General Information
- Times:
- Tuesdays and Fridays from 2:00-3:30
- Location:
- Kerchkoff 024 (basement classroom at the eastern end of the buiding)
- Instructors:
- Eric Mjolsness
- Barbara Wold
- Teaching Assistants:
- Christopher Hart (office hours thursday 2-4)
- Eun Jung Choi
- Luigi Warren
- Mailing List
- Add your self to the class mailing list by going to this
page.
Bi164 Mailing List
Brief Course Description
Investegate, learn about, create, extend, and integrate software tools
supporting current research in cellular biology. Leran about and
apply techniques from pattern recognition, scientific computing,
databases, data mining, circuit inference, and computer algebra in
projects such as:
- Genome-scale mRNA gene expression analysis
- inferring circuit and stochastic models for cell state data
- sequence clues to transcriptional regulation
- cell-cycle regulation modeling
- new tools for model organism databases
- modeling morphogenesis from gene expression imagery
- simulating specialized regulatory network models for
transcription, degradation, multiple phosphorylation, and protein
complex formation.
Requirements: programming skills, cellular biology, familiarity
with numerical or machine learning methods.
Syllabus
- Overview of bioinformatics topics: structure,
sequence, behavior, dynamics, computational methods including
algorithms and software. Biological application areas.
- Exploration via tools: databases, pattern
recognition, cell simulations. Python and Mathematica
programming. UML. Reaction database example.
- Modeling reaction networks and pathways. Gene regulation
networks, protein modification networks, controlled degradation,
protein complex dynamics. Canonical forms for dynamics.
- Pathway models:signal transduction (including
complexes), differentiation, cell cycle. Build database of circuit
models.
- Probabilistic data models and statistical inference:
Clustering, principle components analysis, ICA, SOM.
Classification, ANN, SVM, HMM methods. Expectation-maximization
and cross-validation.
- Sequence analysis: finding genes, motifs,
cis-regulatory elements, evolutionary patterns.
- Gene expression analysis: Clustering, classification.
Available data sets. Medical applications to cancer.
- Circuit inference algorithms: Nonlinear optimization.
Probabilistic models for genomic scale circuits.
- Visualization and GUIs: Information retrieval.
- Developmental models: Evolutionary/developmental
models.
