Genomics, complexity and drug discovery: insights from Boolean network models of cellular regulation

2001 ◽  
Vol 2 (3) ◽  
pp. 203-222 ◽  
Author(s):  
Sui Huang
Keyword(s):  
Drug Discovery ◽  
Boolean Network ◽  
Network Models ◽  
Cellular Regulation

Application of Deep Neural Network Models in Drug Discovery Programs

ChemMedChem ◽  
2021 ◽  
Author(s):  
Christoph Grebner ◽  
Hans Matter ◽  
Daniel Kofink ◽  
Jan Wenzel ◽  
Friedemann Schmidt ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Deep Neural Network ◽  
Network Models ◽  
Neural Network Models

scholarly journals Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Ricardo Ramirez ◽  
Allen Michael Herrera ◽  
Joshua Ramirez ◽  
Chunjiang Qian ◽  
David W. Melton ◽  
...  
Keyword(s):  
Boolean Network ◽  
Macrophage Activation ◽  
Expression Profiles ◽  
Network Models ◽  
Cytokine Expression ◽  
Boolean Networks ◽  
Experimental Results ◽  
Activated Macrophages ◽  
Activation Patterns

Abstract Background Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. Results We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. Conclusion Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

scholarly journals OPTIMIZING INDIVIDUALS’ DAILY FUNCTION: A BOOLEAN NETWORK APPROACH FOR IDENTIFYING INTERVENTION SEQUENCES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S376-S376
Author(s):  
Xiao Yang ◽  
Xiao Yang ◽  
Nilam Ram ◽  
David Conroy ◽  
Aaron Pincus ◽  
...  
Keyword(s):  
Boolean Network ◽  
Healthy Aging ◽  
Network Models ◽  
Developmental Systems ◽  
Intensive Longitudinal Data ◽  
Produce Change ◽  
Network Methods ◽  
Development And Aging ◽  
And Behavior ◽  
Functional Components

Abstract Development and aging are the product of a process wherein an individuals’ functional components co-act to produce change. System dynamics can be described using a variety of methods. In this paper we illustrate how Boolean network methods may be used to describe the sequences of emotion and behavior states that lead to a stable equilibrium – e.g., healthy function; and the interventions needed to push an individual toward healthier equilibria. We applied Boolean network models to intensive longitudinal data obtained from 150 participants (age 18-89 years) to describe individuals’ on-going psychosocial dynamics and identify the specific social behaviors that may be driving them toward undesirable and/or desirable equilibria (e.g., high and low negative emotions). Results are discussed with respect to how they inform theory about developmental systems, and construction of interventions meant to guide individuals toward healthy aging.

scholarly journals Experimental design schemes for learning Boolean network models

2014 ◽  
Vol 30 (17) ◽  
pp. i445-i452 ◽  
Author(s):  
N. Atias ◽  
M. Gershenzon ◽  
K. Labazin ◽  
R. Sharan
Keyword(s):  
Experimental Design ◽  
Boolean Network ◽  
Network Models

COMBINATORIAL FRACTAL GEOMETRY WITH A BIOLOGICAL APPLICATION

Fractals ◽  
2006 ◽  
Vol 14 (02) ◽  
pp. 133-142 ◽  
Author(s):  
JOHN KONVALINA ◽  
IGOR KONFISAKHAR ◽  
JACK HEIDEL ◽  
JIM ROGERS
Keyword(s):  
Fractal Geometry ◽  
Boolean Network ◽  
Biological Systems ◽  
Sierpinski Gasket ◽  
Network Models ◽  
Combinatorial Problem ◽  
Higher Dimensions ◽  
Global Dynamics ◽  
Biological Application ◽  
Sierpiński Gasket

The solution to a deceptively simple combinatorial problem on bit strings results in the emergence of a fractal related to the Sierpinski Gasket. The result is generalized to higher dimensions and applied to the study of global dynamics in Boolean network models of complex biological systems.

Recent Advances on the Network Models in Target-based Drug Discovery

2018 ◽  
Vol 18 (13) ◽  
pp. 1031-1043 ◽  
Author(s):  
Wenying Yan ◽  
Daqing Zhang ◽  
Chen Shen ◽  
Zhongjie Liang ◽  
Guang Hu
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Biological Systems ◽  
Network Models ◽  
New Drugs ◽  
Proteomics Data ◽  
New Era ◽  
Protein Protein Interaction ◽  
Network Parameters ◽  
General Network

With the advancement of “proteomics” data and systems biology, new techniques are needed to meet the new era of drug discovery. Network theory is increasingly applied to describe complex biological systems, thus implying its essential roles in system-based drug design. In this review, we first summarized general network parameters used in describing biological systems, and then gave some recent applications of these network parameters as topological indices in drug design in terms of Protein Structure Networks (PSNs), Protein-Protein Interaction Networks (PPINs) including related structural PPINs, and Elastic Network Models (ENMs). These network models have enabled the development of new drugs relying on allosteric effects, describing anti-cancer targets, targeting hot spots and key proteins at the protein-protein interfaces and PPINs, and helped drug design by modulating conformational flexibility. Accordingly, we highlighted the integration of network models bringing new paradigms into the next-generation target-based drug discovery.

scholarly journals Random Boolean network models and the yeast transcriptional network

2003 ◽  
Vol 100 (25) ◽  
pp. 14796-14799 ◽  
Author(s):  
S. Kauffman ◽  
C. Peterson ◽  
B. Samuelsson ◽  
C. Troein
Keyword(s):  
Boolean Network ◽  
Network Models ◽  
Transcriptional Network

scholarly journals TDR Targets 6: driving drug discovery for human pathogens through intensive chemogenomic data integration

2019 ◽  
Author(s):  
Lionel Urán Landaburu ◽  
Ariel J Berenstein ◽  
Santiago Videla ◽  
Parag Maru ◽  
Dhanasekaran Shanmugam ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Data Integration ◽  
Bioactive Compounds ◽  
Drug Repurposing ◽  
Network Models ◽  
Model Organisms ◽  
Human Pathogens ◽  
Protein Targets ◽  
Chemical Inhibitors ◽  
Automated Screening

Abstract The volume of biological, chemical and functional data deposited in the public domain is growing rapidly, thanks to next generation sequencing and highly-automated screening technologies. These datasets represent invaluable resources for drug discovery, particularly for less studied neglected disease pathogens. To leverage these datasets, smart and intensive data integration is required to guide computational inferences across diverse organisms. The TDR Targets chemogenomics resource integrates genomic data from human pathogens and model organisms along with information on bioactive compounds and their annotated activities. This report highlights the latest updates on the available data and functionality in TDR Targets 6. Based on chemogenomic network models providing links between inhibitors and targets, the database now incorporates network-driven target prioritizations, and novel visualizations of network subgraphs displaying chemical- and target-similarity neighborhoods along with associated target-compound bioactivity links. Available data can be browsed and queried through a new user interface, that allow users to perform prioritizations of protein targets and chemical inhibitors. As such, TDR Targets now facilitates the investigation of drug repurposing against pathogen targets, which can potentially help in identifying candidate targets for bioactive compounds with previously unknown targets. TDR Targets is available at https://tdrtargets.org.

Sign in / Sign up

Export Citation Format

Share Document