Sami Tanveer | HTGAA 2026

Sami Tanveer

Pharm.D Student | HTGAA 2026

University of Poonch

About Me

Pharm.D student exploring the scientific landscape—from mRNA sequencing to drug discovery and clinical applications. A complete beginner, but highly motivated and eager to learn, grow, and contribute to impactful biomedical research.

Connection

HTGAA 2026 Progress

Weekly Assignments

  1. Biological Engineering Application / Tool Application: I want to develop a bacterial biosensor for rapid detection of antibiotic-resistant pathogens in clinical samples. The biosensor uses engineered E. coli containing genetic circuits that activate fluorescent protein expression when they detect beta-lactamase activity or other resistance markers from nearby bacteria.

  • Week 2 — DNA Read, Write, and Edit

    Part 1: Benchling & In-silico Gel Art Purpose This exercise demonstrates applied understanding of restriction enzyme digestion and gel electrophoresis through in-silico modeling. The workflow emphasizes correct experimental logic, lane interpretation, and band pattern analysis using professional bioinformatics tools. Platform and Workflow All simulations were designed and executed using Benchling, a molecular biology platform widely used for DNA analysis, cloning design, and experimental planning. The use of Benchling enabled rapid iteration, accurate restriction mapping, and controlled visualization of gel electrophoresis outcomes.

  • Week 5: Protein Design II

    Part A: SOD1 A4V Therapeutic Peptide Design 1. Project Overview & Pharmacological Target This research targets the A4V mutation (Alanine-to-Valine at residue 4) in human Superoxide Dismutase 1 (SOD1). In Pharmaceutical Sciences, this is a critical target for Familial ALS. The mutation destabilizes the N-terminal “zipper” of the protein, leading to the exposure of hydrophobic residues and subsequent toxic aggregation. Our goal is to design a peptide binder that cap-stabilizes this region.

  • HTGAA 2026: Lab Automation & DNA Design

    1. Laboratory Automation: Opentrons Bio-Art Using the HTGAA26 Opentrons Colab as a framework, I developed a custom automation protocol to translate digital designs into biological patterns. Implementation Documentation Technical Script: sami_tanveer_opentrons.py Protocol Logic: The script utilizes API Level 2.20 and a P20 Single-Channel Gen2 pipette. It features an optimized draw_points function that handles coordinate-based dispensing with batched aspiration to ensure mechanical efficiency and prevent cross-contamination between fluorescent strains. Design Interface: The design was mapped using the Opentrons Art GUI, ensuring precise coordinate placement for Red (mRFP1), Green (mClover3), Blue (Azurite), and Cyan (sfGFP) reporters. Visual Reference of Design Interface:

  • HTGAA 2026: Protein Design Part I

    Part A: Conceptual Questions — Protein Biochemistry & Design These responses explore the molecular logic of protein structures, chirality, and the transition from abiotic chemistry to biological systems, reflecting the professional rigor required for pharmaceutical R&D. Q1: Molecular Abundance in Nutrition How many molecules of amino acids are in 500g of meat? Meat is approximately 20% protein by weight (accounting for water and fat). In 500g of meat, there are roughly 100g of protein.

  • Week 7 Homework — Genetic Circuits Part II: Neuromorphic Circuits

    Part 1: Intracellular Artificial Neural Networks (IANNs) Q1. What advantages do IANNs have over traditional genetic circuits whose input/output behaviors are Boolean functions? Traditional genetic circuits implement Boolean logic — outputs are strictly binary (gene ON or gene OFF). IANNs offer several key advantages over this approach.

Lab Documentation

Research Projects

Sami Tanveer — 2026 Research Portfolio

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