Abstract
With available knowledge and databases, the mining of more information has driven the last decade of computational biology. We validated the existing known information with omics data. There is need in overall shift in our approach; instead of understanding the architecture of hierarchical gene network, we should work on condition-specific shift in hierarchies or partnerships of gene to manage plasticity. We assume that there will be a great shift in metabolomics approach to understand how cell manages to perform at its minimum driving energy level. Transformation of decision-making system with systematic mathematical and multiple soft computing modeling platform will be crucial to untangle the thread of pattern with which the nature follows for the process of evolution, expression, and engineering the cellular machinery.
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References
Alkan F, Wenzel A, Palasca O, Kerpedjiev P, Rudebeck AF, Stadler PF, Hofacker IL, Gorodkin J (2017) RIsearch2: suffix array-based large-scale prediction of RNA–RNA interactions and siRNA off-targets. Nucleic Acids Res 45:e60–e60. https://doi.org/10.1093/nar/gkw1325
Ambardar S, Gupta R, Trakroo D, Lal R, Vakhlu J (2016) High throughput sequencing: an overview of sequencing chemistry. Indian J Microbiol 56:394–404. https://doi.org/10.1007/s12088-016-0606-4
Bartocci E, Lió P (2016) Computational modeling, formal analysis, and tools for systems biology. PLoS Comput Biol 12:e1004591. https://doi.org/10.1371/journal.pcbi.1004591
Bassalo MC, Liu R, Gill RT (2016) Directed evolution and synthetic biology applications to microbial systems. Curr Opin Biotechnol 39:126–133. https://doi.org/10.1016/j.copbio.2016.03.016
Batchelor E, Loewer A (2017) Recent progress and open challenges in modeling p53 dynamics in single cells. Curr Opin Sys Biol 3:54–59. https://doi.org/10.1016/j.coisb.2017.04.007
Belmann P, Dröge J, Bremges A, McHardy AC, Sczyrba A, Barton MD (2015) Bioboxes: standardised containers for interchangeable bioinformatics software. Gigascience 4:47. https://doi.org/10.1186/s13742-015-0087-0
Bhushan A, Joshi J, Shankar P, Kushwah J, Raju SC, Purohit HJ, Kalia VC (2013) Development of genomic tools for the identification of certain Pseudomonas up to species level. Indian J Microbiol 53:253–263. https://doi.org/10.1007/s12088-013-0412-1
Bhushan A, Mukherjee T, Joshi J, Shankar P, Kalia VC (2015) Insights into the origin of Clostridium botulinum strains: evolution of distinct restriction endonuclease sites in rrs (16S rRNA gene). Indian J Microbiol 55:140–150. https://doi.org/10.1007/s12088-015-0514-z
Bohlin J, Eldholm V, Pettersson JH, Brynildsrud O, Snipen L (2017) The nucleotide composition of microbial genomes indicates differential patterns of selection on core and accessory genomes. BMC Genomics 18:151. https://doi.org/10.1186/s12864-017-3543-7
Bracken CP, Scott HS, Goodall GJ (2016) A network-biology perspective of microRNA function and dysfunction in cancer. Nat Rev Genet 17:719–732. https://doi.org/10.1038/nrg.2016.134
Budinska E, Popovici V, Tejpar S, D’ario G, Lapique N, Sikora KO, Di Narzo AF, Yan P, Hodgson JG, Weinrich S, Bosman F, Roth A, Delorenzi M (2013) Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer. J Pathol 231:63–76. https://doi.org/10.1002/path.4212
Cappuccio A, Tieri P, Castiglione F (2016) Multiscale modelling in immunology: a review. Brief Bioinform 17:408–418. https://doi.org/10.1093/bib/bbv012
Cloney R (2017) Metagenomics: uncultivated microbes reveal new CRISPR-Cas systems. Nat Rev Genet 18:146. https://doi.org/10.1038/nrg.2017.1
Cornejo OE, Fisher D, Escalante AA (2015) Genome-wide patterns of genetic polymorphism and signatures of selection in Plasmodium vivax. Genome Biol Evol 7:106–119. https://doi.org/10.1093/gbe/evu267
