Stochastic models and dynamic measures for the characterization of bistable circuits

During the last few years, a great deal of interest has risen concerning the applications of stochastic methods to several biochemical and biological phenomena. Phenomena like gene expression, cellular memory, bet-hedging strategy in bacterial growth and many others, cannot be described by continuous stochastic models due to their intrinsic discreteness and randomness. In this thesis I have used the Chemical Master Equation (CME) technique to modelize some feedback cycles and analyzing their properties, including experimental data. In the first part of this work, the effect of stochastic stability is discussed on a toy model of the genetic switch that triggers the cellular division, which malfunctioning is known to be one of the hallmarks of cancer. The second system I have worked on is the so-called futile cycle, a closed cycle of two enzymatic reactions that adds and removes a chemical compound, called phosphate group, to a specific substrate. I have thus investigated how adding noise to the enzyme (that is usually in the order of few hundred molecules) modifies the probability of observing a specific number of phosphorylated substrate molecules, and confirmed theoretical predictions with numerical simulations. In the third part the results of the study of a chain of multiple phosphorylation-dephosphorylation cycles will be presented. We will discuss an approximation method for the exact solution in the bidimensional case and the relationship that this method has with the thermodynamic properties of the system, which is an open system far from equilibrium.In the last section the agreement between the theoretical prediction of the total protein quantity in a mouse cells population and the observed quantity will be shown, measured via fluorescence microscopy

The GABAB1a isoform mediates heterosynaptic depression at hippocampal mossy fiber synapses

GABA(B) receptor subtypes are based on the subunit isoforms GABA(B1a) and GABA(B1b), which associate with GABA(B2) subunits to form pharmacologically indistinguishable GABA(B(1a,2)) and GABA(B(1b,2)) receptors. Studies with mice selectively expressing GABA(B1a) or GABA(B1b) subunits revealed that GABA(B(1a,2)) receptors are more abundant than GABA(B(1b,2)) receptors at glutamatergic terminals. Accordingly, it was found that GABA(B(1a,2)) receptors are more efficient than GABA(B(1b,2)) receptors in inhibiting glutamate release when maximally activated by exogenous application of the agonist baclofen. Here, we used a combination of genetic, ultrastructural and electrophysiological approaches to analyze to what extent GABA(B(1a,2)) and GABA(B(1b,2)) receptors inhibit glutamate release in response to physiological activation. We first show that at hippocampal mossy fiber (MF)-CA3 pyramidal neuron synapses more GABA(B1a) than GABA(B1b) protein is present at presynaptic sites, consistent with the findings at other glutamatergic synapses. In the presence of baclofen at concentrations <or=1 microm, both GABA(B(1a,2)) and GABA(B(1b,2)) receptors contribute to presynaptic inhibition of glutamate release. However, at lower concentrations of baclofen, selectively GABA(B(1a,2)) receptors contribute to presynaptic inhibition. Remarkably, exclusively GABA(B(1a,2)) receptors inhibit glutamate release in response to synaptically released GABA. Specifically, we demonstrate that selectively GABA(B(1a,2)) receptors mediate heterosynaptic depression of MF transmission, a physiological phenomenon involving transsynaptic inhibition of glutamate release via presynaptic GABA(B) receptors. Our data demonstrate that the difference in GABA(B1a) and GABA(B1b) protein levels at MF terminals is sufficient to produce a strictly GABA(B1a)-specific effect under physiological conditions. This consolidates that the differential subcellular localization of the GABA(B1a) and GABA(B1b) proteins is of regulatory relevance