ID: 1101.4240

Information transmission in genetic regulatory networks: a review

January 21, 2011

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Information Fragmentation, Encryption and Information Flow in Complex Biological Networks

May 28, 2021

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Clifford Bohm, Douglas Kirkpatrick, ... , Adami Christoph
Information Theory
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Assessing where and how information is stored in biological networks (such as neuronal and genetic networks) is a central task both in neuroscience and in molecular genetics, but most available tools focus on the network's structure as opposed to its function. Here we introduce a new information-theoretic tool: "information fragmentation analysis" that, given full phenotypic data, allows us to localize information in complex networks, determine how fragmented (across multiple...

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Adaptive gene regulatory networks

February 17, 2009

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Franck Stauffer, Johannes Berg
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Regulatory interactions between genes show a large amount of cross-species variability, even when the underlying functions are conserved: There are many ways to achieve the same function. Here we investigate the ability of regulatory networks to reproduce given expression levels within a simple model of gene regulation. We find an exponentially large space of regulatory networks compatible with a given set of expression levels, giving rise to an extensive entropy of networks....

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Quantifying efficient information transduction of biochemical signaling cascades

May 11, 2016

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Tatsuaki Tsuruyama
Molecular Networks
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Cells can be considered as systems that utilize changes in thermodynamic entropy as information. Therefore, they serve as useful models for investigating the relationships between entropy production and information transmission, i.e., signal transduction. Based on the hypothesis that cells apply a chemical reaction cascade for the most efficient transduction of information, we adopted a coding design that minimizes the number of bits per concentration of molecules that are em...

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Thomas M. A. Fink, Ryan Hannam
Molecular Networks
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Networks of gene regulation govern morphogenesis, determine cell identity and regulate cell function. But we have little understanding, at the local level, of which logics are biologically preferred or even permitted. To solve this puzzle, we studied the consequences of a fundamental aspect of gene regulatory networks: genes and transcription factors talk to each other but not themselves. Remarkably, this bipartite structure severely restricts the number of logical dependenci...

The large-scale logico-chemical structure of a transcriptional regulation network

February 15, 2005

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M. Cosentino Lagomarsino, P. Jona, B. Bassetti
Molecular Networks
Disordered Systems and Neura...

Identity, response to external stimuli, and spatial architecture of a living system are central topics of molecular biology. Presently, they are largely seen as a result of the interplay between a gene repertoire and the regulatory machinery of the cell. At the transcriptional level, the cis-regulatory regions establish sets of interdependencies between transcription factors and genes, including other transcription factors. These ``transcription networks'' are too large to be...

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New Scaling Relation for Information Transfer in Biological Networks

August 18, 2015

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Hyunju Kim, Paul Davies, Sara Imari Walker
Molecular Networks

Living systems are often described utilizing informational analogies. An important open question is whether information is merely a useful conceptual metaphor, or intrinsic to the operation of biological systems. To address this question, we provide a rigorous case study of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast S. pombe and that of the budding yeast S. cere...

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A statistical method for revealing form-function relations in biological networks

November 30, 2010

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Andrew Mugler, Boris Grinshpun, ... , Wiggins Chris H.
Molecular Networks
Quantitative Methods

Over the past decade, a number of researchers in systems biology have sought to relate the function of biological systems to their network-level descriptions -- lists of the most important players and the pairwise interactions between them. Both for large networks (in which statistical analysis is often framed in terms of the abundance of repeated small subgraphs) and for small networks which can be analyzed in greater detail (or even synthesized in vivo and subjected to expe...

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Information theory and adaptation

November 24, 2010

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Ilya Nemenman
Quantitative Methods

In this Chapter, we ask questions (1) What is the right way to measure the quality of information processing in a biological system? and (2) What can real-life organisms do in order to improve their performance in information-processing tasks? We then review the body of work that investigates these questions experimentally, computationally, and theoretically in biological domains as diverse as cell biology, population biology, and computational neuroscience

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Dissipation in non-steady state regulatory circuits

November 8, 2019

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P. Szymańska-Rożek, D. Villamaina, ... , Walczak A. M.
Molecular Networks

In order to respond to environmental signals, cells often use small molecular circuits to transmit information about their surroundings. Recently, motivated by concrete examples in signaling and gene regulation, a body of work has focused on the properties of circuits that function out of equilibrium and dissipate energy. We briefly review the probabilistic measures of information and dissipation and use simple models to discuss and illustrate trade-offs between information a...

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On The Reconstruction of Interaction Networks with Applications to Transcriptional Regulation

October 28, 2004

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Adam A. Margolin, Ilya Nemenman, Chris Wiggins, ... , Califano Andrea
Molecular Networks
Genomics
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A novel information-theoretic method for reconstruction of interaction networks is introduced. We prove that the method is exact for some class of networks. Performance tests on large synthetic transcriptional regulatory networks produce very encouraging results.

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