Continuous Time Bayesian Networks for Gene Network Reconstruction: A Comparative Study on Time Course Data

2014 ◽  
pp. 176-187 ◽  
Author(s):  
Enzo Acerbi ◽  
Fabio Stella
Keyword(s):  
Comparative Study ◽  
Bayesian Networks ◽  
Continuous Time ◽  
Gene Network ◽  
Time Course ◽  
Network Reconstruction ◽  
Continuous Time Bayesian Networks ◽  
Gene Network Reconstruction ◽  
Time Course Data

scholarly journals Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism

2020 ◽  
Vol 6 (3) ◽  
pp. 108
Author(s):  
Enzo Acerbi ◽  
Marcela Hortova-Kohoutkova ◽  
Tsokyi Choera ◽  
Nancy Keller ◽  
Jan Fric ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Continuous Time ◽  
Regulatory Networks ◽  
Time Course ◽  
Metabolic Networks ◽  
De Novo ◽  
Tryptophan Metabolism ◽  
Reconstruction Method ◽  
Fungal Virulence ◽  
Continuous Time Bayesian Networks

Systems biology approaches are extensively used to model and reverse-engineer gene regulatory networks from experimental data. Indoleamine 2,3-dioxygenases (IDOs)—belonging in the heme dioxygenase family—degrade l-tryptophan to kynurenines. These enzymes are also responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD+). As such, they are expressed by a variety of species, including fungi. Interestingly, Aspergillus may degrade l-tryptophan not only via IDO but also via alternative pathways. Deciphering the molecular interactions regulating tryptophan metabolism is particularly critical for novel drug target discovery designed to control pathogen determinants in invasive infections. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling l-tryptophan metabolism. The method unravels a possible novel approach to target fungal virulence factors during infection. Furthermore, this study represents the first application of continuous-time Bayesian networks as a gene network reconstruction method in Aspergillus metabolism. The experiment showed that the applied computational approach may improve the understanding of metabolic networks over traditional pathways.

scholarly journals Active learning of continuous-time Bayesian networks through interventions*

2021 ◽  
Vol 2021 (12) ◽  
pp. 124001
Author(s):  
Dominik Linzner ◽  
Heinz Koeppl
Keyword(s):  
Experimental Design ◽  
Bayesian Networks ◽  
Continuous Time ◽  
Time Course ◽  
Information Gain ◽  
Learning By Doing ◽  
Variational Approximation ◽  
Real World Data ◽  
Continuous Time Bayesian Networks ◽  
Expected Information Gain

Abstract We consider the problem of learning structures and parameters of continuous-time Bayesian networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences. A popular approach to overcome this is Bayesian optimal experimental design (BOED). However, BOED becomes infeasible in high-dimensional settings, as it involves integration over all possible experimental outcomes. We propose a novel criterion for experimental design based on a variational approximation of the expected information gain. We show that for CTBNs, a semi-analytical expression for this criterion can be calculated for structure and parameter learning. By doing so, we can replace sampling over experimental outcomes by solving the CTBNs master-equation, for which scalable approximations exist. This alleviates the computational burden of integrating over possible experimental outcomes in high-dimensions. We employ this framework in order to recommend interventional sequences. In this context, we extend the CTBN model to conditional CTBNs in order to incorporate interventions. We demonstrate the performance of our criterion on synthetic and real-world data.

scholarly journals Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection

2011 ◽  
Vol 1 (1) ◽  
pp. 27 ◽  
Author(s):  
Konstantina Dimitrakopoulou ◽  
Charalampos Tsimpouris ◽  
George Papadopoulos ◽  
Claudia Pommerenke ◽  
Esther Wilk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Influenza A ◽  
Gene Network ◽  
Network Reconstruction ◽  
Expression Data ◽  
H1n1 Infection ◽  
Influenza A H1n1 ◽  
Gene Network Reconstruction

scholarly journals Algorithm for the Construction of a Global Enzymatic Network to be used for Gene Network Reconstruction

2014 ◽  
Vol 15 (5) ◽  
pp. 400-407
Author(s):  
Andres Quintero ◽  
Jorge Ramírez ◽  
Luis Leal ◽  
Liliana Lopez-Kleine
Keyword(s):  
Gene Network ◽  
Network Reconstruction ◽  
Gene Network Reconstruction

scholarly journals Network Reconstruction from Perturbation Time Course Data

2018 ◽  
Author(s):  
Gregory R Smith ◽  
Deepraj Sarmah ◽  
Mehdi Bouhaddou ◽  
Alan D. Stern ◽  
James Erskine ◽  
...  
Keyword(s):  
Time Course ◽  
Network Inference ◽  
Complex Dynamics ◽  
Self Regulation ◽  
Dynamic Network ◽  
Network Reconstruction ◽  
Biological Interactions ◽  
Inference Problem ◽  
Reconstruction Methods ◽  
Time Course Data

SummaryNetwork reconstruction is an important objective for understanding biological interactions and their role in disease mechanisms and treatment. Yet, even for small systems, contemporary reconstruction methods struggle with critical network properties: (i) edge causality, sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; and (iv) environment-specific effects. Moreover, experimental noise significantly impedes many methods. We report an approach that addresses the aforementioned challenges to robustly and uniquely infer edge weights from sparse perturbation time course data that formally requires only one perturbation per node. We apply this approach to randomized 2 and 3 node systems with varied and complex dynamics as well as to a family of 16 non-linear feedforward loop motif models. In each case, we find that it can robustly reconstruct the networks, even with highly noisy data in some cases. Surprisingly, the results suggest that incomplete perturbation (e.g. 50% knockdown vs. knockout) is often more informative than full perturbation, which may fundamentally change experimental strategies for network reconstruction. Systematic application of this method can enable unambiguous network reconstruction, and therefore better prediction of cellular responses to perturbations such as drugs. The method is general and can be applied to any network inference problem where perturbation time course experiments are possible.

scholarly journals Intrusion Detection using Continuous Time Bayesian Networks

2010 ◽  
Vol 39 ◽  
pp. 745-774 ◽  
Author(s):  
J. Xu ◽  
C. R. Shelton
Keyword(s):  
Intrusion Detection ◽  
Bayesian Networks ◽  
Continuous Time ◽  
Particle Filtering ◽  
Generative Models ◽  
Training Data ◽  
Continuous Time Model ◽  
Time Model ◽  
General Technique ◽  
Continuous Time Bayesian Networks

Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset.

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