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Depicting combinatorial complexity with the molecular interaction map notation.

ABSTRACT: To help us understand how bioregulatory networks operate, we need a standard notation for diagrams analogous to electronic circuit diagrams. Such diagrams must surmount the difficulties posed by complex patterns of protein modifications and multiprotein complexes. To meet that challenge, we have designed the molecular interaction map (MIM) notation (http://discover.nci.nih.gov/mim/). Here we show the advantages of the MIM notation for three important types of diagrams: (1) explicit diagrams that define specific pathway models for computer simulation; (2) heuristic maps that organize the available information about molecular interactions and encompass the possible processes or pathways; and (3) diagrams of combinatorially complex models. We focus on signaling from the epidermal growth factor receptor family (EGFR, ErbB), a network that reflects the major challenges of representing in a compact manner the combinatorial complexity of multimolecular complexes. By comparing MIMs with other diagrams of this network that have recently been published, we show the utility of the MIM notation. These comparisons may help cell and systems biologists adopt a graphical language that is unambiguous and generally understood.

SUBMITTER: Kohn KW

PROVIDER: S-EPMC1681518 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Depicting combinatorial complexity with the molecular interaction map notation.

Kohn Kurt W KW Aladjem Mirit I MI Kim Sohyoung S Weinstein John N JN Pommier Yves Y

Molecular systems biology 20061003

To help us understand how bioregulatory networks operate, we need a standard notation for diagrams analogous to electronic circuit diagrams. Such diagrams must surmount the difficulties posed by complex patterns of protein modifications and multiprotein complexes. To meet that challenge, we have designed the molecular interaction map (MIM) notation (http://discover.nci.nih.gov/mim/). Here we show the advantages of the MIM notation for three important types of diagrams: (1) explicit diagrams that ...[more]

PMID: 17016517

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Project description:BackgroundComputational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine--the scientific approach to medicine in tight relation with basic science--, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).MethodologyDue to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA.SignificanceWe find that some nodes in the network that prove to be topologically important, in particular AKT2, IL6, MAPK1 and TP53, are also known to be associated with drugs used for the treatment of RA. Importantly, based on topological consideration, we are also able to suggest CRKL as a novel potentially relevant molecule for the diagnosis or treatment of RA. This type of finding proves the potential of in silico analyses able to produce highly refined hypotheses, based on vast experimental data, to be tested further and more efficiently. As research on RA is ongoing, the present map is in fieri, despite being--at the moment--a reflection of the state of the art. For this reason we make the network freely available in the standardised and easily exportable .xml CellDesigner format at 'www.picb.ac.cn/ClinicalGenomicNTW/temp.html' and 'www.celldesigner.org'.

Project description:A subfamily of four Phytochrome (phy)-Interacting bHLH transcription Factors (PIFs) collectively promote skotomorphogenic development in dark-grown seedlings. This activity is reversed upon exposure to light, by photoactivated phy molecules that induce degradation of the PIFs, thereby triggering the transcriptional changes that drive a transition to photomorphogenesis. The PIFs function both redundantly and partially differentially at the morphogenic level in this process. To identify the direct targets of PIF transcriptional regulation genome-wide, we analyzed the DNA-binding sites for all four PIFs by ChIP-seq analysis, and defined the genes transcriptionally regulated by each PIF, using RNA-seq analysis of pif mutants. Despite the absence of detectable differences in DNA-binding-motif recognition between the PIFs, the data show a spectrum of regulatory patterns, ranging from single PIF dominance to equal contributions by all four. Similarly, a broad array of promoter architectures was found, ranging from single PIF-binding sites, containing single sequence motifs, through multiple PIF-binding sites, each containing one or more motifs, with each site occupied preferentially by one to multiple PIFs. Quantitative analysis of the promoter occupancy and expression level induced by each PIF revealed an intriguing pattern. Although there is no robust correlation broadly across the target-gene population, examination of individual genes that are shared targets of multiple PIFs shows a gradation in correlation from strongly positive, through uncorrelated, to negative. This finding suggests a dual-layered mechanism of transcriptional regulation, comprising both a continuum of binding-site occupancy by each PIF and a superimposed layer of local regulation that acts differentially on each PIF, to modulate its intrinsic transcriptional activation capacity at each site, in a quantitative pattern that varies between the individual PIFs from gene to gene. These findings provide a framework for probing the mechanisms by which transcription factors with overlapping direct-target genes integrate and selectively transduce signals to their target networks.

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Depicting combinatorial complexity with the molecular interaction map notation.

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Depicting combinatorial complexity with the molecular interaction map notation.

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