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Experimental and Mathematical-Modeling Characterization of Trypanosoma cruzi Epimastigote Motility.

Sosa-Hernández E, Ballesteros-Rodea G, Arias-Del-Angel JA, Dévora-Canales D, Manning-Cela RG, Santana-Solano J, Santillán M - PLoS ONE (2015)

Bottom Line: The present work is aimed at characterizing the motility of parasite T. cruzi in its epimastigote form.Based on the resulting observations, we developed a mathematical model to simulate parasite trajectories.The fact that the model predictions closely match most of the experimentally observed parasite-trajectory characteristics, allows us to conclude that the model is an accurate description of T. cruzi motility.

View Article: PubMed Central - PubMed

Affiliation: Unidad Monterrey, Centro de Investigación y de Estudios Avanzados del IPN, Apodaca NL, México.

ABSTRACT
The present work is aimed at characterizing the motility of parasite T. cruzi in its epimastigote form. To that end, we recorded the trajectories of two strains of this parasite (a wild-type strain and a stable transfected strain, which contains an ectopic copy of LYT1 gene and whose motility is known to be affected). We further extracted parasite trajectories from the recorded videos, and statistically analysed the following trajectory-step features: step length, angular change of direction, longitudinal and transverse displacements with respect to the previous step, and mean square displacement. Based on the resulting observations, we developed a mathematical model to simulate parasite trajectories. The fact that the model predictions closely match most of the experimentally observed parasite-trajectory characteristics, allows us to conclude that the model is an accurate description of T. cruzi motility.

No MeSH data available.


Related in: MedlinePlus

Longitudinal and transverse velocity components for the genetically-modified strain.a) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component transverse to the previous step, during tumbling and persistent motion. b) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component longitudinal to the previous step, during tumbling and persistent motion.
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pone.0142478.g007: Longitudinal and transverse velocity components for the genetically-modified strain.a) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component transverse to the previous step, during tumbling and persistent motion. b) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component longitudinal to the previous step, during tumbling and persistent motion.

Mentions: We computed, by means of Eqs (4) and (5), the longitudinal and transverse components of all the step velocities. We did that for all the recorded trajectories of each strain. Then, we grouped together all the components of each type that correspond to persistent intervals, and repeated the procedure for those corresponding to tumbling intervals. The resulting probability density functions for the wild-type and the genetically modified strains are respectively shown in Figs 6 and 7, together with the corresponding best fitting functions. We found that, in all cases, the PDFs corresponding to the velocity longitudinal components are well fitted by extreme value distributions of the form:ρ(x)=ea-xb-ea-xbb,(10)while the PDF of transverse velocity components are well fitted by Student’s T distributions with 2 degrees of freedom:ρ(x)=18πα2(1+(x)22α2)-3/2.(11)The best fitting parameter values for both strains and for both motility modes are summarized below:


Experimental and Mathematical-Modeling Characterization of Trypanosoma cruzi Epimastigote Motility.

Sosa-Hernández E, Ballesteros-Rodea G, Arias-Del-Angel JA, Dévora-Canales D, Manning-Cela RG, Santana-Solano J, Santillán M - PLoS ONE (2015)

Longitudinal and transverse velocity components for the genetically-modified strain.a) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component transverse to the previous step, during tumbling and persistent motion. b) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component longitudinal to the previous step, during tumbling and persistent motion.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636178&req=5

pone.0142478.g007: Longitudinal and transverse velocity components for the genetically-modified strain.a) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component transverse to the previous step, during tumbling and persistent motion. b) Experimentally-determined probability density functions (dots) and best fitting distributions (solid lines) for the velocity component longitudinal to the previous step, during tumbling and persistent motion.
Mentions: We computed, by means of Eqs (4) and (5), the longitudinal and transverse components of all the step velocities. We did that for all the recorded trajectories of each strain. Then, we grouped together all the components of each type that correspond to persistent intervals, and repeated the procedure for those corresponding to tumbling intervals. The resulting probability density functions for the wild-type and the genetically modified strains are respectively shown in Figs 6 and 7, together with the corresponding best fitting functions. We found that, in all cases, the PDFs corresponding to the velocity longitudinal components are well fitted by extreme value distributions of the form:ρ(x)=ea-xb-ea-xbb,(10)while the PDF of transverse velocity components are well fitted by Student’s T distributions with 2 degrees of freedom:ρ(x)=18πα2(1+(x)22α2)-3/2.(11)The best fitting parameter values for both strains and for both motility modes are summarized below:

Bottom Line: The present work is aimed at characterizing the motility of parasite T. cruzi in its epimastigote form.Based on the resulting observations, we developed a mathematical model to simulate parasite trajectories.The fact that the model predictions closely match most of the experimentally observed parasite-trajectory characteristics, allows us to conclude that the model is an accurate description of T. cruzi motility.

View Article: PubMed Central - PubMed

Affiliation: Unidad Monterrey, Centro de Investigación y de Estudios Avanzados del IPN, Apodaca NL, México.

ABSTRACT
The present work is aimed at characterizing the motility of parasite T. cruzi in its epimastigote form. To that end, we recorded the trajectories of two strains of this parasite (a wild-type strain and a stable transfected strain, which contains an ectopic copy of LYT1 gene and whose motility is known to be affected). We further extracted parasite trajectories from the recorded videos, and statistically analysed the following trajectory-step features: step length, angular change of direction, longitudinal and transverse displacements with respect to the previous step, and mean square displacement. Based on the resulting observations, we developed a mathematical model to simulate parasite trajectories. The fact that the model predictions closely match most of the experimentally observed parasite-trajectory characteristics, allows us to conclude that the model is an accurate description of T. cruzi motility.

No MeSH data available.


Related in: MedlinePlus