Biomodelkit - a framework for modular biomodel-engineering

Otto-von-Guericke-Universität Magdeburg, Fakultät für Naturwissenschaften, Dissertation, 2017von Dipl.-Ing. Mary-Ann BlätkeLiteraturverzeichnis: Seite [177]-18

Coloured Petri nets for multi-level, multiscale, and multi-dimensional modelling of biological systems

Owing to the availability of data of one biological phenomenon at different levels/scales, modelling of biological systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets (ColPNs) have been successfully applied to multilevel, multiscale and multidimensional modelling of some biological systems, addressing many of these challenges. In this article, we first review the basics of ColPNs and some popular extensions, and then their applications for multilevel, multiscale and multidimensional modelling of biological systems. This understanding of how to use ColPNs for modelling biological systems will assist readers in selecting appropriate ColPN classes for specific modelling circumstances

Application of bio-model engineering to model abstract biological behaviours

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