Towards Engineering an Ecosystem: A Review of Computational Approaches to Explore and Exploit the Human Microbiome for Healthcare
Tóm tắt
The diverse and complex microbial community inhabiting the human body, also known as the microbiome, plays a significant role in our health and wellbeing. Any dysbiosis or disruption to this microbial ecosystem can result in several health implications. Engineering and tweaking the ecosystem to restore balance is an active area of modern clinical research. Both conventional probiotics as well as more contemporary efforts towards designing ‘cocktails’ of live microbial cells are being pursued with the aim of modulating the microbiome to our benefit. However, to make such live-biotherapeutic treatment effective and to alleviate any safety concerns, rational design approaches and clarity on mechanisms of action are needed. The current review describes computational approaches towards understanding and modelling the complex ecological interactions amongst microbes, as well as their interactions with the host physiology. Current approaches and some emerging techniques catering to collation of microbe-microbe association data, construction and analysis of complex biological networks, as well as modelling and simulation of microbial cells and ecosystems have been discussed. Ability to make predictions on how the microbiome behaves when subjected to any intervention is expected to help in rational design and informed prescription of novel therapeutics. This will in turn help in engineering this microbial ecosystem to our benefit. While the discussed methods and approaches may not constitute an exhaustive list of currently available resources, the present review article aims to serve as a guideline for building systems level perspectives on microbes and microbial communities.
Tài liệu tham khảo
Allin KH, Nielsen T, Pedersen O (2015) Mechanisms in endocrinology: Gut microbiota in patients with type 2 diabetes mellitus. Eur J Endocrinol 172:R167-177. https://doi.org/10.1530/EJE-14-0874
Allin KH, Tremaroli V, Caesar R et al (2018) Aberrant intestinal microbiota in individuals with prediabetes. Diabetologia 61:810–820. https://doi.org/10.1007/s00125-018-4550-1
Alvarez-Silva C, Kashani A, Hansen TH et al (2021) Trans-ethnic gut microbiota signatures of type 2 diabetes in Denmark and India. Genome Med 13:37. https://doi.org/10.1186/s13073-021-00856-4
Anand S, Mande SS (2018) Diet, microbiota and gut-lung connection. Front Microbiol 9: 2147. Doi: https://doi.org/10.3389/fmicb.2018.02147
Arthur JC, Perez-Chanona E, Mühlbauer M et al (2012) Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 338:120–123. https://doi.org/10.1126/science.1224820
Arumugam M, Raes J, Pelletier E et al (2011) Enterotypes of the human gut microbiome. Nature 473:174–180. https://doi.org/10.1038/nature09944
Ay A, Arnosti DN (2011) Mathematical modeling of gene expression: a guide for the perplexed biologist. Crit Rev Biochem Mol Biol 46:137–151. https://doi.org/10.3109/10409238.2011.556597
Baksi KD, Kuntal BK, Mande SS (2018) ‘TIME’: a web application for obtaining insights into microbial ecology using longitudinal microbiome data. Front Microbiol. https://doi.org/10.3389/fmicb.2018.00036
Baldini F, Heinken A, Heirendt L et al (2019) The Microbiome modeling toolbox: from microbial interactions to personalized microbial communities. Bioinformatics 35:2332–2334. https://doi.org/10.1093/bioinformatics/bty941
Banerjee S, Kirkby CA, Schmutter D et al (2016) Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol Biochem 97:188–198. https://doi.org/10.1016/j.soilbio.2016.03.017
