735hardly imagine today's electronics industry, with its powerful, visually oriented design and automation tools, without having first established standard notations for circuit diagrams. Such was not the case in biology 2 . Despite the visual nature of much of the information exchange, the field was permeated with ad hoc graphical notations having little in common between different researchers, publications, textbooks and software tools. No standard visual language existed for describing biochemical interaction networks, inter-and intracellular signaling gene regulation-concepts at the core of much of today's research in molecular, systems and synthetic biology. The closest to a standard is the notation long used in many metabolic and signaling pathway maps, but in reality, even that lacks uniformity between sources and suffers from undesirable ambiguities (Fig. 1). Moreover, the existing tentative representations, however well crafted, were ambiguous, and only suitable for specific needs, such as representing metabolic networks or signaling pathways or gene regulation.The molecular biology era, and more recently the rise of genomics and other high-throughput technologies, have brought a staggering increase in data to be interpreted. It also favored the routine use of software to help formulate hypotheses, design experiments and interpret results. As a group of biochemists, modelers and computer scientists working in systems biology, we believe establishing standard graphical notations is an important step toward more efficient and accurate transmission of biological knowledge among our different communities. Toward this goal, we initiated the SBGN project in 2005, with the aim of developing and standardizing a systematic and unambiguous graphical notation for applications in molecular and systems biology. Historical antecedentsGraphical representation of biochemical and cellular processes has been used in biochemical textbooks as far back as sixty years ago 3 , reaching an apex in the wall charts hand drawn by Nicholson 4 and Michal 5 . Those graphs describe the processes that transform a set of inputs into a set of outputs, in effect being process, or state transition, diagrams. This style was emulated in the first database systems that depicted metabolic networks, including EMP 6 , EcoCyc 7 and KEGG 8 . More notations have been 'defined' by virtue of their implementation in specialized software tools such as pathway and network designers (e.g., NetBuilder 9 , Patika 10 , JDesigner 11 , CellDesigner 12 ). Those "Un bon croquis vaut mieux qu'un long discours" ("A good sketch is better than a long speech"), said Napoleon Bonaparte. This claim is nowhere as true as for technical illustrations. Diagrams naturally engage innate cognitive faculties 1 that humans have possessed since before the time of our cave-drawing ancestors. Little wonder that we find ourselves turning to them in every field of endeavor. Just as with written human languages, communication involving diagrams requires that authors and readers agr...

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.

Transcription factor binding to DNA in vivo causes the recruitment of chromatin modifiers that can cause changes in chromatin structure, including the modification of histone tails. We previously showed that estrogen receptor (ER) target gene activation is facilitated by peptidylarginine deiminase 2 (PAD2)-catalyzed histone H3R26 deimination (H3R26Cit). Here we report that the genomic distributions of ER and H3R26Cit in breast cancer cells are strikingly coincident, linearly correlated, and observed as early as 2 minutes following estradiol treatment. The H3R26Cit profile is unlike that of previously described histone modifications and is characterized by sharp, narrow peaks. Paired-end MNase ChIP-seq indicates that the charge-neutral H3R26Cit modification facilitates ER binding to DNA by altering the fine structure of the nucleosome. Clinically, we find that PAD2 and H3R26Cit levels correlate with ER expression in breast tumors and that high PAD2 expression is associated with increased survival in ER+ breast cancer patients. These findings provide insight into how transcription factors gain access to nucleosomal DNA and implicate PAD2 as a novel therapeutic target for ER+ breast cancer.

A widely regarded model for glucocorticoid receptor (GR) action postulates that dimeric binding to DNA regulates unfavorable metabolic pathways while monomeric receptor binding promotes repressive gene responses related to its anti-inflammatory effects. This model has been built upon the characterization of the GRdim mutant, reported to be incapable of DNA binding and dimerization. Although quantitative live-cell imaging data shows GRdim as mostly dimeric, genomic studies based on recovery of enriched half-site response elements suggest monomeric engagement on DNA. Here, we perform genome-wide studies on GRdim and a constitutively monomeric mutant. Our results show that impairing dimerization affects binding even to open chromatin. We also find that GRdim does not exclusively bind half-response elements. Our results do not support a physiological role for monomeric GR and are consistent with a common mode of receptor binding via higher order structures that drives both the activating and repressive actions of glucocorticoids.