Aims

Journal of Computational Modelling in Biological Systems (JCMBS) is an international, peer-reviewed, open-access journal devoted to the formal development, analysis, and application of computational modelling frameworks for biological systems.

The journal focuses on the representation of biological processes through explicit mathematical structures, computational architectures, and numerical simulation strategies, and does not consider purely descriptive studies or submissions lacking explicit model formulation. JCMBS emphasises research in which model formulation, structural assumptions, and computational implementation are central to scientific interpretation.

The journal addresses biological systems from molecular to population scales, including cellular, tissue, organ, and organismal levels. Particular attention is given to how modelling choices influence interpretation, prediction, and system-level understanding. Submissions are expected to present clearly defined model structures, justified parameterisation strategies, and systematic evaluation of simulation results in relation to biological behaviour.

JCMBS welcomes conceptual, methodological, and applied contributions that advance reproducible and interpretable modelling of biological systems, including studies situated in biomedical and physiological contexts where modelling structure remains the primary analytical focus.

The journal is published quarterly by Acadlore and follows a structured peer-review process that prioritises clarity of modelling logic, transparency of assumptions, methodological rigour, and robustness of conclusions.

Key features of JCMBS include:

• The journal centres on explicit computational and mathematical modelling of biological systems, rather than on isolated datasets or purely experimental observations.

• Central to the journal’s scope is the formulation and analysis of structured models, whether deterministic, stochastic, or hybrid.

• Submissions must provide clear documentation of modelling assumptions, parameter selection, and validation strategies.

• Contributions should demonstrate how modelling and simulation advance biological interpretation or system-level explanation, rather than merely applying existing methods to new problems.

• Application-oriented studies are welcomed where computational structure, validation, and interpretability remain central.

• The editorial and review process emphasises methodological transparency, reproducibility of computational workflows, and consistency between model design and biological conclusions.

Scope

JCMBS welcomes original research articles, theoretical contributions, systematic reviews, and high-quality computational studies in areas including, but not limited to, the following:

Mathematical and Computational Modelling of Biological Systems

• Deterministic and stochastic models of biological dynamics

• Ordinary and partial differential equation models

• Agent-based and multi-scale modelling approaches

• Hybrid mechanistic–data-driven modelling frameworks

• Parameter estimation, calibration, and identifiability analysis

• Sensitivity and uncertainty analysis in biological models

Biomechanical and Biophysical System Modelling

• Computational modelling of tissues, organs, and musculoskeletal systems

• Fluid–structure interaction in biological environments

• Multiphysics modelling of physiological processes

• Numerical simulation of biological materials and structural behaviour

• Coupled mechanical–biological system analysis

Physiological and Biomedical Modelling Applications

• Organ-level and system-level physiological modelling

• Disease progression and intervention modelling

• Patient-specific and personalised modelling frameworks

• Digital representations of biological processes

• Model-based optimisation in therapeutic or biomedical contexts

Numerical Methods and Computational Frameworks

• Numerical algorithms for biological system simulation

• Model reduction and computational efficiency strategies

• Verification and validation of computational models

• Comparative evaluation of modelling strategies

• High-performance and scalable computing for biological modelling

Uncertainty, Robustness, and Model Evaluation

• Structural and parametric uncertainty in biological models

• Probabilistic and Bayesian modelling approaches

• Model robustness and generalisability analysis

• Reproducibility and validation in simulation studies

• Comparative assessment of modelling assumptions

Cross-Scale and Integrated Biological Modelling

• Multi-scale modelling from molecular to organismal levels

• Coupled biological and physical process modelling

• Structured integration of interacting biological subsystems

• Modelling of genotype–phenotype relationships

• System-level interpretation grounded in computational structure