Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread
Source abstract
Abstract
Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gut microbiota and health · Evolution and Genetic Dynamics · vaccines and immunoinformatics approaches
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens (UGA), GA 30602, USA
Department of Biology, Clarkson University, Potsdam, NY 13699, USA
Escherichia coli Group, Institute—Foundation of Health Research (FIDIS)—University Hospital Complex of Santiago de Compostela University (CHUS), 15706 Santiago de Compostela, Spain
Division of Infection Medicine, Department of Clinical Sciences, Lund University, 221 84 Lund, Sweden
References (100+)
Show source-provided references
1. Resource-limited growth, competition, and predation: A model and experimental studies with bacteria and bacteriophage
2. Feedback dynamics and cell function: Why systems biology is called systems biology10.1039/b502088n ↗
3. Mesarovic, M.D., and Takahara, Y. (1975). General Systems Theory: Mathematical Foundations, Academic Press.
4. (2019, May 20). Systems Biology Home Page. Available online: https://systemsbiology.org/about/what-is-systems-biology/.
5. Equipping scientists for the new biology10.1038/74325 ↗
6. New institute to study systems biology10.1038/11667 ↗
7. A systems biology approach to infectious disease research: Innovating the pathogen-host research paradigm10.1128/mBio.00325-10 ↗
8. Computational systems biology10.1038/nature01254 ↗
9. Systems biology primer: The basic methods and approaches10.1042/EBC20180003 ↗
10. Barrila, J., Crabbe, A., Yang, J., Franco, K., Nydam, S.D., Forsyth, R.J., Davis, R.R., Gangaraju, S., Ott, C.M., and Coyne, C.B. (2018). Modeling host-pathogen interactions in the context of the microenvironment: Three-dimensional cell culture comes of age. Infect. Immun., 86.10.1128/IAI.00282-18 ↗
11. Targeted proteomics and absolute protein quantification for the construction of a stoichiometric host-pathogen surface density model10.1074/mcp.M116.063966 ↗
12. Windels, E.M., Michiels, J.E., Fauvart, M., Wenseleers, T., Van den Bergh, B., and Michiels, J. (2019). Bacterial persistence promotes the evolution of antibiotic resistance by increasing survival and mutation rates. ISME J.10.1038/s41396-019-0344-9 ↗
13. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design10.3390/molecules190710150 ↗
14. Computational assessment of pharmacokinetics and biological effects of some anabolic and androgen steroids10.1007/s11095-018-2353-1 ↗
15. Kahraman, A., Herzog, F., Leitner, A., Rosenberger, G., Aebersold, R., and Malmstrom, L. (2013). Cross-link guided molecular modeling with rosetta. PLoS ONE, 8.10.1371/journal.pone.0073411 ↗
16. Rapid determination of quaternary protein structures in complex biological samples10.1038/s41467-018-07986-1 ↗
17. (1994). Mathematical modeling. Concept paper. Ann. N. Y. Acad. Sci., 740, 271–274.10.1111/j.1749-6632.1994.tb19877.x ↗
18. Domenech de Celles, M., King, A.A., and Rohani, P. (2018). Commentary: Resolving pertussis resurgence and vaccine immunity using mathematical transmission models. Hum. Vaccin Immunother.10.1080/21645515.2018.1549432 ↗
19. Mathematical and computational challenges in population biology and ecosystems science10.1126/science.275.5298.334 ↗
20. Bayesian ranking of sites for engineering safety improvements: Decision parameter, treatability concept, statistical criterion, and spatial dependence10.1016/j.aap.2005.03.012 ↗
21. Applications of bayesian statistical methodology to clinical trial design: A case study of a phase 2 trial with an interim futility assessment in patients with knee osteoarthritis10.1002/pst.1906 ↗
22. Temporal abstraction and temporal bayesian networks in clinical domains: A survey10.1016/j.artmed.2013.12.007 ↗
23. Xing, L., Guo, M., Liu, X., Wang, C., Wang, L., and Zhang, Y. (2017). An improved bayesian network method for reconstructing gene regulatory network based on candidate auto selection. BMC Genom., 18.10.1186/s12864-017-4228-y ↗
24. Liu, M., Stella, F., Hommersom, A., Lucas, P.J.F., Boer, L., and Bischoff, E. (2019). A comparison between discrete and continuous time bayesian networks in learning from clinical time series data with irregularity. Artif. Intell. Med.10.1016/j.artmed.2018.10.002 ↗
