Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them

Observing a column-dependent zeta in dense interstellar sources: the case of the Horsehead Nebula

Context: Observations of small carbon-bearing molecules such as CCH, C4H, c-C3H2, and HCO in the Horsehead Nebula have shown these species to have higher abundances towards the edge of the source than towards the center. Aims: Given the determination of a wide range of values for zeta (s-1), the total ionization rate of hydrogen atoms, and the proposal of a column-dependent zeta(N_H), where N_H is the total column of hydrogen nuclei, we desire to determine if the effects of zeta(N_H) in a single object with spatial variation can be observable. We chose the Horsehead Nebula because of its geometry and high density. Method: We model the Horsehead Nebula as a near edge-on photon dominated region (PDR), using several choices for zeta, both constant and as a function of column. The column-dependent zeta functions are determined by a Monte Carlo model of cosmic ray penetration, using a steep power-law spectrum and accounting for ionization and magnetic field effects. We consider a case with low-metal elemental abundances as well as a sulfur-rich case. Results: We show that use of a column-dependent zeta(N_H) of 5(-15) s-1 at the surface and 7.5(-16) s-1 at Av = 10 on balance improves agreement between measured and theoretical molecular abundances, compared with constant values of zeta.Comment: 12 pages, 6 figures, 5 tables, accepted in A&

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them

FindSim: a Framework for Integrating Neuronal Data and Signaling Models

Current experiments touch only small but overlapping parts of very complex subcellular signaling networks in neurons. Even with modern optical reporters and pharmacological manipulations, a given experiment can only monitor and control a very small subset of the diverse, multiscale processes of neuronal signaling. We have developed FindSim (Framework for Integrating Neuronal Data and SIgnaling Models) to anchor models to structured experimental datasets. FindSim is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so as to systematically refine and validate the model. We use a structured format for encoding the conditions of many standard physiological and pharmacological experiments, specifying which parts of the model are involved, and comparing experiment outcomes with model output. A database of such experiments is run against successive generations of composite cellular models to iteratively improve the model against each experiment, while retaining global model validity. We suggest that this toolchain provides a principled and scalable way to tackle model complexity and diversity of data sources

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them

Walk well:a randomised controlled trial of a walking intervention for adults with intellectual disabilities: study protocol

Background - Walking interventions have been shown to have a positive impact on physical activity (PA) levels, health and wellbeing for adult and older adult populations. There has been very little work carried out to explore the effectiveness of walking interventions for adults with intellectual disabilities. This paper will provide details of the Walk Well intervention, designed for adults with intellectual disabilities, and a randomised controlled trial (RCT) to test its effectiveness. Methods/design - This study will adopt a RCT design, with participants allocated to the walking intervention group or a waiting list control group. The intervention consists of three PA consultations (baseline, six weeks and 12 weeks) and an individualised 12 week walking programme. A range of measures will be completed by participants at baseline, post intervention (three months from baseline) and at follow up (three months post intervention and six months from baseline). All outcome measures will be collected by a researcher who will be blinded to the study groups. The primary outcome will be steps walked per day, measured using accelerometers. Secondary outcome measures will include time spent in PA per day (across various intensity levels), time spent in sedentary behaviour per day, quality of life, self-efficacy and anthropometric measures to monitor weight change. Discussion - Since there are currently no published RCTs of walking interventions for adults with intellectual disabilities, this RCT will examine if a walking intervention can successfully increase PA, health and wellbeing of adults with intellectual disabilities

A reduction in maximal incremental exercise test duration 48 h post down hill run is associated with muscle damage derived exercise induced pain

Purpose: To examine whether exercise induced muscle damage (EIMD) and muscle soreness reduce treadmill maximal incremental exercise (MIE) test duration, and true maximal physiological performance as a consequence of exercise induced pain (EIP) and perceived effort. Methods: Fifty (14 female), apparently healthy participants randomly allocated into a control group (CON, n = 10), or experimental group (EXP, n = 40) visited the laboratory a total of six times: visit 1 (familiarization), visit 2 (pre 1), visit 3 (pre 2), visit 4 (intervention), visit 5 (24 h post) and visit 6 (48 h post). Both groups performed identical testing during all visits, except during visit 4, where only EXP performed a 30 min downhill run and CON performed no exercise. During visits 2, 3, and 6 all participants performed MIE, and the following measurements were obtained: time to exhaustion (TTE), EIP, maximal oxygen consumption [Formula: see text], rate of perceived exertion (RPE), maximum heart rate (HRmax), maximum blood lactate (BLamax), and the contribution of pain to terminating the MIE (assessed using a questionnaire). Additionally during visits 1, 2, 3, 5, and 6 the following markers of EIMD were obtained: muscle soreness, maximum voluntary contraction (MVC), voluntary activation (VA), creatine kinase (CK). Results: There were no significant differences (p ≥ 0.32) between any trials for any of the measures obtained during MIE for CON. In EXP, TTE decreased by 34 s (3%), from pre 2 to 48 h post (p < 0.001). There was a significant association between group (EXP, CON) and termination of the MIE due to "pain" during 48 h post (χ2 = 14.7, p = 0.002). Conclusion: EIMD resulted in premature termination of a MIE test (decreased TTE), which was associated with EIP, MVC, and VA. The exact mechanisms responsible for this require further investigation

Computational Neuroscience Ontology: a new tool to provide semantic meaning to your models

The diversity of modeling approaches in computational neuroscience makes model sharing, retrieval, reuse and reproducibility difficult and even sometimes impossible. To address this problem, standardized languages have been developed by and for the community, such as NeuroML[1], PyNN [2] and NineML (http://software.incf.org/software/nineml). Although these languages enable software interoperability and therefore model reuse and reproducibility, they lack semantic information that would facilitate efficient model sharing and retrieval. In the context of the INCF Multi-Scale Modeling (MSM) program, we have developed an ontology to annotate spiking network models described with NineML and other structured model description languages. Ontologies are formal models of knowledge in a particular domain and composed of classes that represent concepts defining the field as well as the logical relations that link these concepts together [3]. These classes and relations have unique identifiers and definitions that allow unambiguous annotation of digital resources such as web pages or model source code. Implemented in a machine-readable format, these knowledge models can be used to design more efficient and intuitive information retrieval systems for experts in the field. We are proposing the first version of the Computational Neuroscience Ontology or CNO. This ontology is composed of 207 classes representing general concepts related to computational neuroscience organized in a hierarchy of concepts. CNO is currently available on Bioportal (http://bioportal.bioontology.org/ontologies/3003). The design of CNO follows some of the recommendations of the Open Biological and Biomedical Ontologies (OBO) community and is compatible with the ontologies developed and maintained within the Neuroscience Information Framework (NIF, [4]http://www.neuinfo.org). Integration with this large federation of neuroscience ontologies has two main advantages: (1) it allows the linking of models with biological information, creating a bridge between computational and experimental knowledge bases; (2) as ontology development is an iterative process that relies on inputs from the community, NIF has developed NeuroLex (http://neurolex.org), an effective collaborative platform, available for community inputs on the content in CNO. With the further development of CNO based on inputs from the community, we hope CNO will provide a useful framework to federate digital resources in the field of computational neuroscience