Spiking Neural P Systems with Functional Astrocytes

Spiking Neural P Systems (SN P Systems, for short) is a developing field within the universe of P Systems. New variants arise constantly as the study of their properties, such as computational completeness and computational efficiency, grows. Variants frequently incorporate new ingredients into the original model inspired by real neurophysiological structure of the brain. A singular element present within that structure is the astrocyte. Astrocytes, also known collectively as astroglia, are characteristic star-shaped glial cells in the brain and spinal cord. In this paper, a new variant of Spiking Neural P Systems incorporating astrocytes is introduced. These astrocytes are modelled as computing devices capable of performing function computation in a single computation step. In order to experimentally study the action of Spiking Neural P Systems with astrocytes, it is necessary to develop software providing the required simulation tools. Within this trend, P– Lingua offers a standard language for the definition of P Systems. Part of the same software project, pLinguaCore library provides particular implementations of parsers and simulators for the models specified in P–Lingua. Along with the new SN P System variant with astrocytes, an extension of the P–Lingua language allowing definition of these systems is presented in this paper, as well as an upgrade of pLinguaCore, including a parser and a simulator that supports the aforementioned variant.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

A systems biology approach to multi-scale modelling and analysis of planar cell polarity in drosophila melanogaster wing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Systems biology aims to describe and understand biology at a global scale where biological systems function as a result of complex mechanisms that happen at several scales. Modelling and simulation are computational tools that are invaluable for description, understanding and prediction these mechanisms in a quantitative and integrative way. Thus multi-scale methods that couple the design, simulation and analysis of models spanning several spatial and temporal scales is becoming a new emerging focus of systems biology. This thesis uses an exemplar – Planar cell polarity (PCP) signalling – to illustrate a generic approach to model biological systems at different spatial scales, using the new concept of Hierarchically Coloured Petri Nets (HCPN). PCP signalling refers to the coordinated polarisation of cells within the plane of various epithelial tissues to generate sub-cellular asymmetry along an axis orthogonal to their apical-basal axes. This polarisation is required for many developmental events in both vertebrates and non-vertebrates. Defects in PCP in vertebrates are responsible for developmental abnormalities in multiple tissues including the neural tube, the kidney and the inner ear. In Drosophila wing, PCP is seen in the parallel orientation of hairs that protrude from each of the approximately 30,000 epithelial cells to robustly point toward the wing tip. This work applies HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) in Drosophila wing. HCPN facilitate the construction of mathematically tractable, compact and parameterised large-scale models. Different levels of abstraction that can be used in order to simplify such a complex system are first illustrated. The PCP system is first represented at an abstract level without modelling details of the cell. Each cell is then sub-divided into seven virtual compartments with adjacent cells being coupled via the formation of intercellular complexes. A more detailed model is later developed, describing the intra- and inter-cellular signalling mechanisms involved in PCP signalling. The initial model is for a wild-type organism, and then a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP, are constructed. Among them, the largest model consists of 800 cells which when unfolded yields 164,000 places (each of which is described by an ordinary differential equation). This thesis illustrates the power and validity of the approach by showing how the models can be easily adapted to describe well-documented genetic mutations in the Drosophila wing using the proposed approach including clustering and model checking over time series of primary and secondary data, which can be employed to analyse and check such multi-scale models similar to the case of PCP. The HCPN models support the interpretation of biological observations reported in literature and are able to make sensible predictions. As HCPN model multi-scale systems in a compact, parameterised and scalable way, this modelling approach can be applied to other large-scale or multi-scale systems.This study was funded by Brunel University