Statistical inference of the time-varying structure of gene-regulation networks

By Sophie Lèbre, Jennifer Becq, Frédéric Devaux, M. P. Stumpf, Gaëlle Lelandais. BMC Systems Biology, 2010

948 citations recorded by the source corpus.

BackgroundBiological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.MethodsTo overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).ResultsWe demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning.ConclusionsARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/00918c3f090fc1051e2feff5146bedc67f3de4b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. M. P. Stumpf
2. Thalia E. Chan
3. Ann C. Babtie

In Molecular Biology

1. M. P. Stumpf
2. Thalia E. Chan
3. Ann C. Babtie
4. Malte D. Luecken
5. Qing Nie
6. Fabian J Theis
7. Zixuan Cang

Citations in the corpus

• Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, background): “…from population level data (Penfold et al. 2011, Penfold et al. 2015, Bonneau et al. 2006, Margolin et al. 2006a, Villaverde et al. 2013, Villaverde et al. 2013, Liang et al. 2008, Madar et al. 2010, Hill et al. 2012, Lèbre et al. 2010, Beal et al. 2005, Schäfer et al. 2005, Vinciotti et al. 2016).”