Abstract
Integromics is necessitated as the complex diseases require to collate the integrated analysis expression, variation, and regulation of genes involved in trigger, prognosis, and establishment of factors creating the complete disease paradigm. The further involvement of the non-genetic and environmental exogenous factors is also designated to formulate the multitude of data for furthering the “integromic” approaches. Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease models, which are functionally changed during disease development, progression, or treatment. We represent the requirement of the multi-node analyses that goes beyond the binary relationships to enterprise the structured interactions at the interface of genotype-to-phenotype correlations in disease biology. The prevalence and sporadic occurrence of endemic and pandemic infectious diseases, as well as the unmanageable burden of the non-communicable diseases, have emerged as the most burgeoning task of scientific investigations. Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. The systems-level studies have indicated biomolecule to cellular organization requires communication and cross-talk possibilities at the organism levels. Designing newer theranostic regimes thus is required to focus on disease heterogeneity integrating the knowledge base of dynamic physical or functional interactions of network of networks. The chapter is targeted toward the identification, characterization, and a follow through of the experimental and computational tools for evincing the futuristic plan for modular endeavors in disease biology.
References
Allore HG, Murphy TE (2008) An examination of effect estimation in factorial and standardly-tailored designs. Clin Trials 5(2):121–130
Amit I, Garber M, Chevrier N, Leite AP, Donner Y, Eisenhaure T, Guttman M, Grenier JK, Li W, Zuk O et al (2009) Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326:257–263. [PubMed: 19729616]
Barabasi AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68
Bartel PL, Roecklein JA, Sen Gupta D, Fields S (1996) A protein linkage map of Escherichia coli bacteriophage T7. Nat Genet 12:72–77. [PubMed: 8528255]
Bonder MJ, Luijk R, Zhernakova DV, Moed M, Deelen P, Vermaat M, van Iterson M, van Dijk F, van Galen M, Bot J et al (2017) Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet 49:131–138. [PubMed: 27918535]
Cawley S, Bekiranov S, Ng HH, Kapranov P, Sekinger EA, Kampa D, Piccolboni A, Sementchenko V, Cheng J, Williams AJ et al (2004) Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116:499–509. [PubMed: 14980218]
Chan SY, Loscalzo WK (2012a) Deciphering the molecular basis of human cardiovascular disease through network biology. J Curr Opin Cardiol 27(3):202–209
Chan SY, Loscalzo J (2012b) The emerging paradigm of network medicine in the study of human disease. Circ Res 111(3):359–374
Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ et al (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 452:429–435. https://doi.org/10.1038/nature06757
Corradin O, Cohen AJ, Luppino JM, Bayles IM, Schumacher FR, Scacheri PC (2016) Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry. Nat Genet 48:1313–1320. This study identified physical chromatin interactions that are disrupted by risk SNPs and ‘outside variants’. [PubMed: 27643537]
Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JL, Toufighi K, Mostafavi S et al (2010) The genetic landscape of a cell. Science 327:425–431. [PubMed: 20093466]
Debmalya B et al, (2020) In silico disease model: from simple networks to complex diseases bioinformatics and systems biology
Dezs Z, Nikolsky Y, Nikolskaya T, Miller J, Cherba D, Webb C et al (2009) Identifying disease-specific genes based on their topological significance in protein networks. BMC Syst Biol 3:36. https://doi.org/10.1186/1752-0509-3-36
Finley RL Jr, Brent R (1994) Interaction mating reveals binary and ternary connections between Drosophila cell cycle regulators. Proc Natl Acad Sci U S A 91:12980–12984. [PubMed: 7809159]
Fromont-Racine M, Rain JC, Legrain P (1997) Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens. Nat Genet 16:277–282. [PubMed: 9207794]
