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METHODS IN QUANTITATIVE BIOLOGY

BME 780
과목 설명

Focuses on understanding the key methods and principles of quantitative biology through a close reading of the primary literature. Topics covered will include deterministic and stochastic methods for modeling cellular systems, techniques in systems and synthetic biology, image processing tools and image analysis for biology, data-driven network models, genomic approaches, single-molecule approaches, and key computational biology tools. This course is intended for graduate students from a variety of backgrounds who are interested in pursuing quantitative biology during their graduate studies.

선수과목

Graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.85

-0.26% 과거 데이터 대비

수료율
100%

과거 데이터 대비 변화 없음

A 비율
70%

-9.58% 과거 데이터 대비

학급 규모
20

50.54% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

COMPUTATIONAL NETWORK BIOLOGY

BMI/COMPSCI 775

Introduces networks as a powerful representation in many real-world domains including biology and biomedicine. Encompasses theory and applications of networks, also referred to as graphs, to study complex systems such as living organisms. Surveys the current literature on computational, graph-theoretic approaches that use network algorithms for biological modeling, analysis, interpretation, and discovery. Enables hands-on experience in network biology by implementing computational projects.

QUANTITATIVE SYSTEMS BIOLOGY AND DISEASE

MM&I 760

An overview of methods used in quantitative systems biology, with a focus on biochemical systems relevant to the study of host-pathogen interactions, disease and microbial communities.

BIOINFORMATIC INQUIRY

ABT 780

Advances the development of competencies promoting efficient analysis of biological data. Emphasizes matching a research problem with the most effective tools for its completion, balancing use of existing software and de novo software development. Advanced aspects of Python and R, algorithmics, machine learning, simulations, and effective communication of results are emphasized.

STATISTICAL METHODS FOR MOLECULAR BIOLOGY

BMI/STAT 877

Statistical and computational methods in statistical genomics for human and experimental populations. Review methods for quality control, experimental design, clustering, network analysis, and other downstream analysis of next-generation sequencing studies along with methods for genome wide association studies.

MATHEMATICAL METHODS FOR SYSTEMS BIOLOGY

BIOCHEM/BMI/BMOLCHEM/MATH 609

Provides a rigorous foundation for mathematical modeling of biological systems. Mathematical techniques include dynamical systems and differential equations. Applications to biological pathways, including understanding of bistability within chemical reaction systems, are emphasized.