Computional Biology

In spite of Runge-Kutta method is the most used by scientists and engineers, it is not the most powerful method. In this paper, a comparative study between Piece-wise Analytic Method (PAM) and Runge-Kutta Methods is introduced. The result of comparative study shows that PAM is more powerful and gives results better than Runge-Kutta methods. PAM can be considered as a new step in the evolution of solving nonlinear differential equations.

In this paper, a new method is introduced for engineers and scientists which can be used for solving highly nonlinear differential equations. The method is called Piecewise Analytic Method (PAM). PAM is used to solve problems which other methods can't solve. the paper also shows how the accuracy and error can be controlled according to our needs.

We analyzed the water-ferredoxin interaction in mesophilic (moderate temperature) algae (PDB ID: 1AWD) and halophilic (salt-tolerant) archaea (PDB ID: 1DOI) using POWAIND version 2.0 (a protein-water interactions calculation program). It is found that the shell water (SW) is 2.5 fold greater in halophilic ferredoxin than mesophilic ferredoxin. Water-ferredoxin interactions in the core and cavity are the signature of stability. The normalized frequency of such interactions is less in halophilic relative to mesophilic ferredoxin and the halophilic signature for stability by such interactions is negligible. However, the surface dominated with such interactions seems to be important for ferredoxin and oxido-reductase recognition.

Knowledge and understanding of phenotype expression, the taxa level, the traits configuration among sub-populations play a role in monitoring and evaluation of biota performance, selection and hybridization programs. This study aimed to investigate phenotype expression, to determine the taxa level and to map the trait configuration between 32 sub-populations of the Moluccas ring cowrie (MRC), Cypraea annulus based on three data types of morphometric trait. Cypraea annulus samples were used as many as 2926 specimens from 32 subpopulations and 104 test traits. Observation and measurement of morphometric traits done in macrometry and micrometry for three data types namely binary, ordinal and numeric. The results of this study indicate that 32 sub-populations of MRC had various phenotype expressions of morphometric (PEM) for binary and ordinal data types with the highest coefficient values in the range of 0.85 to 0.89 for binary data types and 0.80 to 0.84 for ordinal data types, whereas for numerical data types Its expression was homogeneous with the highest value range 0.95 to 1.00. The morphometric taxa level (MTL) for the binary data type obtained 17 taxa at 70% similarity level; ordinal data type obtained 19 taxa at correlation level 0.700 and numeric data type obtained one taxon at correlation level 0.970. The map of morphometric trait configuration (MTC) of the MRC trait derived four trait classes for binary data types and three trait classes for ordinal and numeric data types. The different variations of PEM and MTL proved that the expression of morphometric proportions of the MRC was strongly influenced by the environment, lifetime and life stages of snails. In addition, the proximity of the similarity of expression between the 32 sub-populations, although living in a geographical location, lifetime and stage of development of different living.