dependent phosphorylation, D2 unresponsiveness depends on protein phosphatase-1 (PP1) inhibition by DARPP-32.
• Simulations suggest that D2 medium spiny neurons could detect transient dips in dopamine level.

The phasic release of dopamine in the striatum determines various aspects of reward and action selection, but the dynamics of the dopamine effect on intracellular signalling remains poorly understood. We used genetically encoded FRET biosensors in striatal brain slices to quantify the effect of transient dopamine on cAMP or PKA-dependent phosphorylation levels, and computational modelling to further explore the dynamics of this signalling pathway. Medium-sized spiny neurons (MSNs), which express either D1 or D2 dopamine receptors, responded to dopamine by an increase or a decrease in cAMP, respectively. Transient dopamine showed similar sub-micromolar efficacies on cAMP in both D1 and D2 MSNs, thus challenging the commonly accepted notion that dopamine efficacy is much higher on D2 than on D1 receptors. However, in D2 MSNs, the large decrease in cAMP level triggered by transient dopamine did not translate to a decrease in PKA-dependent phosphorylation level, owing to the efficient inhibition of protein phosphatase 1 by DARPP-32. Simulations further suggested that D2 MSNs can also operate in a ‘tone-sensing’ mode, allowing them to detect transient dips in basal dopamine. Overall, our results show that D2 MSNs may sense much more complex patterns of dopamine than previously thought.

Motivation: In the post-genomic era, automatic annotation of protein sequences using computational homology-based methods is highly desirable. However, often protein sequences diverge to an extent where detection of homology and automatic annotation transfer is not straightforward. Sophisticated approaches to detect such distant relationships are needed. We propose a new approach to identify deep evolutionary relationships of proteins to overcome shortcomings of the availablemethods. Results: We have developed a method to identify remote homologues more effectively from any protein sequence database by using several cascading events with Hidden Markov Models (C-HMM). We have implemented clustering of hits and profile generation of hit clusters to effectively reduce the computational timings of the cascaded sequence searches. Our C-HMM approach could cover 94, 83 and 40% coverage at family, superfamily and fold levels, respectively, when applied on diverse protein folds. We have compared C-HMM with various remote homology detection methods and discuss the trade-offs between coverage and false positives.

In reward learning, the integration of NMDA-dependent calcium and dopamine by striatal projection neurons leads to potentiation of corticostriatal synapses through CaMKII/PP1 signaling. In order to elicit the CaMKII/PP1-dependent response, the calcium and dopamine inputs should arrive in temporal proximity and must follow a specific (dopamine after calcium) order. However, little is known about the cellular mechanism which enforces these temporal constraints on the signal integration. In this computational study, we propose that these temporal requirements emerge as a result of the coordinated signaling via two striatal phosphoproteins, DARPP-32 and ARPP-21. Specifically, DARPP-32-mediated signaling could implement an input-interval dependent gating function, via transient PP1 inhibition, thus enforcing the requirement for temporal proximity. Furthermore, ARPP-21 signaling could impose the additional input-order requirement of calcium and dopamine, due to its Ca²⁺/calmodulin sequestering property when dopamine arrives first. This highlights the possible role of phosphoproteins in the temporal aspects of striatal signal transduction.

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.