scholarly journals A method for estimating Hill function-based dynamic models of gene regulatory networks

2018 ◽  
Vol 5 (2) ◽  
pp. 171226 ◽  
Author(s):  
Faizan Ehsan Elahi ◽  
Ammar Hasan
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Optimization Problem ◽  
Dynamic Models ◽  
Initial Conditions ◽  
Computational Cost ◽  
Optimization Approach ◽  
Continuous Models ◽  
Gene Regulatory ◽  
The Cost

Gene regulatory networks (GRNs) are quite large and complex. To better understand and analyse GRNs, mathematical models are being employed. Different types of models, such as logical, continuous and stochastic models, can be used to describe GRNs. In this paper, we present a new approach to identify continuous models, because they are more suitable for large number of genes and quantitative analysis. One of the most promising techniques for identifying continuous models of GRNs is based on Hill functions and the generalized profiling method (GPM). The advantage of this approach is low computational cost and insensitivity to initial conditions. In the GPM, a constrained nonlinear optimization problem has to be solved that is usually underdetermined. In this paper, we propose a new optimization approach in which we reformulate the optimization problem such that constraints are embedded implicitly in the cost function. Moreover, we propose to split the unknown parameter in two sets based on the structure of Hill functions. These two sets are estimated separately to resolve the issue of the underdetermined problem. As a case study, we apply the proposed technique on the SOS response in Escherichia coli and compare the results with the existing literature.

scholarly journals A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1022
Author(s):  
Gianluca D’Addese ◽  
Martina Casari ◽  
Roberto Serra ◽  
Marco Villani
Keyword(s):  
Complex Systems ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Computational Cost ◽  
Graph Analysis ◽  
The Past ◽  
Medium Level ◽  
Micro Level ◽  
Gene Regulatory ◽  
High Level

In many complex systems one observes the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its better understanding. We have developed in the past a powerful method to achieve this goal, which however requires a heavy computational cost in several real-world cases. In this work we introduce a modified version of our approach, which reduces the computational burden. The design of the new algorithm allowed the realization of an original suite of methods able to work simultaneously at the micro level (that of the binary relationships of the single variables) and at meso level (the identification of dynamically relevant groups). We apply this suite to a particularly relevant case, in which we look for the dynamic organization of a gene regulatory network when it is subject to knock-outs. The approach combines information theory, graph analysis, and an iterated sieving algorithm in order to describe rather complex situations. Its application allowed to derive some general observations on the dynamical organization of gene regulatory networks, and to observe interesting characteristics in an experimental case.

From Genes to Flower Patterns and Evolution: Dynamic Models of Gene Regulatory Networks

2006 ◽  
Vol 25 (4) ◽  
pp. 278-289 ◽  
Author(s):  
Álvaro Chaos ◽  
Max Aldana ◽  
Carlos Espinosa-Soto ◽  
Berenice García Ponce de León ◽  
Adriana Garay Arroyo ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Models ◽  
Gene Regulatory

scholarly journals Scalable machine learning-assisted model exploration and inference using Sciope

2020 ◽  
Author(s):  
Prashant Singh ◽  
Fredrik Wrede ◽  
Andreas Hellander
Keyword(s):  
Machine Learning ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Computational Cost ◽  
Stochastic Modelling ◽  
Systematic Investigation ◽  
Ease Of Use ◽  
Supplementary Information ◽  
Great Promise ◽  
Gene Regulatory

Abstract Summary Discrete stochastic models of gene regulatory networks are fundamental tools for in silico study of stochastic gene regulatory networks. Likelihood-free inference and model exploration are critical applications to study a system using such models. However, the massive computational cost of complex, high-dimensional and stochastic modelling currently limits systematic investigation to relatively simple systems. Recently, machine-learning-assisted methods have shown great promise to handle larger, more complex models. To support both ease-of-use of this new class of methods, as well as their further development, we have developed the scalable inference, optimization and parameter exploration (Sciope) toolbox. Sciope is designed to support new algorithms for machine-learning-assisted model exploration and likelihood-free inference. Moreover, it is built ground up to easily leverage distributed and heterogeneous computational resources for convenient parallelism across platforms from workstations to clouds. Availability and implementation The Sciope Python3 toolbox is freely available on https://github.com/Sciope/Sciope, and has been tested on Linux, Windows and macOS platforms. Supplementary information Supplementary information is available at Bioinformatics online.

