Teaching

Spring 2024

Math Modeling in Biology (MATH/BCB 423/523):
Introduction to mathematical techniques for modeling and simulation, parameter identification, and analysis of biological systems. Applications drawn from many branches of biology and medicine. Apply differential equations, difference equations, and dynamical systems theory to a wide array of biological problems.

Calculus 3 (MATH 265):
Geometry of space and vectors, multivariable differential calculus, multivariable integral calculus, vector calculus.
Systems Biology (BCB 570) - 1/3 of the course on network inference & analysis:
Algorithmic and statistical approaches in computational functional genomics and systems biology. Analysis of high throughput biological data obtained using system-wide measurements. Topological analysis, module discovery, and comparative analysis of gene and protein networks. Modeling, analysis, and inference of transcriptional regulatory networks, protein-protein interaction networks, and metabolic networks. Dynamic systems and whole-cell models. Ontology-driven, network based, and probabilistic approaches to information integration.

Calculus 3 (MATH 265) - two courses:
Geometry of space and vectors, multivariable differential calculus, multivariable integral calculus, vector calculus.

Math Modeling in Biology (MATH/BCB 423X/523X):
Introduction to mathematical techniques for modeling and simulation, parameter identification, and analysis of biological systems. Applications drawn from many branches of biology and medicine. Apply differential equations, difference equations, and dynamical systems theory to a wide array of biological problems.
Systems Biology (BCB 570) - 1/3 of the course on network inference & analysis:
Algorithmic and statistical approaches in computational functional genomics and systems biology. Analysis of high throughput biological data obtained using system-wide measurements. Topological analysis, module discovery, and comparative analysis of gene and protein networks. Modeling, analysis, and inference of transcriptional regulatory networks, protein-protein interaction networks, and metabolic networks. Dynamic systems and whole-cell models. Ontology-driven, network based, and probabilistic approaches to information integration.

Matrices and Linear Algebra (MATH 207):
Systems of linear equations, determinants, vector spaces, linear transformations, orthogonality, least-squares methods, eigenvalues and eigenvectors. Emphasis on applications and techniques.
Calculus 3 (MATH 265) - small math and stats section:
Geometry of space and vectors, multivariable differential calculus, multivariable integral calculus, vector calculus.

Math Modeling in Biology (MATH/BCB 423X/523X):
Introduction to mathematical techniques for modeling and simulation, parameter identification, and analysis of biological systems. Applications drawn from many branches of biology and medicine. Apply differential equations, difference equations, and dynamical systems theory to a wide array of biological problems.

Calculus 2 (MATH 166) - two courses:
Integral calculus, applications of the integral, parametric curves and polar coordinates, power series and Taylor series.

Math Modeling in Biology (MATH 595 - topics course):
Introduction to mathematical techniques for modeling and simulation, parameter identification, and analysis of biological systems. Applications drawn from many branches of biology and medicine. Apply differential equations, difference equations, and dynamical systems theory to a wide array of biological problems.

Calculus 2 (MATH 166):
Integral calculus, applications of the integral, parametric curves and polar coordinates, power series and Taylor series.

Calculus 2 (MATH 166) - small math and stats section:
Integral calculus, applications of the integral, parametric curves and polar coordinates, power series and Taylor series.