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There remains urgent and unmet need for the treatment of neurological diseases, particularly epilepsy which is a serious chronic brain disease characterised by recurrent seizures. This work is a step towards the development and design of an autonomous implantable living cell system with engineered bio-computing logic gates that sense, compute, and actuate epileptic seizure suppression. These cells will then be implanted into the brain, where they will co-exist with natural neural tissue. Together with engineered cells developed through the molecular communications simulation and the modeling design tool, they will sense and perform logic computing to release GDNF (Glial Cell Line-derived Neurotrophic Factor), providing a novel therapeutic strategy for seizure suppression in epilepsy.
This publication outlines the development of a molecular communications (MC) model to represent the dynamics of 5’ transferRNA-derived fragment (tRF) signalling and its delivery in neurons by abstracting the biological components into MC modules. We then characterised the model to analyse the information throughput and the molecular processing-induced loss to assess model communications performance.
Recent studies have identified three transferRNA-derived fragments (tRFs) – 5’AlaTGC, 5’GluCTC, and 5’GlyGCC – released from neurons and significantly elevated in epilepsy patients. These extracellular tRFs are taken up by neurons and act in inhibiting protein translation, particularly in regulating molecules related to epileptic seizures thereby presenting their potential as seizure biomarkers. We modelled their delivery system as a molecular communications (MC) model to investigate the biological effects of tRFs in neurons. Temporal dynamics and biophysical interactions of tRFs across the neuronal microdomain were expressed mathematically, and system communications performances were evaluated. The devised MC tool aims to aid in the interpretation of tRFs and their uptake and activities in neurons relevant to their potential as seizure biomarkers and neurotherapeutics.
This paper forms part of the PRIME Project entitled “A personalised living cell synthetic computing circuit for sensing and treating neurodegenerative disorders” which aims to provide a transformational diagnostic-therapeutic treatment for epilepsy and other neurological diseases. The result of PRIME is a software design tool for designing engineered cells that compute, diagnose, and produce therapeutic molecules capable of preventing seizures which is governed by Artificial Intelligence (AI) integrated with Molecular Communication simulations that utilize Biophysical and Statistical Mechanics modelling. Our model thereby aims to contribute to the design of these engineered cells by identifying the role of transferRNA-derived fragments (tRFs) in protein translation, particularly for epilepsy-related molecules. The modelling of their efficient delivery system then enables us to amplify the production of proteins that interact with tRFs in promoting the suppression of seizure occurrences in epilepsy.
We aim to explore different modeling strategies outside of the conventional mathematical modeling and molecular communications paradigm, such as using synthetic logic circuit and machine learning models in synthetic biology to characterize the role of epilepsy biomarker molecules such as microRNAs and tsRNAs in the production of epilepsy suppression molecules such as GDNF. The knowledge we get from developing these models will greatly help in optimising the design of the PRIME project device which will be implanted into the brain and co-exist with natural tissues in the brain, and utilize naturally produced epilepsy biomarker molecules such as microRNAs and tsRNAs by detecting them as natural response to seizures, as well as amplifying naturally produced GDNF to act as epilepsy suppressing molecules.
Publication Title: Communication Analysis of tRNA-derived Fragment Uptake and Signalling in Neurons
Authors: Kurt J.A. Pumares, Daniel P. Martins, Steven Fagan, Rachel Stewart, Jochen H.M. Prehn, and Dr Deirdre Kilbane
Name of Conference: 13th International Conference on Bioinformatics and Computational Biology (ICBCB), 27 February to 2 March 2025