Banerjee S, Schlaeppi K, van der Heijden MGA (2018) Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol 16:567–576. https://doi.org/10.1038/s41579-018-0024-1
Bayal N, Nagpal S, Haque MM et al (2019) 16S rDNA based skin microbiome data of healthy individuals and leprosy patients from India. Sci Data 6:225. https://doi.org/10.1038/s41597-019-0232-1
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (methodol) 57:289–300
Berry D, Widder S (2014) Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. https://doi.org/10.3389/fmicb.2014.00219
Bharadwaj A, Singh DP, Ritz A et al (2017) GraphSpace: stimulating interdisciplinary collaborations in network biology. Bioinformatics 33:3134–3136. https://doi.org/10.1093/bioinformatics/btx382
Bose T, Das C, Dutta A et al (2018) Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach. BMC Genomics 19:555
Brohée S, Faust K, Lima-Mendez G et al (2008) NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways. Nucleic Acids Res 36:W444-451. https://doi.org/10.1093/nar/gkn336
Brunner JD, Chia N (2020) Minimizing the number of optimizations for efficient community dynamic flux balance analysis. PLoS Comput Biol 16:e1007786. https://doi.org/10.1371/journal.pcbi.1007786
Bucci V, Tzen B, Li N et al (2016) MDSINE: microbial dynamical systems INference Engine for microbiome time-series analyses. Genome Biol 17:121. https://doi.org/10.1186/s13059-016-0980-6
Budinich M, Bourdon J, Larhlimi A, Eveillard D (2017) A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PLoS ONE 12:e0171744. https://doi.org/10.1371/journal.pone.0171744
Calle ML (2019) Statistical analysis of metagenomics data. Genom Inform. https://doi.org/10.5808/GI.2019.17.1.e6
Carpenter SR, Brock WA, Folke C et al (2015) Allowing variance may enlarge the safe operating space for exploited ecosystems. PNAS 112:14384–14389
Chaffron S, Rehrauer H, Pernthaler J, von Mering C (2010) A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res 20:947–959. https://doi.org/10.1101/gr.104521.109
Chakravarthy SK, Jayasudha R, Ranjith K et al (2018) Alterations in the gut bacterial microbiome in fungal Keratitis patients. PLoS ONE 13:e0199640
Chen X, Huang Y-A, You Z-H et al (2017) A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases. Bioinformatics 33:733–739. https://doi.org/10.1093/bioinformatics/btw715
Chen Y, Chen X, Yu H et al (2019) Oral microbiota as promising diagnostic biomarkers for gastrointestinal cancer: a systematic review. Onco Targets Ther 12:11131. https://doi.org/10.2147/OTT.S230262
Cowley MJ, Pinese M, Kassahn KS et al (2012) PINA v2.0: mining interactome modules. Nucleic Acids Res 40:D862-865. https://doi.org/10.1093/nar/gkr967
Csardi G, Nepusz T (2006) The igraph software package for complex network research. Int J Complex Syst 1695:1–9
Dai L, Vorselen D, Korolev KS, Gore J (2012) Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336:1175–1177. https://doi.org/10.1126/science.1219805
Dai D, Wang T, Wu S et al (2019) Metabolic dependencies underlie interaction patterns of gut microbiota during enteropathogenesis. Front Microbiol. https://doi.org/10.3389/fmicb.2019.01205
Das C, Dutta A, Rajasingh H, Mande SS (2013) Understanding the sequential activation of type III and type VI secretion systems in Salmonella typhimurium using Boolean modeling. Gut Pathog 5:28
Das C, Mokashi C, Mande SS, Saini S (2018) Dynamics and control of flagella assembly in Salmonella typhimurium. Front Cell Infect Microbiol. https://doi.org/10.3389/fcimb.2018.00036
Durot M, Bourguignon P-Y, Schachter V (2009) Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 33:164–190. https://doi.org/10.1111/j.1574-6976.2008.00146.x
Ernst J, Bar-Joseph Z (2006) STEM: a tool for the analysis of short time series gene expression data. BMC Bioinform 7:1–11. https://doi.org/10.1186/1471-2105-7-191
