Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

Convergent and divergent modulatory pathways in {\it Aplysia\/}: Cellular and network studies

Neuromodulation is essential to many functions of the nervous system. In the simple gastropod mollusk Aplysia californica, neuromodulation of the circuits for the defensive withdrawal reflexes has been associated with several forms of learning. In the present work, the neurotransmitters and neural circuitry which contribute to the modulation of the tail-siphon withdrawal reflex were examined.^ A recently-identified neuropeptide transmitter, buccalin A was found to modulate the biophysical properties of the sensory neurons that mediate the reflex. The actions of buccalin A on the sensory neurons were compared with those of the well-characterized modulatory transmitter serotonin, and convergence and divergence in the actions of these two transmitters were evaluated. Buccalin A dramatically increased the excitability of sensory neurons and occluded further enhancement of excitability by serotonin. Buccalin A produced no significant change in spike duration, and it did not block serotonin-induced spike broadening. Voltage-clamp analysis revealed the currents that may be involved in the effects on spike duration and excitability. Buccalin A decreased an outward current similar to the S-K$\sp+$ current (I$\sb{\rm K,S}$). Buccalin A appeared to occlude further modulation of I$\sb{\rm K,S}$ by serotonin, but did not block serotonin-induced modulation of the voltage-dependent delayed rectifier K$\sp+$ current (I$\sb{\rm K,V}$). These results suggest that buccalin A converges on some, but not all, of the same subcellular modulatory pathways as serotonin.^ In order to begin to understand neuromodulation in a more physiological context for the tail-siphon withdrawal reflex, the modulatory circuitry for the tail-withdrawal circuit was examined. Mechanoafferent neurons in the J cluster of the cerebral ganglion were identified as elements of a modulatory circuit for the reflex. Excitatory and inhibitory connections were observed between the J cells and the pleural sensory neurons, the tail motor neurons, and several classes of interneurons for the tail-siphon withdrawal circuit. The J cells produced both fast and slow PSPs in these neurons. Of particular interest was the ability of the J cells to produce slow EPSPs in the pleural sensory neurons. These slow EPSPs were associated with an increase in the excitability of the sensory neurons. The J cells appear to mediate both sensory and modulatory inputs to the circuit for the tail-siphon withdrawal reflex from the anterior part of the animal. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^