25. Saga: A hybrid search algorithm for bayesian network structure learning of transcriptional regulatory networks10.1016/j.jbi.2014.08.010 ↗
26. A mixture copula bayesian network model for multimodal genomic data10.1177/1176935117702389 ↗
27. Applications of bayesian methods to epidemiologic research10.1007/s40471-014-0019-z ↗
28. Spatiotemporal bayesian networks for malaria prediction10.1016/j.artmed.2017.12.002 ↗
29. Comparative efficacy of interventions for reducing injection and sexual risk behaviours to prevent hiv in injection drug users: Protocol for bayesian network meta-analysis10.1136/bmjopen-2018-022811 ↗
30. Inferring population dynamics of hiv-1 subtype c epidemics in eastern africa and southern brazil applying different bayesian phylodynamics approaches10.1038/s41598-018-26824-4 ↗
31. Predictors of pertussis outbreak in urban and rural municipalities of saskatchewan, canada10.17269/s41997-018-0074-1 ↗
32. Correlates of climate variability and dengue fever in two metropolitan cities in bangladesh
33. Aryee, G., Kwarteng, E., Essuman, R., Nkansa Agyei, A., Kudzawu, S., Djagbletey, R., Owusu Darkwa, E., and Forson, A. (2018). Estimating the incidence of tuberculosis cases reported at a tertiary hospital in ghana: A time series model approach. BMC Public Health, 18.10.1186/s12889-018-6221-z ↗
34. Yang, E., Park, H.W., Choi, Y.H., Kim, J., Munkhdalai, L., Musa, I., and Ryu, K.H. (2018). A simulation-based study on the comparison of statistical and time series forecasting methods for early detection of infectious disease outbreaks. Int. J. Environ. Res. Public Health, 15.10.3390/ijerph15050966 ↗
35. De-escalation and discontinuation strategies in high-risk neutropenic patients: An interrupted time series analyses of antimicrobial consumption and impact on outcome10.1007/s10096-018-3328-1 ↗
36. Kim, W.L., Anneducharme, C., and Bucher, B.J. (2011). Development and implementation of a surveillance network system for emerging infectious diseases in the caribbean (aricaba). Online J. Public Health Inform., 3.10.5210/ojphi.v3i2.3856 ↗
37. Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in fiji: A modelling study10.1016/S2542-5196(18)30066-4 ↗
38. Mathematical modeling of infectious disease dynamics10.4161/viru.24041 ↗
39. Coupling multiscale within-host dynamics and between-host transmission with recovery (sir) dynamics10.1016/j.mbs.2019.01.001 ↗
40. Global dynamics of a discrete age-structured sir epidemic model with applications to measles vaccination strategies10.1016/j.mbs.2018.12.003 ↗
41. Sir model for dengue disease with effect of dengue vaccination10.1155/2018/9861572 ↗
42. Nguyen, C., and Carlson, J.M. (2016). Optimizing real-time vaccine allocation in a stochastic sir model. PLoS ONE, 11.10.1371/journal.pone.0152950 ↗
43. A general model for stochastic sir epidemics with two levels of mixing10.1016/S0025-5564(02)00125-6 ↗
44. Stochastic sir epidemics in a population with households and schools10.1007/s00285-015-0901-4 ↗
45. Threshold dynamics of a stochastic sir model with vertical transmission and vaccination10.1155/2017/4820183 ↗
46. Price, D.J., Breuze, A., Dybowski, R., Mastroeni, P., and Restif, O. (2017). An efficient moments-based inference method for within-host bacterial infection dynamics. PLoS Comput. Biol., 13.10.1101/116319 ↗
47. Edge-based compartmental modelling of an sir epidemic on a dual-layer static-dynamic multiplex network with tunable clustering10.1007/s11538-018-0484-5 ↗
48. Savini, L., Candeloro, L., Conte, A., De Massis, F., and Giovannini, A. (2017). Development of a forecasting model for brucellosis spreading in the italian cattle trade network aimed to prioritise the field interventions. PLoS ONE, 12.10.1371/journal.pone.0177313 ↗
49. Chae, S., Kwon, S., and Lee, D. (2018). Predicting infectious disease using deep learning and big data. Int. J. Environ. Res. Public Health, 15.10.3390/ijerph15081596 ↗
50. Artificial intelligence for infectious disease big data analytics10.1016/j.idh.2018.10.002 ↗
51. Leite, D.M.C., Brochet, X., Resch, G., Que, Y.A., Neves, A., and Pena-Reyes, C. (2018). Computational prediction of inter-species relationships through omics data analysis and machine learning. BMC Bioinform., 19.10.1186/s12859-018-2388-7 ↗