Fang H, Huang C, Zhao H, Deng M (2015) CCLasso: correlation inference for compositional data through Lasso. Bioinformatics 31:3172–3180. https://doi.org/10.1093/bioinformatics/btv349
Faust K (2021) Open challenges for microbial network construction and analysis. ISME J. https://doi.org/10.1038/s41396-021-01027-4
Faust K, Raes J (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10:538–550. https://doi.org/10.1038/nrmicro2832
Faust K, Sathirapongsasuti JF, Izard J et al (2012) Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol 8:e1002606. https://doi.org/10.1371/journal.pcbi.1002606
Feist AM, Palsson BØ (2008) The Growing Scope of Applications of Genome-scale Metabolic Reconstructions: the case of E. coli. Nature biotechnology 26:659. https://doi.org/10.1038/nbt1401
Fisher CK, Mehta P (2014) Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9:e102451. https://doi.org/10.1371/journal.pone.0102451
Forkosh E, Ilan Y (2019) The heart-gut axis: new target for atherosclerosis and congestive heart failure therapy. Open Heart 6:e000993. https://doi.org/10.1136/openhrt-2018-000993
Frank DN, Amand ALS, Feldman RA et al (2007) Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. PNAS 104:13780–13785. https://doi.org/10.1073/pnas.0706625104
Freilich S, Kreimer A, Meilijson I et al (2010) The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res 38:3857–3868. https://doi.org/10.1093/nar/gkq118
Gaisawat MB, MacPherson CW, Tremblay J et al (2019) Probiotic supplementation in a clostridium difficile-infected gastrointestinal model is associated with restoring metabolic function of microbiota. Microorganisms. https://doi.org/10.3390/microorganisms8010060
Ganju P, Nagpal S, Mohammed MH et al (2016) Microbial community profiling shows dysbiosis in the lesional skin of Vitiligo subjects. Sci Rep 6:18761. https://doi.org/10.1038/srep18761
Gerasch A, Faber D, Küntzer J et al (2014) BiNA: a visual analytics tool for biological network data. PLoS ONE 9:e87397. https://doi.org/10.1371/journal.pone.0087397
Ghosh TS, Gupta SS, Bhattacharya T et al (2014) Gut microbiomes of Indian children of varying nutritional status. PLoS ONE 9:e95547. https://doi.org/10.1371/journal.pone.0095547
Giuliani C, Marzorati M, Innocenti M et al (2016) Dietary supplement based on stilbenes: a focus on gut microbial metabolism by the in vitro simulator M-SHIME®. Food Funct 7:4564–4575. https://doi.org/10.1039/C6FO00784H
Gloor GB, Reid G (2016) Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can J Microbiol 62:692–703. https://doi.org/10.1139/cjm-2015-0821
Gotelli NJ (2000) Null model analysis of species co-occurrence patterns. Ecology 81:2606–2621. https://doi.org/10.1890/0012-9658(2000)081[2606:NMAOSC]2.0.CO;2
Guo XY, Liu XJ, Hao JY (2020) Gut microbiota in ulcerative colitis: insights on pathogenesis and treatment. J Dig Dis 21:147–159. https://doi.org/10.1111/1751-2980.12849
Gupta SS, Mohammed MH, Ghosh TS et al (2011) Metagenome of the gut of a malnourished child. Gut Pathog 3:7. https://doi.org/10.1186/1757-4749-3-7
Gurung M, Li Z, You H et al (2020) Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine. https://doi.org/10.1016/j.ebiom.2019.11.051
Hajishengallis G, Liang S, Payne MA et al (2011) A low-abundance biofilm species orchestrates inflammatory periodontal disease through the commensal microbiota and the complement pathway. Cell Host Microbe 10:497–506. https://doi.org/10.1016/j.chom.2011.10.006
Hajishengallis G, Darveau RP, Curtis MA (2012) The keystone-pathogen hypothesis. Nat Rev Microbiol 10:717–725. https://doi.org/10.1038/nrmicro2873
