Doctor's degree in Computational and Systems Biology at Claremont Graduate University

 

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Doctor's Degree in Computational and Systems Biology at Claremont Graduate University

Claremont Graduate University
Doctor's degree
Computational and Systems Biology

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Computational and Systems Biology are developing fields of endeavor that aim to understand biological systems through mathematical analysis, numerical simulation, data collection, refinement and improvement of models based on comparison with experimental data and the development of theories to enhance our understanding of biological systems. Fields of modern genetics (including genomics and proteomics) and molecular and cell biology are the current focus of research in systems biology.Myriad computational tools including deterministic and stochastic modeling, Monte Carlo simulations, data mining and data analysis are needed in approaching these problems. Applied mathematical topics including nonlinear dynamical systems (discrete and continuous), partial differential equations, probability and statistics, linear algebra, network theory, numerical analysis, control theory, asymptotic and perturbation methods provide some of the fundamental tools needed to carry out the requisite modeling and analysis.

The doctoral program in Computational and Systems Biology will train scientists, mathematicians, engineers, and/or quantitative biologists via a combination of coursework, independent study, optional laboratory rotations and individual mentoring so as to provide them with a strong grounding in modern biology and genomics as well as in mathematical methods needed for modeling and computational work. This training is applied to an original dissertation that may be characterized by the following:

  • An outstanding original biological problem that motivates the need for the development of mathematical models or computational approaches;
  • A significant component of original, advanced mathematical modeling or computational investigation;
  • The presence of conceptual and/or computational challenges in the implementation of the mathematical models and/or computational methods.


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