Computing Biology. Advancing Discovery.
Research-focused bioinformatics and computational biology solutions for modern biological discovery.
Where Biological Data Meets Computational Science
ZYNEX BIO applies structured computational methods, reproducible bioinformatics workflows, and rigorous biological reasoning to support modern biological research, omics data analysis, and structure-based drug discovery.
Computational Expertise & Analytics
Select a computational capability to explore its analytical workflow, software stack, and output visualizations.
RNA-seq & Transcriptomics
End-to-end transcriptomic workflows from raw read quality control to differential expression and functional pathway enrichment.
NGS & Bioinformatics
High-throughput sequencing data analysis using established, reproducible bioinformatics software pipelines.
Network Pharmacology
Systems-level mapping integrating target prediction, disease networks, protein interaction topology, and pathway mechanisms.
Molecular Docking
Structure-based computational investigation of protein-ligand binding modes, grid box pocket dynamics, and affinity energy.
Structural Bioinformatics
Protein structure cleaning, binding pocket characterization, homology modeling, and sequence-structure relationship analysis.
Scientific Data Analysis
Publication-oriented scientific visualizations designed for high-impact manuscript submission and clear data communication.
RNA-seq & Transcriptomics
Differential Expression Volcano Plot
Our Approach
A structured computational research methodology translating complex biological datasets into validated insights.
Understand
Define the specific biological question, hypothesis, and research scope.
Design
Formulate an optimal computational strategy, selecting algorithms and reference datasets.
Analyze
Execute reproducible computational pipelines using standardized tools.
Interpret
Synthesize raw analytical outputs into biologically meaningful insights.
Deliver
Provide structured documentation, publication-ready vector figures, and reproducible code.
Understand
Define the specific biological question, hypothesis, and research scope.
Design
Formulate an optimal computational strategy, selecting algorithms and reference datasets.
Analyze
Execute reproducible computational pipelines using standardized tools.
Interpret
Synthesize raw analytical outputs into biologically meaningful insights.
Deliver
Provide structured documentation, publication-ready vector figures, and reproducible code.
Selected Computational Work
Demonstrations of structured computational pipelines, analytical workflows, and publication-ready biological data visualizations.
Transcriptomic Analysis of Disease-Associated Gene Expression
Computational workflow processing high-throughput RNA-seq data to quantify differential gene expression, cluster sample trajectories, and pinpoint key enriched metabolic pathways.
Systems Pharmacology of Bioactive Compounds in Complex Targets
Systems-level computational mapping connecting target prediction models, target-disease intersection matrices, protein-protein interaction network topology, and hub protein identification.
Structure-Based Molecular Docking & Binding Pocket Profiling
Computational investigation of protein-ligand binding modes, defining active pocket coordinates, calculating binding affinity energies (kcal/mol), and mapping hydrogen bond contacts.
Scientific Principles
Guiding computational methodologies to maintain scientific integrity and biological relevance.
Scientific Rigor
Selecting computational methods, software tools, and parameters strictly matched to the biological question.
Reproducibility
Structuring workflows to ensure analysis scripts, reference versions, and parameters are fully documented.
Biological Interpretation
Focusing on translating computational outputs into actionable biological context rather than raw data dumps.
Publication-Ready Results
Generating vector-graphics, high-resolution scientific figures, and structured technical documentation.
Scientific Leadership
Guiding computational strategies with research-focused methodology.
Mohd Zubair Khan
Bioinformatics researcher with experience in computational biology, transcriptomic analysis, network pharmacology, molecular docking, structural bioinformatics, and biological data analysis.
Have a Biological Research Problem?
Let's explore the computational approach. Whether you have raw sequencing datasets or target discovery questions, ZYNEX BIO is ready to assist.