Halfvarson J, Brislawn CJ, Lamendella R et al (2017) Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol 2:1–7. https://doi.org/10.1038/nmicrobiol.2017.4
Handcock MS, Hunter DR, Butts CT et al (2008) statnet: software tools for the representation, visualization, analysis and simulation of network data. J Stat Softw 24:1548–7660
Haque MM, Merchant M, Kumar PN et al (2017) First-trimester vaginal microbiome diversity: a potential indicator of preterm delivery risk. Sci Rep 7:16145. https://doi.org/10.1038/s41598-017-16352-y
Hegde SR, Rajasingh H, Das C et al (2012) Understanding communication signals during mycobacterial latency through predicted genome-wide protein interactions and Boolean modeling. PLoS ONE 7:e33893. https://doi.org/10.1371/journal.pone.0033893
Heinken A, Ravcheev DA, Baldini F et al (2019) Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease. Microbiome 7:75. https://doi.org/10.1186/s40168-019-0689-3
Hirschberg S, Gisevius B, Duscha A, Haghikia A (2019) Implications of diet and the gut microbiome in neuroinflammatory and neurodegenerative diseases. Int J Mol Sci. https://doi.org/10.3390/ijms20123109
Hold GL, Smith M, Grange C et al (2014) Role of the gut microbiota in inflammatory bowel disease pathogenesis: What have we learnt in the past 10 years? World J Gastroenterol 20:1192–1210. https://doi.org/10.3748/wjg.v20.i5.1192
Huang Z-A, Chen X, Zhu Z et al (2017) PBHMDA: path-based human microbe-disease association prediction. Front Microbiol. https://doi.org/10.3389/fmicb.2017.00233
Kane AV, Dinh DM, Ward HD (2015) Childhood malnutrition and the intestinal microbiome malnutrition and the microbiome. Pediatr Res 77:256–262. https://doi.org/10.1038/pr.2014.179
Kaur H, Bose C, Mande SS (2019) Tryptophan metabolism by gut microbiome and gut-brain-axis: an in silico analysis. Front Neurosci. https://doi.org/10.3389/fnins.2019.01365
Khandelwal RA, Olivier BG, Röling WFM et al (2013) Community flux balance analysis for microbial consortia at balanced growth. PLoS ONE 8:e64567. https://doi.org/10.1371/journal.pone.0064567
Kim KO, Gluck M (2019) Fecal microbiota transplantation: an update on clinical practice. Clin Endosc 52:137. https://doi.org/10.5946/ce.2019.009
Kim JE, Kim HS (2019) Microbiome of the skin and gut in atopic dermatitis (AD): understanding the pathophysiology and finding novel management strategies. J Clin Med. https://doi.org/10.3390/jcm8040444
Klitgord N, Segrè D (2010) Environments that induce synthetic microbial ecosystems. PLoS Comput Biol 6:e1001002. https://doi.org/10.1371/journal.pcbi.1001002
Konturek PC, Harsch IA, Konturek K et al (2018) Gut-liver axis: how do gut bacteria influence the liver? Med Sci (basel). https://doi.org/10.3390/medsci6030079
Krammer E-M, de Ruyck J, Roos G et al (2018) Targeting dynamical binding processes in the design of non-antibiotic anti-adhesives by molecular simulation—the example of FimH. Molecules. https://doi.org/10.3390/molecules23071641
Kumar SCM, Chugh K, Dutta A et al (2021) Chaperonin abundance enhances bacterial fitness. Front Mol Biosci. https://doi.org/10.3389/fmolb.2021.669996
Kuntal BK, Ghosh TS, Mande SS (2013) Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes. Genomics 102:409–418. https://doi.org/10.1016/j.ygeno.2013.08.004
Kuntal BK, Dutta A, Mande SS (2016) CompNet: a GUI based tool for comparison of multiple biological interaction networks. BMC Bioinform 17:185
Kuntal BK, Chandrakar P, Sadhu S, Mande SS (2019) ‘NetShift’: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets. ISME J 13:442–454. https://doi.org/10.1038/s41396-018-0291-x
Lai D, Lu H, Nardini C (2010) Enhanced modularity-based community detection by random walk network preprocessing. Phys Rev E 81:066118. https://doi.org/10.1103/PhysRevE.81.066118
