Fuzzy Logical on Boolean Networks as Model of Gene Regulatory Networks

2009 ◽  
Author(s):  
Honglin Xu ◽  
Shitong Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Boolean Networks ◽  
Gene Regulatory ◽  
Fuzzy Logical

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data

2021 ◽  
Author(s):  
Marzieh Emadi ◽  
Farsad Zamani Boroujeni ◽  
jamshid Pirgazi
Keyword(s):  
Time Series ◽  
Compressed Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Cognitive Maps ◽  
Boolean Networks ◽  
Fuzzy Cognitive Maps ◽  
Series Data ◽  
Gene Regulatory

Abstract Recently with the advancement of high-throughput sequencing, gene regulatory network inference has turned into an interesting subject in bioinformatics and system biology. But there are many challenges in the field such as noisy data, uncertainty, time-series data with numerous gene numbers and low data, time complexity and so on. In recent years, many research works have been conducted to tackle these challenges, resulting in different methods in gene regulatory networks inference. A number of models have been used in modeling of the gene regulatory networks including Boolean networks, Bayesian networks, Markov model, relational networks, state space model, differential equations model, artificial neural networks and so on. In this paper, the fuzzy cognitive maps are used to model gene regulatory networks because of their dynamic nature and learning capabilities for handling non-linearity and inherent uncertainty. Fuzzy cognitive maps belong to the family of recurrent networks and are well-suited for gene regulatory networks. In this research study, the Kalman filtered compressed sensing is used to infer the fuzzy cognitive map for the gene regulatory networks. This approach, using the advantages of compressed sensing and Kalman filters, allows robustness to noise and learning of sparse gene regulatory networks from data with high gene number and low samples. In the proposed method, stream data and previous knowledge can be used in the inference process. Furthermore, compressed sensing finds likely edges and Kalman filter estimates their weights. The proposed approach uses a novel method to decrease the noise of data. The proposed method is compared to CSFCM, LASSOFCM, KFRegular, ABC, RCGA, ICLA, and CMI2NI. The results show that the proposed approach is superior to the other approaches in fuzzy cognitive maps learning. This behavior is related to the stability against noise and offers a proper balance between data error and network structure.

scholarly journals SYMBOLIC APPROACHES FOR FINDING CONTROL STRATEGIES IN BOOLEAN NETWORKS

2009 ◽  
Vol 07 (02) ◽  
pp. 323-338 ◽  
Author(s):  
CHRISTOPHER JAMES LANGMEAD ◽  
SUMIT KUMAR JHA
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Transfer Functions ◽  
Control Strategies ◽  
Exact Algorithm ◽  
Boolean Networks ◽  
Qualitative Behavior ◽  
Control Policies ◽  
Target Time ◽  
Gene Regulatory

We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean Networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of control signals that deterministically drives the BN from I to F at, or before time t, or else guarantees that no such policy exists. Despite recent hardness-results for finding control policies for BNs, we show that, in practice, our algorithm runs in seconds to minutes on over 13,400 BNs of varying sizes and topologies, including a BN model of embryogenesis in Drosophila melanogaster with 15,360 Boolean variables. We then extend our method to automatically identify a set of Boolean transfer functions that reproduce the qualitative behavior of gene regulatory networks. Specifically, we automatically learn a BN model of D. melanogaster embryogenesis in 5.3 seconds, from a space containing 6.9 × 1010 possible models.

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 An ASP-based Approach for Boolean Networks Representation and Attractor Detection

10.29007/fb4f ◽  
2020 ◽  
Author(s):  
Tarek Khaled ◽  
Belaid Benhamou
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Answer Set Programming ◽  
Boolean Networks ◽  
Stable States ◽  
New Approach ◽  
Significant Step ◽  
The Subject ◽  
Gene Regulatory ◽  
Critical Aspects

In biology, Boolean networks are conventionally used to represent and simulate gene regulatory networks. The attractors are the subject of special attention in analyzing the dynamics of a Boolean network. They correspond to stable states and stable cycles, which play a crucial role in biological systems. In this work, we study a new representation of the dynamics of Boolean networks that are based on a new semantics used in answer set programming (ASP). Our work is based on the enu- meration of all the attractors of asynchronous Boolean networks having interaction graphs which are circuits. We show that the used semantics allows to design a new approach for computing exhaustively both the stable cycles and the stable states of such networks. The enumeration of all the attractors and the distinction between both types of attractors is a significant step to better understand some critical aspects of biology. We applied and evaluated the proposed approach on randomly generated Boolean networks and the obtained results highlight the benefits of this approach, and match with some conjectured results in biology.

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.

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