Publications

BibTex format

@article{Vangelov:2017:10.1101/224063,
author = {Vangelov, B and Barahona, M},
doi = {10.1101/224063},
title = {Modelling the Dynamics of Biological Systems with the Geometric Hidden Markov Model},
url = {http://dx.doi.org/10.1101/224063},
year = {2017}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>ABSTRACT</jats:title><jats:p>Many biological processes can be described geometrically in a simple way: stem cell differentiation can be represented as a branching tree and cell division can be depicted as a cycle. In this paper we introduce the geometric hidden Markov model (GHMM), a dynamical model whose goal is to capture the low-dimensional characteristics of biological processes from multivariate time series data. The framework integrates a graph-theoretical algorithm for dimensionality reduction with a latent variable model for sequential data. We analyzed time series data generated by an in silico model of a biomolecular circuit, the represillator. The trained model has a simple structure: the latent Markov chain corresponds to a two-dimensional lattice. We show that the short-term and long-term predictions of the GHMM reflect the oscillatory behaviour of the genetic circuit. Analysis of the inferred model with a community detection methods leads to a coarse-grained representation of the process.</jats:p>
AU  - Vangelov,B
AU  - Barahona,M
DO  - 10.1101/224063
PY  - 2017///
TI  - Modelling the Dynamics of Biological Systems with the Geometric Hidden Markov Model
UR  - http://dx.doi.org/10.1101/224063
ER  -

Download

ProfessorMauricioBarahona

Contact

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)