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+The following is a guide to the code and data used for data analysis, model simulation and figure generation.
+
+Raw and processed data analyzed via jupyter notebooks
+    The code for analysis of raw timeseries bioreactor data( present in the form of CSVs for ODs, dilutions, and
+    single cell fluorescence) was developed in Python. The data can be loaded as a reactordata object. 
+    All the functions used for analysis utilise this class.
+    The directory contains two different kinds of code.
+    The InBioGitRepo provides the libraries and class definitions that are necessary to load the bioreactor data.
+    The data also contains csv files for colony counts and growth rates under processed data that is also analyzed
+    through the jupyter notebook.
+    The jupyter notebook consists of script and functions that were used to analyse the data once it was loaded.
+    The code was developed in Windows 10 and verified by running in Window 7.
+    Depenendencies for the code to run without hiccups,
+    Python 3.5+
+    Pandas 1.2.4
+    Numpy 1.20
+    Matplotlib 3.4.2
+    Seaborn 0.11.1
+    SciPy 1.6.3
+    SciKit 0.24.2
+    tkinter 8.6
+Matlab data and simulation code
+    Raw timeseries data was processed to obtain relevant information in individualized matlab files that were
+    used to paramterize the stochastic differentiation model and simulate the dynamics given the light sequence
+    (included as csv files).
+    The matlab code is separated for integrated and plasmid strain.