This repository contains example Jupyter Notebooks and Exercises in support of the laboratory component of the biophysical chemistry course (Chem 442) taught at Earlham College during the spring semester of 2016. The Notebooks will use the IPerl kernel of Jupyter Notebooks and the HackaMol library, which is developed by Demian (the subordinate co-instructor).
The Earlham Computer Science department has configured a JupyterHub run on a local research cluster (Thank you CS and Michael Lerner), for which each student has a username and password. The IPerl kernel and HackaMol libraries have been installed on the cluster for use via a JupyterHub login. Thus, the example notebooks can be cloned through a JupyterHub terminal and then loaded directly... or by simple copy and paste of notebook code cells.
The exercises stem from a realization that not everything can be accomplished via the Jupyter Notebooks at present. Files need to be created/managed to be able to interact with the molecular information outside the notebooks, on local computers. Thus, the exercises are intended to build up the local computational toolbox in a series of short exercises that can be completed in around 15 minutes (with a little practice).
Biophysical chemistry 452 develops and applies the principles of chemical thermodynamics and kinetics to understand condensed phase biochemical processes. Classical thermodynamics provides mathematically accessible (with some work!) explanations of our everyday experiences (e.g. temperature, heat flow, buoyancy, etc.). This macroscopic field of science predates atoms and molecules. Thus, the tenets of thermodynamics are conceptually abstract to chemists and biochemists. On the other hand, statistical mechanics provides the microscopic molecular connections to the same macroscopic thermodynamic observations but requires more technical mathematics. The goal of this course is to build analytical tools and intuition that will enhance our understanding of biomolecular transformations between thermodynamically relevant states under varied conditions. To accomplish this goal, we will reside in the sweet spot between microscopic and macroscopic theories; that is, we will use molecular information to explore microscopic interpretations of macroscopic observations. This course is four credits: three credit hours are allotted to the lecture/discussion portion, and the fourth credit is assigned to the laboratory component. Credits will be assessed by your presence, preparedness, participation, and quality of written work.
Chem 452 has a purely theoretical and computational lab component. The main goal of this lab is to expand a student's capacity to do useful work (on computers) and to help each student visualize the molecular implications of class content. Each student (or pair of students) will pursue a single computational project for the entire semester. The product will be a Jupyter Notebook that mixes embedded written and computational analysis. Projects are to be carried out using GitHub to maintain a repository of work; realistically, this may be a part of the final organization of the project. Each week, we will work through exercises and examples to build the tools needed to complete the semester long projects. This GitHub repository contains these exercises and example notebooks, see Repository Description above.
By the end of this lab, if the student is able to carry out the same computational work in a JupyterHub Notebook as on a local computer without notebooks, he or she is on the path to mastery.