With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result.

Metabolism is one of the attributes of life and supplies energy and building blocks to organisms. Therefore, understanding metabolism is crucial for the understanding of complex biological phenomena. Despite having been in the focus of research for centuries, our picture of metabolism is still incomplete. Metabolomics, the systematic analysis of all small molecules in a biological system, aims to close this gap. In order to facilitate such investigations a blueprint of the metabolic network is required. Recently, several metabolic network reconstructions for the model organism Caenorhabditis elegans have been published, each having unique features. We have established the WormJam Community to merge and reconcile these (and other unpublished models) into a single consensus metabolic reconstruction. In a series of workshops and annotation seminars this model was refined with manual correction of incorrect assignments, metabolite structure and identifier curation as well as addition of new pathways. The WormJam consensus metabolic reconstruction represents a rich data source not only for in silico network-based approaches like flux balance analysis, but also for metabolomics, as it includes a database of metabolites present in C. elegans, which can be used for annotation. Here we present the process of model merging, correction and curation and give a detailed overview of the model. In the future it is intended to expand the model toward different tissues and put special emphasizes on lipid metabolism and secondary metabolism including ascaroside metabolism in accordance to their central role in C. elegans physiology.

The mechanism of cell-penetrating peptides entry into cells is unclear, preventing the development of more efficient vectors for biotechnological or therapeutic purposes. Here, we developed a protocol relying on fluorometry to distinguish endocytosis from direct membrane translocation, using Penetratin, TAT and R9. The quantities of internalized CPPs measured by fluorometry in cell lysates converge with those obtained by our previously reported mass spectrometry quantification method. By contrast, flow cytometry quantification faces several limitations due to fluorescence quenching processes that depend on the cell line and occur at peptide/cell ratio >6.108 for CF-Penetratin. The analysis of cellular internalization of a doubly labeled fluorescent and biotinylated Penetratin analogue by the two independent techniques, fluorometry and mass spectrometry, gave consistent results at the quantitative and qualitative levels. Both techniques revealed the use of two alternative translocation and endocytosis pathways, whose relative efficacy depends on cell-surface sugars and peptide concentration. We confirmed that Penetratin translocates at low concentration and uses endocytosis at high μM concentrations. We further demonstrate that the hydrophobic/hydrophilic nature of the N-terminal extremity impacts on the internalization efficiency of CPPs. We expect these results and the associated protocols to help unraveling the translocation pathway to the cytosol of cells.

The need to build a tool to facilitate the quick creation and editing of models encoded in the Systems Biology Markup language (SBML) has been growing with the number of users and the increased complexity of the language. SBMLeditor tries to answer this need by providing a very simple, low level editor of SBML files. Users can create and remove all the necessary bits and pieces of SBML in a controlled way, that maintains the validity of the final SBML file.

The incidence of Neonatal Abstinence Syndrome (NAS) continues to rise and there remains a critical need to develop non-pharmacological interventions for managing opioid withdrawal in newborns. Objective physiologic markers of opioid withdrawal in the newborn remain elusive. Optimal treatment strategies for improving short-term clinical outcomes and promoting healthy neurobehavioral development have yet to be defined. This dual-site randomized controlled trial (NCT02801331) is designed to evaluate the therapeutic efficacy of stochastic vibrotactile stimulation (SVS) for reducing withdrawal symptoms, pharmacological treatment, and length of hospitalization, and for improving developmental outcomes in opioid-exposed neonates. Hospitalized newborns (n = 230) receiving standard clinical care for prenatal opioid exposure will be randomly assigned within 48-hours of birth to a crib with either: 1) Intervention (SVS) mattress: specially-constructed SVS crib mattress that delivers gentle vibrations (30-60 Hz, ~12 μm RMS surface displacement) at 3-hr intervals; or 2) Control mattress (treatment as usual; TAU): non-oscillating hospital-crib mattress. Infants will be studied throughout their hospitalization and post discharge to 14-months of age. The study will compare clinical measures (i.e., withdrawal scores, cumulative dose and duration of medications, velocity of weight gain) and characteristic progression of physiologic activity (i.e., limb movement, cardio-respiratory, temperature, blood-oxygenation) throughout hospitalization between opioid-exposed infants who receive SVS and those who receive TAU. Developmental outcomes (i.e., physical, social, emotional and cognitive) within the first year of life will be evaluated between the two study groups. Findings from this randomized controlled trial will determine whether SVS reduces in-hospital severity of NAS, improves physiologic function, and promotes healthy development.

Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data.

In this contribution, we describe a multi-omics systems biology study of the metabolic changes that occur during aging in Caenorhabditis elegans. Sampling several time points from young adulthood until early old age, our study covers the full duration of aging and include transcriptomics, and targeted MS-based metabolomics. In order to focus on the metabolic changes due to age we used two strains that are metabolically close to wild-type, yet are conditionally non-reproductive. Using these data in combination with a whole-genome model of the metabolism of C. elegans and mathematical modeling, we predicted metabolic fluxes during early aging. We find that standard Flux Balance Analysis does not accurately predict in vivo measured fluxes nor age-related changes associated with the Citric Acid cycle. We present a novel Flux Balance Analysis method where we combined biomass production and targeted metabolomics information to generate an objective function that is more suitable for aging studies. We validated this approach with a detailed case study of the age-associated changes in the Citric Acid cycle. Our approach provides a comprehensive time-resolved multi-omics and modeling resource for studying the metabolic changes during normal aging in C. elegans.

BioModels serves as a central repository of mathematical models representing biological processes. It offers a platform to make mathematical models easily shareable across the systems modelling community, thereby supporting model reuse. To facilitate hosting a broader range of model formats derived from diverse modelling approaches and tools, a new infrastructure for BioModels has been developed that is available at http://www.ebi.ac.uk/biomodels. This new system allows submitting and sharing of a wide range of models with improved support for formats other than SBML. It also offers a version-control backed environment in which authors and curators can work collaboratively to curate models. This article summarises the features available in the current system and discusses the potential benefit they offer to the users over the previous system. In summary, the new portal broadens the scope of models accepted in BioModels and supports collaborative model curation which is crucial for model reproducibility and sharing.

Studies in Shaker, a voltage-dependent potassium channel, suggest a coupling between activation and inactivation. This coupling is controversial in hERG, a fast-inactivating voltage-dependent potassium channel. To address this question, we transferred to hERG the S3-S4 linker of the voltage-independent channel, rolf, to selectively disrupt the activation process. This chimera shows an intact voltage-dependent inactivation process consistent with a weak coupling, if any, between both processes. Kinetic models suggest that the chimera presents only an open and an inactivated states, with identical transition rates as in hERG. The lower sensitivity of the chimera to BeKm-1, a hERG preferential closed-state inhibitor, also suggests that the chimera presents mainly open and inactivated conformations. This chimera allows determining the mechanism of action of hERG blockers, as exemplified by the test on ketoconazole.

PANDIT is a database of homologous sequence alignments accompanied by estimates of their corresponding phylogenetic trees. It provides a valuable resource to those studying phylogenetic methodology and the evolution of coding-DNA and protein sequences. Currently in version 17.0, PANDIT comprises 7738 families of homologous protein domains; for each family, DNA and corresponding amino acid sequence multiple alignments are available together with high quality phylogenetic tree estimates. Recent improvements include expanded methods for phylogenetic tree inference, assessment of alignment quality and a redesigned web interface, available at the URL http://www.ebi.ac.uk/goldman-srv/pandit.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

