How can one model biological data using python?
Python is an open-source programming language which gives the option of designing and utilizing various customized packages which cater to specific needs of the code. Due to relaxed restrictions on syntax and yet the ability to perform complicated tasks along with flexible packages, it is one of the most used languages in the field of bioinformatics. Packages like scipy (scientific python) and numpy (numerical python) are used to solve numerical equations which represent interactions between species in ecological setting, hence are used to model species interaction. 'networkx' package used to construct and analyze network data for which input is a set of interactions, biological networks could be better explored and represented using this package. Besides these customized packages, there are others like 'pandas' (importing and exporting biological data from different formats), 'pyplot' (representing biological data in form of graphs) etc.
What are Periodic Boundary Conditions (PBS) in molecular simulations?
Molecular simulations give a rough estimate of the time evolution of molecules. One of the most basic step in simulations is defining space in which the molecule is placed, with space comes boundary (since it is computationally not feasible to conduct simulations in infinite space). Depending on pre-defined conditions, the space can either be a cube,hexagon etc. The problem, however, with fixed space is of fixed and rigid boundary. During the course of simulations, it is very likely that the molecule will interact with these rigid boundaries, this might result in certain energy changes which are of no interest with respect to the simulation. To overcome this issue of rigid boundary, Periodic Boundary Conditions (PBC) are designed. In PBC, infinite copies of the simulation box are replicated in all the sides of the original simulation box. If an atom decides to exit the simulation box from one side, then it would enter the original simulation box from the other side. Hence atoms are free to interact with mirror images of other atoms.This solves the problem of unwanted energy fluctuations in the system.
What is graph theory? And, what are it's applications in the field of Biology?
Graph theory describes interaction data in the form of a graph, which consists of: nodes : these represent interacting species (ex: metabolites in a metabolic network, individuals in a social network etc) edges : these represent the interaction in between interacting species (ex: inhibitory activity/ exhibitory activity of one metabolite on the other, friend/mutual friend in a social network etc) These 'edges' essentially connect the 'nodes'. Depending on the type of interaction, these 'edges' can either be uni-directional or bi-directional. Due to the increasing availability of interaction data in biology (regulatory factor-gene, gene-gene. protein- protein interaction etc), it has become important to draw conclusions on what the data means. With graph theory, it is not only possible to represent the interaction data in a way that it can be perceived, but one can also perform various forms of statistical analysis (like degree distribution, path length etc) to understand which nodes would likely be switched off if one of the nodes is deleted. Hence, this has substantial contribution in the field of medicine too.