The specification of SBML Level 1 is freely available from http://www.sbml.org/

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

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...

Nicotinic acetylcholine receptors (nAChRs) expressed by dopaminergic (DA) neurons have long been considered as potential therapeutic targets for the treatment of several neuropsychiatric diseases, including nicotine and cocaine addiction or Parkinson's disease. However, DA neurons express mRNAs coding for most, if not all, neuronal nAChR subunits, and the subunit composition of functional nAChRs has been difficult to establish. Immunoprecipitation experiments performed on mouse striatal extracts allowed us to identify three main types of heteromeric nAChRs (alpha4beta2*, alpha6beta2*, and alpha4alpha6beta2*) in DA terminal fields. The functional relevance of these subtypes was then examined by studying nicotine-induced DA release in striatal synaptosomes and recording ACh-elicited currents in DA neurons fromalpha4, alpha6, alpha4alpha6, and beta2 knock-out mice. Our results establish that alpha6beta2* nAChRs are functional and sensitive to alpha-conotoxin MII inhibition. These receptors are mainly located on DA terminals and consistently do not contribute to DA release induced by systemic nicotine administration, as evidenced by in vivo microdialysis. In contrast, (nonalpha6)alpha4beta2* nAChRs represent the majority of functional heteromeric nAChRs on DA neuronal soma. Thus, whereas a combination of alpha6beta2* and alpha4beta2* nAChRs may mediate the endogenous cholinergic modulation of DA release at the terminal level, somato-dendritic (nonalpha6)alpha4beta2* nAChRs most likely contribute to nicotine reinforcement.