The systems approach
In the previous steps we outlined that a biological system is constructed with biochemical components including metabolites and genes, and the biological function or phenotype is controlled by the interactions between components.
The interactions can be investigated at a single functional level, for example protein-protein interactions. We can also investigate the interactions between the components present in different functional levels (outlined in the previous article). For example, metabolism is the interaction of metabolites and enzymes (which are a set of proteins). The impact of these interactions on biological function and the phenotype will vary; a small number of interactions may have the same or a greater effect on the phenotype than a large or global set of interactions between the functional levels.
These concepts demonstrate that we must study the system as a whole to understand the whole system. Let us imagine a jigsaw puzzle. To fully understand the picture represented by the jigsaw we need to know what each of the jigsaw pieces (the components) represent but more importantly we need to place the jigsaw pieces together in a systematic way to accurately depict and understand the meaning of the jigsaw picture. We cannot view the picture if the jigsaw pieces are not connected together. If the jigsaw is correctly connected we can view the picture. Simply, the components are important but how the components interact is more important.
Systems biology is a scientific strategy applied to study the complete system. One definition of systems biology is the study of interactions between components in a biological system that contribute, maintain and regulate the biological function or phenotype. The components and interactions are studied in a holistic approach rather than a reductionist approach.
The Systems biology approach has been built on a traditional reductionist approach. The reductionist approach simplifies the complex networks down in to individual components and interactions. For example, the complete metabolic network composed of thousands of metabolites has been broken down over more than 100 years of biochemistry in to metabolic pathways including the glycolysis pathway and the Kreb’s cycle. These simpler and reduced metabolic pathways normally contain 20 or fewer components and interactions. The reductionist approach has built the foundations one brick (or metabolic pathway) at a time on which systems biology can now operate. From this reductionist approach we have gained valuable information on how the metabolites, proteins, RNA and genes interact and the interaction networks.
Systems biology is a multi-disciplinary science that requires biologists and chemists to perform biological studies, informaticians to provide complex data analysis and synthesis and systems modellers to construct and operate in-silico models of the biological system. Systems biology can be broken down in to three central disciplines – biological experiments, data synthesis and systems modeling.
In the omics era biological experiments have provided, for the first time, very large holistic datasets representing the components and their interactions observed across one or multiple functional levels at the global level. We can measure thousands of biochemical in one sample. The emergence of the omics disciplines has provided fresh impetus to the systems biology approach.
Data analysis and synthesis deconstructs the data to quantitatively define the important components and more importantly the interactions that drive the biological function or phenotype.
Systems modeling is the construction, calibration and validation of a computational model which reflects the interactions of the biological system that drive the biological function or phenotype. This model is constructed first with one or multiple networks to qualitatively define the components and interactions, then additional quantitative biological data is added as the next layer on to the networks. The model is then calibrated and validated to assess whether it robustly defines the system. If the model is not validated then further biological data is required and the cycle starts again to provide more data to the model to improve its predictability. If the model is validated then it can be applied to in-silico predict how a perturbation to the biological system will influence the function or phenotype. Think about the yeast Saccharomyces cerevisiae whose genome contains approximately 6500 genes. If we wanted to study the effect on metabolism of knocking out each of the genes one at a time then we would have to perform 6500 biological studies – this would require many years of work in the laboratory. However, by applying systems biology this can be performed in a much shorter period of time and at a lower cost. This demonstrates some of the advantages of applying a systems biology approach.
If you would like to investigate the systems biology concept further please look at the following article. This article neatly describes how systems biology is applied.
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