scholarly journals Transient hysteresis and inherent stochasticity in gene regulatory networks

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Pájaro ◽  
I. Otero-Muras ◽  
C. Vázquez ◽  
A. A. Alonso
Keyword(s):  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Cellular Response ◽  
Regulatory Networks ◽  
Initial Conditions ◽  
Quantitative Description ◽  
Hysteresis Phenomenon ◽  
Cell Fate Determination ◽  
Fate Determination ◽  
Gene Regulatory

Abstract Cell fate determination, the process through which cells commit to differentiated states is commonly mediated by gene regulatory motifs with mutually exclusive expression states. The classical deterministic picture for cell fate determination includes bistability and hysteresis, which enables the persistence of the acquired cellular state after withdrawal of the stimulus, ensuring a robust cellular response. However, stochasticity inherent to gene expression dynamics is not compatible with hysteresis, since the stationary solution of the governing Chemical Master Equation does not depend on the initial conditions. We provide a quantitative description of a transient hysteresis phenomenon reconciling experimental evidence of hysteretic behaviour in gene regulatory networks with inherent stochasticity: under sufficiently slow dynamics hysteresis is transient. We quantify this with an estimate of the convergence rate to the equilibrium and introduce a natural landscape capturing system’s evolution that, unlike traditional cell fate potential landscapes, is compatible with coexistence at the microscopic level.

TOPAC: ALIGNMENT OF GENE REGULATORY NETWORKS USING TOPOLOGY-AWARE COLORING

2012 ◽  
Vol 10 (01) ◽  
pp. 1240001 ◽  
Author(s):  
GÜNHAN GÜLSOY ◽  
BHAVIK GANDHI ◽  
TAMER KAHVECI
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Biological Network ◽  
Optimal Solution ◽  
Alignment Score ◽  
Target Network ◽  
Gene Regulatory ◽  
The Cost ◽  
Confidence Value ◽  
Made In

We consider the problem of finding a subnetwork in a given biological network (i.e. target network) that is most similar to a given small query network. We aim to find the optimal solution (i.e. the subnetwork with the largest alignment score) with a provable confidence bound. There is no known polynomial time solution to this problem in the literature. Alon et al. has developed a state-of-the-art coloring method that reduces the cost of this problem. This method randomly colors the target network prior to alignment for many iterations until a user-supplied confidence is reached. Here we develop a novel coloring method, named k-hop coloring (k is a positive integer), that achieves a provable confidence value in a small number of iterations without sacrificing the optimality. Our method considers the color assignments already made in the neighborhood of each target network node while assigning a color to a node. This way, it preemptively avoids many color assignments that are guaranteed to fail to produce the optimal alignment. We also develop a filtering method that eliminates the nodes that cannot be aligned without reducing the alignment score after each coloring instance. We demonstrate both theoretically and experimentally that our coloring method outperforms that of Alon et al., which is also used by a number network alignment methods, including QPath and QNet, by a factor of three without reducing the confidence in the optimality of the result. Our experiments also suggest that the resulting alignment method is capable of identifying functionally enriched regions in the target network successfully.

scholarly journals Parallelization of ARACNe, an Algorithm for the Reconstruction of Gene Regulatory Networks

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 25
Author(s):  
Uxía Casal ◽  
Jorge González-Domínguez ◽  
María J. Martín
Keyword(s):  
Cellular Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Parallel Implementation ◽  
Computational Cost ◽  
Spearman Correlation ◽  
Mathematical Methods ◽  
Other Substances ◽  
Gene Regulatory ◽  
High Computational Cost

Gene regulatory networks are graphical representations of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression. There are different computational approaches for the reverse engineering of these networks. Most of them require all gene-gene evaluations using different mathematical methods such as Pearson/Spearman correlation, Mutual Information or topology patterns, among others. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) is one of the most effective and widely used tools to reconstruct gene regulatory networks. However, the high computational cost of ARACNe prevents its use over large biologic datasets. In this work, we present a hybrid MPI/OpenMP parallel implementation of ARACNe to accelerate its execution on multi-core clusters, obtaining a speedup of 430.46 using as input a dataset with 41,100 genes and 108 samples and 32 nodes (each of them with 24 cores).

Sign in / Sign up

Export Citation Format

Share Document