| Bahan Kajian | - Dasar pemrograman bahasa R
- Statistika univariat data biokimia
- Statistika bivariat data biokimia
- Metode optimasi dengan metode response surface methodology
- Analisis data kinetika enzim monosubstrat, multisubstrat dan alosterik
- Analisis data biofisika untuk studi stabilitas protein
- Analisis data biokimia multivariat dengan machine learning
- Pengantar bioinformatika
- Analisis data struktur dan proteomik
| - Fundamentals of R programming
- Univariate statistical analysis of biochemical data
- Bivariate statistical analysis of biochemical data
- Optimization methods using response surface methodology
- Analysis of enzyme kinetics data for mono-substrate, multi-substrate, and allosteric enzymes
- Analysis of biophysical data for protein stability studies
- Multivariate biochemical data analysis using machine learning
- Introduction to bioinformatics
- Structural and proteomic data analysis
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| Capaian Pembelajaran Mata Kuliah (CPMK) | - Menjelaskan prinsip fisika dan kimia di balik simulasi biomolekul, meliputi representasi struktur, medan gaya, termodinamika statistik, dan hubungannya dengan parameter eksperimen.
- Menggunakan dan mengevaluasi perangkat komputasi untuk mempelajari struktur, dinamika, dan interaksi biomolekul, termasuk molecular docking, MD, dan free energy calculations, secara kritis dan berbasis konsep
- Menganalisis dan menginterpretasi data komputasi secara biokimiawi, termasuk trajektori MD, hasil docking, analisis konformasi protein, dan profil energi bebas, dalam kaitannya dengan eksperimen struktural dan termodinamika
- Mengkritisi keterbatasan metode komputasi dalam konteks eksperimen nyata, yakni menilai validitas, akurasi, dan generalisabilitas temuan simulasi, serta memahami isu mutakhir dan etika penggunaan AI/ML dalam biokimia komputasi.
- Merancang dan melaksanakan mini-project biokimia komputasi secara mandiri: memilih sistem biomolekul, memformulasikan pertanyaan ilmiah, melakukan analisis, dan menyampaikan hasil dalam format ilmiah (laporan & presentasi).
| - Explaining the physical and chemical principles underlying biomolecular simulations, including structural representation, force fields, statistical thermodynamics, and their relationship with experimental parameters.
- Use and evaluate computational tools to study the structure, dynamics, and interactions of biomolecules, including molecular docking, molecular dynamics (MD), and free energy calculations, in a concept-based and critical manner.
- Analyse and interpret computational data from a biochemical perspective, including MD trajectories, docking results, protein conformational analysis, and free energy profiles, in relation to structural and thermodynamic experiments.
- Critically evaluate the limitations of computational methods in the context of real experimental systems by assessing the validity, accuracy, and generalisability of simulation findings, as well as understanding current issues and ethical considerations in the use of AI/ML in computational biochemistry.
- Design and conduct an independent mini-project in computational biochemistry, including selecting a biomolecular system, formulating a scientific question, performing computational analysis, and communicating the results in a scientific format through a report and presentation.
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| Metode Pembelajaran | Tatap muka konseptual, diskusi literatur, tugas reflektif, presentasi, mini-project | Conceptual face-to-face lectures, literature discussions, reflective assignments, presentations, and a mini-project. |
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