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 G olf -and G i/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 D1Rϩ MSNs via ACh/M4R/G i/o and an ACh dip is required to gate a subset of D1R/G olf -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 D2ϩ MSNs is under basal inhibition via D2R/G i/o and a DA dip leads to a PKA increase by disinhibiting A2aR/G olf , but D2ϩ 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 G i/o and the importance of interactions between G i/o and G olf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.

Development and regeneration of the nervous system requires the precise formation of axons and dendrites. Kinases and phosphatases are pervasive regulators of cellular function and have been implicated in controlling axodendritic development and regeneration. We undertook a gain-offunction analysis to determine the functions of kinases and phosphatases in the regulation of neuron morphology. Over 300 kinases and 124 esterases and phosphatases were studied by highcontent analysis of rat hippocampal neurons. Proteins previously implicated in neurite growth, such as ERK1, GSK3, EphA8, FGFR, PI3K, PKC, p38, and PP1a, were confirmed to have effects in our functional assays. We also identified novel positive and negative neurite growth regulators. These include neuronal-developmentally regulated kinases such as the activin receptor, interferon regulatory factor 6 (IRF6) and neural leucine-rich repeat 1 (LRRN1). The protein kinase N2 (PKN2) and choline kinase a (CHKA) kinases, and the phosphatases PPEF2 and SMPD1, have little or no established functions in neuronal function, but were sufficient to promote neurite growth. In addition, pathway analysis revealed that members of signaling pathways involved in cancer progression and axis formation enhanced neurite outgrowth, whereas cytokine-related pathways significantly inhibited neurite formation.

Striatum, which is the input nucleus of the basal ganglia, integrates cortical and thalamic glutamatergic inputs with dopaminergic afferents from the substantia nigra pars compacta. The combination of dopamine and glutamate strongly modulates molecular and cellular properties of striatal neurons and the strength of corticostriatal synapses. These actions are performed via intracellular signaling networks, containing several intertwined feedback loops. Understanding the role of dopamine and other neuromodulators requires the development of quantitative dynamical models for describing the intracellular signaling, in order to provide precise unambiguous descriptions and quantitative predictions. Building such models requires integration of data from multiple data sources containing information regarding the molecular interactions, the strength of these interactions, and the subcellular localization of the molecules. Due to the uncertainty, variability, and sparseness of these data, parameter estimation techniques are critical for inferring or constraining the unknown parameters, and sensitivity analysis evaluates which parameters are most critical for a given observed macroscopic behavior. Here, we briefly review the modeling approaches and tools that have been used to investigate biochemical signaling in the striatum, along with some of the models built around striatum. We also suggest a future direction for the development of such models from the, now becoming abundant, high-throughput data.