Levy R, Borenstein E (2013) Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. PNAS 110:12804–12809. https://doi.org/10.1073/pnas.1300926110
Ley RE, Bäckhed F, Turnbaugh P et al (2005) Obesity alters gut microbial ecology. PNAS 102:11070–11075. https://doi.org/10.1073/pnas.0504978102
Ley RE, Turnbaugh PJ, Klein S, Gordon JI (2006) Human gut microbes associated with obesity. Nature 444:1022–1023. https://doi.org/10.1038/4441022a
Li L, Abou-Samra E, Ning Z et al (2019) An in vitro model maintaining taxon-specific functional activities of the gut microbiome. Nat Commun 10:4146. https://doi.org/10.1038/s41467-019-12087-8
Lo C, Marculescu R (2017) MPLasso: Inferring microbial association networks using prior microbial knowledge. PLoS Comput Biol 13:e1005915. https://doi.org/10.1371/journal.pcbi.1005915
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8
Lynch SV, Pedersen O (2016) The human intestinal microbiome in health and disease. N Engl J Med 375:2369–2379. https://doi.org/10.1056/NEJMra1600266
Ma W, Zhang L, Zeng P et al (2017) An analysis of human microbe–disease associations. Brief Bioinform 18:85–97. https://doi.org/10.1093/bib/bbw005
MacArthur BD, Lachmann A, Lemischka IR, Ma’ayan A (2010) GATE: software for the analysis and visualization of high-dimensional time series expression data. Bioinformatics 26:143–144. https://doi.org/10.1093/bioinformatics/btp628
Magni P, Ferrazzi F, Sacchi L, Bellazzi R (2008) TimeClust: a clustering tool for gene expression time series. Bioinformatics 24:430–432. https://doi.org/10.1093/bioinformatics/btm605
Magnúsdóttir S, Heinken A, Kutt L et al (2017) Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol 35:81–89. https://doi.org/10.1038/nbt.3703
Maithreye R, Mande SS (2007) Modelling of the regulation of the hilA promoter of type three secretion system of Salmonella enterica serovar Typhimurium. Syst Synth Biol 1:129–137. https://doi.org/10.1007/s11693-007-9009-5
Marchesi JR, Adams DH, Fava F et al (2016) The gut microbiota and host health: a new clinical frontier. Gut 65:330–339. https://doi.org/10.1136/gutjnl-2015-309990
Mathipa MG, Thantsha MS (2017) Probiotic engineering: towards development of robust probiotic strains with enhanced functional properties and for targeted control of enteric pathogens. Gut Pathogens 9:28. https://doi.org/10.1186/s13099-017-0178-9
McMurdie PJ, Holmes S (2014) Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol 10:e1003531. https://doi.org/10.1371/journal.pcbi.1003531
Mellbye BL, Giguere AT, Murthy GS et al (2018) Genome-scale, constraint-based modeling of nitrogen oxide fluxes during coculture of Nitrosomonas europaea and Nitrobacter winogradskyi. mSystems 3:e00170-e217. https://doi.org/10.1128/mSystems.00170-17
Menees S, Chey W (2018) The gut microbiome and irritable bowel syndrome. F1000Res. https://doi.org/10.12688/f1000research.14592.1
Mills JP, Rao K, Young VB (2018) Probiotics for prevention of Clostridium difficile infection. Curr Opin Gastroenterol 34:3. https://doi.org/10.1097/MOG.0000000000000410
Mounier J, Monnet C, Vallaeys T et al (2008) Microbial interactions within a cheese microbial community. Appl Environ Microbiol 74:172–181. https://doi.org/10.1128/AEM.01338-07
Nagpal S, Baksi KD, Kuntal BK, Mande SS (2020a) NetConfer: a web application for comparative analysis of multiple biological networks. BMC Biol. https://doi.org/10.1186/s12915-020-00781-9
Nagpal S, Singh R, Yadav D, Mande SS (2020b) MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks. Nucleic Acids Res 48:W572–W579. https://doi.org/10.1093/nar/gkaa254
Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582. https://doi.org/10.1073/pnas.0601602103