Ghiassian SD, Menche J, Barabasi AL (2015) A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Comput Biol 11:e1004120. [PubMed: 25853560]
Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, Lucau-Danila A, Anderson K, Andre B et al (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418:387–391. [PubMed: 12140549]
Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS, Zhang R, Hartmann BM, Zaslavsky E, Sealfon SC et al (2015) Understanding multicellular function and disease with human tissue-specific networks. Nat Genet 47:569–576. [PubMed: 25915600]
Grove CA, De Masi F, Barrasa MI, Newburger DE, Alkema MJ, Bulyk ML, Walhout AJ (2009) A multiparameter network reveals extensive divergence between C. elegans bHLH transcription factors. Cell 138:314–327. [PubMed: 19632181]
Gunsalus KC, Ge H, Schetter AJ, Goldberg DS, Han JD, Hao T, Berriz GF, Bertin N, Huang J, Chuang LS et al (2005) Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis. Nature 436:861–865. [PubMed: 16094371]
Hood L, Heath JR, Phelps ME, Lin B (2014) Systems biology and new technologies enable predictive and preventative medicine. Science 306(5696):640–643
Joshi A, Rienks M, Theofilatos K, Mayr M (2021) Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol 18(5):313–330
Kim SK, Lund J, Kiraly M, Duke K, Jiang M, Stuart JM, Eizinger A, Wylie BN, Davidson GS (2001) A gene expression map for Caenorhabditis elegans. Science 293:2087–2092. [PubMed: 11557892]
Kitano H (2002) Computational systems biology. Nature 420(6912):206–210
Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I et al (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298:799–804. [PubMed: 12399584]
Leiserson MDM, Vandin F, Wu H-T, Dobson JR, Eldridge JV, Thomas JL et al (2015) Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat Genet 47:106–114. https://doi.org/10.1038/ng.3168
Li P, Fu Y, Wang Y (2015) Network based approach to drug discovery: a mini review. Mini Rev Med Chem 15(8):687–695
Licatalosi DD, Darnell RB (2010) RNA processing and its regulation: global insights into biological networks. Nat Rev Genet 11:75–87
Lim WA, Lee CM, Tang C (2013) Design principles of regulatory networks: searching for the molecular algorithms of the cell. Mol Cell 49(2):202–212
Liu R, Wang X, Aihara K, Chen L (2013) Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers. Med Res Rev. https://doi.org/10.1002/med.21293. [Epub ahead of print]
Liu ZP, Wang Y, Zhang XS, Chen L (2012) Network-based analysis of complexdiseases. IET Syst Biol 6:22–33
Loscalzo J, Barabási A-L, Silverman EK (2017) Network medicine: complex systems in human disease and therapeutics. Harvard University Press
Mani R, St Onge R, Jt H, Giaever G, Roth F (2008) Defining genetic interaction. Proc Natl Acad Sci U S A 105:3461–3466. [PubMed: 18305163]
Marcotte E, Date S (2001) Exploiting big biology: integrating large-scale biological data for function inference. Brief Bioinform 2:363–374. [PubMed: 11808748]
Marcotte R, Sayad A, Brown KR, Sanchez-Garcia F, Reimand J, Haider M, Virtanen C, Bradner JE, Bader GD, Mills GB, Pe’er D, Moffatand J, Neel BG (2016) Functional genomic Landscape of Human Breast Cancer drivers, vulnerabilities, and resistance. Cell 164:293–309. https://doi.org/10.1016/j.cell.2015.11.062
Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabasi AL (2015) Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347:1257601. [PubMed: 25700523]
Macleod M (2021) The applicability of mathematics in computational systems biology and its experimental relations. Eur J Philos Sci 11:84
Mohr S, Bakal C, Perrimon N (2010) Genomic screening with RNAi: results and challenges. Annu Rev Biochem 79:37–64. [PubMed: 20367032]
Motter AE, Gulbahce N, Almaas E, Barabási A-L (2008) Predicting synthetic rescues in metabolic networks. Mol Syst Biol 4:168. https://doi.org/10.1038/msb.2008.1
Murrugarra D, Laubenbacher RJ (2011) Regulatory patterns in molecular interaction networks. Theor Biol 7(288):66–72
Nibbe RK, Koyuturk M, Chance MR (2010) An integrative-omics approach toidentify functional sub-networks in human colorectal cancer. PLoSComput Biol 6:e1000639
