Bioe6403 Jun 2026

: Covering theoretical design and problem-solving. Final Examination : An in-person assessment consisting of: 20 multiple-choice questions. 4 short-answer or calculation-style questions. Important Academic Policies

The course covers the lifecycle of a biological signal from its origin to digital analysis:

For students enrolled in the current semester, the assessment typically includes: bioe6403

: Hands-on construction and validation of simple clinical measurement devices.

: Identifying sources of electrical noise in instrumentation and using hardware or software techniques to correct them. Electromedical Safety : Covering theoretical design and problem-solving

Teams of 3–4 propose a quantitative bioengineering solution to a clinical problem. Recent BIOE6403 projects have included:

: Test simple clinical measurement devices to ensure they meet professional standards for accuracy and reliability. Important Academic Policies The course covers the lifecycle

| Week | Topic | Hands-on Lab / Computational Exercise | |------|----------------------------|----------------------------------------| | 1 | Introduction to systems biology; central dogma review | Setting up Python/R environment; accessing GEO/ArrayExpress | | 2 | High-throughput data overview (microarray, bulk RNA-seq, scRNA-seq) | FASTQ to count matrix; quality control with FastQC & MultiQC | | 3 | Network representations (graphs, adjacency matrices, motifs) | Building protein interaction networks using STRING + NetworkX | | 4 | Network inference I: Correlation & mutual information | ARACNE & CLR algorithm implementation | | 5 | Network inference II: Bayesian & regression-based (GENIE3) | Comparing inference methods on DREAM challenge data | | 6 | ODE modeling of gene circuits | Simulating a repressilator (toggle switch) with SciPy/odeint | | 7 | Parameter estimation & sensitivity analysis | Fitting a model to synthetic data; LHS-PRCC analysis | | 8 | Single-cell RNA-seq analysis pipeline | Using Scanpy: filtering, normalization, highly variable genes | | 9 | Dimensionality reduction & trajectory inference | UMAP visualization; Monocle 3 / PAGA trajectory | | 10 | Machine learning for genomic prediction | Regularized regression (LASSO) for TF binding site prediction | | 11 | Multi-omics integration (MOFA, Seurat v4) | Integrating scRNA-seq + scATAC-seq from PBMCs | | 12 | Spatial transcriptomics & image-based omics | Analyzing a Visium dataset; spot deconvolution | | 13 | Model validation: Knockouts, perturbations, and causal inference | Using DoRothEA + PROGENy for activity inference | | 14 | Final project presentations | Peer feedback & reproducibility check |

You measure the expression of genes X, Y, and Z under 100 genetic perturbations. Partial correlation analysis shows: pcor(X,Y | Z) = 0.02 (p=0.8) pcor(X,Z | Y) = 0.65 (p=0.001) pcor(Y,Z | X) = 0.01 (p=0.9)

: Understanding the origin and significance of biological signals such as electroencephalography (EEG) , electrocardiography (ECG) , and blood pressure .

If you can share your (or even a photo of the course description), I can tailor this content precisely to your instructor’s topics, textbook, and assignments. Otherwise, the above is a comprehensive, realistic representation of a BIOE6403 graduate bioengineering course.