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TimeTeller: A tool to probe the circadian clock as a multigene dynamical system
Denise Vlachou1, Maria Veretennikova1, Laura Usselmann2
1Mathematics Institute & Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom.
Abstract:
Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine.
Insights
TimeTeller is a new machine learning tool that analyzes the molecular circadian clock as a noisy dynamical system. It estimates circadian clock function from transcriptomic data, aiding circadian medicine advancements.
Area of Science:
- Chronobiology
- Systems Biology
- Machine Learning
Background:
- The molecular circadian clock regulates critical physiological processes and influences disease pathogenesis, including cancer and heart disease.
- Accurate assessment of circadian clock function is crucial for understanding disease and developing effective therapies.
- Existing tools are limited in analyzing the clock as a complex, noisy, multi-gene dynamical system.
Purpose of the Study:
- To introduce TimeTeller, a novel machine learning tool designed to analyze the circadian clock as a noisy multigene dynamical system.
- To estimate circadian clock function from single transcriptome data by modeling its multi-dimensional state.
- To demonstrate TimeTeller's utility in assessing clock systems across different species and data types.
Main Methods:
- Development of TimeTeller, a machine learning algorithm for analyzing noisy multigene dynamical systems.
- Application of TimeTeller to mouse, baboon, and human microarray and RNA-seq data.
- Utilizing TimeTeller to visualize and quantify clock structure, stratify samples by clock dysfunction, and compare clocks across diverse contexts.
Main Results:
- TimeTeller successfully models the multi-dimensional state of the circadian clock from transcriptomic data.
- The tool enables visualization and quantification of global clock structure.
- TimeTeller can quantitatively stratify samples based on circadian clock dysfunction and compare clocks across individuals, conditions, and tissues.
Conclusions:
- TimeTeller provides a novel methodology for analyzing the molecular circadian clock as a noisy dynamical system.
- This tool has significant potential for advancing circadian medicine by enabling precise assessment of clock function.
- TimeTeller facilitates a deeper understanding of circadian clock dynamics in health and disease across species.
