(β2c-tubulin) (refer to β5-tubulin β1-tubulin) 1.6. Systems Biology Systems Biology is an inter-disciplinary field that is rapidly is gaining attention in bioscience research as a technique for studying complex biological systems [, , ]. It is especially relevant in this post-genomic era where many areas of research are rich in data but hypothesis-poor []. Although Systems Biology is a relatively new field in oncology research, it is proving a valuable tool for understanding the biological mechanisms underlying tumour development and for identifying novel targets for cancer therapeutics [, ]. A key aspect of Systems Biology is that it can be both data-driven and []. The two approaches complement each other when attempting to understand the relationships between and []. A hypothesis-driven ‘causes’ ‘effects’ approach links a proposed or to observed and and ‘cause’ ‘theory’ ‘effects’ ‘evidence’ is often applied to prove or disprove a hypothesis. A data-driven approach evaluates multiple to predict a in order to formulate a new hypothesis; this can ‘effects’ ‘cause’ then be tested experimentally via a second hypothesis-driven approach, thereby forming a of []. This iterative cycle enables the wealth of ‘cycle knowledge’ information that can be generated through quantitative high-throughput technologies, and data from the rapidly expanding bioinformatic databases, to be maximised []. Computational mechanistic or dynamical modelling is becoming an integral part of Systems Biology, due in part to being able to manage the large number of molecular components and their complex interactions when conducting a system-wide study.
(β2c-tubulin) β5-tubulin (refer to β1-tubulin) 1.6. Systems Biology Systems Biology is an inter-disciplinary field that is rapidly is gaining attention in bioscience research as a technique for studying complex biological systems [, , ]. It is especially relevant in this post-genomic era where many areas of research are rich in data but hypothesis-poor []. Although Systems Biology is a relatively new field in oncology research, it is proving a valuable tool for understanding the biological mechanisms underlying tumour development and for identifying novel targets for cancer therapeutics [, ]. A key aspect of Systems Biology is that it can be both data-driven and []. The two approaches complement each other when attempting to understand the relationships between ‘causes’ and ‘effects’ []. A hypothesis-driven approach links a proposed ‘cause’ or ‘theory’ to observed ‘effects’ and ‘evidence’ and is often applied to prove or disprove a hypothesis. A data-driven approach evaluates multiple ‘effects’ to predict a ‘cause’ in order to formulate a new hypothesis; this can then be tested experimentally via a second hypothesis-driven approach, thereby forming a ‘cycle of knowledge’ []. This iterative cycle enables the wealth of information that can be generated through quantitative high-throughput technologies, and data from the rapidly expanding bioinformatic databases, to be maximised []. Computational mechanistic or dynamical modelling is becoming an integral part of Systems Biology, due in part to being able to manage the large number of molecular components and their complex interactions when conducting a system-wide study.
PhD_Thesis_%28KilnerJul13%29.docx