Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks

Abstract

Motivation: Mechanistic models based on ordinary differential equations provide powerful and accurate means to describe the dynamics of molecular machinery which orchestrates gene regulation. When combined with appropriate statistical techniques, mechanistic models can be calibrated using experimental data and, in many cases, also the model structure can be inferred from time–course measurements. However, existing mechanistic models are limited in the sense that they rely on the assumption of static network structure and cannot be applied when transient phenomena affect, or rewire, the network structure. In the context of gene regulatory network inference, network rewiring results from the net impact of possible unobserved transient phenomena such as changes in signaling pathway activities or epigenome, which are generally difficult, but important, to account for.Results: We introduce a novel method that can be used to infer dynamically evolving regulatory networks from time–course data. Our method is based on the notion that all mechanistic ordinary differential equation models can be coupled with a latent process that approximates the network structure rewiring process. We illustrate the performance of the method using simulated data and, further, we apply the method to study the regulatory interactions during T helper 17 (Th17) cell differentiation using time–course RNA sequencing data. The computational experiments with the real data show that our method is capable of capturing the experimentally verified rewiring effects of the core Th17 regulatory network. We predict Th17 lineage specific subnetworks that are activated sequentially and control the differentiation process in an overlapping manner.Availability and Implementation: An implementation of the method is available at http://research.ics.aalto.fi/csb/software/lem/.Contacts: jukka.intosalmi@aalto.fi or harri.lahdesmaki@aalto.fi

Explicit Modeling of RNA Stability Improves Large-Scale Inference of Transcription Regulation

“…The results of this study encourage further developments of the Inferelator algorithm that would allow for an efficient incorporation and recovery of biophysical parameters, such as RNA decay rates and interaction terms between co-regulating TFs, a more careful separation of the transcription term into transcriptional activation and repression, which has only been done on the small scale (Noman & Iba, 2005;Liu & Wang, 2008;Bonneau & Aijo, 2016;Intosalmi et al, 2016), and modeling functional modifications of TFs that can affect their transcriptional activity. Given the growing body of literature on RNA-binding proteins (RBPs) (Hogan et al, 2008;Mittal et al, 2009;Janga & Mittal, 2011), our results also inspire potential approaches to model the RNA decay term explicitly as a sum of contributions from RNA degradation factors.…”

“…Various machine learning approaches are used to infer regulatory networks. They have multiple levels of model complexity, ranging from the earliest Boolean network and network module approaches (Shmulevich et al, 2002;Lähdesmäki et al, 2003;Segal et al, 2003;Pe'er et al, 2001) to approaches that explicitly model dynamics, TF interactions and transcription factor post-transcriptional activity (Honkela et al, 2010;Äijö et al, 2013;Intosalmi et al, 2016;Studham et al, 2014). Advancements in genomics and transcriptomics technologies spurred the development of more complex methods, involving Mutual Information (Margolin et al, 2006a,b;Faith et al, 2007;Butte & Kohane, 2000), correlation (Butte & Kohane, 2000), ANOVA (Küffner et al, 2012), conditional entropy (Karlebach & Shamir, 2012), Random Forest (Huynh-Thu et al, 2010;Petralia et al, 2015), Bayesian causality (Mani & Cooper, 2004;Mani et al, 2012;Friedman et al, 2000), expression module clustering (Reiss et al, , 2015, and constrained regression of biophysical models Greenfield et al, 2013;Arrieta-Ortiz et al, 2015).…”

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

“…Various machine learning approaches are then used to infer the network. The approaches have multiple levels of complexity, ranging from Boolean networks and network module approaches ( Shmulevich et al, 2002 ; Lähdesmäki et al, 2003 ; Segal et al, 2003 ; Pe’er et al, 2001 ) to approaches that explicitly or implicitly model dynamics, TF interactions, and activity ( Honkela et al, 2010 ; Äijö et al, 2013 ; Intosalmi et al, 2016 ; Studham et al, 2014 ).…”

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

“…ODE models are particularly valuable as they can be used to predict the temporal evolution of latent variables [4,5]. Moreover, they provide executable formulations of biological hypotheses and therefore allow the rigorous falsification of hypotheses [6,7,8,9,10,11], thereby deepening the biological understanding. Furthermore, ODE models have been applied to derive model-based biomarkers [12,13,14], that enable a personalized design of targeted therapies in precision medicine.…”