Critical transitions refer to sudden or abrupt shifts in the state of complex dynamical systems that happens when certain conditions of the system pass through a critical or bifurcation point. In this section of the report, we investigate if critical transitions also occur in the human innate immune system. By using the baseline model of the innate immunity for patients undergoing cardiac surgery, we perturb the system by adding stochasticity to the concentration of inflammation triggering moieties by introducing various types of noise. We then use Early Warning Signals to detect critical transitions in the stochastic human innate immune system.
- Clone this repository to your local
git clone [email protected]:UvaCsl/immune-ct.git
- Create an environment named
immune
using therequirements.txt
file
conda create --name <env> --file requirements.txt
The repository contains 6 folders:
datasets
: contains patient dataimages
: contains generated gif imagesnotebooks
: does the heavy lifting in computational modelingresult
: contains results of the simulationsscratch
: contains test codesutils
: contains codes of helper functions
The science happens in the notebooks. Below is a short list and description of each one of them.
Part 0 - 3D Plots.ipynb
: plots the time series data of patients in 3DPart 0 - Dimensionality Reduction.ipynb
: deals with dimensionality reduction using different methodsPart 1 - Bifurcation (Deterministic Model of HIIS).ipynb
: looks into the bifurcation analyses of the time seriesPart 1 - PCA.ipynb
: focuses on PCAPart II - Stochastic Immunity (Manual Run).ipynb
: adding different types of noise, where each experiment is run manuallyPart II - Stochastic Immunity (On-Off).ipynb
: explores switching off/probability of nonproduction of certain concentrationsPart III - EWS.ipynb
: code for early warning signalsPart IV - EWS on Data.ipynb
: explores early warning signals on data