Cooperative Target Tracking by a Mobile Bionanosensor Network

Okaie, Yutaka; Nakano, Tadashi; Hara, Takahiro; Obuchi, Takuya; Hosoda, Kazufumi; Hiraoka, Yasushi; Nishio, Shojiro

doi:10.1109/tnb.2014.2343237

Cited by 64 publications

(57 citation statements)

References 35 publications

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“…Similarly, anisotropy can be taken into account by changing the rates in (1) for different directions. We are currently investigating the analysis in instances where distinct bacterial populations use quorum sensing signaling to enhance exchange of environmental information (e.g., as discussed in [30]). …”

Section: Resultsmentioning

confidence: 99%

“…Thornlow et al [28] show that increasing the motility of anerobic bacteria, Salmonella and generating homogeneous population of highly motile Salmonella, enhanced tumor penetration in vitro. Okaie et al [30] developed a microsensor network equivalent of bacterial chemotaxis. They exploited the model in [31] (later extended in [32]) where bacteria were modeled as microsensors and base stations were placed to monitor the different sensors.…”

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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors Affecting Bacterial Propagation towards Tumor Microenvironments

Koduvayur¹

2016

MOJPB

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“…Information may be encoded in DNA strands and then transported by flagellated bacteria [46]. Attractant and repellent molecules are sometimes used to drive bacteria to the receiver [54].…”

Section: Other Modelsmentioning

confidence: 99%

Molecular Communication in Nanonetworks

Yan¹,

Ge²,

Sun³

et al. 2016

Nano BioMed ENG

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With the development of nanotechnology, bioengineering and biology, it is envisioned that biological nanomachines may flourish in assorted valuable applications considering their unique characteristics including energy efficiency, bio-compatibility and extremely small scale. However, current biological nanomachines are only able to perform simple tasks at nano-level. Therefore, nanonetworks which interconnect bio-nanomachines into a network have been proposed to overcome the limitations of individual biological nanomachine. Among the possible communication schemes for nanonetworks, modern electromagnetic communication techniques are not good solutions due to the limitation of antenna size. Inspired by nature, one promising candidate is molecular communication proposed from the perspective of communication and computer engineering. Integrated with the knowledge from communication and computer engineering, molecular communication enables biological nanomachines to interface with other biological nanomachines and existing biological systems. Their interconnections form a bio-nanonetwork which is capable to provide functions that individual nanomachines cannot accomplish. In this paper, we introduce the state-of-the-art progress in the emerging field of molecular communication. The framework, design and engineering of components and theoretical modeling of molecular communication are discussed. The research challenges and opportunities are also talked about to inspire future researches of more feasible molecular communication systems.

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“…One approach to modeling the target tracking scenario described above is to use individual-based models [20]. For example, a bacterium-based self-propelling biosensor (i.e., bionanomachines in our definition) may be modeled as an individual object with a direction of movement θ and the constant velocity v (Fig.…”

Section: Overviewmentioning

confidence: 99%

“…Our early efforts introduced, for the first time in the area of nanoscale and molecular networking, the problem of target tracking using molecular communication, developed an individual-based model, and demonstrated that a group of bio-nanomachines interacts through the use of attractants and repellents to track moving targets [20]. While the individualbased modeling approach provides flexibility in modeling individual behaviors, the computational cost is expensive and does not scale to the number of individuals (e.g., the number of bio-nanomachines and targets).…”

Section: Introductionmentioning

confidence: 99%

Modeling and performance evaluation of mobile bionanosensor networks for target tracking

Okaie

Nakano

Hara

et al. 2014

2014 IEEE International Conference on Communications (ICC)

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This paper considers mobile bionanosensor networks designed for target tracking in molecular environments. The mobile bionanosensor network considered in this paper consists of nano-to-microscale bio-nanomachines that coordinate their activity by propagating the two types of signaling molecules: attractants for a group of bio-nanomachines to move toward targets, and repellents to spread over the environment. A mathematical model for target tracking is developed and the performance of mobile bionanosensor networks is defined based on distributions of targets and bio-nanomachines. Numerical results are then shown to facilitate discussion of the impact of attractants and repellents on target tracking performance.

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Cooperative Target Tracking by a Mobile Bionanosensor Network

Cited by 64 publications

References 35 publications

Factors Affecting Bacterial Propagation towards Tumor Microenvironments

Factors Affecting Bacterial Propagation towards Tumor Microenvironments

Molecular Communication in Nanonetworks

Modeling and performance evaluation of mobile bionanosensor networks for target tracking

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