This scope focuses on mathematical models—such as differential equations and stochastic processes—to understand how biological populations fluctuate over time and space, and how evolutionary forces like mutation, genetic drift, and natural selection shape species diversity and adaptive traits across generations.

This area involves the use of quantitative compartmental models (e.g., SIR, SEIR) and network-based approaches to analyze the transmission dynamics of human, animal, and plant pathogens, ultimately helping to predict disease outbreaks, calculate reproduction numbers, and evaluate the effectiveness of public health interventions like vaccines and quarantines.

This scope centers on the holistic mathematical modeling of complex biological systems—such as gene regulatory networks and intracellular signaling cascades—alongside the development of novel computational algorithms to analyze large-scale multi-omics data and decode intricate molecular mechanisms.

Research in this category applies mathematical rigor and control theory to the design, construction, and optimization of artificial biological pathways, genetic circuits, and cellular factories, ensuring their robust and predictable behavior for next-generation bioengineering technologies.

This area is dedicated to the development and application of sophisticated statistical methods, probabilistic models, Bayesian inference, and machine learning algorithms specifically tailored to handle complex, high-dimensional life science datasets and rigorously validate mathematical models against empirical biological evidence.

This scope bridges biomathematics with bioeconomic applications by employing mathematical models to design optimal, sustainable harvesting and bioprocessing strategies in agriculture, forestry, and fisheries, ensuring that resource extraction maximizes economic yield without crossing critical ecological thresholds.

Exploring the intersection of mathematical biology, economics, and environmental policy, this area utilizes bioeconomic models and game theory to evaluate the economic impacts of biodiversity loss, assign value to ecosystem services, and optimize the allocation of limited funds for effective conservation strategies.

This scope applies mathematics to human physiology, pathology, and clinical treatments, encompassing areas like oncology modeling, cardiovascular dynamics, and pharmacokinetics/pharmacodynamics (PK/PD) to assist in personalized medicine, clinical trial design, and the diagnosis of complex diseases.

Submissions in this category use advanced optimization algorithms and optimal control theory (e.g., Pontryagin's Maximum Principle) to identify the most mathematically effective interventions in biology and medicine, such as optimal scheduling for multi-drug cancer therapies or optimal time-dependent harvesting policies.

This area focuses on developing and implementing advanced numerical methods, finite element analyses, and fast stochastic simulation algorithms that leverage parallel and high-performance computing (HPC) architectures to overcome the computational bottlenecks of large-scale biomathematical simulations.

This scope utilizes mathematical models, frequently employing differential equations, to study the complex interactions between organisms and their abiotic environments, analyzing phenomena such as food web dynamics, spatial pattern formation, traveling waves of invasive species, and the resilience of ecological networks against climate change.