Estimation of Parameters of Mycobacterium tuberculosis Growth: A Multi-Agent-Based Simulation Approach

The infectious disease Tuberculosis still causes many death around the world nowadays. The study of the bacillus that causes Tuberculosis, M. tuberculosis, is therefore very important. One special aspect to be studied is its growth. In this work we try a first attempt in estimating the parameters of a Multi-Agent-Based Simulation that can reproduce the growth of the real bacteria. In a previous work we have developed a Multi-Agent-Based Simulation and observed that the proper setting of parameters in order to reproduce the real behaviour of the growth is not a trivial task. Moreover, it is hard to tell if the adjustment of parameters is incorrect or if the model needs to be more detailed. Hence, in this work, we proceeded with the estimation of the parameters using a numerical method. The results are promising and show a very interesting venue for further research, both in terms of the growth behaviour as in the general aspects of modelling with Multi-Agent-Based Simulation.

[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]  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.

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

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

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

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

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

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

[10]  Luis Antunes,et al.  Multi-Agent-Based Simulation VII, International Workshop, MABS 2006, Hakodate, Japan, May 8, 2006, Revised and Invited Papers , 2007, MABS.

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

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

[13]  Diana F. Adamatti,et al.  Multi-Agent-Based Simulation of Mycobacterium Tuberculosis Growth , 2013, MABS.

[14]  Takao Terano Exploring the Vast Parameter Space of Multi-Agent Based Simulation , 2006, MABS.

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

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

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

[18]  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.

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