Python Project: Stochastic
Simulation of Patient Virus Population Dynamics And
Treatment Regimens
Links
to Python Project: Stochastic
Simulation of Patient Virus Population Dynamics And
Treatment Regimens
- Simulations for drug and disease industries,
where you have a number of diseased cells, antibiotics,
resistances to antibiotics, and a percentage you need to
achieve to remove the illness, and a calculation to
compare using and not using a drug for it
- A Stochastic Simulation of Patient Virus Population
Dynamics And Treatment Regimens: Exercises In
Python
- In this assignment, we used Python and Pylab to design
and implement a stochastic simulation of patient and
virus population dynamics, and reach conclusions about
treatment regimens based on the simulation
results.
- In biology class, you learn that traits of an organism
are determined by its genetic code.
- When organisms reproduce, their offspring will inherit
genetic information from their parent.
- Their genetic info. will be modified due to mixing or
mutations in the genome replication process, thus
introducing diversity into a population.
- Viruses are no exception. Two characteristics of
viruses make them particularly difficult to treat. The
first is that their replication mechanism often lacks
the error checking mechanisms that are present in more
complex organisms. This speeds up the rate of mutation.
- Secondly, viruses replicate extremely quickly (orders
of magnitude faster than humans): thus while we may be
used to thinking of evolution as a process which occurs
over long time scales, populations of viruses can
undergo substantial evolutionary changes within a single
patient over the course of treatment.
- These 2 characteristics allow a virus population to
acquire genetic resistance to therapy quickly.
- In this problem set, we make use of simulations to
explore the effect of drugs on the virus population and
determine how best to address the treatment challenges
in a simplified model.
