TY - GEN
T1 - Estimating the Molecular Information Through Cell Signal Transduction Pathways
AU - Sakkaff, Zahmeeth
AU - Immaneni, Aditya
AU - Pierobon, Massimilano
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the NIH National Institutes of General Medical Sciences through grant 5P20GM113126-02 (Nebraska Center for Integrated Biomolecular Communication P20-GM113126). REFERENCES
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/24
Y1 - 2018/8/24
N2 - The development of reliable abstractions, models, and characterizations of biochemical communication channels that propagate information from/to biological cells is one of the first challenges for the engineering of systems able to pervasively interface, control, and communicate through these channels, i.e., the Internet of Bio-N ano Things. Signal transduction pathways in eukaryotic cells are important examples of these channels, especially since their performance is directly linked to organisms' health, such as in cancer. In this paper, a novel computational approach is proposed to characterize the communication performance of signal transduction pathways based on chemical stochastic simulation tools, and the estimation of information-theoretic parameters from sample distributions. Differently from previous literature, this approach does not have constraints on the size of the data, accounts for the information contained in the dynamic pathway evolution, and estimates not only the end-to-end information propagation, but also the information through each component of the pathway. Numerical examples are provided as a case study focused on the popular JAK-STAT pathway, linked to immunodeficiency and cancer.
AB - The development of reliable abstractions, models, and characterizations of biochemical communication channels that propagate information from/to biological cells is one of the first challenges for the engineering of systems able to pervasively interface, control, and communicate through these channels, i.e., the Internet of Bio-N ano Things. Signal transduction pathways in eukaryotic cells are important examples of these channels, especially since their performance is directly linked to organisms' health, such as in cancer. In this paper, a novel computational approach is proposed to characterize the communication performance of signal transduction pathways based on chemical stochastic simulation tools, and the estimation of information-theoretic parameters from sample distributions. Differently from previous literature, this approach does not have constraints on the size of the data, accounts for the information contained in the dynamic pathway evolution, and estimates not only the end-to-end information propagation, but also the information through each component of the pathway. Numerical examples are provided as a case study focused on the popular JAK-STAT pathway, linked to immunodeficiency and cancer.
KW - Cell Signal Transduction Pathways
KW - Gillespie Stochastic Simulation
KW - Information Theory
KW - Internet of Bio-N ano Things
KW - Molecular Communication
KW - N anonetworks
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U2 - 10.1109/SPAWC.2018.8445884
DO - 10.1109/SPAWC.2018.8445884
M3 - Conference contribution
AN - SCOPUS:85053452731
SN - 9781538635124
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
BT - 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
Y2 - 25 June 2018 through 28 June 2018
ER -