Homeostasis is an open-access quarterly journal published by Science Exploration Press, dedicated to advancing the science of homeostasis across molecular, cellular, organ, and whole-organism levels. By bridging experimental biology with artificial intelligence, machine learning, computational sciences, and clinical applications, the journal provides a platform for mechanistic and translational research that explains, measures, and modulates dynamic regulatory processes in living systems. Homeostasis aims to foster predictive diagnostics, precision interventions, and integrative tools that enhance patient care and population health, shaping the future of precision medicine through a deeper understanding of physiological stability, adaptation, and resilience.

Aims and Scope

Aims

Homeostasis refers to the dynamic process through which internal variables are actively regulated to remain within life-sustaining limits, even in the face of external perturbations. The journal Homeostasis publishes mechanistic and translational studies that explain, measure, and modulate control processes across molecular, cellular, organ, and whole-organism levels. The aim is to develop diagnostic, monitoring, and therapeutic tools that make use of homeostasis predictions. We place special emphasis on how artificial intelligence (AI), machine learning and computational modeling can transform these aims by enabling deep learning, predictive diagnostics, and precision interventions.

Scope

We encourage work that integrates AI-driven approaches to map these dynamic trade-offs and predict maladaptation before clinical manifestation. We publish data showing that disease often reflects failure or maladaptation of dynamic homeostatic controls. We consider studies that quantify stability, disturbance, and control performance in living systems, including
• Feedforward control, adaptive gain, and set-point resetting that refine performance across circadian phase, stress, infection, growth, and aging
• Thermoregulation and energy balance
• Neuroendocrine and autonomic control
• Immunometabolic and inflammatory regulation
• Oxidative and nitrosative stress and proteostasis
• Microbiome–host interactions
• Sleep and circadian timing
• Development, aging, and homeodynamics
• Comparative and environmental physiology
• Behavior that anticipates or counters disturbance
• Fluid, electrolyte, and acid–base balance
• Osmoregulation and volume regulation
• AI-enhanced modeling of physiological resilience and adaptive capacity
• Digital biomarkers and wearable sensor integration for continuous homeostasis monitoring

Methods and approaches

• Interventional studies and clinical trials with rigorous design and statistics
• Hybrid experimental–computational pipelines that combine laboratory data with AI-driven inference to accelerate mechanistic discovery
• Human, animal, and in-vitro models, including organoids, organ-on-chip, and closed-loop devices
• Systems identification, control theory, and dynamical modeling
• Causal inference and analysis of physiological time series
• Advanced AI and machine learning for multimodal physiological data integration, digital twin models, and predictive simulations of system stability
• Single-cell and spatial omics
• Proteomics, metabolomics, and lipidomics
• Imaging and biosensors

Disease and application domains

• Metabolic and endocrine disorders
• Critical illness and sepsis
• Heart and kidney failure
• Hepatic and pulmonary disease
• Neurological and psychiatric conditions
• Exercise, altitude, and environmental physiology
• Pharmacological and toxicological perturbations
• AI-enabled precision medicine approaches for diagnosis, prognosis, and therapy optimization in these conditions

Publication Frequency

Quarterly.

Publishing Model

Homeostasis is an open-access journal that ensures all content is freely available to users and their institutions without any associated costs. Users are granted full rights to read, download, copy, distribute, print, search, and link to the full texts of articles, as well as to use them for any other lawful purposes, without the need for prior permission from the publisher or the author. This policy aligns with the principles of open access, promoting the broad dissemination of scientific knowledge to a global audience and enhancing the accessibility of high-quality research.

Licensing and Copyright

Homeostasis is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain the copyright of their work and agree to make their original works freely available for use, copying, and redistribution in all formats without needing permission, provided that proper citation is given to the authors and the original source. This license allows others to share, adapt, and build upon the work, fostering collaboration and further innovation in the field.

Digital Archive

All content published in Homeostasis is archived and made permanently available through the Portico digital archive, ensuring long-term access and preservation of the journal's articles.