Publications

Books on Systems Biology and Systems Medicine

Translational Bioinformatics and Systems Biology Methods for Personalized Medicine
(ISBN: 978-0128043288)

Cellular Rhythms and Networks: Implications for Systems Medicine
(ISBN: 978-3319228181)

Psychoneuroimmunology: Systems Biology Approaches to Mind-Body Medicine
(ISBN: 978-3319832135)

Systems Biology in Drug Discovery and Development: Methods and Protocols
(ISBN: 978-1607617990)

Psychoneuroimmunology: Methods and Protocols
(ISBN: 978-1071641996)

Pharmacogenomics in Drug Discovery and Development
(ISBN: 978-1071625729)

Research Papers on  Systems Biology and Systems Medicine

Yan, Q. (2026). A Nonlinear Model of Complementary Regulatory Dynamics (CRD): Implications for Systems Biology and Dynamical Systems Medicine. Preprints, 2026101142. https://doi.org/10.20944/preprints202610.1142.v1

Abstract:
Complementary Regulatory Dynamics (CRD) is a proposed integrative theory for understanding Complex Adaptive Systems (CAS). This paper develops a minimal, nonlinear Activator-Regulator (A-R) model that is extensible and falsifiable for studying CRD in CAS. Within defined boundaries and relative to a specified outcome, the functional Activator role promotes and amplifies, whereas the functional Regulator role constrains, inhibits, or restores. The model formally interprets five CRD principles: functional complementarity, mutual regulation, dynamic balance, transformation, and multiscale recurrence. It may support representations of resilience, regulatory failure, compensatory recruitment, regime shifts, and multiscale connections in CAS. Potential biological applications include gene regulation, cell-cycle progression, protein homeostasis, immune activation and resolution, neural activities, and glucose-insulin and host-microbiome homeostasis. The preliminary mathematical model may shift the focus from static biomarker measurements to patient-specific dynamical biopatterns. It can help classify disease trajectories and guide the timing and sequence of interventions. These applications may help integrate and enrich mechanistic systems biology, network medicine, quantitative systems pharmacology, causal inference, and clinical testing. Validated implementations could support analysis of dynamical biotargets, estimation of regulatory reserve, prediction of recovery or transition risks, and hypothesis formulation. CRD may help make personalized therapy and dynamical systems medicine more achievable.
Keywords: Complementary Regulatory Dynamics (CRD); Complex Adaptive Systems (CAS); nonlinear dynamics; systems biology; dynamical systems medicine; dynamic balance; adaptation; resilience; mathematical model; differential equations

Yan, Q. (2026). Complementary Regulatory Dynamics: An Integrative Theory for Systems Biology and Dynamical Systems Medicine. Preprints, 2026092067. https://doi.org/10.20944/preprints202609.2067.v1

Abstract:
Essential challenges persist in systems biology, especially data and model integration. These issues may explain the lack of a universally accepted framework in modern biology for organizing the dynamic behaviors of whole organisms and diseases across scales. This paper proposes Complementary Regulatory Dynamics (CRD) as an integrative theory for studying how complex adaptive systems generate necessary changes while preserving viable functions. CRD assigns functional activator and regulator roles to processes relative to a defined system boundary and focal outcome. The theory is organized around five provisional, testable principles. CRD may help integrate and enrich models from established theories, including nonlinear dynamical systems theory, homeostasis and allostasis, and resilience theory. CRD could provide a common logic for organizing complex biological networks. It may help identify causal variables and build explicit bridges linking multiple scales. As a falsifiable theory, it can improve experimental design and clinical testing by shifting from static biomarkers to dynamical biopatterns and time-sensitive approaches. In translational settings, this could help detect early-warning signals, transition-prone states, and intervention windows. Therapeutic strategies can focus on systems-based biotargets to restore dynamic balance. CRD theory may provide potentially unifying guidance for translating systems biology into dynamical systems medicine.
Keywords: Complementary Regulatory Dynamics (CRD); systems biology; nonlinear dynamics; dynamical systems medicine; dynamical biopatterns; homeostasis; allostasis; robustness; resilience; feedback

Yan Q. (2025). Personality and Psychoneuroimmunology: A Systems Biology Perspective. Methods in molecular biology (Clifton, N.J.), 2868, 15–36.

