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3.15. Biochemistry and Molecular Biology (BMB)

Biochemistry and Molecular Biology explores chemical processes within living organisms at the molecular level. This computing-focused subset emphasizes biomolecular structure, enzyme kinetics, molecular genetics, and bioinformatics methods directly relevant to protein structure prediction, AlphaFold, sequence analysis, genomics, and AI applications in biology.

Knowledge Area (KA)	CS Core	KA Core
3.15.1 Structure and Function of Biomolecules	Elective
3.15.2 Molecular Genetics and Enzymology	Elective
3.15.3 Bioinformatics and Computational Biology	Elective

3.15.1. BMB/Structure and Function of Biomolecules ↑ Back to top

Chemical architecture and biological roles of amino acids, proteins, and nucleic acids, with emphasis on protein folding and three-dimensional structure prediction relevant to AlphaFold and structural bioinformatics.
Topics:
Core

• Amino acids: chemical properties, chirality, side chain classes, and peptide bond formation
• Protein structure hierarchy: primary, secondary, tertiary, and quaternary levels and stabilizing forces
• Protein folding problem, energy landscape perspective, misfolding, and relevance to AlphaFold
• DNA and RNA structure: base pairing rules, double helix geometry, and secondary structure motifs
• Protein Data Bank (PDB) and structural databases as data sources for bioinformatics pipelines

Learning Outcomes:
Core:

1. Describe the chemical properties of amino acids and nucleotides as building blocks of proteins and nucleic acids [Familiarity]
2. Analyze protein structures from PDB files, identifying secondary structure elements, active sites, and key functional residues [Usage]
3. Evaluate protein structure prediction methods including energy-based approaches and deep-learning tools such as AlphaFold [Assessment]

3.15.2. BMB/Molecular Genetics and Enzymology ↑ Back to top

Mechanisms of genetic information flow, enzyme kinetics, and gene regulation providing the molecular foundations for genomics, transcriptomics, and systems biology in computing.
Topics:
Core

• The central dogma of molecular biology: replication, transcription, and translation mechanisms
• Michaelis-Menten enzyme kinetics: Km, Vmax, turnover number, and modes of inhibition
• Gene regulation: transcription factors, operons, epigenetic mechanisms, and regulatory network motifs
• Recombinant DNA techniques: PCR, CRISPR-Cas9, and gene expression systems used in biotechnology
• High-throughput omics technologies: genomics, transcriptomics, proteomics, and metabolomics data generation

Learning Outcomes:
Core:

1. Describe the central dogma of molecular biology and identify known exceptions such as reverse transcription and RNA editing [Familiarity]
2. Analyze enzyme kinetic data using Michaelis-Menten and Lineweaver-Burk plots to determine Km and Vmax [Usage]
3. Interpret omics datasets to identify differentially expressed genes or enriched biological pathways in a given condition [Assessment]

3.15.3. BMB/Bioinformatics and Computational Biology ↑ Back to top

Computational methods for analyzing biological sequences, structures, and genome-scale data, directly relevant to sequence alignment, homology modeling, structural bioinformatics, and AI applications in biology.
Topics:
Core

• Sequence alignment algorithms: Needleman-Wunsch (global), Smith-Waterman (local), and BLAST for database search
• Phylogenetic tree construction, evolutionary distance measures, and multiple sequence alignment
• Comparative (homology) modeling and template-based protein structure prediction
• Structural bioinformatics: molecular docking, structure comparison metrics (RMSD, TM-score), and binding site prediction
• Genome-scale data analysis: variant calling, RNA-seq differential expression, and pathway enrichment
• Machine learning in bioinformatics: protein function prediction, drug-target interaction, and single-cell data analysis

Learning Outcomes:
Core:

1. Explain the algorithmic differences between global and local sequence alignment and identify appropriate use cases for each [Familiarity]
2. Perform sequence database searches using BLAST and interpret results in terms of homology, e-values, and evolutionary conservation [Usage]
3. Evaluate the accuracy of computational protein structure predictions using standard metrics such as GDT-TS and TM-score [Assessment]