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.

scholarly journals An Open-Source Cloud-FPGA Gene Regulatory Accelerator

2021 ◽  
Author(s):  
Lucas Bragança ◽  
Jeronimo Penha ◽  
Michael Canesche ◽  
Dener Ribeiro ◽  
José Augusto M. Nacif ◽  
...  
Keyword(s):  
Open Source ◽  
Gene Regulatory Networks ◽  
High Performance ◽  
Regulatory Networks ◽  
Cloud Services ◽  
On Demand ◽  
Speed Up ◽  
Gene Regulatory ◽  
High Level ◽  
Better Than

FPGAs are suitable to speed up gene regulatory network (GRN) algorithms with high throughput and energy efficiency. In addition, virtualizing FPGA using hardware generators and cloud resources increases the computing ability to achieve on-demand accelerations across multiple users. Recently, Amazon AWS provides high-performance Cloud's FPGAs. This work proposes an open source accelerator generator for Boolean gene regulatory networks. The generator automatically creates all hardware and software pieces from a high-level GRN description. We evaluate the accelerator performance and cost for CPU, GPU, and Cloud FPGA implementations by considering six GRN models proposed in the literature. As a result, the FPGA accelerator is at least 12x faster than the best GPU accelerator. Furthermore, the FPGA reaches the best performance per dollar in cloud services, at least 5x better than the best GPU accelerator.

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 Macroscopic dynamics of gene regulatory networks revealed by individual entropy

2021 ◽  
Author(s):  
Cong Liu ◽  
Lijie Hao ◽  
Jinzhi Lei
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Temporal Dynamics ◽  
Transition Process ◽  
Network Structures ◽  
Structure Variation ◽  
Macroscopic Variable ◽  
Gene Regulatory

Complex systems are usually high-dimensional with intricate interactions among internal components, and may display complicated dynamics under different conditions. While it is difficult to measure detailed dynamics of each component, proper macroscopic description of a complex system is crucial for quantitative studies. In biological systems, each cell is a complex system containing a huge number of molecular components that are interconnected with each other through intricate molecular interaction networks. Here, we consider gene regulatory networks in a cell, and introduce individual entropy as a macroscopic variable to quantify the transcriptional dynamics in response to changes in random perturbations and/or network structures. The proposed individual entropy measures the information entropy of a system at each instant with respect to a basal reference state, and may provide temporal dynamics to characterize switches of system states. Individual entropy provides a method to quantify the stationary macroscopic dynamics of a gene set that is dependent on the gene regulation connections, and can be served as an indicator for the evolution of network structure variation. Moreover, the individual entropy with reference to a preceding state enables us to characterize different dynamic patterns generated from varying network structures. Our results show that the proposed individual entropy can be a valuable macroscopic variable of complex systems in characterizing the transition processes from order to disorder dynamics, and to identify the critical events during the transition process.

Improved Model Checking Techniques for State Space Analysis of Gene Regulatory Networks

2010 ◽  
pp. 386-404
Author(s):  
Hélio C. Pais ◽  
Kenneth L. McMillan ◽  
Ellen M. Sentovich ◽  
Ana T. Freitas ◽  
Arlindo L. Oliveira
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Basins Of Attraction ◽  
Model Checker ◽  
Biologically Relevant ◽  
Qualitative Models ◽  
A Cell ◽  
Gene Regulatory ◽  
Improved Model ◽  
High Level

A better understanding of the behavior of a cell, as a system, depends on our ability to model and understand the complex regulatory mechanisms that control gene expression. High level, qualitative models of gene regulatory networks can be used to analyze and characterize the behavior of complex systems, and to provide important insights on the behavior of these systems. In this chapter, we describe a number of additional functionalities that, when supported by a symbolic model checker, make it possible to answer important questions about the nature of the state spaces of gene regulatory networks, such as the nature and size of attractors, and the characteristics of the basins of attraction. We illustrate the type of analysis that can be performed by applying an improved model checker to two well studied gene regulatory models, the network that controls the cell cycle in the yeast S. cerevisiae, and the network that regulates formation of the dorsal-ventral boundary in D. melanogaster. The results show that the insights provided by the analysis can be used to understand and improve the models, and to formulate hypotheses that are biologically relevant and that can be confirmed experimentally.

