Studies of long-term potentiation and depression of inhibitory transmission by mathematical modeling of post-synaptic processes

Gastrodiae Rhizoma Ethanol Extract Enhances Pentobarbital-Induced Sleeping Behaviors and Rapid Eye Movement Sleep via the Activation of GABA A -ergic Transmission in Rodents

This research was designed to identify whether Gastrodiae Rhizoma ethanol extract (GREE) enhances pentobarbital-induced sleep via   γ-aminobutyric acid- (GABA-) ergic systems and modulated sleep architectures in animals. GREE (25, 50, and 100 mg/kg, p.o.) inhibited locomotor activity in mice, in a dose-dependent manner. GREE not only prolonged total sleep time, but also reduced sleep latency time in pentobarbital (42 mg/kg)-treated mice. Subhypnotic pentobarbital (28 mg/kg, i.p.) also increased the number of total sleeping animals in concomitant administration of GREE. GREE (100 mg/kg) alone reduced the count of sleep-wake cycles in electroencephalogram. Furthermore, GREE increased total sleep time and rapid eye movement (REM) sleep. From the in vitro experiments, GREE increased intracellular chloride level in primary cultured cerebellar granule cells. Protein expressions of glutamine acid decarboxylase (GAD) and GABA_A receptors subtypes by western blot were increased. Therefore, our study suggested that GREE enhances pentobarbital-induced sleeping behaviors and increased REM via the activation of GABA_A-ergic transmission in rodents.

Postsynaptic signal transduction models for long-term potentiation and depression

More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed. The models range from simple models with a single reversible reaction to detailed models with several hundred kinetic reactions. In this study, more than a hundred models are reviewed, and their features are compared and contrasted so that similarities and differences are more readily apparent. The models are classified according to the type of synaptic plasticity that is modeled (LTP or LTD) and whether they include diffusion or electrophysiological phenomena. Other characteristics that discriminate the models include the phase of synaptic plasticity modeled (induction, expression, or maintenance) and the simulation method used (deterministic or stochastic). We find that models are becoming increasingly sophisticated, by including stochastic properties, integrating with electrophysiological properties of entire neurons, or incorporating diffusion of signaling molecules. Simpler models continue to be developed because they are computationally efficient and allow theoretical analysis. The more complex models permit investigation of mechanisms underlying specific properties and experimental verification of model predictions. Nonetheless, it is difficult to fully comprehend the evolution of these models because (1) several models are not described in detail in the publications, (2) only a few models are provided in existing model databases, and (3) comparison to previous models is lacking. We conclude that the value of these models for understanding molecular mechanisms of synaptic plasticity is increasing and will be enhanced further with more complete descriptions and sharing of the published models.

Phenomenological and molecular-level Petri net modeling and simulation of long-term potentiation

Petri net-based modeling methods have been used in many research projects to represent biological systems. Among these, the hybrid functional Petri net (HFPN) was developed especially for biological modeling in order to provide biologists with a more intuitive Petri net-based method. In the literature, HFPNs are used to represent kinetic models at the molecular level. We present two models of long-term potentiation previously represented by differential equations which we have transformed into HFPN models: a phenomenological synapse model and a molecular-level model of the CaMKII regulation pathway. Through simulation, we obtained results similar to those of previous studies using these models. Our results open the way to a new type of modeling for systems biology where HFPNs are used to combine different levels of abstraction within one model. This approach can be useful in fully modeling a system at the molecular level when kinetic data is missing or when a full study of a system at the molecular level it is not within the scope of the research.