Classification-based Inference of Dynamical Models of Gene Regulatory Networks
David A. Fehr,Manu,Yen Lee Loh +2 more
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TLDR
This work presents FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters that is faster than global non-linear optimization by nearly three orders of magnitude and its computational complexity scales much better with GRN size.Abstract:
Cell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimization problems that are amenable to computationally efficient supervised learning techniques. We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR9s effectiveness on synthetic data as well as experimental data from the gap gene system of Drosophila. FIGR is faster than global non-linear optimization by nearly three orders of magnitude and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before. FIGR Source code is freely available at http://github.com/mlekkha/FIGR.read more
Citations
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Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis
Noa Novershtern,Noa Novershtern,Noa Novershtern,Aravind Subramanian,Lee N. Lawton,Raymond H. Mak,W. Nicholas Haining,Marie McConkey,Naomi Habib,Nir Yosef,Cindy Y. Chang,Cindy Y. Chang,Tal Shay,Garrett M. Frampton,Adam Drake,Ilya B. Leskov,Björn Nilsson,Björn Nilsson,Fred Preffer,David Dombkowski,John W. Evans,Ted Liefeld,John S. Smutko,Jianzhu Chen,Nir Friedman,Richard A. Young,Todd R. Golub,Todd R. Golub,Todd R. Golub,Aviv Regev,Aviv Regev,Aviv Regev,Benjamin L. Ebert,Benjamin L. Ebert,Benjamin L. Ebert +34 more
TL;DR: This work profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry.
Journal ArticleDOI
Reconstructing blood stem cell regulatory network models from single-cell molecular profiles
Fiona K. Hamey,Sonia Nestorowa,Sarah Kinston,David G. Kent,Nicola K. Wilson,Berthold Göttgens +5 more
TL;DR: In this article, the authors developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells.
Journal ArticleDOI
Genetic Complexity of the Human Genome in Health and Disease: Basic Concepts
TL;DR: The great variation of genome sequence and regulatory elements of the genome architecture are exploited in studies of genome-wide association with disease, in the framework of Precision Medicine and in general of Genomic Medicine.
References
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Lucas Sánchez,Denis Thieffry +1 more
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Synergy between the hunchback and bicoid morphogens is required for anterior patterning in Drosophila.
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Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data.
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