Call for Methods and Data Submissions

The IEEE Transactions on Molecular, Biological, and Multi-Scale Communications is actively soliciting submissions for our “Methods and Data” article type. 

“Methods and Data” papers allow authors to submit manuscripts that include experimental protocols, data sets, and data analysis methods. These manuscripts should encourage growth in the use of experimental methods and the use and analysis of established data sets.

All submissions must be based on high-quality research that has already been published or accepted in a peer-reviewed venue, either by the same authors or by another group, and this must be clearly indicated. Thus, a “Methods and Data” submission does not need to verify the underlying research. Instead, a submission should focus on the description of the experimental setup and the value of the provided data set.

For additional details on how to submit a “Methods and Data” paper, please read our Information for Authors. When submitting, please select the “Transactions Methods Submissions” manuscript type on the ScholarOne submission portal.

As a sample submission, please refer to the published “Methods and Data” paper:

For any questions, please contact our “Methods and Data” editors:

Prof. Adam Noel (
Prof. Werner Haselmayr (

Looking forward to your submissions!

Volume 7, Issue 1 is Out Now

Be sure to check out TMBMC’s 1st issue of 2021, which came out recently on IEEE Xplore. There is an interesting variety of topics to explore. Enjoy!


A Quantized Representation of Intertemporal Choice in the Brain (J. Tee and D. P. Taylor)

Machine Learning in Nano-Scale Biomedical Engineering (A.-A. A. Boulogeorgos, S. E. Trevlakis, S. A. Tegos, V. K. Papanikolaou, and G. K. Karagiannidis)

Embedded Codes for Reassembling Non-Overlapping Random DNA Fragments (S. Nassirpour and A. Vahid)

Molecular Type Spread Molecular Shift Keying for Multiple-Access Diffusive Molecular Communications (W. Gao, T. Mak, and L.-L. Yang)


A Droplet-Based Signal Reconstruction Approach to Channel Modeling in Molecular Communication (F. Gulec and B. Atakan)

Volume 6, Issue 3 Is Out Now

Be sure to check out TMBMC’s 3rd issue of 2020 which came out recently on IEEE Xplore. There is a great variety of topics, including an invited article by Profs. C.-L. Tai and I. F. Akyildiz. Enjoy!


Generalized Molecular-Shift Keying (GMoSK): Principles and Performance Analysis (X. Chen, Y. Huang, L.-L. Yang, and M. Wen)

Optimal Detection Interval for Absorbing Receivers in Molecular Communication Systems With Interference (T. N. Cao, N. Zlatanov, P. L. Yeoh, and J. S. Evans)

Is Information in the Brain Represented in Continuous or Discrete Form? (J. Tee and D. P. Taylor)

Impacts of Unintended Nanomachine in Diffusion-Based Molecular Communication System (L. Chouhan, P. K. Sharma, P. K. Upadhyay, P. Garg, and N. Varshney)

Capacities and Optimal Input Distributions for Particle-Intensity Channels (N. Farsad, W. Chuang, A. Goldsmith, C. Komninakis, M. Médard, C. Rose, L. Vandenberghe, E. E. Wesel, and R. D. Wesel)

[INVITED] A Novel Framework for Capacity Analysis of Diffusion-Based Molecular Communication Incorporating Chemical Reactions (C.-L. Tai and I. F. Akyildiz)


3-D Diffusive Molecular Communication With Two Fully-Absorbing Receivers: Hitting Probability and Performance Analysis (N. V. Sabu, N. Varshney, and A. K. Gupta)

Volume 6, Issue 2 is Now Out

The second issue of 2020 for TMBMC is out now. There is a great variety of papers with our usual fantastic line-up of feature articles, plus a Methods paper and a Letters paper. Check it out at IEEE Xplore, and let us know what you think!


Wireless Communication in Nanonetworks: Current Status, Prospect and Challenges (Y. Lu, R. Ni, and Q. Zhu)

Information Devices Based on Quantized Liénard-Hermite Oscillators (G. L. Viviani)

Analysis of Multi-Chemical Transmission in the Macro-Scale (D. T. McGuiness, S. Giannoukos, S. Taylor, and A. Marshall)

A Comprehensive Survey on Hybrid Communication in Context of Molecular Communication and Terahertz Communication for Body-Centric Nanonetworks (K. Yang, D. Bi, Y. Deng, R. Zhang, M. M. Ur Rahman, N. A. Ali, M. A. Imran, J. M. Jornet, Q. H. Abbasi, and A. Alomainy)

2-D Channel Characterization of a Molecular Motor Signal (A. Gaur and M. R. Bhatnagar)


Molecular Signal Tracking and Detection Methods in Fluid Dynamic Channels (M. Abbaszadeh, I. U. Atthanayake, P. J. Thomas, and W. Guo)


Molecular Type Permutation Shift Keying for Molecular Communication (Y. Tang, M. Wen, X. Chen, Y. Huang, and L.-L. Yang)

Special Issue Call for Papers: Advances in Artificial Intelligence and Mathematical Modelling for Epidemic Diseases and Healthcare Applications

For a PDF of the Call for Papers, please click here.

Call for Papers

As the coronavirus pandemic deepens, there is an urgent need to develop advanced epidemic models that can further improve the efficiency of monitoring, tracking, prevention, control, and treatment. While traditional mathematical modelling methods are considered strong tools to predict the course of COVID-19, healthcare responses are hindered by the lack of standardization, which has prevented universal and coordinated strategies to contain and mitigate the spread of virus. Nonetheless, there are still many challenges, which require researchers in different interdisciplinary areas such as computer science, bioinformatics, epidemiology, and molecular modeling, to work towards cognizing the problem in depth. Artificial intelligence-based models are expected to play a major role in responding to the current and future generations of viruses, which are becoming more complex and much smarter. With the aid of artificial intelligence, there are renewed efforts specifically focusing on machine learning techniques to enhance the computational and data integration capabilities by exploiting many diverse sources of information.

Therefore, the main objective of this special issue is to report on the most recent progress and state-of-the-art investigations on AI-Assisted modeling, including designing, testing, and evaluating, as well as any new standardization initiatives. We specifically seek outstanding work of AI and mathematical modelling that can accurately project the spread of the epidemic, including but not limited to the following topics:

Read more