Network Inference and Biological Dynamics

By Chris J. Oates, Sach Mukherjee. 2011

81 citations recorded by the source corpus.

Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods have been proposed for this setting, but the connections and differences between their statistical formulations have received less attention. In this paper, we show how a broad class of statistical network inference methods, including a number of existing approaches, can be described in terms of variable selection for the linear model. This reveals some subtle but important differences between the methods, including the treatment of time intervals in discretely observed data. In developing a general formulation, we also explore the relationship between single-cell stochastic dynamics and network inference on averages over cells. This clarifies the link between biochemical networks as they operate at the cellular level and network inference as carried out on data that are averages over populations of cells. We present empirical results, comparing thirty-two network inference methods that are instances of the general formulation we describe, using two published dynamical models. Our investigation sheds light on the applicability and limitations of network inference and provides guidance for practitioners and suggestions for experimental design.

Subfields: Molecular Biology

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Citations in the corpus

Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics (2016, methodology): “(see [1]–[4], [40]–[42] and references therein).”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “…approaches aiming to reconstruct GRN structure from mRNA profiles and are discussed in depth elsewhere (Penfold & Wild 2011, Marbach et al. 2012, Villaverde & Banga 2013, Oates & Mukherjee 2012), but it is worth emphasising a few key points that affect how we should interpret and use our results.”
Network inference using steady-state data and Goldbeter-Koshland kinetics (2012, background): “Indeed, such bias can prevent recovery of the correct network even in favourable asymptotic limits of large sample size and low noise (Oates and Mukherjee, 2012a).”
Causal network inference using biochemical kinetics (2014, unknown): “The appropriateness of normality, additivity and the uncorrelatedness of errors necessarily depends on the data generating and measurement processes, as well as the time intervals t j (cid:6) t j (cid:6) 1 between consecutive observations, as discussed in Oates and Mukherjee (2012).”
Learning Large Scale Ordinary Differential Equation Systems (2017, methodology): “Finally, we note that the generalised profiling method described by Ramsay et al. (2007) is another variation on the inverse collocation method.”