We are pleased to announce an upcoming special issue in IEEE Transactions on Molecular, Biological, and Multi-scale Communications, entitled "Seeing Through the Crowd: Molecular Communication in Crowded and Multi-Cellular Environments". You can find the full CFP on the T-MBMC websiteLink opens in a new window. Please consider submitting to the issue and sharing this call with your collaborators.

 

We do welcome contributions from authors outside of IEEE/ComSoc. To facilitate this, initial submissions do not need to follow the IEEE formatting guidelines, as long as the scientific and technical ideas are conveyed clearly.

In short, the aim of this Special Issue is to raise a spotlight on the diversity of complex environments and the tools that are needed to describe molecular signaling and communication in these systems. We have a particular interest in biophysical models that have not yet been subject to extensive molecular communication analysis, but contributions using any models that are appropriate for “large” cellular systems are welcome.

 

The list of invited topics includes (but is not limited to) research articles and surveys on the following:

• Signal propagation models designed for intercellular environments such as organoids, tissues, organs, tumors, and biofilms
• Signal propagation models designed for intracellular environments such as cytoplasm, nuclei, and organelles
• Signal propagation models designed for crowded environments (biological or non-biological) such as porous media
• Unconventional diffusion modeling, including anisotropic diffusion, anomalous diffusion (superdiffusion or subdiffusion), fractional Brownian motion, and multi-phase diffusion
• Biophysical flow modelling, including turbulent and laminar flow
• Multi-cellular chemical reaction networks
• Design of synthetic or natural communication systems in multi-cellular environments
• Communication system analysis in multi-cellular environments
• Decentralized cellular decision-making and computation
• Experimental methods to apply or detect signals in multi-cellular environments, either in aggregate or at single-cell resolution