Gallery — Recent

Gene regulatory networks have an important role to study the behaviour of genes. By analysing these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its qualitative dynamical behaviour. René Thomas discrete modelling of gene regulatory network (GRN) is a well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN. We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs. As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra features i.e. CTL editor, parameters filtering and input/output files management. GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification, K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours and days of manual computation

1509평가원B30

Anjana Sasindran's CV

Lagrange's Theorem

This is the homework my students use for typing up their weekly homework in LaTeX.

A questionnaire example.

Il s'agit d'un gabarit simple destiner à des étudiants qui n'ont jamais utilisé LaTeX. J'ai inclus plusieurs commentaires afin de donner quelques explications quant aux commandes utilisées.

Muons compose the penetrating component of Cosmic Rays. At sea level, they constitute the largest part of Secondary Cosmic Rays, giving an average flux of ≈ 100 m−2s−1sr−1. The aim of our experiment is to estimate, from muon decay, the mean lifetime and the mass of invisible products. Our experimental setup includes four detectors: three of them are plastic scintillators and compose the trigger system, while the last one is a liquid scintillator which measures the particles energy. All these scintillators are read by photomultipliers. Trigger and pulse thresholds are computed by logical and temporal modules in a VME crate. The Data Acquisition System has been verified to work properly. It is composed of two fADCs modules, one I/O Register, one Motorola computer and a Farm. The liquid scintillator has been calibrated in energy using both passing muons and 60CO gamma source. Thanks to the charge-energy conversion factor we estimated electron energy spectrum. In particular we selected a sample of decay events by estimating muon mean lifetime τμ = 2.19 ± 0.34 μs; then we finally extrapolated an upper limit for invisible products mass mν < 5.99 ± 0.73 MeV/c2.

A template for problem sets, set up to enter blocks of equations, images and arrays.