00937nas a2200205 4500008004100000022001400041245014800055210006900203260001600272300001600288490000800304100002200312700002200334700002300356700001900379700002200398700002900420700002300449856025900472 2018 eng d a0378-190900aThe effect of environmental conditions on Atlantic salmon smolts’ (Salmo salar) bioenergetic requirements and migration through an inland sea0 aeffect of environmental conditions on Atlantic salmon smolts Sal cJan-10-2018 a1467 - 14820 v1011 aStrople, Leah, C.1 aFilgueira, Ramón1 aHatcher, Bruce, G.1 aDenny, Shelley1 aBordeleau, Xavier1 aWhoriskey, Frederick, G.1 aCrossin, Glenn, T. uhttp://link.springer.com/10.1007/s10641-018-0792-5http://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdfhttp://link.springer.com/article/10.1007/s10641-018-0792-5/fulltext.htmlhttp://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdf01967nas a2200169 4500008004100000022001300041245007200054210006900126260001600195300001200211490000700223520128000230100001901510700001901529700001601548856023301564 2017 eng d a1364815200aModular and spatially explicit: A novel approach to system dynamics0 aModular and spatially explicit A novel approach to system dynami cJan-08-2017 a48 - 620 v943 a
The Open Modeling Environment (OME) is an open-source System Dynamics (SD) simulation engine which has been created as a joint project between Oregon State University and the US Environmental Protection Agency. It is designed around a modular implementation, and provides a standardized interface for interacting with spatially explicit data while still supporting the standard SD model components. OME can be run as a standalone simulation or as a plugin to a larger simulation framework, and is capable of importing Models from several SD model formats, including Simile model files, Vensim model files, and the XMILE interchange format. While it has been released, OME is still under development, and a number of potential future improvements are discussed. To help illustrate the utility of OME, an example model design process is provided as an Appendix.
1 aWingo, Patrick1 aBrookes, Allen1 aBolte, John uhttps://linkinghub.elsevier.com/retrieve/pii/S1364815216308453https://api.elsevier.com/content/article/PII:S1364815216308453?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1364815216308453?httpAccept=text/plain02611nas a2200277 4500008003900000022001400039245008700053210006900140520178500209100001401994700001402008700001202022700001402034700001402048700001502062700001402077700001402091700001602105700001402121700001302135700001602148700002002164700001802184700001602202856011502218 2014 d a1091-649000aMultiscale digital Arabidopsis predicts individual organ and whole-organism growth0 aMultiscale digital Arabidopsis predicts individual organ and who3 aUnderstanding how dynamic molecular networks affect whole-organism physiology, analogous to mapping genotype to phenotype, remains a key challenge in biology. Quantitative models that represent processes at multiple scales and link understanding from several research domains can help to tackle this problem. Such integrated models are more common in crop science and ecophysiology than in the research communities that elucidate molecular networks. Several laboratories have modeled particular aspects of growth in Arabidopsis thaliana, but it was unclear whether these existing models could productively be combined. We test this approach by constructing a multiscale model of Arabidopsis rosette growth. Four existing models were integrated with minimal parameter modification (leaf water content and one flowering parameter used measured data). The resulting framework model links genetic regulation and biochemical dynamics to events at the organ and whole-plant levels, helping to understand the combined effects of endogenous and environmental regulators on Arabidopsis growth. The framework model was validated and tested with metabolic, physiological, and biomass data from two laboratories, for five photoperiods, three accessions, and a transgenic line, highlighting the plasticity of plant growth strategies. The model was extended to include stochastic development. Model simulations gave insight into the developmental control of leaf production and provided a quantitative explanation for the pleiotropic developmental phenotype caused by overexpression of miR156, which was an open question. Modular, multiscale models, assembling knowledge from systems biology to ecophysiology, will help to understand and to engineer plant behavior from the genome to the field.1 aChew, Y H1 aWenden, B1 aFlis, A1 aMengin, V1 aTaylor, J1 aDavey, C L1 aTindal, C1 aThomas, H1 aOugham, H J1 aReffye, P1 aStitt, M1 aWilliams, M1 aMuetzelfeldt, R1 aHalliday, K J1 aMillar, A J uhttp://www.pnas.org/content/early/2014/08/27/1410238111.full.pdf+html?sid=66edb45d-8e99-4d84-a072-a47729a65e1400503nam a2200157 4500008003900000020001900039245006200058210006100120260001000181300000800191653002800199653000800227100002100235700001900256856007000275 2003 d a978-047149618200aEnvironmental Modelling: Finding Simplicity in Complexity0 aEnvironmental Modelling Finding Simplicity in Complexity bWiley a43010aenvironmental modelling10aGIS1 aWainwright, John1 aMulligan, Mark uhttp://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471496189.html