52. Cockrell, R.C., and An, G. (2018). Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation. PLoS Comput. Biol., 14.10.1371/journal.pcbi.1005876 ↗
53. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist?10.1186/s40168-018-0603-4 ↗
54. Spatio-temporal spillover risk of yellow fever in Brazil10.1186/s13071-018-3063-6 ↗
55. Sharaha, U., Rodriguez-Diaz, E., Sagi, O., Riesenberg, K., Lapidot, I., Segal, Y., Bigio, I.J., Huleihel, M., and Salman, A. (2019). Detection of extended-spectrum beta-lactamase-producing escherichia coli using infrared microscopy and machine-learning algorithms. Anal. Chem.10.1021/acs.analchem.8b05497 ↗
56. Bailey, A., Ledeboer, N., and Burnham, C.D. (2018). Clinical microbiology is growing up: The total laboratory automation revolution. Clin. Chem.10.1373/clinchem.2017.274522 ↗
57. Patchy promiscuity: Machine learning applied to predict the host specificity of salmonella enterica and escherichia coli
58. Wheeler, N.E., Gardner, P.P., and Barquist, L. (2018). Machine learning identifies signatures of host adaptation in the bacterial pathogen salmonella enterica. PLoS Genet., 14.10.1371/journal.pgen.1007333 ↗
59. Integrated signaling pathways mediate bordetella immunomodulation, persistence, and transmission10.1016/j.tim.2018.09.010 ↗
60. De, R.K., and Tomar, N. (2014). Immunoinformatics: A brief review. Immunoinformatics, Springer New York.10.1007/978-1-4939-1115-8 ↗
61. Thakar, J., Pilione, M., Kirimanjeswara, G., Harvill, E.T., and Albert, R. (2007). Modeling systems-level regulation of host immune responses. PLoS Comput. Biol., 3.10.1371/journal.pcbi.0030109 ↗
62. Host-microbe protein interactions during bacterial infection10.1016/j.chembiol.2015.09.015 ↗
63. Understanding the dynamics of t-cell activation in health and disease through the lens of computational modeling10.1200/CCI.18.00057 ↗
64. An immunology primer for computational modelers10.1007/s10928-014-9384-y ↗
65. Natural selection and infectious disease in human populations10.1038/nrg3734 ↗
66. Systems immunology: Just getting started10.1038/ni.3768 ↗
67. A system dynamics model to predict the human monocyte response to endotoxins10.3389/fimmu.2017.00915 ↗
68. Toward a genome-wide systems biology analysis of host-pathogen interactions in group a streptococcus10.1016/S0002-9440(10)61232-1 ↗
69. Aziz, R.K., Kansal, R., Aronow, B.J., Taylor, W.L., Rowe, S.L., Kubal, M., Chhatwal, G.S., Walker, M.J., and Kotb, M. (2010). Microevolution of group a streptococci in vivo: Capturing regulatory networks engaged in sociomicrobiology, niche adaptation, and hypervirulence. PLoS ONE, 5.10.1371/journal.pone.0009798 ↗
70. Streptococcus pyogenes infection and the human proteome with a special focus on the immunoglobulin g-cleaving enzyme ides10.1074/mcp.RA117.000525 ↗
71. A comprehensive analysis of the streptococcus pyogenes and human plasma protein interaction network10.1039/C3MB70555B ↗
72. Quantitative proteogenomics of human pathogens using dia-ms10.1016/j.jprot.2015.09.012 ↗
73. Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics10.1038/ncomms10261 ↗
74. Dynamics of salmonella infection of macrophages at the single cell level10.1098/rsif.2012.0163 ↗
75. Grant, A.J., Restif, O., McKinley, T.J., Sheppard, M., Maskell, D.J., and Mastroeni, P. (2008). Modelling within-host spatiotemporal dynamics of invasive bacterial disease. PLoS Biol., 6.10.1371/journal.pbio.0060074 ↗
76. Quantification of the effects of antibodies on the extra- and intracellular dynamics of salmonella enterica10.1098/rsif.2012.0866 ↗
77. Proteo-transcriptomic dynamics of cellular response to hiv-1 infection10.1038/s41598-018-36135-3 ↗
78. Microbe-mediated host defence drives the evolution of reduced pathogen virulence10.1038/ncomms13430 ↗
79. From evolutionary genetics to human immunology: How selection shapes host defence genes10.1038/nrg2698 ↗
80. Within-host evolution of bacterial pathogens10.1038/nrmicro.2015.13 ↗
81. Chao, D.L., Davenport, M.P., Forrest, S., and Perelson, A.S. (2003, January 11–14). Stochastic stage-structured modeling of the adaptive immune system. Proceedings of the IEEE Computational Systems Bioinformatics Conference, Stanford, CA, USA.
