Bumpy Rides: Modeling the Dynamics of Chemotactic Interacting Bacteria

Wei, Guopeng; Bogdan, Paul; Mărculescu, Radu

doi:10.1109/jsac.2013.sup2.12130020

Cited by 54 publications

(19 citation statements)

References 46 publications

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“…Initially, at the point of injection, the bacteria are unaware of the exact location of the tumor thus they propagate according to a random walk model [34][35][36][37]. Once the bacteria reach the tumor microenvironment, they then move towards the tumor, by active chemotaxis in response to chemical signal from the tumor [17,29] and grow according to the model discussed in [33].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Until, the bacteria injected in region depicted by the grid (0, 0, 0) reach the tumor microenvironment, i.e., until the bacteria reach the region, (m, m, m), they move according to a random walk process [36,37] by spending a random time in each of the regions shown in Figure 1. The random time is exponentially distributed [35].…”

Section: Markov Modelmentioning

confidence: 99%

“…The model was verified with experiments and it was shown that bacteria typically propagate only unidirectional to neighboring lattices. Lavrentovich & Nelson [35] and Deng [36] separately studied the time for survival of bacteria in any geographic region using diffusion model [35] and by colonial communication model [36,37] and showed that the time spent in any geographical region is exponentially distributed. Therefore, the propagation of engineered bacteria can be modeled as a stochastic random walk [36,37].…”

Section: Introductionmentioning

confidence: 99%

“…Lavrentovich & Nelson [35] and Deng [36] separately studied the time for survival of bacteria in any geographic region using diffusion model [35] and by colonial communication model [36,37] and showed that the time spent in any geographical region is exponentially distributed. Therefore, the propagation of engineered bacteria can be modeled as a stochastic random walk [36,37]. However, the nature of these findings has not yet been exploited to analyze the time taken for bacteria to reach tumor microenvironments and determine the factors that impact this time.…”

Section: Introductionmentioning

confidence: 99%

“…However, the nature of these findings has not yet been exploited to analyze the time taken for bacteria to reach tumor microenvironments and determine the factors that impact this time. Wei et al [37] presented simulations on first passage time to tumors. A diffusion equation was formulated that used spatial extensions to Gillespei's algorithm [38].…”

Section: Introductionmentioning

confidence: 99%

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Factors Affecting Bacterial Propagation towards Tumor Microenvironments

Koduvayur¹

2016

MOJPB

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This paper provides a detailed analysis to identify factors that improve bacterial targeting times to tumor microenvironments. We utilize the "active flagellar" motion characteristic of the bacterial movement to present a continuous time Markov model and a first passage time analysis to determine the factors that affect the mean time taken for injected bacteria to reach a tumor micro environment. We have determined 3 major factors that play a role in determining mean targeting times: (i) the population of the injected bacteria, (ii) the distance between the region of injection and the tumor microenvironment and (iii) the directional efficiency of the injected bacteria. Of these 3 factors our results show that the directional efficiency is the most significant factor that affects the time taken by different bacterial species to reach the tumor micro environment. While distance between the tumor micro environment and the region of injection also plays an important role, population of bacteria is most ineffective, especially for large directional efficiency. Our model matches well with some existing experimental results.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Markov Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factors Affecting Bacterial Propagation towards Tumor Microenvironments

Koduvayur¹

2016

MOJPB

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Two‐way chemotaxis‐based communication for biological nanonetworks

Grammenos

Kantelis

Nicopolitidis

et al. 2022

Int J Communication

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Summary Chemotaxis has been studied as a means to improve communication performance in the promising field of biological nanonetworks. However, as of today, no protocols for reliable two‐way communications have been proposed in such networks. This work proposes a new protocol for switching between different chemotactic substances as a novel scheme to improve reliability of two‐way communications in biological nanonetworks. Multiple simulations, involving message exchange between stations and chemotactic release, were performed to evaluate the performance of the proposed strategies and achieve a deeper understanding of the behaviour of molecular communication nanonetworks. Furthermore, a novel message sending strategy utilising chemorepellent is presented, which does not involve the release of new molecules into the medium. Simulating chemotactic substance as individual molecules allowed for a more accurate modelling of the system in the boundaries of the chemotactic interface, bringing out the wave‐like behaviour of the latter. The results of the experiments indicate the effectiveness of the proposed controlled release strategy in improving message transmission quality. Furthermore, based on the findings of this work, we propose amendments of existing protocols that incorporate switching between chemotactic substances.

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