Dar SA, Gupta AK, Thakur A, Kumar M (2016) SMEpred workbench: a web server for predicting efficacy of chemically modified siRNAs. RNA Biol 13:1144–1151. https://doi.org/10.1080/15476286.2016.1229733
Dumas E, Christina Boritsch E, Vandenbogaert M, RodrÃguez de la Vega RC, Thiberge JM, Caro V, Gaillard JL, Heym B, Girad-Misquich F, Brosch R, Sapriel G (2016) Mycobacterial pan-genome analysis suggests important role of plasmids in the radiation of type VII secretion systems. Genome Biol Evol 8:387–402. https://doi.org/10.1093/gbe/evw001
Duncan KR, Crüsemann M, Lechner A, Sarkar A, Li J, Ziemert N, Wang M, Bandeira N, Moore BS, Dorrestein PC, Jensen PR (2015) Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species. Chem Biol 22:460–471. https://doi.org/10.1016/j.chembiol.2015.03.010
Fitzpatrick MC, Keller SR (2015) Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett 18:1–16. https://doi.org/10.1111/ele.12376
Gifford SM, Sharma S, Booth M, Moran MA (2013) Expression patterns reveal niche diversification in a marine microbial assemblage. The ISME J 7:281–298. https://doi.org/10.1038/ismej.2012.96
Goh C, Knight JC (2017) Enhanced understanding of the host–pathogen interaction in sepsis: new opportunities for omic approaches. The Lancet Resp Med 5:212–223. https://doi.org/10.1016/S2213-2600(17)30045-0
He F, Han Y, Gong J, Song J, Wang H, Li Y (2017) Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level. Sci Rep 7:44836. https://doi.org/10.1038/srep44836
Kalia VC, Kumar P (2015) Genome wide search for biomarkers to diagnose Yersinia infections. Indian J Microbiol 55:366–374. https://doi.org/10.1007/s12088-015-0552-6
Kalia VC, Kumar P, Kumar R, Mishra A, Koul S (2015) Genome wide analysis for rapid identification of Vibrio species. Indian J Microbiol 55:375–383. https://doi.org/10.1007/s12088-015-0553-5
Kalia VC, Kumar R, Kumar P, Koul S (2016) A genome-wide profiling strategy as an aid for searching unique identification biomarkers for Streptococcus. Indian J Microbiol 56:46–58. https://doi.org/10.1007/s12088-015-0561-5
Kalia VC, Kumar R, Koul S (2017) In silico analytical tools for phylogenetic and functional bacterial genomics. In: Arora G, Sajid A, Kalia VC (eds) Drug resistance in bacteria, fungi, malaria and cancer. Springer Nature, Cham, pp 339–355. ISBN 978-3-319-48682-6. https://doi.org/10.1007/978-3-319-48683-3_15
Karp PD, Krummenacker M, Paley S, Wagg J (1999) Integrated pathway – genome databases and their role in drug discovery. Trends Biotechnol 17:275–81. https://doi.org/10.1016/S0167-7799(99)01316-5
Kekre A, Bhushan A, Kumar P, Kalia VC (2015) Genome wide analysis for searching novel markers to rapidly identify Clostridium strains. Indian J Microbiol 55:250–257. https://doi.org/10.1007/s12088-015-0535-7
Koul S, Kalia VC (2016) Comparative genomics reveals biomarkers to identify Lactobacillus species. Indian J Microbiol 56:253–263. https://doi.org/10.1007/s12088-016-0605-5
Kumar A, Mohanty NN, Chacko N, Yogisharadhya R, Shivachandra SB (2015) Structural features of a highly conserved Omp16 protein of Pasteurella multocida strains and comparison with related peptidoglycan-associated lipoproteins (PAL). Indian J Microbiol 55:50–56. https://doi.org/10.1007/s12088-014-04896-1
Kumar R, Koul S, Kumar P, Kalia VC (2016) Searching biomarkers in the sequenced genomes of Staphylococcus for their rapid identification. Indian J Microbiol 56:64–71. https://doi.org/10.1007/s12088-016-0565-9
Kumar R, Koul S, Kalia VC (2017) Exploiting bacterial genomes to develop biomarkers for identification. In: Arora G, Sajid A, Kalia VC (eds) Drug resistance in bacteria, fungi, malaria and cancer. Springer Nature, Cham, pp 357–370. ISBN 978-3-319-48682-6. https://doi.org/10.1007/978-3-319-48683-3_16
