Mohin Sapara

Introduction Epigenetic regulation is one of the most fundamental mechanisms controlling gene expression in human cells. Although every somatic cell contains nearly identical DNA sequences, different cell types exhibit highly specialized functions because distinct transcriptional programs are activated or suppressed through chromatin remodeling. Histone modifications represent a major component of this epigenetic control system, regulating chromatin accessibility and transcription factor recruitment. Among the most important histone-modifying enzymes involved in enhancer activation is KMT2D, also known as Lysine Methyltransferase 2D or MLL2. KMT2D encodes a large nuclear histone methyltransferase located on chromosome 12q13.12. The protein belongs to the COMPASS-like family of chromatin regulators and primarily catalyzes mono- and di-methylation of histone H3 lysine 4 (H3K4me1 and H3K4me2). These histone modifications are characteristic epigenetic signatures of active enhancers, which ...

Abstract High Mobility Group Nucleosome-binding protein 5 (HMGN5) has emerged as an important chromatin architectural regulator involved in the epigenetic control of transcription, chromatin accessibility, and oncogenic transformation. Recent evidence demonstrates that aberrant HMGN5 expression contributes significantly to breast cancer progression through modulation of chromatin dynamics and activation of proliferation-associated transcriptional programs. HMGN5 belongs to the HMGN family of non-histone chromosomal proteins that interact directly with nucleosomes and regulate higher-order chromatin structure. Unlike sequence-specific transcription factors, HMGN proteins exert genome-wide regulatory effects by altering nucleosomal stability, histone modification accessibility, and transcriptional competency. In breast carcinoma, elevated HMGN5 expression correlates with aggressive clinical phenotypes, enhanced proliferative capacity, increased DNA replication activity, and poor prognosi...

1. Introduction and Motivation Quantitative Structure–Activity Relationship (QSAR) modeling represents one of the earliest and most enduring attempts to formalize the relationship between chemical structure and biological or physicochemical activity. At its core, QSAR is founded on a deceptively simple premise: that measurable properties derived from molecular structure encode information relevant to how a compound behaves in a given experimental or biological context. Despite its long history, QSAR remains highly relevant in contemporary computational chemistry, cheminformatics, and early-stage drug discovery, particularly as a baseline framework against which more complex machine-learning approaches are evaluated. However, while the conceptual foundations of QSAR are widely taught, the practical construction of a QSAR pipeline that is methodologically sound, reproducible, and diagnostically transparent is far less frequently demonstrated in a complete and auditable manner. Many publi...

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...

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...

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 ...

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...