The purpose of this lab was to illustrate the validity of the law of conservation of energy along with the determination of the spring constant of a given spring. For the first part the spring constantk was to be found from a given spring. Through the suspension of various known metal masses on a vertically suspended spring, the spring constant was determined. Two methods were used: the algebraic rearrangement of Hooke's Law and a slope analysis of a linear regression on a Force (N) against Stretch Length (m) scatter plot. The spring constant k was determined to be 26.438 ± 1.063. For the second part of the lab, the aim was to validate the law of conservation of energy through the oscillation of a vertically suspended spring. Data was collected using a Vernier Motion Detector 2 machine and the various energies (kinetic energy, gravitational potential energy and spring potential energy) were collected and summed up. The sum of these energies yielded a fairly constant energy total (2.287 J ± 0.025 J) which supports the authenticity of the law of conservation of energy. While there were some uncertainties due to the lab setup, human error and equipment error it did not affect the validity of the methods during experimentation. Overall, the spring constant k of a given spring was determined and the law of conservation of energy was validated through the calculation of total energy during a suspended mass' oscillation.
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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