Piano F, Schetter AJ, Morton DG, Gunsalus KC, Reinke V, Kim SK, Kemphues KJ (2002) Gene clustering based on RNAi phenotypes of ovary-enriched genes in C. elegans. Curr Biol 12:1959–1964. [PubMed: 12445391]
Pichlmair A, Kandasamy K, Alvisi G, Mulhern O, Sacco R, Habjan M et al (2012) Viral immune modulators perturb the human molecular network by common and unique strategies. Nature 487:486–490. https://doi.org/10.1038/nature11289
Pujana MA, Han JDJ, Starita LM, Stevens KN, Tewari M, Ahn JS, Rennert G, Moreno V, Kirchhoff T, Gold B et al (2007) Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet 39:1338–1349. [PubMed: 17922014]
Reece-Hoyes JS, Deplancke B, Shingles J, Grove CA, Hope IA, Walhout AJ (2005) A compendium of Caenorhabditis elegans regulatory transcription factors: a resource for mapping transcription regulatory networks. Genome Biol 6:R110. [PubMed: 16420670]
Roberts PM (2006) Mining literature for systems biology. Brief Bioinform 7:399–406. PubMed:17032698
Rolland T, Tasan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, Mosca R et al (2014) A proteome-scale map of the human interactome network. Cell 159:1212–1226. [PubMed: 25416956]
Saiker IH (2021) Machine learning: algorithms, real- world applications and research directions. SN Comp Sci 2:160
Simon R (2005) Development and validation of therapeutically relevant multi-gene biomarker classifiers. J Natl Cancer Inst 97:866–867
Srivas R, Shen JP, Yang CC, Sun SM, Li J, Gross AM et al (2016) A network of conserved synthetic lethal interactions for exploration of precision cancer therapy. Mol Cell 63:514–525. https://doi.org/10.1016/j.molcel.2016.06.022
Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302:249–255. [PubMed: 12934013]
Sun S, Yang F, Tan G, Costanzo M, Oughtred R, Hirschman J, Theesfeld CL, Bansal P, Sahni N, Yi S et al (2016) An extended set of yeast-based functional assays accurately identifies human disease mutations. Genome Res 26:670–680. [PubMed: 26975778]
van Leeuwen J, Pons C, Mellor JC, Yamaguchi TN, Friesen H, Koschwanez J, Usaj MM, Pechlaner M, Takar M, Usaj M et al (2016) Exploring genetic suppression interactions on a global scale. Science 354
Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM (2009) A census of human transcription factors: function, expression and evolution. Nat Rev Genet 10:252–263. [PubMed: 19274049]
Vermeirssen V, Barrasa MI, Hidalgo CA, Babon JA, Sequerra R, Doucette-Stamm L, Walhout BA, AJ. (2007) Transcription factor modularity in a gene-centered C. elegans core neuronal protein- DNA interaction network. Genome Res 17:1061–1071. [PubMed: 17513831]
Vidal M et al (2011) Interactome networks and human disease. Cell 144(6):986–998. https://doi.org/10.1016/j.cell.2011.02.016
Vidal M (2001) Vidal M. a biological atlas of functional maps. Cell 104:333–339. [PubMed: 11239391]
Voit EO (2000) Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists. Cambridge University Press
Walhout AJ, Reboul J, Shtanko O, Bertin N, Vaglio P, Ge H, Lee H, Doucette-Stamm L, Gunsalus KC, Schetter AJ et al (2002) Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline. Curr Biol 12:1952–1958. [PubMed: 12445390]
Walhout AJ, Vidal M (2001) Protein interaction maps for model organisms. Nat Rev Mol Cell Biol 2:55–62. [PubMed: 11413466]
Wang X (2011) Role of clinical bioinformatics in the development of network-based Biomarkers. J Clin Bioinforma 1:28
Winslow RL, Trayanova N, Geman D, Mille MI (2012) Computational medicine: translating models to clinical care. Sci Transl Med 4(158):158rv11
Wu D, Rice CM, Wang X (2012) Cancer bioinformatics: a new approach to systems clinical medicine. BMC Bioinform 13:71
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:17. https://doi.org/10.2202/1544-6115.1128
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Sharda, S., Avasthi, A., Bose, S., Kaur, N. (2024). Cellular Interactions Network in Cancer: Integrative Disease Models. In: Sobti, R.C., Ganguly, N.K., Kumar, R. (eds) Handbook of Oncobiology: From Basic to Clinical Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-99-6263-1_43
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