Describing Membrane Computations with a Chemical Calculus

Membrane systems are nature motivated computational models inspired by certain basic features of biological cells and their membranes. They are examples of the chemical computational paradigm which describes computation in terms of chemical solutions where molecules interact according to rules de ning their reaction capabilities. Chemical models can be presented by rewriting systems based on multiset manipulations, and they are usually given as a kind of chemical calculus which might also allow nondeterministic and non-sequential computations. Here we study membrane systems from the point of view of the chemical computing paradigm and show how computations of membrane systems can be described by such a chemical calculus

Mutual Mobile Membranes with Timers

A feature of current membrane systems is the fact that objects and membranes are persistent. However, this is not true in the real world. In fact, cells and intracellular proteins have a well-defined lifetime. Inspired from these biological facts, we define a model of systems of mobile membranes in which each membrane and each object has a timer representing their lifetime. We show that systems of mutual mobile membranes with and without timers have the same computational power. An encoding of timed safe mobile ambients into systems of mutual mobile membranes with timers offers a relationship between two formalisms used in describing biological systems

Formal executable descriptions of biological systems

The similarities between systems of living entities and systems of concurrent processes may support biological experiments in silico. Process calculi offer a formal framework to describe biological systems, as well as to analyse their behaviour, both from a qualitative and a quantitative point of view. A couple of little examples help us in showing how this can be done. We mainly focus our attention on the qualitative and quantitative aspects of the considered biological systems, and briefly illustrate which kinds of analysis are possible. We use a known stochastic calculus for the first example. We then present some statistics collected by repeatedly running the specification, that turn out to agree with those obtained by experiments in vivo. Our second example motivates a richer calculus. Its stochastic extension requires a non trivial machinery to faithfully reflect the real dynamic behaviour of biological systems

Simulating Membrane Systems and Dissolution in a Typed Chemical Calculus

We present a transformation of membrane systems, possibly with pro- moter/inhibitor rules, priority relations, and membrane dissolution, into formulas of the chemical calculus such that terminating computations of membranes correspond to terminating reduction sequences of formulas and vice versa. In the end, the same result can be extracted from the underlying computation of the membrane system as from the reduction sequence of the chemical term. The simulation takes place in a typed chemical calculus, but we also give a short account of the untyped case

Multiscale Computations on Neural Networks: From the Individual Neuron Interactions to the Macroscopic-Level Analysis

We show how the Equation-Free approach for multi-scale computations can be exploited to systematically study the dynamics of neural interactions on a random regular connected graph under a pairwise representation perspective. Using an individual-based microscopic simulator as a black box coarse-grained timestepper and with the aid of simulated annealing we compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level. We also exploit the scheme to perform a rare-events analysis by estimating an effective Fokker-Planck describing the evolving probability density function of the corresponding coarse-grained observables

Flux Analysis in Process Models via Causality

We present an approach for flux analysis in process algebra models of biological systems. We perceive flux as the flow of resources in stochastic simulations. We resort to an established correspondence between event structures, a broadly recognised model of concurrency, and state transitions of process models, seen as Petri nets. We show that we can this way extract the causal resource dependencies in simulations between individual state transitions as partial orders of events. We propose transformations on the partial orders that provide means for further analysis, and introduce a software tool, which implements these ideas. By means of an example of a published model of the Rho GTP-binding proteins, we argue that this approach can provide the substitute for flux analysis techniques on ordinary differential equation models within the stochastic setting of process algebras