Systems biology is the scientific analysis and modelling which displays systemic properties as well as dynamic interactions in biological objects. This holistic approach is used in a quantitative and qualitative manner by combining different experimental studies with mathematical modelling (Klipp et al., 2016). Systems biology can be used in order to establish the relationship between bodily systems which cause changes in the biology of individuals, altering their BMI and playing a role in other metabolic disorders associated with obesity. Obesity is due to the excessive of abnormal fat accumulation in adipose tissue, impairing health (WHO, 2000). The prevalence of obesity has increased in Australia in recent years, and has been predicted to continue to rise (WHO, 2000). Both genetics and environment are attributes to obesity, however the extent and understanding of these causes are still not completely understood. The microbiome, being the usually microbial inhabitants present in the body, has been studied as a contributor to obesity. Additionally, the metabolic activity of individuals in relation to their microbiome is being studied in order to determine various causes for obesity and metabolic syndrome. Systems biology allows for large-scale analysis of data from several studies to understand the obesity epidemic, and therefore treat it effectively.
Systems biology creates an analysis of behaviours and interactions of numerous systems, providing a comprehensive understanding of how different relationships cause different diseases and disorders (Klipp et al., 2016). Obesity is a component of metabolic syndrome which can be studied through systems biology of a population. Genome-wide association studies (GWAS) is an example of systems research which identifies small genetic variations across individual, whilst linking this in with traditional genetics. This gives scientists the ability to establish crucial functional relationships between genes, found in different systems, associated with the progression of obesity and metabolic syndrome (Lusis et al., 2008). When GWAS was conducted over 130 independent studies, CD44, a cell surface adhesion receptor involved in cell-cell interactions, was found to have the top role in modulating adipose tissue inflammation and glycaemic control, as it was the highest differentially expressed gene (Meng et al., 2012). Additionally, GWAS has identified links between the FTO gene and melanocortin-4 receptor (MC4R), which then caused changes in BMI and therefore the progression of obesity in both children and adults (Tung & Yeo, 2011). Without systems biology, this first-hand information about causal relationships between variations in genes and disease would not be formed, emphasising its importance in helping to understand and treat obesity.
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Systems biology can be used to gain a better understanding of the microbiome found in the gut in order to link microbial activity to the prevalence of obesity and metabolic syndrome. The microbiome functions within the body to assist with metabolism, and when imbalanced can lead to disease, such as obesity and metabolic syndrome (Kinross et al., 2011). Parallel computing for systems biology allows for a large-scale analysis of GI tract’s under various physiological conditions that both are human’s or mimic human microbiota (Maruvada et al., 2017). Profiles of parallel microbial communities on murine models, such as human babies, give insights to microbiomes which have not adapted to their environment yet. These show that microbiomes with higher Actinobacteria are linked to a higher rate of development of obesity and metabolic syndrome (Martin et al., 2007). Other examples of components of metabolic syndrome which this profiling shows is the mice with an increase in Clostridium perfringens had increased liver triglyceride as well as higher plasma glycerol, indicating towards the progression of diabetes (Martin et al., 2007). The modelling of the gut ecosystem microbial levels are vital for disease prevention that is specifically targeted to each individual (Kinross et al., 2011). It can be seen, using systems biology strategies, that varying levels of microbes in the gut microbiome can cause a higher chance of development of obesity or metabolic syndrome.
The functioning relationships between an individual’s microbiome and their metabotype is associated with the progression of obesity and metabolic syndrome, shown by systems biology. A metabotype is an individual’s metabolic phenotype and systems analysis allows for the differences from person to person to be highlighted (Calvani et al., 2010). Using a systems-based approach for a FISH analysis showed significantly lower microbial population in obese mice, compared to mice within a healthy weight range (Waldram et al., 2008). The obese mice also had decreased creatine production, found via a urine analysis and Hydrogen Nuclear Magnetic Resonance (H-NMR), indicating obese and lean mice have different metabotypes therefore making obese mice more inclined to be obese due to their biology (Waldram et al., 2008). Additionally, urine analyses from humans while initially obese, and once becoming lean showed a significant difference in their metabotypes. Variations in their microbiomes shown via H-NMR are a probable attribute to this difference in metabotypes (Calvani et al., 2010). The metabonics used here allowed for a complex study of the metabolite profiles of these biological samples, showing the benefits of a systems approach in order to understand metabolic regulation on a global level. Using systems biology for both FISH analyses and H-NMR shows evidence of relationships between metabotypes and they microbial environments and the development of obesity.
Therefore, the development of metabolic syndrome and, a component of it, obesity within a population can be analysed on a large-scale using systems biology. This has been seen through systems biology experimental strategies including GWAS, parallel microbiological profiling, FISH analyses and H-NMR which display underlying mechanisms which were not identified previously. Systems biology is extremely beneficial due to its ability to provide an in-depth analysis of the current obesity epidemic, and creates the opportunity for a better and wider array of treatment options, but more importantly prevention.
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