The module is divided into three blocks namely, Data Science, Complex systems, and Advanced algorithms. You can find the project reports for each block, and presentation on **Project 1** (given on 18th November 2022) in the respective folders.
## Data Science
Contributors: Jule Brenningmeyer, Maike Herkenrath, Abhinav Mishra, Se Yeon Kim
## Data Science
Contributors: Jule Brenningmeyer, Maike Herkenrath, Abhinav Mishra, Se Yeon Kim
Language: Python, R
### Project 1
The data, and figures used in writing the report section for **Project 1** can be found in the respective subfolders. The scripts has ran successfully on a system, and the compiled R markdown pdf has been added for better understanding. Here's some description of the script if you'd like to read:
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**Implementation** - Write a small program to generate the stoichiometric matrix and propensity function vector. Regrading the former, only use stoichiometric coefficients -1, 0 or 1.
### Exercise 2
**Programming**
1. Write a program implementing SIR (usceptible-infected-recovere) model and generate trajectories using the stochastic simulation algorithm (SSA; also called Gillespie’s algorithm).
2. Write a program implementing Slögl model and generate trajectories using the stochastic simulation algorithm.
### Exercise 3
1.
- Write a program implementing predator-prey model.
- Extend your program above by solving the ODEs with the built-in ODE-solver `scipy.integrate.solve_ivp` using ’RK45’ for integration.
- Plot the simulation output of the explicit Euler method and superimpose the solution obtained from the adaptive step-size method (’RK45’).
2.
- Implement a pharmacokinetic model and perform simulations using the stochastic simulation algorithm (SSA).
- Simulate the corresponding ordinary differential equations (ODEs) with the built-in ODE-solver `scipy.integrate.solve_ivp` using ’RK45’ for integration.
### Exercise 4
1. Parameter estimation using `curve_fit`.
2. Perform stochastic simulations with the SSA algorithm to study how the infection probability depends on the number of viruses that an individual is exposed
with.
3. Perform 300 stochastic simulations. Stop your simulation when
a) either the viral infection has been eliminated, or
b) the number of viruses exceeds 50, i.e. X2(t) ≥ 50. Count the former as an elimination event and the latter as an infection event. Based on the outcome of the 300 simulations, estimate the infection probability.
c) Plot the infection probability (y-axis) as a function of the viral exposure (x-axis).
d) Can you come up with a simple formula that estimates the elimination probability after exposure with n viruses, based on the elimination probability after exposure with a single virus?
1. You are given a reference text, which has parts of the first chromosome of the human genome. You also got a list of markers ’illumina reads XYZ.fasta.gz’. You need to figure out how many of these markers appear in the reference.
2. Implement a naive search algorithm (don’t use an index).
3. Implement a suffix array based search.
4. Benchmark (runtime and memory) your solutions for 1’000, 10’000, 100’000 1’000’000 queries of length 100.
5. Benchmark (runtime) queries of the length 40, 60, 80, and 100 with a suitable numberof queries.
6. Implement an fmindex based search.
7. Benchmark (runtime and memory) your solutions for 1’000, 10’000, 100’000 1’000’000 queries of length 100.
8. Which algorithm is best suited?
9. Download the Humane Reference Genome https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/ (1. click on download assembly, 2. Source database RefSeq3. File type: Genomic FASTA (.fna), 3. Download)
10. Benchmark (runtime) of queries with k = 0, k = 1 and k = 2 errors of length 40, 60, 80, and 100 with a suitable number of queries.
