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Applications of network biology
Published in Karthik Raman, An Introduction to Computational Systems Biology, 2021
Barabási, Vidal, and co-workers first developed the concept of disease networks [12]. They represented the disease network as a bipartite graph, where there are two sets of nodes, corresponding to diseases (genetic disorders) and disease genes. Being a bipartite graph (see §2.3.4), edges connect only diseases and genes, with no edges between the diseases or genes themselves, as shown in Figure 4.1. The network, which they called the diseasome, contained 1,284 genetic disorders and 1,777 disease genes from the Online Mendelian Inheritance in Man (OMIM) database [13]. Figure 4.1 (centre panel) shows a small subset of these disorder–disease gene associations, with circles and rectangles representing disorders and disease genes, respectively. An edge connects a disorder to a disease gene if mutations in the gene lead to that disorder. The size of each circle is proportional to the number of genes participating in the disorder; the disease nodes themselves are coloured based on the class to which the disorder belongs.
Naturally Occurring Polymers—Animals
Published in Charles E. Carraher, Carraher's Polymer Chemistry, 2017
The association of a particular disease with a particular gene or group of genes is rapidly increasing. A spot check of www.ncbi.nlm.nih.gov/omim, the online version of Mendelian Inheritance in Man (OMIM) gives an ongoing updated progress report of this activity. Currently, about 1500 disease mutations have been entered. With the advent of the mapping comes a number of shifts in thinking and activity. Thus, we will move from the so-called map-based gene discovery to looking at the particular activity of gene sequences; from association of particular gene-associated diseases to looking at tendency and susceptibility for given conditions and the variation with tendency/susceptibility between individuals; from looking at the activity of a single gene or gene location to investigating combined activities of several genes from varying locations; from the so-called genomics or studying of genes themselves to proteomics and studying of the interaction between the genes and proteins; from gene action to gene regulation; and from specific mutations to the mechanisms and causes of such mutations. Much of this is a guessing game—hopefully an educated and educating guessing game and it is currently very costly. As new techniques and strategies are developed, the cost should decrease.
Systems toxicology approach explores target-pathway relationship and adverse health impacts of ubiquitous environmental pollutant bisphenol A
Published in Journal of Toxicology and Environmental Health, Part A, 2022
Manigandan Nagarajan, Gobichettipalayam Balasubramaniam Maadurshni, Jeganathan Manivannan
In case of BPA disease targets, the Online Mendelian Inheritance in Man (OMIM) diseases indicated occurrence of hypertension as evidenced by alterations in phenylethanolamine n-methyltransferase (PNMT), nitric oxide synthase 3 (NOS3) and nuclear receptor subfamily 3, group C, member 2 (NR3C2) gene polymorphism (NR3C2). Thids gene catalyzes production of nitric oxide (NO) in the endothelium and its deficiency leads to alterations in NO metabolism and pathogenesis of hypertension (Zintzaras, Kitsios, and Stefanidis 2006). In this regard, Zhang et al. (2012) observed significant associations with NOS3 variants and coronary heart disease (CHD) and heart failure associated with significant pharmacogenetic effects for stroke and all-cause mortality. Subsequently, the gene polymorphism is known to be associated with risk of gestational hypertension in Han Chinese women (Cui, Xu, and Jiang 2019). Further, a study using genetic model of hypertension reported novel molecular mechanisms involved in the dysregulation of cardiac, the terminal enzyme in the catecholamine biosynthetic pathway that is responsible for adrenaline biosynthesis (Peltsch et al. 2016).
Medical diagnosis and treatment is NP-complete
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2021
Jeffrey. E. Arle, Kristen W. Carlson
Another example of diagnosis-treatment pairs would be digested from the ~8,000 known single-gene inherited disorders Online Mendelian Inheritance in Man (OMIM) (Amberger et al., 2015). The diagnosis-treatment matrix would consist of gene-by-gene alleles of the human genome and their clinical synopsis and phenotypes as the symptoms in the top row, left side, with treatments added in the right side. Bits in the row vector below each allele on the left side would indicate if the gene was implicated in the disorder and its observable symptoms, and for the prescribed tests or treatments for each disorder on the right side. The power set of all possible subsets of OMIM gene sets is on the order of 102048, but these single-allele disorders occupy only 1/102045 of the total combinatorial space (we omit |T| in these calculations as |T| << |S|). The sparseness is greater for polygenetic disorders across the entire human genome. Given 21,000 genes, there are on the order of 106031 subsets. The number of polygenic diseases and degree of polygenicity is unknown, assumed to be large, but with few dominant factors (International Common Disease Alliance, 2019). Assuming six dominant gene factors on average, n choose k, where n = 21,000 and k = 6, the resulting ~1023 combinations would occupy just 1/106008 of the genetic disease diagnosis subset space. Assuming 100 factors on average per disease boosts the number of combinations to 10274, but they would occupy just 1/105757 of the total space.
A pilot study of exome sequencing in a diverse New Zealand cohort with undiagnosed disorders and cancer
Published in Journal of the Royal Society of New Zealand, 2018
Colina McKeown, Samantha Connors, Rachel Stapleton, Tim Morgan, Ian Hayes, Katherine Neas, Joanne Dixon, Kate Gibson, David M. Markie, Peter Tsai, Cherie Blenkiron, Sandra Fitzgerald, Paula Shields, Patrick Yap, Ben Lawrence, Cristin Print, Stephen P. Robertson
The search space within which to find a specific genetic explanation for monogenic disorders is becoming well-refined. There are approximately 19,000 protein-coding genes in the human genome, covering approximately 50 Mb of DNA sequence, and it is in this portion of the genome—the exome—that an estimated 85% of Mendelian molecular diagnoses will be found (Blackburn et al. 2015; Chong et al. 2015; Valencia et al. 2015). Whole exome sequencing (WES) is increasingly being used worldwide with studies of its utility demonstrating diagnostic rates of 16%–57% (de Ligt et al. 2012; Soden et al. 2014; Yang et al. 2014; Chong et al. 2015; Valencia et al. 2015; Wright et al. 2015; Monroe et al. 2016; Stark et al. 2016). Currently, mutations in 3849 genes have been proven to be responsible for 6121 Mendelian phenotypes (OMIM Gene Map Statistics 2017).