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I'm a junior math major starting to take his first courses in Algebra, Analysis, Topology, Nonlinear and Partial Differential Equations, etc.

I am just considering my career options at this point and somebody told be biotech/biology could have some interesting work. So I have come here to hear some advice if that is okay! Is there anything highly mathematical about the search for anti-aging or achieving the whole "biologically immortal" thing? I just don't know about any of this stuff so please inform me :)

PS: I also know Prob/Stats at a decently advanced level. So there's that too. Thanks!!

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closed as primarily opinion-based by David, Bryan Krause, canadianer, Chris Sep 4 '17 at 8:45

Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.

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Off course you can! Here are career paths for you as per my limited knowledge:

  1. BioStats

There are lot of applications in this domain. For example in Microarray Gene Expression or Proteomics analysis (for example, this paper).

  1. Bioinformatics

Sequence alignments use common techniques like Dynamic Programming, Probabilistic or Heuristic methods, etc.

  1. Computational Biology

There are lot of applications like Lagrangian and Eulerian Mechanics (Computational Anatomy), Computational Phylogenetics (Computational Evolutionary Biology), etc.

  1. Systems Biology - In Systems Biology, you can have applications in:

1-) Probability and Hypothesis Testing - usually used to find associations between genes (Ref: S. Krawetz (ed.), Bioinformatics for Systems Biology, DOI 10.1007/978-1-59745-440-7_8, Chapter 8)

2-) Stochastic Modelling of Biological Patterns - ODEs,PDEs (applied to lot of problems), Markov Chains, HMM, PSSM are common mathematical implementations in this domain.

3-) Population Genetics - uses Statistical models (for example Genetic Distance), exponential equations (for example, Cumulative Effect of Mutation)

P.S: Here are some tools you might be interested in:

1-) Bioconductor - one of the main reasons why R is preferred language by BioStatisticians.

2-) BioPython

Wish you best of luck with your career!

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