Lee S, Rho JY (2016) Development of a specific diagnostic system for detecting Turnip Yellow Mosaic Virus from Chinese cabbage in Korea. Indian J Microbiol 56:103–107. https://doi.org/10.1007/s12088-015-0557-1
Lee S, Kim CS, Shin YG, Kim JH, Kim YS, Jheong WH (2016) Development of nested PCR-based specific markers for detection of peach rosette mosaic virus in plant quarantine. Indian J Microbiol 56:108–111. https://doi.org/10.1007/s12088-015-0548-2
Li GW, Burkhardt D, Gross C, Weissman JS (2014) Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157:624–635. https://doi.org/10.1016/j.cell.2014.02.033
Liu F, Heiner M, Yang M (2016) Fuzzy stochastic petri nets for modeling biological systems with uncertain kinetic parameters. PLoS One 11:e0149674. https://doi.org/10.1371/journal.pone.0149674
Madhavaram A (2016) Biofilm production in response to DNA methylation in Pseudomonas aeruginosa. Int J Sci Eng Res 7:1183–1188
Mahata A, Mondal SP, Alam S, Roy B (2017) Mathematical model of glucose-insulin regulatory system on diabetes mellitus in fuzzy and crisp environment. Ecol Genet Genom 2:25–34
Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, Harney E, Stewardson K, Fernandes D, Novak M, Sirak K, Gamba C, Jones ER, Llamas B, Dryomov S, Pickrell J, Arsuaga JL, de Castro JM, Carbonell E, Gerritsen F, Khokhlov A, Kuznetsov P, Lozano M, Meller H, Mochalov O, Moiseyev V, Guerra MA, Roodenberg J, Vergès JM, Krause J, Cooper A, Alt KW, Brown D, Anthony D, Lalueza-Fox C, Haak W, Pinhasi R, Reich D (2015) Genome-wide patterns of selection in 230 ancient Eurasians. Nature 528:499–503. https://doi.org/10.1038/nature16152
Mc Auley MT, Mooney KM, Angell PJ, Wilkinson SJ (2015) Mathematical modelling of metabolic regulation in aging. Meta 5:232–251. https://doi.org/10.3390/metabo5020232
Meza-Lucas A, Pérez-Villagómez M, MartÃnez-López JP, GarcÃa-Rodea R, MartÃnez-Castelán MG, Escobar-Gutiérrez A, de la Rosa-Arana JL, Villanueva-Zamudio A (2016) Comparison of DOT-ELISA and Standard-ELISA for detection of the Vibrio cholerae toxin in culture supernatants of bacteria isolated from human and environmental samples. Indian J Microbiol 56:379–382. https://doi.org/10.1007/s12088-016-0596-2
Mohanraju P, Makarova KS, Zetsche B, Zhang F, Koonin EV, van der Oost J (2016) Diverse evolutionary roots and mechanistic variations of the CRISPR-Cas systems. Science 353:aad5147. https://doi.org/10.1126/science.aad5147
Monat C, Pera B, Ndjiondjop MN, Sow M, Tranchant-Dubreuil C, Bastianelli L, Ghesquiere A, Sabot F (2017) De novo assemblies of three Oryza glaberrima accessions provide first insights about pan-genome of African rices. Genome Biol Evol 9:1–6. https://doi.org/10.1093/gbe/evw253
Mostafavi S, Morris Q (2012) Combining many interaction networks to predict gene function and analyze gene lists. Proteomics 12:1687–1696. https://doi.org/10.1002/pmic.201100607
Nickerson ML, Witte N, Im KM, Turan S, Owens C, Misner K, Tsang SX, Cai Z, Wu S, Dean M, Costello JC, Theodorescu D (2017) Molecular analysis of urothelial cancer cell lines for modeling tumor biology and drug response. Oncogene 36:35–46. https://doi.org/10.1038/onc.2016.172
Palazzo AF, Gregory TR (2014) The case for junk DNA. PLoS Genet 10:e1004351. https://doi.org/10.1371/journal.pgen.1004351
Pan Y, Ni BJ, Lu H, Chandran K, Richardson D, Yuan Z (2015) Evaluating two concepts for the modelling of intermediates accumulation during biological denitrification in wastewater treatment. Wat Res 71:21–31. https://doi.org/10.1016/j.watres.2014.12.029
