876 research outputs found
Optimization of synchronization in gradient clustered networks
We consider complex clustered networks with a gradient structure, where sizes
of the clusters are distributed unevenly. Such networks describe more closely
actual networks in biophysical systems and in technological applications than
previous models. Theoretical analysis predicts that the network
synchronizability can be optimized by the strength of the gradient field but
only when the gradient field points from large to small clusters. A remarkable
finding is that, if the gradient field is sufficiently strong,
synchronizability of the network is mainly determined by the properties of the
subnetworks in the two largest clusters. These results are verified by
numerical eigenvalue analysis and by direct simulation of synchronization
dynamics on coupled-oscillator networks.Comment: PRE, 76, 056113 (2007
Robust dynamics in minimal hybrid models of genetic networks
Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast
Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments
Signaling cascades are triggered by environmental stimulation and propagate the signal to regulate transcription. Systematic reconstruction of the underlying regulatory mechanisms requires pathway-targeted, informative experimental data. However, practical experimental design approaches are still in their infancy. Here, we propose a framework that iterates design of experiments and identification of regulatory relationships downstream of a given pathway. The experimental design component, called MEED, aims to minimize the amount of laboratory effort required in this process. To avoid ambiguity in the identification of regulatory relationships, the choice of experiments maximizes diversity between expression profiles of genes regulated through different mechanisms. The framework takes advantage of expert knowledge about the pathways under study, formalized in a predictive logical model. By considering model-predicted dependencies between experiments, MEED is able to suggest a whole set of experiments that can be carried out simultaneously. Our framework was applied to investigate interconnected signaling pathways in yeast. In comparison with other approaches, MEED suggested the most informative experiments for unambiguous identification of transcriptional regulation in this system
Evaluation of large language models for discovery of gene set function
Gene set analysis is a mainstay of functional genomics, but it relies on
manually curated databases of gene functions that are incomplete and unaware of
biological context. Here we evaluate the ability of OpenAI's GPT-4, a Large
Language Model (LLM), to develop hypotheses about common gene functions from
its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene
sets with names that summarize their consensus functions, substantiated by
analysis text and citations. Benchmarking against named gene sets in the Gene
Ontology, GPT-4 generated very similar names in 50% of cases, while in most
remaining cases it recovered the name of a more general concept. In gene sets
discovered in 'omics data, GPT-4 names were more informative than gene set
enrichment, with supporting statements and citations that largely verified in
human review. The ability to rapidly synthesize common gene functions positions
LLMs as valuable functional genomics assistants
Identifying functional modules in protein–protein interaction networks: an integrated exact approach
Motivation: With the exponential growth of expression and protein–protein interaction (PPI) data, the frontier of research in systems biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature
enoLOGOS: a versatile web tool for energy normalized sequence logos
enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at
Cytoscape 2.8: new features for data integration and network visualization
Summary: Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features—Custom Node Graphics and Attribute Equations—which can be used jointly to greatly enhance Cytoscape's data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties
Analysis of Gene Sets Based on the Underlying Regulatory Network
Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78147/1/cmb.2008.0081.pd
Webb Miller and Trey Ideker To Receive Top International Bioinformatics Awards for 2009 from the International Society for Computational Biology
- …