Ni J, Wu GD, Albenberg L, Tomov VT (2017) Gut microbiota and IBD: causation or correlation? Nat Rev Gastroenterol Hepatol 14:573–584. https://doi.org/10.1038/nrgastro.2017.88
Nissen L, Casciano F, Gianotti A (2020) Intestinal fermentation in vitro models to study food-induced gut microbiota shift: an updated review. FEMS Microbiol Lett. https://doi.org/10.1093/femsle/fnaa097
Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614
Ou J, Carbonero F, Zoetendal EG et al (2013) Diet, microbiota, and microbial metabolites in colon cancer risk in rural Africans and African Americans. Am J Clin Nutr 98:111–120. https://doi.org/10.3945/ajcn.112.056689
Paine RT (1969) A note on trophic complexity and community stability. Am Nat 103:91–93. https://doi.org/10.1086/282586
Paulson JN, Stine OC, Bravo HC, Pop M (2013) Differential abundance analysis for microbial marker-gene surveys. Nat Methods 10:1200–1202. https://doi.org/10.1038/nmeth.2658
Peng L-H, Yin J, Zhou L et al (2018) Human microbe-disease association prediction based on adaptive boosting. Front Microbiol. https://doi.org/10.3389/fmicb.2018.02440
Perisin MA, Sund CJ (2018) Human gut microbe co-cultures have greater potential than monocultures for food waste remediation to commodity chemicals. Sci Rep 8:15594. https://doi.org/10.1038/s41598-018-33733-z
Pinna NK, Anjana RM, Saxena S et al (2021) Trans-ethnic gut microbial signatures of prediabetic subjects from India and Denmark. Genome Med 13:36. https://doi.org/10.1186/s13073-021-00851-9
Qin J, Li R, Raes J et al (2010) A human gut microbial gene catalog established by metagenomic sequencing. Nature 464:59–65. https://doi.org/10.1038/nature08821
Qu J, Zhao Y, Yin J (2019) Identification and analysis of human microbe-disease associations by matrix decomposition and label propagation. Front Microbiol. https://doi.org/10.3389/fmicb.2019.00291
Rizvi A, Shankar A, Chatterjee A et al (2019) Rewiring of Metabolic network in Mycobacterium tuberculosis during adaptation to different stresses. Front Microbiol 10:2417
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616
Rouanet A, Bolca S, Bru A et al (2020) Live biotherapeutic products, a road map for safety assessment. Front Med (lausanne). https://doi.org/10.3389/fmed.2020.00237
Ruan Q, Dutta D, Schwalbach MS et al (2006) Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors. Bioinformatics 22:2532–2538. https://doi.org/10.1093/bioinformatics/btl417
Saadatpour A, Albert R (2013) Boolean modeling of biological regulatory networks: a methodology tutorial. Methods 62:3–12. https://doi.org/10.1016/j.ymeth.2012.10.012
Salavert F, García-Alonso L, Sánchez R et al (2016) Web-based network analysis and visualization using cell maps. Bioinformatics 32:3041–3043. https://doi.org/10.1093/bioinformatics/btw332
Santillán M, Mackey MC (2004) Influence of catabolite repression and inducer exclusion on the bistable behavior of the lac operon. Biophys J 86:1282–1292. https://doi.org/10.1016/S0006-3495(04)74202-2
Scheffer M, Bascompte J, Brock WA et al (2009) Early-warning signals for critical transitions. Nature 461:53–59. https://doi.org/10.1038/nature08227
Schmidt TSB, Raes J, Bork P (2018) the human gut microbiome: from association to modulation. Cell 172:1198–1215. https://doi.org/10.1016/j.cell.2018.02.044
Sedlar K, Videnska P, Skutkova H et al (2016) Bipartite graphs for visualization analysis of microbiome data: supplementary issue: bioinformatics methods and applications for big metagenomics data. Evol Bioinform. https://doi.org/10.4137/EBO.S38546
Sen P, Orešič M (2019) Metabolic modeling of human gut microbiota on a genome scale: an overview. Metabolites 9:22. https://doi.org/10.3390/metabo9020022
