arXiv subjects

Agniva Datta

Publications and source records attributed to Agniva Datta.

6 recordsLinked to original sources

arXiv2025

The random walk of intermittently self-propelled particles

Motivated by various recent experimental findings, we propose a dynamical model of intermittently self-propelled particles: active particles that recurrently switch between two modes of motion, namely an active run-state and a turn state, in which self-propulsion is absent. The durations of these motility modes are derived from arbitrary waiting-time distributions. We derive the expressions for exact forms of transport characteristics like mean-square displacements and diffusion coefficients to describe such processes. Furthermore, the conditions for the emergence of sub- and superdiffusion in the long-time limit are presented. We give examples of some important processes that occur as limiting cases of our system, including run-and-tumble motion of bacteria, Lévy walks, hop-and-trap dynamics, intermittent diffusion and continuous time random walks.

cond-mat.soft↗

arXiv2025

Swimming patterns of a multi-mode bacterial swimmer in fluid shear flow

Bacterial swimming is well characterized in uniform liquids at rest. The natural habitat of bacterial swimmers, however, is often dominated by moving fluids and interfaces, resulting in shear flows that may strongly alter bacterial navigation strategies. Here, we study how fluid shear flow affects the swimming motility of the soil bacterium Pseudomonas putida, a bacterial swimmer that moves in a versatile pattern composed of three different swimming modes, where the flagella may push, pull, or wrap around the cell body (multi-mode swimmer). We introduce a computer automated cell tracking and swimming mode detection tool to show that shear induced alignment depends on the swimming mode, while motility and proximity to surfaces counteract the alignment effect. Moreover, filament wrapping becomes less efficient with increasing shear stress. Numerical simulations of realistic swimmer geometries complement our experimental results, providing more detailed mechanistic insights into movement patterns of bacterial swimmers in a shear flow.

cond-mat.soft↗

arXiv2025

Deciphering the dual chemotaxis strategy of bacteria in porous media

Chemotaxis of bacterial swimmers that move in a run-and-turn pattern is well studied in uniform bulk fluid. It is primarily based on modulating the run time in dependence on the swimming direction with respect to the source of chemoattractant (run time bias). Here, we provide evidence that the lophotrichously flagellated soil bacterium Pseudomonas putida may also perform chemotaxis in porous media where the free path length is severely restricted. Besides the classical run time bias, we identify a second chemotactic strategy: the change in swimming direction upon a turn event is adjusted, so that the direction of the next run phase is biased towards the source of chemoattractant (turn angle bias). Agent based simulations, based on the experimentally observed statistical properties of the swimming pattern, indicate that turn angle bias is the predominant chemotaxis strategy of bacteria in porous environments.

cond-mat.soft↗

arXiv2024

Intermittent Run Motility of Bacteria in Gels Exhibits Power-Law Distributed Dwell Times

While bacterial swimming has been well characterized in uniform liquid environments, only little is known about how bacteria propagate through complex environments, such as gel-like matrices or porous media that are typically encountered in tissue or soil. Here, we study swimming motility of the soil bacterium Pseudomonas putida (P. putida) in polysaccharide matrices formed by different concentrations of agar. P. putida cells display intermittent run-motility in the gel, where run times are exponentially distributed and intermittently occurring dwell times follow a waiting-time distribution with a power-law decay. An analysis of the turn angle distribution suggests that both, flagella mediated turning as well as mechanical trapping in the agar matrix play a role in the overall swimming pattern. Based on the experimentally observed motility pattern and measured waiting-time distributions, we propose a minimal active particle model which correctly describes the observed time dependence of the mean square displacement of the bacterial swimmers.

cond-mat.soft↗

arXiv2021

Modelling the Spread of an Epidemic in Presence of Vaccination using Cellular Automata

The results of Kermack-McKendrick SIR model are planned to be reproduced by cellular automata (CA) lattice model. The CA algorithms are proposed to study the model of epidemic, systematically. The basic goal is to capture the effects of spreading of infection over a scale of length. This CA model can provide the rate of growth of the infection over the space which was lacking in the mean-field like SIR model. The motion of the circular front of an infected cluster shows a linear behaviour in time. The correlation of a particular site to be infected, with respect to the central site is also studied. The outcomes of CA model are in good agreement with that obtained from SIR model. The results of vaccination have been also incorporated in the CA algorithm with a satisfactory degree of success. The advantage of the present model is that it can shed considerable amount of light to the physical properties of the spread of a typical epidemic in a simple, yet robust way.

q-bio.PE↗

arXiv2021

Phase transition in Kermack-McKendrick Model of Epidemic: Effects of Additional Nonlinearity and Introduction of Medicated Immunity

Mathematical modelling of the spread of epidemics has been an interesting challenge in the field of epidemiology. The SIR Model proposed by Kermack and McKendrick in 1927 is a prototypical model of epidemiology. However, it has its limitations. In this paper, we show two independent ways of generalizing this model, the first one if the vaccine isn't discovered or ready to use and the next one, if the vaccine is discovered and ready to use. In the first part, we have pointed out a major over-simplification, i.e., assumption of variation of the time derivatives of the variables with the linear or quadratic powers of the individual variables and introduce two new parameters to incorporate further nonlinearity in the number of infected people in the model. As a result of this, we show how this additional nonlinearity, in the newly introduced parameters, can bring a significant shift in the peak time of infection, i.e., the time at which the infected population reaches maximum. We show that in special cases, even we can get a transition from epidemic to a non-epidemic stage of a particular infectious disease. We further study one such special case and treat it as a problem of phase transition. Then, we investigate all the necessary parameters of this phase transition, like the order parameter and critical exponent. We observe that $O_p \sim (q_c-q)^β$. {\it As far as we know the phase transition and its quantification in terms of the scaling behaviour is not yet know in the context of pandemic}. In the second part, we incorporate in the model, a consideration of artificial herd immunity and show how we can decrease the peak time of infection with a subsequent decrease in the maximum number of infected people.

q-bio.PE↗