Pooja S, Pushpanathan M, Jayashree S, Gunasekaran P, Rajendhran J (2015) Identification of periplasmic a-amylase from cow dung metagenome by product induced gene expression profiling (Pigex). Indian J Microbiol 55:57–65. https://doi.org/10.1007/s12088-014-0487-3
Puranik S, Purohit HJ (2015) Dynamic interactive events in gene regulation using E. Coli dehydrogenase as a model. Funct Integ Genom 15:175–188. https://doi.org/10.1007/s10142-014-0418-8
Puri A, Rai A, Dhanaraj PS, Lal R, Patel DD, Kaicker A, Verma M (2016) An in silico approach for identification of the pathogenic species, Helicobacter pylori and its relatives. Indian J Microbiol 56:277–286. https://doi.org/10.1007/s12088-016-0575-7
Qiu Z, Yang H, Rong L, Ding W, Chen J, Zhong L (2017) Targeted metagenome based analyses show gut microbial diversity of inflammatory bowel disease patients. Indian J Microbiol 57. https://doi.org/10.1007/s12088-017-0652-6
Ram R, Mehta M, Nguyen QT, Larma I, Boehm BO, Pociot F, Concannon P, Morahan G (2016) Systematic evaluation of genes and genetic variants associated with type 1 diabetes susceptibility. J Immunol 196:3043–3053. https://doi.org/10.4049/jimmunol.1502056
Ricker N, Shen SY, Goordial J, Jin S, Fulthorpe RR (2016) PacBio SMRT assembly of a complex multi-replicon genome reveals chlorocatechol degradative operon in a region of genome plasticity. Gene 586:239–247. https://doi.org/10.1016/j.gene.2016.04.018
Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273:1516–1517
Shaik R, Ramakrishna W (2014) Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Pl Physiol 164:481–495. https://doi.org/10.1104/pp.113.225862
Spetale FE, Tapia E, Krsticevic F, Roda F, Bulacio P (2016) A factor graph approach to automated GO annotation. PLoS One 11:e0146986. https://doi.org/10.1371/journal.pone.0146986
Tsigkinopoulou A, Baker SM, Breitling R (2017) Respectful modeling: addressing uncertainty in dynamic system models for molecular biology. Trends Biotechnol 35:518–529. https://doi.org/10.1016/j.tibtech.2016.12.008
Vandecraen J, Chandler M, Aertsen A, Van Houdt R (2017) The impact of insertion sequences on bacterial genome plasticity and adaptability. Crit Rev Microbiol 43; https://doi.org/10.1080/1040841X.2017
Vernikos G, Medini D, Riley DR, Tettelin H (2015) Ten years of pan-genome analyses. Curr Opin Microbiol 23:148–154. https://doi.org/10.1016/j.mib.2014.11.016
Wright AV, Nuñez JK, Doudna JA (2016) Biology and applications of CRISPR systems: harnessing nature’s toolbox for genome engineering. Cell 164:29–44. https://doi.org/10.1016/j.cell.2015.12.035
Wu S, Joseph A, Hammonds AS, Celniker SE, Yu B, Frise E (2016) Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks. Proc Nat Acad Sci 113:4290–4295. https://doi.org/10.1073/pnas.1521171113
Yu S, Peng Y, Zheng Y, Chen W (2015) Comparative genome analysis of Lactobacillus casei: insights into genomic diversification for niche expansion. Indian J Microbiol 55:102–107. https://doi.org/10.1007/s12088-014-0496-2
Yugendran T, Harish BN (2016) Global DNA methylation level among ciprofloxacin-resistant clinical isolates of Escherichia coli. J Clin Diagnos Res 10:DC27. https://doi.org/10.7860/JCDR/2016/19034.7830
Zhu X, Need AC, Petrovski S, Goldstein DB (2014) One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat Neuroscience 17:773–781. https://doi.org/10.1038/nn.3713
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Purohit, H.J., Tikariha, H., Kalia, V.C. (2018). Future Perspectives of Computational Biology: Demanding Shifts in Analytical Thinking to Unfold Biological Complexities. In: Purohit, H., Kalia, V., More, R. (eds) Soft Computing for Biological Systems. Springer, Singapore. https://doi.org/10.1007/978-981-10-7455-4_17
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