期刊
BRIEFINGS IN BIOINFORMATICS
卷 24, 期 3, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbad113
关键词
non-equilibrium system; transition rate; energy landscape; transition path; data-driven approach
In this study, we proposed a data-driven approach to estimate non-equilibrium transition rate, combining kernel density estimation and non-equilibrium transition rate theory. Our approach showed superior performance in estimating transition rate from data compared to previous methods, due to the introduction of a nonparametric density estimation method and consideration of flux effects in determining the new saddle point. We demonstrated the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.
The dynamical properties of many complex physical and biological systems can be quantified from the energy landscape theory. Previous approaches focused on estimating the transition rate from landscape reconstruction based on data. However, for general non-equilibrium systems (such as gene regulatory systems), both the energy landscape and the probability flux are important to determine the transition rate between attractors. In this work, we proposed a data-driven approach to estimate non-equilibrium transition rate, which combines the kernel density estimation and non-equilibrium transition rate theory. Our approach shows superior performance in estimating transition rate from data, compared with previous methods, due to the introduction of a nonparametric density estimation method and the new saddle point by considering the effects of flux. We demonstrate the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.
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