Modeling Extracellular Vesicles-Mediated Interactions of Cells in the Tumor Microenvironment.

Interactions of cells via extracellular vesicles (EVs) manipulate various actions, including cancer initiation and progression, inflammation, anti-tumor signaling and cell migration, proliferation and apoptosis in the tumor microenvironment. EVs as the external stimulus can activate or inhibit some receptor pathways in a way that amplify or attenuate a kind of particle release at target cells. This can also be carried out in a biological feedback-loop where the transmitter is affected by the induced release initiated by the target cell due to the EVs received from the donor cell, to create a bilateral process. In this paper, at first we derive the frequency response of internalization function in the framework of a unilateral communication link. This solution is adapted to a closed-loop system to find the frequency response of a bilateral system. The overall releases of the cells, given by the combination of the natural release and the induced release, are reported at the end of this paper and the results are compared in terms of distance between the cells and reaction rates of EVs at the cell membranes.

Optimization of Extracellular Vesicle Release for Targeted Drug Delivery.

Targeted drug delivery is a promising approach for many serious diseases, such as glioblastoma multiforme, one of the most common and devastating brain tumor. In this context, this work addresses the optimization of the controlled release of drugs which are carried by extracellular vesicles. Towards this goal, we derive and numerically verify an analytical solution for the end-to-end system model. We then apply the analytical solution either to reduce the disease treatment time or to reduce the amount of required drugs. The latter is formulated as a bilevel optimization problem, whose quasiconvex/quasiconcave property is proved here. For solving the optimization problem, we propose and utilize a combination of bisection method and golden-section search. The numerical results demonstrate that the optimization can significantly reduce the treatment time and/or the required drugs carried by extracellular vesicles for a therapy compared to the steady state solution.

Duality Between Coronavirus Transmission and Air-Based Macroscopic Molecular Communication.

This contribution exploits the duality between a viral infection process and macroscopic air-based molecular communication. Airborne aerosol and droplet transmission through human respiratory processes is modeled as an instance of a multiuser molecular communication scenario employing respiratory-event-driven molecular variable-concentration shift keying. Modeling is aided by experiments that are motivated by a macroscopic air-based molecular communication testbed. In artificially induced coughs, a saturated aqueous solution containing a fluorescent dye mixed with saliva is released by an adult test person. The emitted particles are made visible by means of optical detection exploiting the fluorescent dye. The number of particles recorded is significantly higher in test series without mouth and nose protection than in those with a well-fitting medical mask. A simulation tool for macroscopic molecular communication processes is extended and used for estimating the transmission of infectious aerosols in different environments. Towards this goal, parameters obtained through self experiments are taken. The work is inspired by the recent outbreak of the coronavirus pandemic.

Equivalent Discrete-Time Channel Modeling for Molecular Communication With Emphasize on an Absorbing Receiver.

This paper introduces the equivalent discrete-time channel model (EDTCM) to the area of diffusion-based molecular communication (DBMC). Emphasis is on an absorbing receiver, which is based on the so-called first passage time concept. In the wireless communications community the EDTCM is well known. Therefore, it is anticipated that the EDTCM improves the accessibility of DBMC and supports the adaptation of classical wireless communication algorithms to the area of DBMC. Furthermore, the EDTCM has the capability to provide a remarkable reduction of computational complexity compared to random walk based DBMC simulators. Besides the exact EDTCM, three approximations thereof based on binomial, Gaussian, and Poisson approximation are proposed and analyzed in order to further reduce computational complexity. In addition, the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is adapted to all four channel models. Numerical results show the performance of the exact EDTCM, illustrate the performance of the adapted BCJR algorithm, and demonstrate the accuracy of the approximations.

Low-Complexity Adaptive Threshold Detection for Molecular Communication.

This paper studies detection algorithms for diffusion-based molecular communication systems, where molecules freely diffuse as information carrier from a transmitter to a receiver in a fluid medium. The main limitations are strong intersymbol interference due to the random propagation of the molecules, and the low-energy/low-complexity assumption regarding future implementations in so-called nanomachines. In this contribution, a new biologically inspired detection algorithm suitable for binary signaling, named adaptive threshold detection, is proposed, which deals with these limitations. The proposed detector is of low complexity, does not require explicit channel knowledge, and seems to be biologically reasonable. Numerical results demonstrate that the proposed detector can outperform the common low-complexity fixed threshold detector under certain conditions. As a benchmark, maximum-likelihood sequence estimation (MLSE) and reduced-state sequence estimation (RSSE) are also analyzed by means of numerical simulations. In addition, the effect of molecular denaturation on the detection performances is studied. It is shown that denaturation generally improves the detection performances, while RSSE is able to outperform MLSE in the case of no denaturation.