Submission Deadline: 31 August 2026 View: 27 Submit to Special Issue
Prof. Dulf Eva-H.
Email: eva.dulf@aut.utcluj.ro
Affiliation: Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, 400014, Romania
Homepage: https://users.utcluj.ro/~dulfe/
Research Interests: modeling of complex processes, medical applications, bio-inspired optimization, bio-inspired control
Prof. Kovacs Levente
Email: kovacs@uni-obuda.hu
Affiliation: Physiological Controls Research Center, University Research and Innovation Center, Obuda University, Budapest, 1034, Hungary
Research Interests: physiological modeling and control, modern robust control theory, cyber-medical systems, biomedical engineering
Dr. Denes-Fazakas Lehel
Email: denes-fazakas.lehel@nik.uni-obuda.hu
Affiliation: Physiological Controls Research Center, University Research and Innovation Center, Obuda University, Budapest, 1034, Hungary
Research Interests: biomedical engineering, machine learning and deep learning, big data
MSc.Eng. Pintea Paul-Andrei
Email: paul.pintea@aut.utcluj.ro
Affiliation: Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, 400014, Romania
Research Interests: medical applications, applied mathematics, advanced control systems
Biological systems—from cellular processes to complex organisms and ecological networks exhibit remarkable behaviours such as adaptation, self-organisation, resilience, and efficiency. These properties provide powerful foundations both for modelling real biological phenomena and for developing bio-inspired computational, optimisation, and control techniques applicable in engineering and technology.
This Special Issue invites high-quality research contributions in the modelling of biological processes and the development of optimisation and control techniques inspired by natural systems. We welcome submissions presenting innovative computational models, robust and adaptive control strategies, evolutionary and swarm-based optimisation techniques, and emerging data-driven or hybrid approaches. Both theoretical developments and application-oriented studies are encouraged. The goal is to provide a comprehensive forum showcasing novel methodologies and impactful engineering solutions that push the boundaries of modern modelling and optimisation.
Scope
The scope of this Special Issue spans biological system modelling, bio-inspired computation, and engineering applications. We welcome high-quality contributions in areas including, but not limited to:
Modelling and Simulation of Biological Systems
· Mathematical and computational models of cellular, physiological, biomechanical, neural, or ecological systems
· Multi-scale and multi-physics modelling of biological processes
· Data-driven and hybrid modelling approaches integrating experimental and computational biology
· Machine learning and AI for predicting biological behaviour or system dynamics
· Simulation frameworks for complex biological or biomedical systems
Bio-Inspired Optimisation and Control
· Evolutionary algorithms, swarm intelligence, and population-based optimisation
· Bio-inspired control strategies and adaptive/robust control frameworks
· Nature-derived heuristics for engineering design, prediction, and decision-making
· Hybrid optimisation techniques combining biological inspiration with ML/AI
· Applications of bio-inspired methods in robotics, autonomous systems, and smart engineering
Biologically Motivated System Analysis
· Stability, sensitivity, and parameter analysis of biological models
· Identification of biological parameters using optimisation techniques
· Modelling of emergent behaviours such as pattern formation, learning, or collective motion
Applications in Engineering, Medicine, and Biotechnology
· Biomedical engineering: tumour modelling, drug delivery optimisation, biomechanics, tissue growth
· Healthcare technologies: personalised medicine, diagnostic modelling, physiological control systems
· Environmental and ecological engineering: ecosystem modelling, resource optimisation
· Bio-inspired materials, structures, and design principles applied to engineering systems
Emerging Trends and Interdisciplinary Approaches
· Neuro-inspired computation, spiking neural models, and brain-inspired optimisation
· Synthetic biology modelling and control
· Modelling and optimisation in bio-cyber-physical systems
· AI-enhanced model discovery and automated scientific modelling
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