Mathematical and Computational Biology (MCB)

Section Information

Mathematical and Computational Biology focuses on the development and application of mathematical models, computational methods, and quantitative analysis to understand biological systems. It integrates principles from biology, mathematics, physics, statistics, and computer science to study processes across molecular, cellular, organismal, and population levels.

Modern research in this field includes systems biology, bioinformatics, biophysical modeling, population dynamics, machine learning in biology, structural biology, genomics, and simulation of complex biological networks. Advances in algorithms, high-performance computing, and data-driven modeling continue to expand the scope and impact of computational biology.

This section publishes theoretical studies, computational analyses, simulations, algorithm development, and interdisciplinary research that apply mathematical and computational tools to biological questions, biomedical applications, and life-science innovation.

Scope

Bioinformatics and Computational Genomics
- Genome sequencing, annotation, and comparative genomics
- Transcriptomics, proteomics, and multi-omics integration
- Sequence analysis, alignment algorithms, and data mining
- Machine learning in genomics and biomarker discovery
Systems Biology and Network Modeling
- Gene regulatory networks, signaling pathways, and metabolic models
- Dynamic modeling of cellular processes
- Network topology, robustness, and control
- Multi-scale modeling from cells to tissues
Mathematical Modeling of Biological Processes
- Deterministic and stochastic models
- Differential equations, reaction–diffusion systems, and pattern formation
- Biophysical and biomechanical modeling
- Applications in physiology, neuroscience, and developmental biology
Computational Neuroscience
- Neuronal modeling, synaptic dynamics, and neural coding
- Network simulations and brain-inspired computing
- Signal processing and electrophysiological data analysis
- Cognitive modeling and neural systems theory
Population Biology and Evolutionary Dynamics
- Population growth, competition, and ecosystem models
- Evolutionary game theory and adaptive dynamics
- Phylogenetics, coalescent theory, and evolutionary computation
- Modeling of infectious disease transmission and immunity
Biostatistics and Quantitative Biology
- Statistical modeling and inference in biological data
- Bayesian frameworks, regression, and survival analysis
- Experimental design and uncertainty quantification
- High-dimensional data analysis and predictive modeling
Structural Biology and Molecular Simulation
- Molecular dynamics and Monte Carlo simulations
- Protein structure prediction and folding
- Computational chemistry and docking studies
- Energy landscapes and biophysical interactions
Computational Methods and Algorithm Development
- Numerical methods for biological simulation
- Data-driven modeling, AI, and deep learning applications
- Optimization techniques and high-performance computing
- Software tools, pipelines, and reproducible workflows