BioModels (http://www.ebi.ac.uk/biomodels/) is a repository of mathematical models of biological processes. A large set of models is curated to verify both correspondence to the biological process that the model seeks to represent, and reproducibility of the simulation results as described in the corresponding peer-reviewed publication. Many models submitted to the database are annotated, cross-referencing its components to external resources such as database records, and terms from controlled vocabularies and ontologies. BioModels comprises two main branches: one is composed of models derived from literature, while the second is generated through automated processes. BioModels currently hosts over 1200 models derived directly from the literature, as well as in excess of 140,000 models automatically generated from pathway resources. This represents an approximate 60-fold growth for literature-based model numbers alone, since BioModels' first release a decade ago. This article describes updates to the resource over this period, which include changes to the user interface, the annotation profiles of models in the curation pipeline, major infrastructure changes, ability to perform online simulations and the availability of model content in Linked Data form. We also outline planned improvements to cope with a diverse array of new challenges.

The biological pathway exchange language (BioPAX) and the systems biology markup language (SBML) belong to the most popular modeling and data exchange languages in systems biology. The focus of SBML is quantitative modeling and dynamic simulation of models, whereas the BioPAX specification concentrates mainly on visualization and qualitative analysis of pathway maps. BioPAX describes reactions and relations. In contrast, SBML core exclusively describes quantitative processes such as reactions. With the SBML qualitative models extension (qual), it has recently also become possible to describe relations in SBML. Before the development of SBML qual, relations could not be properly translated into SBML. Until now, there exists no BioPAX to SBML converter that is fully capable of translating both reactions and relations.

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.

Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification.

Computing accurate nucleic acid melting temperatures has become a crucial step for the efficiency and the optimisation of numerous molecular biology techniques such as in situ hybridization, PCR, antigene targeting, and microarrays. MELTING is a free open source software which computes the enthalpy, entropy and melting temperature of nucleic acids. MELTING 4.2 was able to handle several types of hybridization such as DNA/DNA, RNA/RNA, DNA/RNA and provided corrections to melting temperatures due to the presence of sodium. The program can use either an approximative approach or a more accurate Nearest-Neighbor approach.

Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.

Phosphatidylinositol-4,5-bisphosphate (PIP(2)) is a phospholipid that has been shown to modulate several ion channels, including some voltage-gated channels like Kv11.1 (hERG). From a biophysical perspective, the mechanisms underlying this regulation are not well characterized. From a physiological perspective, it is critical to establish whether the PIP(2) effect is within the physiological concentration range. Using the giant-patch configuration of the patch-clamp technique on COS-7 cells expressing hERG, we confirmed the activating effect of PIP(2). PIP(2) increased the hERG maximal current and concomitantly slowed deactivation. Regarding the molecular mechanism, these increased amplitude and slowed deactivation suggest that PIP(2) stabilizes the channel open state, as it does in KCNE1-KCNQ1. We used kinetic models of hERG to simulate the effects of the phosphoinositide. Simulations strengthened the hypothesis that PIP(2) is more likely stabilizing the channel open state than affecting the voltage sensors. From the physiological aspect, we established that the sensitivity of hERG to PIP(2) comes close to that of KCNE1-KCNQ1 channels, which lies in the range of physiological PIP(2) variations.

Triacylglycerols (TG) are synthesized at the endoplasmic reticulum (ER) bilayer and packaged into organelles called lipid droplets (LDs). LDs are covered by a single phospholipid monolayer contiguous with the ER bilayer. This connection is used by several monotopic integral membrane proteins, with hydrophobic membrane association domains (HDs), to diffuse between the organelles. However, how proteins partition between ER and LDs is not understood. Here, we employed synthetic model systems and found that HD-containing proteins strongly prefer monolayers and returning to the bilayer is unfavorable. This preference for monolayers is due to a higher affinity of HDs for TG over membrane phospholipids. Protein distribution is regulated by PC/PE ratio via alterations in monolayer packing and HD-TG interaction. Thus, HD-containing proteins appear to non-specifically accumulate to the LD surface. In cells, protein editing mechanisms at the ER membrane would be necessary to prevent unspecific relocation of HD-containing proteins to LDs.