This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonLife in nature is defined by many characteristics. Whether something can move, communicate,
response to the others, reproduce or die, indicate if it is alive or not. Among these features,
communication can be considered the most basic and yet the most important as it happens both
inside and outside an organism; between every molecule and every cell there are signals to be
passed and to be responded to. Communication defines biology.
A network of molecules or a society of organisms are both complex systems. The smallest
change in this snarled network affects the whole system and changes the output significantly.
Comprehending and manipulating them in detail is time and resources consuming and involves
human error. But there is a way to simplify the process of inspecting the living creatures.
Bio-model engineering lies at the crossroads of biology, mathematics, computer science,
engineering and is a branch of systems biology. In this field of science, biological models are created
and/or re-designed for simplification, abstraction and description of biological networks. Modelling
these networks based on past experimental observations in silico with a set of pre-designed models
and a collection of components would make this process faster and simpler.
This thesis contributes to science by providing a collection of model components built in
Petri nets with Snoopy. These components each describe a specific behaviour and they can be
used individually or as a combination. The set of behaviours in this collection include chemotaxis,
reproduction, death, communication and response. These are a few of the most basic behaviours
in nature that mark something as alive. These basic behaviours choose that a piece of stone is
not alive but the small microscopic bacteria on it are.
Starting with small achievable steps, these components are modelled in abstract, meaning
they demonstrate only the critical parts of the behaviours. Not only the models, but also
the process of modelling and combining the components is provided from the adaptation and
manipulation of a general protocol.
The components in this library are categorised based on their complexity. In this categorisation,
the models have four levels, with each level more complex than the former. The
more complex levels, are built from the simpler ones in a hierarchical manner. There are two
application of the models to two different microorganisms, each from one of the main biological superkingdoms to demonstrate the practicality of this collection. The chosen microorganisms are
from: the domain of Prokaryotes E. coli and Eukaryotes Dictyostelium a.k.a slime mould.
Each model contains a set of rate constants that define the speed of the reactions. A set
of expected behaviours based on biological literature is defined for these models to be compared
with the outcome result of the analysis of the models. The models are simulated by Spike, a
command line programme for simulation of models built in Snoopy, and are analysed with R and
Python. To achieve the expected results, optimisation methods are used to find the best rates
possible in the models in order to achieve a defined behaviour. In this thesis the optimisation is
applied to Dictyostelium model to achieve the best rates for the accumulation of Dictyostelium
cells in one location to create fruiting bodies. Random Restart Hill Climbing and Simulated
Annealing are the chosen methods for optimisation