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303
Shaw GT-W, Pao Y-Y, Wang D (2016) MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles. BMC Bioinform 17:488. https://doi.org/10.1186/s12859-016-1359-0
Singh R, Haque MM, Mande SS (2019) Lifestyle-induced microbial gradients: an Indian perspective. Front Microbiol. https://doi.org/10.3389/fmicb.2019.02874
Sola-Oladokun B, Culligan EP, Sleator RD (2017) Engineered probiotics: applications and biological containment. Annu Rev Food Sci Technol 8:353–370. https://doi.org/10.1146/annurev-food-030216-030256
Srivastava D, Baksi KD, Kuntal BK, Mande SS (2019) “EviMass”: a literature evidence-based miner for human microbial associations. Front Genet. https://doi.org/10.3389/fgene.2019.00849
Steele JA, Countway PD, Xia L et al (2011) Marine bacterial, archaeal and protistan association networks reveal ecological linkages. ISME J 5:1414–1425. https://doi.org/10.1038/ismej.2011.24
Stubbendieck RM, Vargas-Bautista C, Straight PD (2016) Bacterial communities: interactions to scale. Front Microbiol 7:1234. https://doi.org/10.3389/fmicb.2016.01234
Tandon D, Haque MM, Mande SS (2016) Inferring intra-community microbial interaction patterns from metagenomic datasets using associative rule mining techniques. PLoS ONE 11:e0154493. https://doi.org/10.1371/journal.pone.0154493
Tang L (2019) In vitro intestine model for gut microbiome. Nat Methods 16:578–578. https://doi.org/10.1038/s41592-019-0489-5
Tilg H, Cani PD, Mayer EA (2016) Gut microbiome and liver diseases. Gut 65:2035–2044. https://doi.org/10.1136/gutjnl-2016-312729
Valdes AM, Walter J, Segal E, Spector TD (2018) Role of the gut microbiota in nutrition and health. BMJ. https://doi.org/10.1136/bmj.k2179
Valitutti F, Cucchiara S, Fasano A (2019) Celiac disease and the microbiome. Nutrients. https://doi.org/10.3390/nu11102403
Venturelli OS, Carr AC, Fisher G et al (2018) Deciphering microbial interactions in synthetic human gut microbiome communities. Mol Syst Biol. https://doi.org/10.15252/msb.20178157
Wang Y, Thilmony R, Gu YQ (2014) NetVenn: an integrated network analysis web platform for gene lists. Nucleic Acids Res 42:W161-166. https://doi.org/10.1093/nar/gku331
Watane A, Cavuoto KM, Banerjee S, Galor A (2019) The microbiome and ocular surface disease. Curr Ophthalmol Rep 7:196–203. https://doi.org/10.1007/s40135-019-00217-w
Watts SC, Ritchie SC, Inouye M, Holt KE (2019) FastSpar: rapid and scalable correlation estimation for compositional data. Bioinformatics 35:1064–1066. https://doi.org/10.1093/bioinformatics/bty734
Yadav D, Ghosh TS, Mande SS (2016) Global investigation of composition and interaction networks in gut microbiomes of individuals belonging to diverse geographies and age-groups. Gut Pathog 8:1–21. https://doi.org/10.1186/s13099-016-0099-z
Yang J, Li D, Yang Z et al (2020) Establishing high-accuracy biomarkers for colorectal cancer by comparing fecal microbiomes in patients with healthy families. Gut Microbes 11:918–929. https://doi.org/10.1080/19490976.2020.1712986
Zhou G, Xia J (2018) OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Res 46:W514–W522. https://doi.org/10.1093/nar/gky510
Zhou J, Deng Y, Luo F et al (2010) Functional molecular ecological networks. mBBol. https://doi.org/10.1128/mBio.00169-10
Zhou G, Soufan O, Ewald J et al (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47:W234–W241. https://doi.org/10.1093/nar/gkz240
Zhuang K, Izallalen M, Mouser P et al (2011) Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 5:305–316. https://doi.org/10.1038/ismej.2010.117
Zomorrodi AR, Maranas CD (2012) OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol 8:e1002363. https://doi.org/10.1371/journal.pcbi.1002363