Encoding Threshold Boolean Networks into Reaction Systems for the Analysis of Gene Regulatory Networks

2021 ◽  
Vol 179 (2) ◽  
pp. 205-225
Author(s):  
Roberto Barbuti ◽  
Pasquale Bove ◽  
Roberta Gori ◽  
Damas Gruska ◽  
Francesca Levi ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Boolean Network ◽  
Reaction System ◽  
Boolean Networks ◽  
Specific Cell ◽  
Reaction Systems ◽  
Gene Regulatory ◽  
Closed Reaction System

Gene regulatory networks represent the interactions among genes regulating the activation of specific cell functionalities and they have been successfully modeled using threshold Boolean networks. In this paper we propose a systematic translation of threshold Boolean networks into reaction systems. Our translation produces a non redundant set of rules with a minimal number of objects. This translation allows us to simulate the behavior of a Boolean network simply by executing the (closed) reaction system we obtain. This can be very useful for investigating the role of different genes simply by “playing” with the rules. We developed a tool able to systematically translate a threshold Boolean network into a reaction system. We use our tool to translate two well known Boolean networks modelling biological systems: the yeast-cell cycle and the SOS response in Escherichia coli. The resulting reaction systems can be used for investigating dynamic causalities among genes.

scholarly journals The Dynamics of Canalizing Boolean Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Elijah Paul ◽  
Gleb Pogudin ◽  
William Qin ◽  
Reinhard Laubenbacher
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Boolean Network ◽  
Boolean Networks ◽  
Molecular Networks ◽  
Biological Applications ◽  
Expected Number ◽  
Modeling Framework ◽  
Gene Regulatory ◽  
The Stability

Boolean networks are a popular modeling framework in computational biology to capture the dynamics of molecular networks, such as gene regulatory networks. It has been observed that many published models of such networks are defined by regulatory rules driving the dynamics that have certain so-called canalizing properties. In this paper, we investigate the dynamics of a random Boolean network with such properties using analytical methods and simulations. From our simulations, we observe that Boolean networks with higher canalizing depth have generally fewer attractors, the attractors are smaller, and the basins are larger, with implications for the stability and robustness of the models. These properties are relevant to many biological applications. Moreover, our results show that, from the standpoint of the attractor structure, high canalizing depth, compared to relatively small positive canalizing depth, has a very modest impact on dynamics. Motivated by these observations, we conduct mathematical study of the attractor structure of a random Boolean network of canalizing depth one (i.e., the smallest positive depth). For every positive integer ℓ, we give an explicit formula for the limit of the expected number of attractors of length ℓ in an n-state random Boolean network as n goes to infinity.

scholarly journals A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired models

2020 ◽  
Vol 2 (3) ◽  
pp. 207-226 ◽  
Author(s):  
Roberto Barbuti ◽  
Roberta Gori ◽  
Paolo Milazzo ◽  
Lucia Nasti
Keyword(s):  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Quantitative Methods ◽  
Environmental Changes ◽  
Boolean Networks ◽  
Specific Cell ◽  
Qualitative Approaches ◽  
Gene Regulatory ◽  
Gillespie’S Algorithm

Abstract Gene Regulatory Networks (GRNs) represent the interactions among genes regulating the activation of specific cell functionalities, such as reception of (chemical) signals or reaction to environmental changes. Studying and understanding these processes is crucial: they are the fundamental mechanism at the basis of cell functioning, and many diseases are based on perturbations or malfunctioning of some gene regulation activities. In this paper, we provide an overview on computational approaches to GRN modelling and analysis. We start from the biological and quantitative modelling background notions, recalling differential equations and the Gillespie’s algorithm. Then, we describe more in depth qualitative approaches such as Boolean networks and some computer science formalisms, including Petri nets, P systems and reaction systems. Our aim is to introduce the reader to the problem of GRN modelling and to guide her/him along the path that goes from classical quantitative methods, through qualitative methods based on Boolean network, up to some of the most relevant qualitative computational methods to understand the advantages and limitations of the different approaches.

scholarly journals Construction Method of Probabilistic Boolean Networks Based on Imperfect Information

