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The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical for-mulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems.
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Applying Molecular Communication Theory to Multi-Scale Integrated Models of Biological Pathways
The natural communication ability of cells is explored in this paper by providing preliminary results in the estimation of the Mutual Information (MI) of signaling pathway communication channels. These results, based on an application of Molecular Communication (MC) and information theory concepts to multi-scale integrated Flux-Balance Analysis (iFBA) models are a first step to evaluate the potential of cells and their biochemical processes as a substrate for enabling engineered MC channels for the future internet of Bio-Nano Things.
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- PAR ID:
- 10195247
- Date Published:
- Journal Name:
- NANOCOM '19: Proceedings of the Sixth Annual ACM International Conference on Nanoscale Computing and Communication
- Page Range / eLocation ID:
- 1 to 2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3345312.3345495
