Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships

Nykter, Matti; Price, Nathan D.; Larjo, Antti; Aho, Tommi; Kauffman, Stuart; Yli‐Harja, Olli; Shmulevich, Ilya

doi:10.1103/physrevlett.100.058702

Cited by 84 publications

(72 citation statements)

References 28 publications

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“…There are other important methods that analyze expression level changes in the cellular process (5). Many are local (i.e., bottom up), such as the Bayesian methods based on elucidating the relationships between a few genes at a time (6)(7)(8)(9)(10)(11). The approaches based on information theory that rely on the concept of statistical entropy (8)(9)(10)12) differ from the present work in that we use thermodynamics to define the physical entropy, the free energy of the transcripts, and thereby the steady state.…”

mentioning

confidence: 94%

“…Many are local (i.e., bottom up), such as the Bayesian methods based on elucidating the relationships between a few genes at a time (6)(7)(8)(9)(10)(11). The approaches based on information theory that rely on the concept of statistical entropy (8)(9)(10)12) differ from the present work in that we use thermodynamics to define the physical entropy, the free energy of the transcripts, and thereby the steady state. Reverse-engineering algorithms based on chemical kinetic-like differential equations identify causal interactions through the rate constants of mutual influence (11,(13)(14)(15)(16)(17).…”

mentioning

confidence: 94%

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On a fundamental structure of gene networks in living cells

Kravchenko‐Balasha

Levitzki

Goldstein³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

109

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Computers are organized into hardware and software. Using a theoretical approach to identify patterns in gene expression in a variety of species, organs, and cell types, we found that biological systems similarly are comprised of a relatively unchanging hardware-like gene pattern. Orthogonal patterns of software-like transcripts vary greatly, even among tumors of the same type from different individuals. Two distinguishable classes could be identified within the hardware-like component: those transcripts that are highly expressed and stable and an adaptable subset with lower expression that respond to external stimuli. Importantly, we demonstrate that this structure is conserved across organisms. Deletions of transcripts from the highly stable core are predicted to result in cell mortality. The approach provides a conceptual thermodynamic-like framework for the analysis of gene-expression levels and networks and their variations in diseased cells.

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mentioning

confidence: 94%

mentioning

confidence: 94%

On a fundamental structure of gene networks in living cells

Kravchenko‐Balasha

Levitzki

Goldstein³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

109

View full text Add to dashboard Cite

show abstract

“…This method of computation is very useful because it can potentially applied to genomic sequences, networked structures, time series data, and in general to any objects that can be represented using comput-ers. The interplay between networked structures and dynamics using Boolean networks as well as the phylogenetic analysis of the metabolism of 107 organisms were conducted using NCD information [39]. Basically the information distance can be applied to any object.…”

Section: Ncd(x Y) = C(xy) Min{c(x)c(y)} Max{c(x)c(y)}mentioning

confidence: 99%

Emergent Principles in Gene Expression Dynamics

Nacher

Ochiai²

2011

TOBIOIJ

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Rapid advances in data processing of genome-wide gene expression have allowed us to get a first glimpse of some fundamental laws and principles involved in the intra-cellular organization as well as to investigate its complex regulatory architecture. However, the identification of commonalities in dynamical processes involved in networks has not followed the same development. In particular, the coupling between dynamics and structural features remains largely uncovered. Here, we review several works that have addressed the issue of uncovering the gene expression dynamics and principles using micro-array time series data at different environmental conditions and disease states as well as the emergence of criticality in gene expression systems by using information theory. Moreover, we also describe the efforts done to explore the question of characterizing gene networks by using transcriptional dynamics information. The combination of the emergent principles uncovered in the transcriptional organization with dynamic information, may lead to reconstruct, characterize and complete gene networks. We also discuss several methods based on simulations of a series of enzyme-catalyzed reaction routes and Markov processes as well as combination of complex network properties with stochastic theory.

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“…Examples include the extraction of genetic interaction networks from microarray data, the inference of the modularity of genomic networks, or for information processing in neural networks (9)(10)(11)(12)(13)(14)(15)(16)(17)(18). The present application differs in two ways.…”

mentioning

confidence: 95%

Maximal entropy inference of oncogenicity from phosphorylation signaling

Graeber

Heath

Skaggs

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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Point mutations in the phosphorylation domain of the Bcr-Abl fusion oncogene give rise to drug resistance in chronic myelogenous leukemia patients. These mutations alter kinase-mediated signaling function and phenotypic outcome. An information theoretic analysis of the correlation of phosphoproteomic profiling and transformation potency of the oncogene in different mutants is presented. The theory seeks to predict the leukemic transformation potency from the observed signaling by constructing a distribution of maximal entropy of site-specific phosphorylation events. The theory is developed with special reference to systems biology where high throughput measurements are typical. We seek sets of phosphorylation events most contributory to predicting the phenotype by determining the constraints on the signaling system. The relevance of a constraint is measured by how much it reduces the value of the entropy from its global maximum, where all events are equally likely. Application to experimental phospho-proteomics data for kinase inhibitor-resistant mutants shows that there is one dominant constraint and that other constraints are not relevant to a similar extent. This single constraint accounts for much of the correlation of phosphorylation events with the oncogenic potency and thereby usefully predicts the trends in the phenotypic output. An additional constraint possibly accounts for biological fine structure.high-throughput measurements | information theory | phospho proteomics | signal transduction networks | systems biology B iological systems use complex networks of molecular events, such as signaling and transcription, to regulate cellular outcome. Experimental biology is now able to measure up to thousands of these events from individual samples, inspiring efforts to identify both the events most contributory to cellular phenotypes and the nature of how multiple regulatory mechanisms are coordinated. Such understanding is crucial to the next generation of single agent and mixture molecularly targeted therapeutics. Kinase-mediated signaling is central to the execution of cellular programs and to communication with the environment and other cells. Aberrant signaling is implicated in many diseases and is a hallmark of cancer (1). Mass spectrometry-and antibody-based phospho proteomics allow the global profiling of the state of the signaling network (2-5). These approaches permit site-specific monitoring of phosphorylation and, thus, provide discrimination of the sometimes multiple and differential regulatory phosphorylation events on individual proteins. Here, we develop and apply an information theoretic maximal entropy analysis of the correlation of signaling events to cellular outcome to identify the dominant constraints that describe and predict the phenotypic output.There are equivalent ways of motivating the choice of a distribution of maximal entropy (6). From an information theoretic point of view it is the distribution that is consistent with the data at hand and is otherwise least informative. From ...

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Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships

Cited by 84 publications

References 28 publications

On a fundamental structure of gene networks in living cells

On a fundamental structure of gene networks in living cells

Emergent Principles in Gene Expression Dynamics

Maximal entropy inference of oncogenicity from phosphorylation signaling

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