2 research outputs found
Bioinformatics
This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here
Modelling and measurement in synthetic biology
Synthetic biology applies engineering principles to make progress in the study of complex
biological phenomena. The aim is to develop understanding through the praxis of
construction and design. The computational branch of this endeavour explicitly brings
the tools of abstraction and modularity to bear. This thesis pursues two distinct lines of
inquiry concerning the application of computational tools in the setting of synthetic biology.
One thread traces a narrative through multi-paradigm computational simulations,
interpretation of results, and quantification of biological order. The other develops computational
infrastructure for describing, simulating and discovering, synthetic genetic
circuits.
The emergence of structure in biological organisms, morphogenesis, is critically
important for understanding both normal and pathological development of tissues. Here,
we focus on epithelial tissues because models of two dimensional cellular monolayers
are computationally tractable. We use a vertex model that consists of a potential energy
minimisation process interwoven with topological changes in the graph structure of the
tissue. To make this interweaving precise, we define a language for propagators from
which an unambiguous description of the simulation methodology can be constructed.
The vertex model is then used to reproduce laboratory results of patterning in engineered
mammalian cells. The assertion that the claim of reproduction is justified is based on
a novel measure of structure on coloured graphs which we call path entropy. This
measure is then extended to the setting of continuous regions and used to quantify
the development of structure in house mouse (Mus musculus) embryos using three
dimensional segmented anatomical models.
While it is recognised that DNA can be considered a powerful computational
environment, it is far from obvious how to program with nucleic acids. Using rule-based
modelling of modular biological parts, we develop a method for discovering synthetic
genetic programs that meet a specification provided by the user. This method rests on
the concept of annotation as applied to rule-based programs. We begin with annotating
rules and proceed to generating entire rule-based programs from annotations themselves.
Building on those tools we describe an evolutionary algorithm for discovering genetic
circuits from specifications provided in terms of probability distributions. This strategy
provides a dual benefit: using stochastic simulation captures circuit behaviour at low
copy numbers as well as complex properties such as oscillations, and using standard
biological parts produces results that are implementable in the laboratory