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 268
Author(s):  
Katsuaki Umiji ◽  
Koichi Kobayashi ◽  
Yuh Yamashita
Keyword(s):  
Gene Regulatory Networks ◽  
Boolean Functions ◽  
Regulatory Networks ◽  
Imperfect Information ◽  
Boolean Network ◽  
Construction Method ◽  
Boolean Networks ◽  
Probabilistic Boolean Network ◽  
Gene Regulatory ◽  
Selection Probabilities

A probabilistic Boolean network (PBN) is well known as one of the mathematical models of gene regulatory networks. In a Boolean network, expression of a gene is approximated by a binary value, and its time evolution is expressed by Boolean functions. In a PBN, a Boolean function is probabilistically chosen from candidates of Boolean functions. One of the authors has proposed a method to construct a PBN from imperfect information. However, there is a weakness that the number of candidates of Boolean functions may be redundant. In this paper, this construction method is improved to efficiently utilize given information. To derive Boolean functions and those selection probabilities, the linear programming problem is solved. Here, we introduce the objective function to reduce the number of candidates. The proposed method is demonstrated by a numerical example.

INFERENCE OF GENE REGULATORY NETWORKS USING BOOLEAN-NETWORK INFERENCE METHODS

2009 ◽  
Vol 07 (06) ◽  
pp. 1013-1029 ◽  
Author(s):  
GRAHAM J. HICKMAN ◽  
T. CHARLIE HODGMAN
Keyword(s):  
Regulatory Networks ◽  
Boolean Network ◽  
Network Inference ◽  
Genetic Network ◽  
Boolean Networks ◽  
Genetic Networks ◽  
Related Data ◽  
Boolean Network Model ◽  
Gene Regulatory ◽  
Inference Methods

The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks. The review outlines the various different types of Boolean network developed to date, from the original Random Boolean Network to the current Probabilistic Boolean Network. In addition, some of the different inference methods available to infer these genetic networks are also examined. Where possible, particular attention is paid to input requirements as well as the efficiency, advantages and drawbacks of each method. Though the Boolean network model is one of many models available for network inference today, it is well established and remains a topic of considerable interest in the field of genetic network inference. Hybrids of Boolean networks with other approaches may well be the way forward in inferring the most informative networks.

scholarly journals Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers

2017 ◽  
Vol 39 (3) ◽  
pp. 407-417 ◽  
Author(s):  
Dimple Chudasama ◽  
Valeria Bo ◽  
Marcia Hall ◽  
Vladimir Anikin ◽  
Jeyarooban Jeyaneethi ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Prognostic Value ◽  
Regulatory Networks ◽  
Cancer Biomarkers ◽  
Specific Gene ◽  
Lung Cancers ◽  
Gene Regulatory

scholarly journals The Role of Gene Conversion between Transposable Elements in Rewiring Regulatory Networks

2019 ◽  
Vol 11 (7) ◽  
pp. 1723-1729
Author(s):  
Jeffrey A Fawcett ◽  
Hideki Innan
Keyword(s):  
Transposable Elements ◽  
Gene Conversion ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Alternative Mechanism ◽  
Beneficial Mutations ◽  
Future Studies ◽  
Cellular Processes ◽  
Gene Regulatory

Abstract Nature has found many ways to utilize transposable elements (TEs) throughout evolution. Many molecular and cellular processes depend on DNA-binding proteins recognizing hundreds or thousands of similar DNA motifs dispersed throughout the genome that are often provided by TEs. It has been suggested that TEs play an important role in the evolution of such systems, in particular, the rewiring of gene regulatory networks. One mechanism that can further enhance the rewiring of regulatory networks is nonallelic gene conversion between copies of TEs. Here, we will first review evidence for nonallelic gene conversion in TEs. Then, we will illustrate the benefits nonallelic gene conversion provides in rewiring regulatory networks. For instance, nonallelic gene conversion between TE copies offers an alternative mechanism to spread beneficial mutations that improve the network, it allows multiple mutations to be combined and transferred together, and it allows natural selection to work efficiently in spreading beneficial mutations and removing disadvantageous mutations. Future studies examining the role of nonallelic gene conversion in the evolution of TEs should help us to better understand how TEs have contributed to evolution.

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