scholarly journals Conceptual Confusion: The case of Epigenetics

2016 ◽  
Author(s):  
Angela Oliveira Pisco ◽  
Aymeric Fouquier d’Hérouël ◽  
Sui Huang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Activation ◽  
Epigenetic Landscape ◽  
The Past ◽  
Time Period ◽  
Formal Concepts ◽  
Gene Regulatory ◽  
Mathematical Foundations ◽  
Conceptual Confusion

ABSTRACTThe observations of phenotypic plasticity have stimulated the revival of ‘epigenetics’. Over the past 70 years the term has come in many colors and flavors, depending on the biological discipline and time period. The meanings span from Waddington’s “epigenotype” and “epigenetic landscape” to the molecular biologists’ “epigenetic marks” embodied by DNA methylation and histone modifications. Here we seek to quell the ambiguity of the name. First we place “epigenetics” in the various historical contexts. Then, by presenting the formal concepts of dynamical systems theory we show that the “epigenetic landscape” is more than a metaphor: it has specific mathematical foundations. The latter explains how gene regulatory networks produce multiple attractor states, the self-stabilizing patterns of gene activation across the genome that account for “epigenetic memory”. This network dynamics approach replaces the reductionist correspondence of molecular epigenetic modifications with concept of the epigenetic landscape, by providing a concrete and crisp correspondence.

scholarly journals Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies

2020 ◽  
Vol 117 (21) ◽  
pp. 11589-11596 ◽  
Author(s):  
Héloïse D. Dufour ◽  
Shigeyuki Koshikawa ◽  
Cédric Finet
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Window ◽  
Single Gene ◽  
Wing Pattern ◽  
The Past ◽  
Novel Traits ◽  
Temporal Flexibility ◽  
Gene Regulatory ◽  
Anterior Posterior

Organisms have evolved endless morphological, physiological, and behavioral novel traits during the course of evolution. Novel traits were proposed to evolve mainly by orchestration of preexisting genes. Over the past two decades, biologists have shown that cooption of gene regulatory networks (GRNs) indeed underlies numerous evolutionary novelties. However, very little is known about the actual GRN properties that allow such redeployment. Here we have investigated the generation and evolution of the complex wing pattern of the flySamoaia leonensis. We show that the transcription factor Engrailed is recruited independently from the other players of the anterior–posterior specification network to generate a new wing pattern. We argue that partial cooption is made possible because 1) the anterior–posterior specification GRN is flexible over time in the developing wing and 2) this flexibility results from the fact that every single gene of the GRN possesses its own functional time window. We propose that the temporal flexibility of a GRN is a general prerequisite for its possible cooption during the course of evolution.

scholarly journals Artificial Gene Regulatory Networks—A Review

2019 ◽  
Vol 24 (4) ◽  
pp. 296-328 ◽  
Author(s):  
Sylvain Cussat-Blanc ◽  
Kyle Harrington ◽  
Wolfgang Banzhaf
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Environmental Signals ◽  
The Past ◽  
Current State ◽  
Behavioral Expression ◽  
Gene Regulatory ◽  
Living Organisms ◽  
Artificial Gene ◽  
Engineering Modeling

In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.

scholarly journals Macroscopic dynamics of gene regulatory networks revealed by individual entropy

2021 ◽  
Author(s):  
Cong Liu ◽  
Lijie Hao ◽  
Jinzhi Lei
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Temporal Dynamics ◽  
Transition Process ◽  
Network Structures ◽  
Structure Variation ◽  
Macroscopic Variable ◽  
Gene Regulatory

Complex systems are usually high-dimensional with intricate interactions among internal components, and may display complicated dynamics under different conditions. While it is {difficult} to measure detail dynamics of each component, proper macroscopic description of a complex system is crucial for quantitative studies. In biological systems, each cell is a complex system containing a huge number of molecular components that are interconnected with each other through intricate molecular interaction networks. Here, we consider gene regulatory networks in a cell, and introduce individual entropy as a macroscopic variable to quantify the transcriptional dynamics in response to changes in random perturbations and/or network structures. The proposed individual entropy measures the information entropy of a system at each instant with respect to a basal reference state, and may provide temporal dynamics to characterize switches of system states. Individual entropy provides a method to quantify the stationary macroscopic dynamics of a gene set that is dependent on the gene regulation connections, and can be served as an indicator for the evolution of network structure variation. Moreover, the individual entropy with reference to a preceding state enable us to characterize different dynamic patterns generated from varying network structures. Our results show that the proposed individual entropy can be a valuable macroscopic variable of complex systems in characterizing the transition processes from order to disorder dynamics, and to identify the critical events during the transition process.

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 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).

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