Stochastic and executable models of synaptic processes
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1
University of Pisa, Italy
Neurons communicate with each other at synapses. These are plastic communication units that modulate signal transmission. Synaptic plasticity, i.e. the possibility of being modified by activity, contributes to neuro-computational capabilities, underlying phenomena like memory and learning. The synaptic signal transduction involves complex intra- and inter-cellular biochemical reactions, which, under several respects, appear to be most suitably described by stochastic models. On these bases, we have developed a stochastic and computationally executable model of the calyx of Held synapse. The aim of this work is to provide formal descriptive techniques for the analysis of such a complex and systemic behaviour. We exploit an interpretation of the structure of life systems as interacting computational entities from which the overall behaviour of the system emerges. We have exploited "process calculi", developed within concurrency theory in computer science, as representation language. These calculi describe the interactive behaviour of a system in terms of the behaviour of its active processes. A distinguished feature is that these representations have a direct executable interpretation, which allows the behaviour of a system, like the calyx, to be qualitatively simulated. Often, these models and their analysis enjoy nice compositional properties. Process behaviour is defined starting from basic interaction steps, which model simple molecular interactions, and compositional operators, through which we build up the complex behaviour of a structured system. The semantics of process calculi is generally expressed by a transition system, with states representing the current configuration of a system and transition representing its capability to act and move to one or more future states. Stochastic semantics has been developed in order to study system performances when relevant events occur according to a given probability distribution. Many of these semantics are based on the Gillespie’s Stochastic Simulation Algorithm, originally proposed within chemical reaction modeling. This has further fostered the use of process calculi for biology and lead to the definition of new calculi for this purpose.The approach we followed benefits from conjugating the abstract and compositional algebraic models, the possibility of precisely describe their semantics and formally reasoning about them, and the quantitative analysis provided by stochastic semantics, accounting for the non-continuous, nor discrete, nature of many phenomena. Building on available data in literature, fitting some unknown parameters and developing working hypotheses, we have defined a model which, to our knowledge, is the first computational model of synaptic activity based on process calculi. The in-silico simulations performed are coherent with experimental data in literature and faithfully comprise short-term synaptic plasticity phenomena, like facilitation, depression and potentiation. Overall, the simulation results represent a quite articulate description of the presynaptic and postsynaptic activity. Additionally, this multidisciplinary work validates the application of the technique in life sciences, suggesting possibly improvements, such as a refined representation of space that overcomes the standard assumption of spatial uniformity (well-stirred space).
References
1. Bracciali, Brunelli, Cataldo, Degano. Expressive Models for Synaptic Plasticity. CMSB 2007, LNBI 4695, 152-167, 2007. Springer.
2. Bracciali, Brunelli, Cataldo, Degano. Stochastic models for the in silico simulation of synaptic processes. BMC Bioinformatics 2008, 9:S7.
Conference:
Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008.
Presentation Type:
Poster Presentation
Topic:
General Neuroinformatics
Citation:
Cataldo
E,
Bracciali
A,
Brunelli
M and
Degano
P
(2008). Stochastic and executable models of synaptic processes.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2008.
doi: 10.3389/conf.neuro.11.2008.01.012
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Received:
28 Jul 2008;
Published Online:
28 Jul 2008.
*
Correspondence:
Enrico Cataldo, University of Pisa, Pisa, Italy, nemoABS01@frontiersin.org