Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure

Identification of neutral biochemical network models from time series data

<p>Abstract</p> <p>Background</p> <p>The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, <it>i.e</it>., if it is constructed according to strict guidelines.</p> <p>Results</p> <p>In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity.</p> <p>Conclusion</p> <p>The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium <it>Lactococcus lactis </it>and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.</p

A Kinetic Model of Dopamine- and Calcium-Dependent Striatal Synaptic Plasticity

Corticostriatal synapse plasticity of medium spiny neurons is regulated by glutamate input from the cortex and dopamine input from the substantia nigra. While cortical stimulation alone results in long-term depression (LTD), the combination with dopamine switches LTD to long-term potentiation (LTP), which is known as dopamine-dependent plasticity. LTP is also induced by cortical stimulation in magnesium-free solution, which leads to massive calcium influx through NMDA-type receptors and is regarded as calcium-dependent plasticity. Signaling cascades in the corticostriatal spines are currently under investigation. However, because of the existence of multiple excitatory and inhibitory pathways with loops, the mechanisms regulating the two types of plasticity remain poorly understood. A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. The model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. Calcium input modulated this loop through the PP2B (phosphatase 2B)-CK1 (casein kinase 1)-Cdk5 (cyclin-dependent kinase 5)-Thr75 pathway and PP2A, whereas calcium and dopamine input activated the loop via PKA activation by cyclic AMP (cAMP). The positive feedback loop displayed robust bi-stable responses following changes in the reaction parameters. Increased basal dopamine levels disrupted this dopamine-dependent plasticity. The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction

Selection of sucrose concentration depends on the effort required to obtain it: studies using tetrabenazine, D₁, D₂, and D₃ receptor antagonists

RationaleLow doses of dopamine (DA) antagonists and accumbens DA depletions reduce food-reinforced instrumental behavior but do not impair primary food motivation, causing animals to reallocate behavior away from food-reinforced tasks with high response requirements and select less effortful alternatives. However, it is uncertain if this same pattern of effects would occur if sucrose was used as the reinforcer. Objectives These experiments studied the impact of DA depletion and antagonism on performance of an effort-related choice task using sucrose as the reinforcer, as well as sucrose consumption, preference, and taste reactivity tests. Methods The effects of DA manipulations were assessed using a task in which rats chose between lever pressing on a fixed ratio 7 schedule for 5.0 % sucrose versus freely consuming a less concentrated solution (0.3 %). Results The DA depleting agent tetrabenazine shifted effort-related choice, decreasing lever pressing for 5.0 % sucrose but increasing intake of the concurrently available 0.3 % sucrose. Tetrabenazine did not affect sucrose appetitive taste reactivity, or sucrose consumption or preference, in free consumption tests. The D1 antagonist ecopipam and the D2 antagonist haloperidol also shifted choice behavior at doses that did not alter sucrose consumption or preference. In contrast, sucrose pre-exposure reduced consumption across all conditions. D3 antagonism had no effects. Conclusions D1 and D2 receptor blockade and DA depletion reduce the tendency to work for sucrose under conditions that leave fundamental aspects of sucrose motivation (intake, preference, hedonic reactivity) intact. These findings have implications for studies employing sucrose intake or preference in animal models of depression

Botulinum toxin: Description of injection techniques and examination of controversies surrounding toxin diffusion

10.1111/j.1600-0404.2007.00931.xActa Neurologica Scandinavica117273-8

Opioid-dependent anticipatory negative contrast and binge-like eating in rats with limited access to highly preferred food

Binge eating and an increased role for palatability in determining food intake are abnormal adaptations in feeding behavior linked to eating disorders and body weight dysregulation. The present study tested the hypothesis that rats with limited access to highly preferred food would develop analogous opioid-dependent learned adaptations in feeding behavior, with associated changes in metabolism and anxiety-like behavior. For this purpose, adolescent female Wistar rats were daily food deprived (2h) and then offered 10-min access to a feeder containing chow followed sequentially by 10-min access to a different feeder containing either chow (chow/chow; n=7) or a highly preferred, but macronutrient-comparable, sucrose-rich diet (chow/preferred; n=8). Chow/preferred-fed rats developed binge-like hyperphagia of preferred diet from the second feeder and anticipatory chow hypophagia from the first feeder with a time course suggesting associative learning. The feeding adaptations were dissociable in onset, across individuals, and in their dose-response to the opioid-receptor antagonist nalmefene, suggesting that they represent distinct palatability-motivated processes. Chow/preferred-fed rats showed increased anxiety-like behavior in relation to their propensity to binge as well as increased feed efficiency, body weight, and visceral adiposity. Chow/preferred-fed rats also had increased circulating leptin levels and decreased growth hormone and 'active' ghrelin levels. Thus, the short-term control of food intake in rats with restricted access to highly preferred foods comes to rely more on hedonic, rather than nutritional, properties of food, through associative learning mechanisms. Such rats show changes in ingestive, metabolic, endocrine, and anxiety-related measures, which resemble features of binge eating disorders or obesity