Academic & Research Profile

This page provides a consolidated overview of my academic training, laboratory experience, translational research work, and scientific communication activities.

📄 Research Portfolio

A detailed academic portfolio describing my education, molecular biology training, clinical research experience, and bioinformatics skill development:

👉 View Full Research Portfolio

💼 Professional Profile

Verified professional profile including education, research appointments, and institutional affiliations:

👉 View LinkedIn Profile

💻 Bioinformatics & Computational Work

Reproducible bioinformatics workflows, analytical scripts, and method-focused repositories developed in R and Python:

👉 View GitHub Profile

🎥 Science Communication & Educational Channels

StoryLens Depot — science narratives & documentaries
https://www.youtube.com/@storylensdepot

Mohin Sapara — molecular biology, cancer & research education
https://www.youtube.com/@mohinsapara

QuHub — interdisciplinary science, AI & future technologies
https://www.youtube.com/@quhub

This post serves as a centralized public entry point to my research, computational work, and scientific outreach activities.

Comments

KRAS-Driven Oncogenic Signalling in Pancreatic Ductal Adenocarcinoma: Molecular Mechanisms, Regulatory Pathways, and Therapeutic Frontiers

Pancreatic ductal adenocarcinoma (PDAC) is a characteristically aggressive tumour resistant to chemotherapy, and at the centre of this malignant phenotype lies an almost universal dependency on activating mutations in the KRAS oncogene. More than 90 %of PDAC tumours present with alterations in the KRAS oncogene, most frequently at codon 12, and these mutations represent the primary cause of the tumour’s signalling complexity, metabolic heterogeneity and stromal orchestration. The predominance of KRAS in PDAC reflects the capacity of mutant KRAS to adversely affect cellular processes in the tumour microenvironment that sustain the tumour’s growth, plasticity, survival and resistance to therapy. The biochemical behaviour of KRAS is rooted in its role as a molecular switch cycling between inactive GDP-bound and active GTP-bound conformations. In physiologically normal cells, this transition is carefully modulated by guanine nucleotide exchange factors and GTPase-activat...

From Script Usage to Computational Reasoning: My Progression in Python, R, and Bioinformatics

This article is a reflective and technical account of how my understanding and practical usage of Python , R , RStudio , and bioinformatics workflows has evolved over time. It is written deliberately without hype. The goal is not to exaggerate proficiency, but to document growth, limitations, scale of data handled, and a statistically reasoned trajectory of where my computational capacity is realistically headed over the next three years. 1. Starting Point: Computational Literacy, Not Expertise My initial engagement with programming languages was not as a formally trained computer scientist, but as a biomedical researcher responding to data pressure. Early usage of R and Python was functional and problem-driven: plotting figures, running basic statistics, reshaping tables, and automating repetitive tasks. I did not begin with algorithmic depth; I began with necessity. At this stage, my interaction with code was characterized by: Script reuse with modification Heavy reli...

My Year with ChatGPT (2025): Quantifying the Growth of an AI-Literate Scientist

In 2025, my interaction with artificial intelligence transitioned from occasional consultation to sustained intellectual collaboration. This post documents that trajectory using structured metrics, annotated summaries, and reflective analysis, treating AI usage as a measurable component of scientific skill development rather than a casual productivity aid. 1. Temporal Scope and Engagement Intensity Metric Scientifically Interpretable Value Annotation Calendar window January–December 2025 Continuous annual engagement, not episodic usage Total active days Multi-month distributed activity Indicates integration into routine research workflow Session depth High (20–30+ conversational turns/session) Reflects iterative hypothesis refinement rather than query–response use Cumulative active time Equivalent to several full working weeks Comparable to time invested in a structured training module Interpretation: Engagement patterns resemble supervised intelle...

Mohin Sapara

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