scholarly journals Noise Control In Gene Regulatory Networks With Negative Feedback

2016 ◽  
Author(s):  
Michael Hinczewski ◽  
D. Thirumalai
Keyword(s):  
Negative Feedback ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Noise Suppression ◽  
Regulatory Gene ◽  
Genetic Circuits ◽  
Cellular Functions ◽  
Complex Circuits ◽  
Regulatory Functions ◽  
Tetracycline Repressor

AbstractGenes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

2020 ◽  
Vol 27 (4) ◽  
pp. 265-278 ◽  
Author(s):  
Ying Han ◽  
Liang Cheng ◽  
Weiju Sun
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Comprehensive Information ◽  
Protein Interaction Prediction ◽  
Or Gene ◽  
Experimental Findings

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

scholarly journals Model-guided design of mammalian genetic programs

2021 ◽  
Vol 7 (8) ◽  
pp. eabe9375
Author(s):  
J. J. Muldoon ◽  
V. Kandula ◽  
M. Hong ◽  
P. S. Donahue ◽  
J. D. Boucher ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Computational Models ◽  
Biological Complexity ◽  
Genetic Circuits ◽  
Cellular Functions ◽  
High Performing ◽  
Design Objective ◽  
Complex Functions ◽  
Mammalian Genetic ◽  
Analog Processing

Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multifunctional proteins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

scholarly journals Transcription Factors and microRNA Genes Regulatory Network Construction Under Drought Stress in Sesame (Sesamum indicum L.)

2020 ◽  
Author(s):  
Mohammad Amin Baghery ◽  
Seyed Kamal Kazemitabar ◽  
Ali Dehestani ◽  
Pooyan Mehrabanjoubani ◽  
Mohammad Mehdi Naghizadeh ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Drought Stress ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Regulatory Gene ◽  
Sesamum Indicum ◽  
Regulatory Mechanisms ◽  
Oilseed Crop ◽  
Sesame Genotypes ◽  
Almost All

Abstract Background: Drought is one of the most common environmental stresses affecting crops yield and quality. Sesame is an important oilseed crop that most likely faces drought during its growth due to growing in semi-arid and arid areas. Plants responses to drought controlled by regulatory mechanisms. Despite this importance, there is little information about Sesame regulatory mechanisms against drought stress. Results: 458 drought-related genes were identified using comprehensive RNA-seq data analysis of two susceptible and tolerant sesame genotypes under drought stress. These drought-responsive genes were included secondary metabolites biosynthesis-related Like F3H, sucrose biosynthesis-related like SUS2, transporters like SUC2, and protectives like LEA and HSP families. Interactions between identified genes and regulators including TFs and miRNAs were predicted using bioinformatics tools and related regulatory gene networks were constructed. Key regulators and relations of Sesame under drought stress were detected by network analysis. TFs belonged to DREB (DREB2D), MYB (MYB63), ZFP (TFIIIA), bZIP (bZIP16), bHLH (PIF1), WRKY (WRKY30) and NAC (NAC29) families were found among key regulators. mRNAs like miR399, miR169, miR156, miR5685, miR529, miR395, miR396, and miR172 also found as key drought regulators. Furthermore, a total of 117 TFs and 133 miRNAs that might be involved in drought stress were identified with this approach. Conclusions: Most of the identified TFs and almost all of the miRNAs are introduced for the first time as potential regulators of drought response in Sesame. These regulators accompany with identified drought-related genes could be valuable candidates for future studies and breeding programs on Sesame under drought stress. Keywords: Sesamum indicum, Drought stress, Regulatory networks, miRNA, Transcription Factors.

Measuring Information Propagation and Processing in Biological Systems

2009 ◽  
pp. 190-226
Author(s):  
Juha Kesseli ◽  
Andre S. Ribeiro ◽  
Matti Nykter
Keyword(s):  
Information Processing ◽  
Dynamical Systems ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Dynamical Behavior ◽  
Measurement Data ◽  
Distribution Data ◽  
Information Propagation ◽  
Information Management Systems ◽  
Element Activity

In this chapter the authors study the propagation and processing of information in dynamical systems. Various information management systems can be represented as dynamical systems of interconnected information processing units. Here they focus mostly on genetic regulatory networks that are information processing systems that process and propagate information stored in genome. Boolean networks are used as a dynamical model of regulation, and different ways of parameterizing the dynamical behavior are studied. What are called critical networks are in particular under study, since they have been hypothesized as being the most effective under evolutionary pressure. Critical networks are also present in man-made systems, such as the Internet, and provide a candidate application area for findings on the theory of dynamical networks in this chapter. The authors present approaches of annealed approximation and find that avalanche size distribution data supports criticality of regulatory networks. Based on Shannon information, they then find that a mutual information measure quantifying the coordination of pairwise element activity is maximized at criticality. An approach of algorithmic complexity, the normalized compression distance (NCD), is shown to be applicable to both dynamical and topological features of regulatory networks. NCD can also be seen to enable further utilization of measurement data to estimate information propagation and processing in biological networks.

scholarly journals Biological network analysis with deep learning

2020 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O’Bray ◽  
Karsten Borgwardt
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Biological Networks ◽  
Protein Function ◽  
Regulatory Networks ◽  
Biological Network ◽  
Gene Interaction ◽  
Disease Diagnosis ◽  
Interaction Prediction ◽  
Graph Neural Networks

Abstract Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

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