Stochastic and executable models of synaptic processes

Neurons communicate with each other at synapses. These are plastic\u2028 communication units that modulate signal transmission. Synaptic\u2028 plasticity, i.e. the possibility of being modified by activity,\u2028 contributes to neuro-computational capabilities, underlying phenomena\u2028 like memory and learning. The synaptic signal transduction involves \u2028complex intra- and inter-cellular biochemical reactions, which, under\u2028 several respects, appear to be most suitably described by stochastic \u2028models.\u2028\u2028 On these bases, we have developed a stochastic and computationally \u2028executable model of the calyx of Held synapse. The aim of this work is \u2028to provide formal descriptive techniques for the analysis of such a \u2028complex and systemic behaviour. We exploit an interpretation of the\u2028 structure of life systems as interacting computational entities from \u2028which the overall behaviour of the system emerges. We have exploited \u2028"process calculi", developed within concurrency theory in computer \u2028science, as representation language. These calculi describe the\u2028 interactive behaviour of a system in terms of the behaviour of its \u2028active processes. A distinguished feature is that these \u2028representations have a direct executable interpretation, which allows \u2028the behaviour of a system, like the calyx, to be qualitatively\u2028 simulated. Often, these models and their analysis enjoy nice \u2028compositional properties.\u2028\u2028 Process behaviour is defined starting from basic interaction steps,\u2028 which model simple molecular interactions, and compositional \u2028operators, through which we build up the complex behaviour of a\u2028 structured system. The semantics of process calculi is generally \u2028expressed by a transition system, with states representing the current\u2028 configuration of a system and transition representing its capability\u2028 to act and move to one or more future states. Stochastic semantics has \u2028been developed in order to study system performances when relevant \u2028events occur according to a given probability distribution. Many of \u2028these semantics are based on the Gillespie’s Stochastic Simulation \u2028 Algorithm, originally proposed within chemical reaction modeling. This \u2028has further fostered the use of process calculi for biology and lead\u2028 to the definition of new calculi for this purpose.\u2028\u2028 The approach we followed benefits from conjugating the abstract and\u2028 compositional algebraic models, the possibility of precisely describe\u2028 their semantics and formally reasoning about them, and the\u2028 quantitative analysis provided by stochastic semantics, accounting for\u2028 the non-continuous, nor discrete, nature of many phenomena. Building\u2028 on available data in literature, fitting some unknown parameters and\u2028 developing working hypotheses, we have defined a model which, to our\u2028 knowledge, is the first computational model of synaptic activity based \u2028on process calculi. The in-silico simulations performed are coherent\u2028 with experimental data in literature and faithfully comprise\u2028 short-term synaptic plasticity phenomena, like facilitation,\u2028 depression and potentiation. Overall, the simulation results represent \u2028a quite articulate description of the presynaptic and postsynaptic\u2028 activity.\u2028\u2028 Additionally, this multidisciplinary work validates the application of\u2028 the technique in life sciences, suggesting possibly improvements, such\u2028 as a refined representation of space that overcomes the standard\u2028 assumption of spatial uniformity (well-stirred space).\u2028\u2028\u202