Yan Q. (2025). Personality, Aging, and Alzheimer’s Disease: Implications for Psychoneuroimmunology and Personalized Medicine. Methods in molecular biology (Clifton, N.J.), 2868, 37–48.

Yan Q. (2022). The Yin-Yang Dynamics in Cancer Pharmacogenomics and Personalized Medicine. Methods in molecular biology (Clifton, N.J.), 2547, 141–163.

Yan Q. (2022). The Yin-Yang Dynamics in Cardiovascular Pharmacogenomics and Personalized Medicine. Methods in molecular biology (Clifton, N.J.), 2547, 255–266.

Yan Q. (2018). Stress and Systemic Inflammation: Yin-Yang Dynamics in Health and Diseases. Methods in molecular biology (Clifton, N.J.), 1781, 3–20.

Yan Q. (2018). Neuroimmune Imbalances and Yin-Yang Dynamics in Stress, Anxiety, and Depression. Methods in molecular biology (Clifton, N.J.), 1781, 77–85.

Yan Q. (2017). Toward dynamical systems medicine: personalized and preventive strategies. Personalized medicine, 14(5), 551–554.

Yan Q. (2014). From pharmacogenomics and systems biology to personalized care: a framework of systems and dynamical medicine. Methods in molecular biology (Clifton, N.J.), 1175, 3–17.

Yan Q. (2014). Translational bioinformatics approaches for systems and dynamical medicine. Methods in molecular biology (Clifton, N.J.), 1175, 19–34.

Yan Q. (2012). The role of psychoneuroimmunology in personalized and systems medicine. Methods in molecular biology (Clifton, N.J.), 934, 3–19.

Yan Q. (2012). Translational bioinformatics in psychoneuroimmunology: methods and applications. Methods in molecular biology (Clifton, N.J.), 934, 383–400.

Yan Q. (2012). Translational implications of inflammatory biomarkers and cytokine networks in psychoneuroimmunology. Methods in molecular biology (Clifton, N.J.), 934, 105–120.

Yan Q. (2011). Translation of psychoneuroimmunology into personalized medicine: a systems biology perspective. Personalized medicine, 8(6), 641–649.

Yan Q. (2011). Toward the integration of personalized and systems medicine: challenges, opportunities and approaches. Personalized medicine, 8(1), 1–4.

Yan Q. (2010). Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML. Methods in molecular biology (Clifton, N.J.), 637, 23–45.

Yan Q. (2010). Immunoinformatics and systems biology methods for personalized medicine. Methods in molecular biology (Clifton, N.J.), 662, 203–220.

Yan Q. (2010). Membrane transporters and drug development: relevance to pharmacogenomics, nutrigenomics, epigenetics, and systems biology. Methods in molecular biology (Clifton, N.J.), 637, 1–21.

Yan Q. (2010). Systems biology of influenza: understanding multidimensional interactions for personalized prevention and treatment. Methods in molecular biology (Clifton, N.J.), 662, 285–302.

Yan Q. (2010). Translational bioinformatics and systems biology approaches for personalized medicine. Methods in molecular biology (Clifton, N.J.), 662, 167–178.

Yan Q. (2008). Bioinformatics databases and tools in virology research: an overview. In silico biology, 8(2), 71–85.

Yan Q. (2008). The integration of personalized and systems medicine: bioinformatics support for pharmacogenomics and drug discovery. Methods in molecular biology (Clifton, N.J.), 448, 1–19.

Yan Q. (2005). Biomedical informatics methods in pharmacogenomics. Methods in molecular medicine, 108, 459–486.

Yan Q. (2005). Pharmacogenomics and systems biology of membrane transporters. Molecular biotechnology, 29(1), 75–88.

Yan Q. (2003). Bioinformatics and data integration in membrane transporter studies. Methods in molecular biology (Clifton, N.J.), 227, 37–60.

Yan Q. (2003). Pharmacogenomics of membrane transporters an overview. Methods in molecular biology (Clifton, N.J.), 227, 1–20.