PhD Thesis A Natural Computation Approach To Biology: Modelling Cellular Processes and Populations of Cells With Stochastic Models of P Systems


This thesis presents three new computational models designed to represent biological systems, based on models of membrane computing: Membrane Systems with Peripheral Proteins (MSPP), Membrane Systems with Peripheral and Integral Proteins (MSPIP) and Colonies of Synchronizing Agents (CSA). Membranes and membrane proteins are fundamental to the operation of biological cells, hence MSP(I)P is close to biologists’ prevailing view of the cell and is highly compatible with existing biochemical models. CSA is an hierarchical paradigm designed to represent complex systems such as populations of cells and tissues. This work extends the corpus of knowledge about biology and the theory of computation by proving technical results related to these models.
The MSPP, MSPIP and CSA models have associated software implementations which allow the simulation of the temporal evolution of biological models by means of multiset rewriting under the control of a stochastic algorithm. One of these, Cyto-Sim (implementing the MSPP and MSPIP models), being most developed, is presented in detail with several examples. Stochastic simulation is inherently computationally intensive and hierarchical systems particularly so. This thesis presents a new state of the art stochastic simulation algorithm for hierarchical and agent-based systems (the Method of Partial Propensities) and uses this result to improve the state of the art of stochastic simulation algorithms for well stirred chemical systems (the Method of Arbitrary Partial Propensities).
The noise evident in stochastic simulations is a potentially useful characteristic, containing information about the system being simulated. To extract detailed measures of stochasticity and the behaviour of a system, a new technique using Fourier analysis is presented and illustrated. With this it is possible to characterise the distance between models and the performance of simulation algorithms.

Paper Details


S. Sedwards


PhD Thesis