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1.
PLoS One ; 6(10): e26690, 2011.
Article in English | MEDLINE | ID: mdl-22066004

ABSTRACT

Variability is a hallmark of microbial systems. On the one hand, microbes are subject to environmental heterogeneity and undergo changeable conditions in their immediate surroundings. On the other hand, microbial populations exhibit high cellular diversity. The relation between microbial diversity and variability of population dynamics is difficult to assess. This connection can be quantitatively studied from a perspective that combines in silico models and thermodynamic methods and interpretations. The infection process of Plasmodium falciparum parasitizing human red blood cells under laboratory cultivation conditions is used to illustrate the potential of Individual-based models in the context of predictive microbiology and parasitology. Experimental data from several in vitro cultures are compared to the outcome of an individual-based model and analysed from a thermodynamic perspective. This approach allows distinguishing between intrinsic and external constraints that give rise to the diversity in the infection forms, and it provides a criterion to quantitatively define transient and stationary regimes in the culture. Increasing the ability of models to discriminate between different states of microbial populations enhances their predictive capability which finally leads to a better the control over culture systems. The strategy here presented is of general application and it can substantially improve modelling of other types of microbial communities.


Subject(s)
Cellular Senescence , Erythrocytes/pathology , Erythrocytes/parasitology , Malaria, Falciparum/blood , Malaria, Falciparum/parasitology , Plasmodium falciparum/physiology , Animals , Computer Simulation , Humans , Life Cycle Stages , Parasitemia/blood , Parasitemia/parasitology , Plasmodium falciparum/growth & development , Thermodynamics , Time Factors
2.
J Theor Biol ; 252(1): 56-68, 2008 May 07.
Article in English | MEDLINE | ID: mdl-18329047

ABSTRACT

The lag phase is the initial phase of a culture that precedes exponential growth and occurs when the conditions of the culture medium differ from the pre-inoculation conditions. It is usually defined by means of cell density because the number of individuals remains approximately constant or slowly increases, and it is quantified with the lag parameter lambda. The lag phase has been studied through mathematical modelling and by means of specific experiments. In recent years, Individual-based Modelling (IbM) has provided helpful insights into lag phase studies. In this paper, the definition of lag phase is thoroughly examined. Evolution of the total biomass and the total number of bacteria during lag phase is tackled separately. The lag phase lasts until the culture reaches a maximum growth rate both in biomass and cell density. Once in the exponential phase, both rates are constant over time and equal to each other. Both evolutions are split into an initial phase and a transition phase, according to their growth rates. A population-level mathematical model is presented to describe the transitional phase in cell density. INDividual DIScrete SIMulation (INDISIM) is used to check the outcomes of this analysis. Simulations allow the separate study of the evolution of cell density and total biomass in a batch culture, they provide a depiction of different observed cases in lag evolution at the individual-cell level, and are used to test the population-level model. The results show that the geometrical lag parameter lambda is not appropriate as a universal definition for the lag phase. Moreover, the lag phase cannot be characterized by a single parameter. For the studied cases, the lag phases of both the total biomass and the population are required to fully characterize the evolution of bacterial cultures. The results presented prove once more that the lag phase is a complex process that requires a more complete definition. This will be possible only after the phenomena governing the population dynamics at an individual level of description, and occurring during the lag and exponential growth phases, are well understood.


Subject(s)
Bacteria/growth & development , Models, Biological , Bacteriological Techniques , Biomass , Culture Media , Population Density
3.
J Theor Biol ; 248(3): 448-59, 2007 Oct 07.
Article in English | MEDLINE | ID: mdl-17632129

ABSTRACT

Malaria is still one of the most fatal diseases in the world. Development of an effective treatment or vaccine requires the cultivation of the parasite that causes it: Plasmodium falciparum. Several methods for in vitro cultivation of P. falciparum infected erythrocytes have been successfully developed and described in the last 30 years. Some problems arising from the current harvests are the low parasitaemia and daily human supervision requirements. The lack of a suitable model for global culture behavior makes the assay of new methodologies a costly and tenuous task. In this paper we present a model and simulation tool for these systems. We use the INDividual DIScrete SIMulation protocol (INDISIM) to qualitatively reproduce the temporal evolution of the erythrocyte and merozoite populations. Whole system dynamics are inferred by setting the rules of behavior for each individual red blood cell, such as the nutrient uptake, metabolism and infection processes, as well as the properties and rules for the culture medium: composition, diffusion and external manipulation. We set the individual description parameters according to the values in published data, and allow population heterogeneity. Cells are arranged in a three-dimensional grid and the study is focused on the geometric constraints and physical design of experimental sets. Several published experimental cultures have been reproduced with computer simulations of this model, showing that the observed experimental behavior can be explained by means of individual interactions and statistical laws.


Subject(s)
Erythrocytes/parasitology , Malaria, Falciparum/blood , Animals , Cell Death/physiology , Cells, Cultured , Culture Media , Erythrocytes/metabolism , Humans , Merozoites/physiology , Models, Biological , Parasitemia/blood , Plasmodium falciparum/pathogenicity , Plasmodium falciparum/physiology , Reproducibility of Results
4.
J Theor Biol ; 241(4): 939-53, 2006 Aug 21.
Article in English | MEDLINE | ID: mdl-16524598

ABSTRACT

The lag phase has been widely studied for years in an effort to contribute to the improvement of food safety. Many analytical models have been built and tested by several authors. The use of Individual-based Modelling (IbM) allows us to probe deeper into the behaviour of individual cells; it is a bridge between theories and experiments when needed. INDividual DIScrete SIMulation (INDISIM) has been developed and coded by our group as an IbM simulator and used to study bacterial growth, including the microscopic causes of the lag phase. First of all, the evolution of cellular masses, specifically the mean mass and biomass distribution, is shown to be a determining factor in the beginning of the exponential phase. Secondly, whenever there is a need for an enzyme synthesis, its rate has a direct effect on the lag duration. The variability of the lag phase with different factors is also studied. The known decrease of the lag phase with an increase in the temperature is also observed in the simulations. An initial study of the relationship between individual and collective lag phases is presented, as a complement to the studies already published. One important result is the variability of the individual lag times and generation times. It has also been found that the mean of the individual lags is greater than the population lag. This is the first in a series of studies of the lag phase that we are carrying out. Therefore, the present work addresses a generic system by making a simple set of assumptions.


Subject(s)
Bacteria/growth & development , Models, Biological , Bacteria/enzymology , Bacteriological Techniques/methods , Biomass , Computer Simulation , Culture Media , Enzymes/biosynthesis , Food Microbiology
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