Multi-Agent-Based Simulation of Mycobacterium Tuberculosis Growth

Tuberculosis is an infectious disease that still causes many deaths around the world nowadays. It is caused by the M. tuberculosis bacillus. The study of the growth curve of this infectious organism is relevant as it has wide applications in tuberculosis research. In this work a Multi-Agent-Based Simulation is proposed to pursue the reproduction in silico of the observed in vitro M. tuberculosis growth curves. Simulation results are qualitatively compared with growth curves obtained in vitro with a recent proposed methodology. The results are promising and indicate that the chosen simulation methodology has the potential to serve as a platform for testing different bacterial growing behaviour as well as bacteria growth under different conditions.

[1]  R. C. Whiting,et al.  When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves , 1997 .

[2]  M. Glickman,et al.  Mycolic acid cyclopropanation is essential for viability, drug resistance, and cell wall integrity of Mycobacterium tuberculosis. , 2009, Chemistry & biology.

[3]  P. V. van Helden,et al.  Time to detection of Mycobacterium tuberculosis in BACTEC systems as a viable alternative to colony counting. , 2008, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[4]  B. Hanna,et al.  Testing of susceptibility of Mycobacterium tuberculosis to isoniazid and rifampin by mycobacterium growth indicator tube method , 1996, Journal of clinical microbiology.

[5]  M. Collins,et al.  A model for analyzing growth kinetics of a slowly growing Mycobacterium sp , 1988, Applied and environmental microbiology.

[6]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[7]  J. Tyagi,et al.  Mycobacterium tuberculosis rrnPromoters: Differential Usage and Growth Rate-Dependent Control , 1999, Journal of bacteriology.

[8]  G. Schoolnik,et al.  Mycobacterium tuberculosis gene expression during adaptation to stationary phase and low-oxygen dormancy. , 2004, Tuberculosis.

[9]  K. Luh,et al.  Comparison of the BACTEC MGIT 960 with Löwenstein-Jensen medium for recovery of mycobacteria from clinical specimens. , 2000, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[10]  P. V. van Helden,et al.  Novel Method for Rapid Measurement of Growth of Mycobacteria in Detergent-Free Media , 1998, Journal of Clinical Microbiology.

[11]  C. Sifri.  Healthcare epidemiology: quorum sensing: bacteria talk sense. , 2008, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[12]  A. Drogoul,et al.  Multi-Agent Simulation as a Tool for Modeling Societies: Application to Social Differentiation in Ant Colonies , 1992, MAAMAW.

[13]  E. Rubin,et al.  Genes required for mycobacterial growth defined by high density mutagenesis , 2003, Molecular microbiology.

[14]  Jaime Simão Sichman,et al.  MAS and Social Simulation: A Suitable Sommitment , 1998, MABS.

[15]  Mahavir Singh,et al.  Fitness of Mycobacterium tuberculosis Strains of the W-Beijing and Non-W-Beijing Genotype , 2010, PloS one.

[16]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[17]  E. Chan,et al.  Multidrug-resistant and extensively drug-resistant tuberculosis: a review , 2008, Current opinion in infectious diseases.

[18]  M. Collins,et al.  Detection of mycobacteria by radiometric and standard plate procedures , 1983, Journal of clinical microbiology.