82. The dynamics of acute inflammation10.1016/j.jtbi.2004.04.044 ↗
83. Agent-based dynamic knowledge representation of pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis10.1186/1742-4682-8-33 ↗
84. Mathematical model of innate and adaptive immunity of sepsis: A modeling and simulation study of infectious disease10.1155/2015/504259 ↗
85. Shi, Z., Chapes, S.K., Ben-Arieh, D., and Wu, C.H. (2016). An agent-based model of a hepatic inflammatory response to salmonella: A computational study under a large set of experimental data. PLoS ONE, 11.10.1371/journal.pone.0161131 ↗
86. Miller, S.E., Bell, C.S., McClain, M.S., Cover, T.L., and Giorgio, T.D. (2016). Dynamic computational model of symptomatic bacteremia to inform bacterial separation treatment requirements. PLoS ONE, 11.10.1371/journal.pone.0163167 ↗
87. Lamping, F., Jack, T., Rubsamen, N., Sasse, M., Beerbaum, P., Mikolajczyk, R.T., Boehne, M., and Karch, A. (2018). Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious sirs in critically ill children—A data-driven approach using machine-learning algorithms. BMC Pediatr., 18.10.1186/s12887-018-1082-2 ↗
88. Using statistical and machine learning methods to evaluate the prognostic accuracy of sirs and qsofa10.4258/hir.2018.24.2.139 ↗
89. Garcia-Gallo, J.E., Fonseca-Ruiz, N.J., Celi, L.A., and Duitama-Munoz, J.F. (2018). A machine learning-based model for 1-year mortality prediction in patients admitted to an intensive care unit with a diagnosis of sepsis. Med. Intensiv.
90. Meiring, C., Dixit, A., Harris, S., MacCallum, N.S., Brealey, D.A., Watkinson, P.J., Jones, A., Ashworth, S., Beale, R., and Brett, S.J. (2018). Optimal intensive care outcome prediction over time using machine learning. PLoS ONE, 13.10.1371/journal.pone.0206862 ↗
91. Masino, A.J., Harris, M.C., Forsyth, D., Ostapenko, S., Srinivasan, L., Bonafide, C.P., Balamuth, F., Schmatz, M., and Grundmeier, R.W. (2019). Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS ONE, 14.10.1371/journal.pone.0212665 ↗
92. Taylor, R.A., Moore, C.L., Cheung, K.H., and Brandt, C. (2018). Predicting urinary tract infections in the emergency department with machine learning. PLoS ONE, 13.10.1371/journal.pone.0194085 ↗
93. Machine learning in critical care: State-of-the-art and a sepsis case study10.1186/s12938-018-0569-2 ↗
94. Delahanty, R.J., Alvarez, J., Flynn, L.M., Sherwin, R.L., and Jones, S.S. (2019). Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis. Ann. Emerg. Med.10.1016/j.annemergmed.2018.11.036 ↗
95. A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier10.1016/j.ijmedinf.2018.12.002 ↗
96. Saqib, M., Sha, Y., and Wang, M.D. (2018, January 18–21). Early prediction of sepsis in emr records using traditional ml techniques and deep learning lstm networks. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.10.1109/EMBC.2018.8513254 ↗
97. Development and validation of a novel automated gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test10.1038/s41391-018-0067-4 ↗
98. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the picu10.1097/PCC.0000000000001666 ↗
99. An interpretable machine learning model for accurate prediction of sepsis in the icu10.1097/CCM.0000000000002936 ↗
100. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care10.1038/s41591-018-0213-5 ↗