Study on moving direction and survival index of Persian sturgeon (Acipenser persicus) fingerlings using mark-recapture method in Caspian Sea (Guilan province coasts)

To study moving direction and survival index of Persian sturgeon fingerlings, a total of 390200 individuals of the fish in three weight classes: less than 3g, 3 to 5g and 6 to10g were marked by coded wire tags (CWT) during 2003 to 2008. In 2003, 101500 of these individuals were marked in Shahid Beheshti, Shahid Rajaee and Shahid Marjani Sturgeon Rearing Centers, in north of Iran and then released in Sephidrud, Tajan and Gorganrud rivers. During 2004 to 2008, 288700 pieces were marked by Shahid Beheshti Rearing Centers and released in Sephidrud River. Catch and detection of fingerlings carried out by gill net prepared from nylon with mesh sizes 22, 26, 33 (2 filaments for each mesh) and one 40mm mesh size. Totally, 175 meters of net was used to study fishes in waters under 10m depth in Guilan province. In all, 2827 pieces of this fish were caught of which 40 had CWT and these belonged to weight classes 6-10g (22 pieces), 3-5g (17 pieces) and under 3g (one piece). Results on release and catch of the fingerlings for Sephirud River showed that more than 70% of fingerlings moved to eastern parts of the estuary and eastern coasts of Guilan province (stations like 12 Bahman, Dastak and Chaboksar). Of the fingerlings released in Gorganrud and Tajan rivers, only one piece was caught in Chaboksar and another in Lisar after 15 months. Hence, we postulated that the fingerlings released in Mazandaran and Golestan provices migrated to Guilan province coasts. Statistical analysis of the survival index in different weight classes indicated that the class 6-10g had higher survival rate, twice than class 3-5g and 20 times more than that of the class under 3g. Considering these results, probably the higher weight at release time can be effective in increasing the survival rate of the Persian sturgeon fingerlings

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems