Abstract
Network Physiology provides a unifying framework for understanding how molecular, cellular, and organ-level systems integrate as a network to generate distinct physiological states and sustain human function. Spaceflight offers a unique environment—characterized by microgravity, radiation, isolation, and circadian disruption—that perturbs interconnected physiological systems and networks. Network Physiology in Space, an emerging area of research and clinical practice within the multidisciplinary field of Network Physiology, examines how multiscale interactions, from genomic and metabolic pathways to organ system dynamics, adapt and reorganize in response to spaceflight stressors to maintain homeostasis at the organism level. Using systems biology, multi-omics, nonlinear analyses of physiological systems dynamics, computational modeling, and AI-enhanced analysis, researchers have traditionally focused on individual systems to investigate regulatory mechanisms underpinning adaptations to spaceflight, including muscle and bone loss, cardio-vascular and cardio-respiratory deconditioning, immune function shifts, neuro-vestibular dysregulation, circadian, and sleep fragmentation. However, physiological systems and organs continuously interact across levels to synchronize dynamics and coordinate functions. Changes in a system in response to perturbations are often interlinked with other systems, leading to diversity of effects, which underscores the need for an integrative framework capable of linking molecular signals to system-level physiological function and crew functionality. In this context, Network Physiology provides a unifying theoretical and analytical approach to identify, quantify and model dynamic interactions among physiological systems across spatio-temporal scales, integrating multi-omics, physiological, and behavioral data into dynamical network representations. This systems-level perspective enables spaceflight-induced adaptations to be interpreted as coordinated reconfigurations of interacting physiological networks, rather than isolated responses of individual components. As many adaptations are common with disuse pathology, spaceflight becomes a living laboratory for probing frailty and resilience, revealing principles relevant to aging, metabolic and immune disorders, neurodegeneration, and rehabilitation on Earth. Recent methodological advances in inferring functional forms of coupling and causality in dynamic systems interactions, and novel integrative and adaptive network approaches in Network Physiology offer new perspectives to human and animal studies in space or analogue environments, for the development of translational applications to clinical practice and hybrid mechanistic–machine-learning models that simulate system-wide responses and guide personalized countermeasures strategies and personalized medicine.
Introduction
Despite significant advances in systems biology and integrative physiology over recent decades, a complete understanding of how diverse physiological systems and organs coordinate their dynamics across spatial and temporal scales is currently lacking. Specifically, the mechanisms through which systems integrate both vertically, from genomic, proteomic, and metabolic interactions to cellular signaling pathways, tissues, and organs, and horizontally, as interconnected networks, to generate distinct physiological states, environmental adaptations, maintain hemostasis (health), precipitate disease phenotypes, and influence pathophysiological, symptomatic and functional associations between diseases, remain unclear. Recent developments in coupled nonlinear systems, statistical and computational physics, signal processing, information theory, and adaptive networks of dynamical systems offer new tools to uncover the complexity of physiological systems, their interactions, and their effects. These tools form foundational elements of the theoretical frameworks and methodological approaches to yield a new age of physiology, to inform new dawn in (personalized) medicine.
A central challenge in any biological, including physiological systems is quantifying and mechanistically understanding how global behavior emerges from network interactions among dynamically evolving entities, particularly when the coupling between components is time dependent. In the context of systems biology, human physiology, and medicine, this challenge has led to the emergence of the new field of Network Physiology (; Ivanov and Bartsch, 2014; Ivanov, 2021), propelled by advances in biomedical technologies, computational modeling, and theoretical approaches (Figure 1).
FIGURE 1
Traditional systems biology, following the reductionist paradigm, has largely focused on identifying key molecular elements within cells and elucidating their roles in cellular function by evaluating the effects of functional knockouts. Integrative network approaches have extended these efforts by constructing knowledge graphs that represents static maps of relationships (statistical associations) among cellular components, aiming to uncover complex intra-cellular signaling pathways and systematically organize genomic, proteomic, and metabolic data. Such approaches have catalyzed a paradigm shift, enabling the identification of associations between clusters of disease phenotypes and shared genes.
Graph theory and complex networks have also been employed to predict the roles of gene and proteins in disease expression based on their network neighborhood, through motifs and modules, which has highlighted novel genes involved in numerous disease phenotypes (Milo et al., 2002; Stuart et al., 2003; Song et al., 2005; Oti et al., 2006; Köhler et al., 2008; Ivanov et al., 2016). This static graph-based perspective has facilitated the identification of many previously unknown disease-associated genes, yet much work remains given that only 10%–15% of all human genes have known disease associations (; ).
Moreover, integrative frameworks have expanded understanding of intra-cellular networks to encompass tissue- and organ-specific gene connectivity, establishing principles of gene interactions across cells and tissues (Lage et al., 2008; Reverter et al., 2008; Kirouac et al., 2010; Lage et al., 2010). Recent innovations in layered multiplex networks integrate genetic networks with co-morbidity factors, providing insights into how various risk factors influence gene function, and thus, the likelihood of disease expression in complex clinical scenarios (Lee et al., 2008; Park et al., 2009) as well as the temporal evolution of co-morbidity and diseases networks (Haug et al., 2021; ).
While systems biology has primarily addressed vertical integration (Joyner, 2011), from sub-cellular to cellular and tissue levels, its methodological focus has traditionally been on bottom-up mechanistic causation and static cross-scale associations. As a result, important gaps remain both in understanding the dynamic nature of vertical integration across levels, where differences in time scales and governing principles complicate cross-communication, as well as in horizontal integration among interacting physiological and organ systems (Figure 1), where heterogeneous dynamics and regulatory mechanisms coexist within the same level of organization (; ). These limitations constrain our ability to explain how coherent organism-level behaviors emerge from distributed physiological processes.
Network Physiology (; Ivanov and Bartsch, 2014; Ivanov et al., 2016; Ivanov et al., 2017; Ivanov, 2021) addresses these challenges by studying how physiological systems coordinate, synchronize, and integrate as a network to optimize functions and maintain health, and the cascade failure may underpin their failure. In this framework, network nodes represent distinct dynamical physiological systems operating at different scales, each with distinct dynamics and control mechanisms, while network links reflect transient dynamical coordination between systems. A central question in Network Physiology is how global physiological states (and adaptations) emerge from the collective dynamics of interconnected organ systems (; Liu et al., 2015b). A key challenge is that small temporal variations in individual system dynamics or network interactions appear to lead to markedly different global behaviors, even in networks with unchanged topology (; Liu et al., 2015a; Lehnertz et al., 2020; Lin et al., 2020; ).
In fact, physiological interactions occur across multiple levels and spatio-temporal scales, producing distinct physiological states such as rest and fatigue, wakefulness and sleep, consciousness, and unconsciousness. As such, investigations in Network Physiology focus on: (i) the structural and functional connectivity within individual physiological and organ systems and their sub-systems (Liu et al., 2015a; ), and (ii) how organism-level behaviors arise from interactions among systems, shaping health or disease outcomes (; Ivanov and Bartsch, 2014; Rizzo et al., 2020; Günther et al., 2022; Rizzo et al., 2023). Understanding these interactions is critical as disruption of networked communications can not only lead to dysfunction of individual systems, but precipitate rapid systemic collapse, such as coma or multi-organ failure (; Moorman et al., 2016; ). Consequently, Network Physiology complements traditional approaches by emphasizing the importance of system-wide coordination and hierarchical network integration as hallmarks of physiological state and function (Lin et al., 2016; Ivanov et al., 2021; Rizzo et al., 2022).
Physiological systems: vertical and horizontal integration from molecules to organ networks
Building on the framework outlined above, physiological systems can be conceptualized along two complementary dimensions: vertical integration, spanning molecular, cellular, tissue, and organ levels; and horizontal integration, representing the dynamic coordination among organs and systems within the body (Figure 1). Considering these dimensions jointly is essential for understanding how homeostasis in health is maintained, and how pathological states emerge, as well as how interventions at one level propagate through the physiological network.
Vertical integration: from molecules to organs
Vertical integration describes the cascade of biological interactions that originates at the molecular level and manifests as organ-level function. At the molecular level, proteins, nucleic acids, lipids, and small molecules interact within defined pathways to regulate cellular behavior. For example, the binding of insulin to its receptor triggers intracellular signaling through the phosphoinositide 3-kinase (PI3K)/Akt pathway, which modulates glucose uptake and glycogen synthesis in hepatocytes and myocytes (Taniguchi et al., 2006). This molecular signaling underlies the physiological response of glucose homeostasis at the organ and systemic levels.
At the cellular level, vertical integration encompasses how cells coordinate their internal processes and respond to external stimuli. Cellular activities such as metabolism, ion transport, gene expression, and signal transduction collectively define the functional state of tissues. In cardiac tissue, cardiomyocytes exhibit a tightly regulated excitation-contraction coupling mechanism, whereby the molecular dynamics of ion channels and calcium handling proteins generate coordinated contractions across the tissue (). This cellular orchestration directly underpins the pumping function of the heart, demonstrating a clear link between molecular events and organ-level output.
Tissue-level integration reflects how specialized cell populations organize into functional units. For instance, in the liver, hepatocytes, Kupffer cells, and hepatic stellate cells interact within the lobular architecture to maintain metabolic homeostasis, detoxification, and immune surveillance. Disruption at any cellular or molecular node, such as in non-alcoholic fatty liver disease (NAFLD), propagates through the tissue architecture, altering organ function (Tilg and Moschen, 2010). These examples illustrate that vertical integration is not merely additive; it is a hierarchical networked system where molecular perturbations can generate organ-level phenotypes.
Organ-level function represents the culmination of vertical integration. Physiological outputs, such as cardiac output, renal filtration, or pulmonary gas exchange, emerge from the integrated activity of tissues and cellular networks. Furthermore, these organ-level functions are subject to feedback regulation. The kidneys, for instance, regulate systemic blood pressure through the renin-angiotensin-aldosterone system (RAAS), which is initiated by molecular sensing of perfusion pressure and sodium concentration, and coordinated by cellular and tissue-level responses. This exemplifies how vertical integration ensures that organ function is not an isolated process, but a dynamic product of multiscale interactions.
The multidisciplinary field of Network Physiology provides a unifying theoretical framework for the hierarchical perspective of vertical integration by emphasizing the dynamic coupling among networks operating at different spatial and temporal scales, conceptualizing each level of biological organization—metabolic, genomic, cellular, tissue, and organ—as an inter-connected network embedded within a larger “network of networks.” Vertical integration, in this framework, reflects the coordinated exchange of information and dynamical interactions across levels, whereby changes in one node or network alter the coupling structure and functional organization of others. Thus, physiological states emerge not only from structural hierarchy but from multiscale dynamical coordination, temporal synchronization, and adaptive network reconfiguration among components across layers of biological organization.
Horizontal integration: inter-organ network dynamics
Horizontal integration refers to the continuously dynamical interplay among physiological and organ systems to achieve systemic homeostasis. Whereas vertical integration concerns interactions across hierarchical levels of biological organization, horizontal integration refers to interactions among systems operating at the same level of organization. Physiological systems in the organism do not act in isolation; instead, physiological states and functions at the organism level emerge from coordinated multi-organ responses. For example, the regulation of blood glucose involves the pancreas (insulin/glucagon secretion), liver (glycogen storage and gluconeogenesis), skeletal muscle (glucose uptake), and adipose tissue (lipid metabolism). Disruption in any node or alteration in inter-system coupling can precipitate systemic dysfunction, as observed in type 2 diabetes mellitus ().
Another canonical example of horizontal integration is the cardiovascular and renal systems. Blood pressure and volume are controlled by a tightly coupled network involving the heart, kidneys, vasculature, and endocrine organs. The heart pumps blood, the kidneys adjust fluid and electrolyte balance, and the vasculature modulates resistance—all coordinated through neural and hormonal signals. This coordination exemplifies network-level dynamics: feedback from one organ alters the function of others through an adaptive network of time-varying “elastic” interactions, and systemic stability emerges from these inter-organ interactions (Ivanov and Bartsch, 2014; Hall, 2016; Ivanov, 2021).
The neuroendocrine system provides another demonstration of horizontal integration. Hypothalamic-pituitary axes regulate diverse physiological processes, including stress response, reproduction, and growth, by coordinating hormone secretion across multiple target organs. The hypothalamic-pituitary-adrenal (HPA) axis, for instance, modulates cortisol secretion, which in turn affects liver metabolism, immune function, and cardiovascular tone (; Goswami et al., 2019). Disruption in this network can lead to systemic pathophysiology, emphasizing the integrative nature of organ communication (Voliotis et al., 2018).
Horizontal integration also extends to organ systems that traditionally appear disparate. The gut-brain axis exemplifies a bidirectional network where the microbiome influences central nervous system function via immune, endocrine, and neural pathways. Metabolites such as short-chain fatty acids and neurotransmitter precursors produced by gut microbes modulate brain function, while neural and hormonal outputs from the brain regulate gut motility, secretion, and microbial composition (; Takayasu et al., 2017). Horizontal integration among key organ systems—brain, cardiac, respiratory, muscular, ocular—is essential in facilitating basic physiological states such as wake and sleep, rest and exercise, where distinct network structure and hierarchy in the strength and directionality of systems interactions uniquely define each physiological state, and where specific pathways of network reorganization underlie transitions across states (; Liu et al., 2015b; Ivanov et al., 2021; ; Rizzo et al., 2023; ; Ivanov and Bartsch, 2025). This inter-organ communication demonstrates that physiological systems are networked across traditional anatomical boundaries.
Network physiology: bridging vertical and horizontal dimensions
The true complexity of physiological systems emerges when vertical and horizontal integration are considered jointly across multiple organ systems and time scales (Figure 1). A classic example is exercise physiology. During exercise, skeletal muscle metabolism is modulated at the molecular level by AMP-activated protein kinase (AMPK) and calcium-calmodulin-dependent protein kinase pathways, which enhance glucose uptake and mitochondrial biogenesis (Hardie et al., 2012). Cellular energy consumption triggers tissue-level responses, such as increased capillary perfusion, while organ-level outputs include elevated cardiac output and respiratory ventilation to match, but not exceed metabolic demand. Horizontally, these responses are coordinated across the cardiovascular, respiratory, endocrine, and nervous systems to ensure oxygen delivery, substrate utilization, and thermoregulation (; ; ). This multiscale, multi-organ integration illustrates the inherent sophistication of physiological regulation.
Pathophysiological states further underscore the necessity of concurrent understanding both vertical and horizontal dimensions. Hypertension, for instance, can result from molecular alterations in ion channels, cellular changes in vascular smooth muscle, tissue remodeling in arteries, and systemic dysregulation of inter-organ networks involving the kidneys, vasculature, heart, and central nervous system (). Effective therapeutic interventions often require targeting multiple levels simultaneously, pharmacologically modulating molecular pathways, improving tissue function, and restoring inter-organ coordination, and thus, functionality and organism homeostasis.
Current challenges in spaceflight-related biomedical risks
After over seven decades of human spaceflight, our frameworks for understanding and mitigating space-related biomedical risks remain nascent (Goswami et al., 2026a). Traditional space medicine is geared to risk prevention by selecting out risk factors, requiring extensive exercise, whilst providing basic acute care (managing injuries or sudden illness in orbit) with periodic health assessment including physician consultation monitoring “classic” physiological parameters in near-real time. However, space medicine is far from modern precision medicine approaches on Earth that leverage big data, advanced analytical approaches, and population-based genomics, and ultimately assumes medical evacuation is an option.
Despite major advances in biomedical science over the past decades, health and disease are still largely defined in terms of individual molecular pathways, physiological and organ systems, rather than through a holistic understanding of their dynamic coordination and network integration across levels of organization in the human organism. The traditional reductionist paradigm in basic science and medicine limits our ability to capture the regulatory network mechanisms that generate vital physiological states and functions, and sustain physiological homeostasis, stability and adaptability under stress. Moreover, reductionist approaches do not provide the framework necessary to explain how perturbations to a given system or sub-system propagate across systems to produce systemic dysfunctions, cascade of failures and multi-system breakdown. In extreme environments, such as spaceflight, where multiple stressors act simultaneously on diverse systems, the limitations of the reductionist paradigm become particularly consequential. This motivates the development of an integrative Network Physiology framework, to explore the laws of cross-system communication and the principles of hierarchical network integration among diverse systems in the human organism as fundamental determinants of health and disease (Ivanov and Bartsch, 2014; Ivanov et al., 2017; Ivanov, 2021; Ivanov et al., 2021).
As deep-space missions venture beyond Low Earth Orbit, without real-time medical support from Earth, there is a pressing need to transition to Earth-independent healthcare systems and optimized countermeasures (Scott et al., 2020). This means equipping crews with autonomous health monitoring and decision-support tools that can predict, and guide treatment of illness before they become mission-threatening (Goswami et al., 2012; Goswami et al., 2013). Such a paradigm shift in astronaut healthcare will rely on advanced technologies: (i) AI for data interpretation and decision support, and informed multi-omics profiling. However, a key impediment to AI and multi-omic approaches is a lack of systematically acquired data volume that commercial spaceflight may be better able to deliver than traditional ‘professional’ spaceflight (Green, 2024); (ii) Integrated platforms of medical devices and sensor networks of spatially distributed, autonomous devices (nodes) that cooperatively monitor and synchronously record high-frequency multi-channel physiological data in space (Ivanov, 2021). However, even with increasing availability of systematically recorded data, a critical challenge lies in understanding how spaceflight perturbs the dynamic interactions among physiological systems, rather than isolated molecular pathways or organs. Such interactions may underpin the high levels of intra-individual variability seen in physiological adaptation to spaceflight, and even its ground-based analogues such as long duration head down tilt bed rest (Scott et al., 2021). Addressing this challenge requires the development of novel analytic methodologies and an integrative Network Physiology framework, and their application to space physiology and medicine. Within this framework, physiological adaptation and dysfunction are understood as network-level processes, reflecting changes in the structure, dynamics, causality, and integration of interacting systems under conditions in which the combined effects of microgravity, radiation exposure, circadian and metabolic disruption, confinement, and isolation may be active. These approaches will also help address the challenge of relatively low volumes of multi-modal data recorded in space compared to some terrestrial studies.
Network physiology as a unifying framework for precision space health
Spaceflight challenges human health through the combined action of microgravity, radiation exposure, isolation, confinement, and circadian disruption. However, these stressors do not affect physiological systems independently, but instead also perturb the dynamic interactions and functional coupling among organ systems that collectively sustain global physiological states.
Traditional space medicine has largely focused on isolated molecular, cellular, or organ-level changes (Ivanov et al., 1999; Goswami et al., 2021; Siddiqui et al., 2021; Mason et al., 2024; ), leaving a critical gap in understanding how spaceflight alters the integration, coordination, and adaptability of physiological networks. Network Physiology provides a multidisciplinary theoretical and analytical framework to evaluate these complex systems by explicitly characterizing how physiological systems interact as a coordinated adaptive network across hierarchical levels of physiological organization, from genomic and metabolic networks to organ systems and whole-organism function (Figure 2).
FIGURE 2
Over the past decade, terrestrial studies in Network Physiology have demonstrated that (i) distinct physiological states are characterized by unique network structure and dynamics of systems interactions, and that (ii) transitions across states and conditions in health and disease are associated with coordinated reconfiguration of cross-system interactions (; Liu et al., 2015a; Liu et al., 2015b; Rizzo et al., 2022; Rizzo et al., 2023). Skeletal muscle function, for example, is governed by inter-muscular coordination networks that degrade with fatigue and aging, and reorganize with structured training (; ), while maladaptive responses to prolonged stress have been conceptualized as emergent network dysregulation in complex systems such as overtraining syndrome (Romero-Ortuño et al., 2021; ). Network Physiology of aging research further shows progressive breakdown of inter-organ coordination and resilience (Romero-Ortuño et al., 2021; ), and restoration of muscle network organization in older adults accompanying functional recovery after targeted intervention (). Similarly, physiological stressors related to circadian and sleep disruption were found to reorganize synchronization and functional coupling across behavioral, metabolic, and neurophysiological systems (Healy et al., 2021; Tubbs et al., 2022), and that cardio-respiratory regulation exhibits state- and age-dependent changes in coupling strength and directionality (; ; ). Time-varying information measures further reveal adaptive brain–heart interactions reflecting dynamic autonomic and cognitive states (). Although the Network Physiology framework has not been applied to in-flight data yet, the accumulated findings from terrestrial studies collectively establish a conceptual and analytical foundation for investigating how the multi-stressor environment during spaceflight may affect the vertical and horizontal network integration across physiological systems and would be essential to develop more comprehensive strategies for prevention, recovery and treatment of space-related disorders.
The Network Physiology in Space perspective (Figure 2) extends to a mission-phase timeframe investigations encompassing pre-flight, in-flight, and post-flight assessment. Spaceflight provides a dynamic, complex challenge characterized not only by data collection in flight but also pre- and post-flight—termed baseline data collection (BDC). BDC is a key part of any mission not only because comprehensive data collection in flight is challenging or impossible (e.g., MRI), but also because of the need to prepare for spaceflight, and to track and promote re-adaptation and rehabilitation for life back on Earth.
Earth-based training and baseline adaptation
During Earth-based training and pre-flight preparation, astronauts undergo controlled physiological challenges such as short-arm centrifugation to experience loading, confinement, altered loading such as acute exposure to microgravity in parabolic flight, and familiarity with inflight exercise paradigms. Network Physiology provides quantitative tools to establish baseline patterns of multi-system coordination, capturing normative interactions among systems including the cardiovascular, respiratory, neuromuscular, and neural systems known to be modulated in space (Goswami et al., 2026a). Previous studies on Earth have shown that physiological network structure and dynamics systematically reorganize across fundamental states such as sleep and wake, rest and fatigue, and aging, offering a reference framework against which spaceflight-induced deviations can be assessed (Figure 2).
Spaceflight exposure and in-flight adaptation
During spaceflight, exposure to microgravity, radiation, and psychosocial stressors leads to profound alterations in circulation, musculoskeletal loading, neuro-vestibular control, immune regulation, and psychophysiological state (Goswami et al., 2026a). Within the Network Physiology framework, these changes are interpreted as state-dependent reconfigurations of interacting physiological networks, rather than isolated adaptions/dysfunction of individual systems. Studies of brain–heart (; Lin et al., 2016; Valenza et al., 2025), brain–muscular (Rizzo et al., 2020; Roeder et al., 2020; Rizzo et al., 2022; Roeder et al., 2024), carido-respiratory (; ), cardio–muscular (), and inter-muscular (Kerkman et al., 2018; ; ) interactions on Earth demonstrate that coupling strength, directionality, and characteristic time delays are sensitive markers of functional state and stress, suggesting that similar network-level signatures may reveal early indicators of maladaptation during spaceflight (Figure 2).
Return to earth, recovery, and rehabilitation
Following return to Earth, astronauts undergo a prolonged period of physiological recovery and rehabilitation, during which the restoration of ‘normal’ (that is, 1 appropriate) function depends on the integrated response of multi-system networks. Network Physiology enables longitudinal tracking of how physiological coupling patterns recover, remain altered, or reorganize into compensatory configurations. Evidence from aging, fatigue, and neurodegenerative conditions indicates that incomplete recovery is often associated with persistent breakdowns in network coordination (Rizzo et al., 2023; ; ), underscoring the importance of the network-based metrics for evaluating rehabilitation strategies and effectiveness, as well as long-term health risks after spaceflight (Figure 2).
Methodological approaches to physiological systems integration in spaceflight
Recent advances in systems biology, multi-omics, nonlinear dynamics and information theory for multi-variate physiological systems interactions, and computational modeling have enabled deeper exploration of vertical and horizontal integration (Guyton et al., 1955; Guyton et al., 1972; Kitano, 2002; ; ; Moorman et al., 2016). Molecular profiling (genomics, proteomics, metabolomics) provides detailed insight into cellular and tissue-level mechanisms, while imaging and physiological monitoring capture organ-level dynamics. Data-driven network and modeling approaches, including adaptive network theory of dynamical systems provide structural and dynamical representations of inter-organ connectivity and facilitate the study of horizontal integration by quantifying inter-organ interactions (Morandotti et al., 2025; ; ; Lin et al., 2016; ; Sawicki et al., 2022). Complementing structural network models, dynamical systems analysis characterizes the temporal evolution, stability, causality, and cross-scale propagation of physiological processes. Within the Network Physiology framework, analytical approaches—such as information-theoretic measures, synchronization analysis, causal inference techniques, and adaptive network modeling—enable quantitative assessment of coupling strength, directionality, time delays, and state-dependent reconfiguration among interacting physiological systems (Figure 3). For example, computational models of the cardiovascular-respiratory system can predict how perturbations in heart rate or lung function propagate through the organism, providing mechanistic insights that are difficult to achieve experimentally or comprehend from reductionist experiments.
FIGURE 3
Furthermore, integrative experimental platforms, such as organ-on-chip and multi-organ micro-physiological systems, allow researchers to model vertical and horizontal interactions in vitro. By combining human cell-derived organoids with microfluidic networks, these platforms recapitulate tissue-specific functions and inter-organ communication, enabling mechanistic studies of drug responses and disease pathophysiology (Wang and Qin, 2023), and providing controlled experimental platforms to investigate hierarchical interactions within a network-of-networks architecture.
Mathematical modeling of systems dynamics and network interactions from spaceflight data
Spaceflight imposes persistent stressors that reshape interactions among physiological systems, altering feedback mechanisms and control structures. These changes give rise to emergent, network-level effects—such as cardiovascular deconditioning, orthostatic intolerance, immune dysfunction, and musculoskeletal wasting—rather than discrete failures of individual systems. Mathematical modeling (Figure 4) offers a rigorous framework to: (i) Describe coupled physiological sub-systems through differential equations, delay dynamics, and control formulations; (ii) Characterize feedback behavior, stability, resilience, and adaptive responses in altered gravity environments; (iii) Forecast whole-system behavior when direct experimentation is limited; (iv) Test and refine countermeasures computationally prior to implementation.
FIGURE 4
In the context of space physiology, such models are particularly critical because available data are limited, costly to obtain, and subject to ethical constraints.
Mathematical modeling is a valuable tool for analyzing cardiovascular function during spaceflight (Keith Sharp et al., 2013). Previous research has reported how cardiovascular system models support space life sciences research, using post-flight orthostatic intolerance (POI)—a major spaceflight-related concern—as a representative example. POI affects a large proportion of astronauts during post-flight stand tests and manifests as dizziness, fainting, and related symptoms that can impair crew performance and pose safety risks. These effects could be even more severe during missions to the Moon or Mars. For more than a decade, POI has been the primary focus of cardiovascular modeling in bioastronautics, with numerous physiological mechanisms explored. Modeling strategies range from computational models with varying levels of hemodynamic detail to physical models evaluated in parabolic and orbital flight. Mathematical techniques, including parameter sensitivity analysis, can help identify dominant system mechanisms and inform the development of more effective countermeasures. Model validation remains a persistent challenge, underscoring the need for targeted experimental data to improve interpretation of cardiovascular responses. Future work is expected to emphasize subject-specific modeling and integrated physiological analyses, enabling assessment of individual susceptibility to POI and evaluation of cardiovascular effects on musculoskeletal, visual, and cognitive function.
Mathematical modeling, especially when grounded in rigorous control theory and validation methods (; ), is essential for transforming space physiology from descriptive observation into predictive, network-based, and optimizable science. Additional mathematical models that could be used in Network Physiology include: (i) Mechanistic modeling of cardiovascular, cardio–respiratory, cardio-muscular, and inter-muscular control systems, explicitly addressing nonlinear feedback and transport delays. Such models could become a gold standard for exploration and validation of hypotheses reflecting sub-system interactions, for analyzing system level function, and sensitivity analysis of sub-system parameters; (ii) Model validation, parameter identifiability, and sensitivity analysis, which are critical when working with limited astronaut data; (iii) Optimization of experimental design, ensuring models extract maximal information from minimal measurements; (iv) Application of control theory and adaptive network concepts (stability, regulation, robustness, causality, directionality, synchronization, time-delays, adaptive feedbacks, synergetics and emergence) to physiological systems and their interactions (Rosenblum et al., 1996; Tass et al., 1998; Pikovsky et al., 2001; Ott, 2002; ; Xu et al., 2006; Keener and Sneyd, 2009; Haken, 2012; ; ; Zabczyk, 2020; Ivanov, 2021).
Work in this area directly supports the transition from descriptive models to predictive, actionable physiological network models, making it highly relevant for space medicine and long-duration missions. When extended beyond single systems, mechanistic models naturally integrate with Network Physiology, where: (i) nodes represent physiological systems and sub-systems; (ii) network links represent time-varying regulatory coupling, feedback, or information flow; (iii) network structure, dynamics and parameters evolve across spaceflight mission phases (Figure 4).
This hybrid approach bridges network science and control-oriented physiological modeling, enabling both interpretability and prediction. In addition, combination models incorporating mechanistic modelling approaches reflecting known physiological processes and machine learning approaches providing black box input/output modelling using known data when specific physiology is unknown allow for expanded modelling of physiological systems and the use of a variety of data types (Procopio et al., 2023).
Integration with AI and translational perspectives
Importantly, Network Physiology provides the conceptual physiological structure necessary to guide applications of AI or data-driven modeling. AI and machine learning techniques facilitate pattern discovery, network inference, and prediction in high-dimensional data, and are essential components of the Network Physiology methodological instrumentarium necessary for comprehensive quantification and interpretation in terms of meaningful physiological interactions. To address challenges in Network Physiology, a new generation of physiologically inspired AI and machine learning algorithms are needed, specificity trained to simultaneously respond to both spatial and temporal features of dynamic network and track real-time changes of states and conditions. Hybrid approaches combining mechanistic modeling and machine learning enable computational physiological networks capable of simulating system-wide responses, predicting intervention outcomes, and guiding personalized countermeasures across the entire spaceflight architecture (Ivanov, 2021).
AI and digital twins: clinical implementation
AI-enabled digital twins have strong potential to advance aerospace medicine through personalized physiological modeling, risk prediction, and decision support, not least in extreme operational environments including spaceflight (; ; Hargens and Vico, 2016; Viceconti et al., 2016; ; Topol, 2019; ; Smuck et al., 2021; Nasarian et al., 2024; ). However, clinical implementation depends on robust validation across heterogeneous and longitudinal data, as well as mission-relevant stressors such as microgravity, hypoxia, circadian disruption, and radiation (). Operational readiness requires integration with clinical workflows, real-time data from wearable and onboard systems, and compliance with safety-critical regulatory standards (Topol, 2019; Smuck et al., 2021). Transparent and interpretable AI is essential to support effective human–AI teaming and clinical oversight (; Nasarian et al., 2024). Aerospace systems engineering practices provide a proven framework for addressing these challenges and enabling responsible deployment of digital twins in clinical and aerospace medical settings () (Figure 4).
Whilst spaceflight is interesting and timely in itself, the integrative framework of Network Physiology positions space as a living laboratory for understanding resilience, adaptation, and aging. Insights gained from extreme environments have been used to inform diagnostics, therapies, and rehabilitation strategies for aging, but also conditions associated with metabolic disease, neurodegeneration, and muscle-skeletal rehabilitation on Earth. Thus, Network Physiology provides an opportunity to reinforce the bidirectional impact of space physiology and terrestrial medicine upon each other.
Artificial gravity and countermeasures
Artificial gravity, particularly via short-arm human centrifugation, is a promising countermeasure for mitigating multisystem deconditioning during long-duration spaceflight (Lackner and DiZio, 2000; ; ; Hargens and Vico, 2016; Viceconti et al., 2016; ; ). These systems allow controlled, intermittent hyper gravity exposure within compact and operationally feasible platforms, with precise modulation of gravity dose and timing that is effective (; Laing et al., 2020), but also tolerable (). From an AI and digital twin perspective, artificial gravity provides an effective testbed for personalized countermeasure development, as key physiological responses can be continuously monitored using multi-modal sensing and biomarker data (Hargens and Vico, 2016). Integration of AI-driven digital twins with artificial gravity systems enables closed-loop, individualized optimization of countermeasure protocols based on real-time physiological feedback and mission requirements (; Goswami et al., 2015; ; Stead et al., 2025). This approach supports precision aerospace medicine and offers a controlled environment for validating digital twin models prior to clinical or mission deployment ().
The human physiolome on earth and in space
The Human Physiolome (; Ivanov, 2021) represents a comprehensive, data-driven atlas of the dynamic networks of physiological interactions which uniquely define various physiological states, functions and conditions in health and disease that underline life on Earth. The Human Physiolome aims to quantify and catalog the structure and dynamics of physiological systems cross-communications through large-scale multi-modal synchronized recordings and analytical methods, producing blueprint reference network maps (approximately maps (Ivanov, 2021)) that link patterns of systems interaction and network integration to specific states and conditions in health and disease. By capturing billions of data points of physiological signals and coupling dynamics, the Human Physiolome seeks to demonstrate the fundamental principles of network integration and control that govern organism-level functions. Extending this database beyond Earth, by building the Human Physiolome under spaceflight conditions offers opportunities to understand how vertical and horizontal integration among physiological systems is perturbed in space, with implications for astronaut health, countermeasures development, and broader biological insights into adaptability, resilience, systemic organization, and patterns of cross-communication.
Conclusions and future directions
In summary, hierarchical vertical and horizontal network integration jointly shape physiological function and dysfunction. Vertical integration ensures that molecular and cellular processes coalesce to generate functional organ outputs, while horizontal integration maintains systemic homeostasis through dynamic inter-organ communication. Understanding these dimensions is critical for elucidating the mechanisms underlying health and disease, and for developing interventions that operate across multiple biological scales. Future research leveraging systems biology, computational modeling, and organ-on-chip platforms promises to deepen our mechanistic understanding of physiological integration, paving the way for precision medicine and targeted therapeutics.
Novel computational tools and analytic formalism developed in the field of Network Physiology (; Ivanov and Bartsch, 2014) have added new rich dimensions to our understanding of physiologic states and functions. The integrative perspective has redefined physiologic states from the point of view of dynamic adaptive networks of systems interactions. This has helped establish the first associations between distinct physiologic states and conditions and both network topology and the temporal characteristics of systems and organ interactions (network links), even when network topology remains unchanged. It has been shown that brain–organ interactions have preferred channels of communication (frequency bands) that are specific for each organ (; Rizzo et al., 2020; Rizzo et al., 2023). Recent efforts have focused on brain–heart (Lin et al., 2016; Valenza et al., 2016), cardio-respiratory (; ; ), muscle fiber and inter-muscle coordination (; ), and cardio-muscular networks () to identify new aspects of coupling dynamics and feedback mechanisms. By developing the theoretical framework necessary to uncover basic principles of (i) integration among diverse physiologic systems (from genomics to organs) that leads to complex physiologic functions at the organism level, and (ii) hierarchical reorganization of physiological networks and their evolution across states and conditions, investigations in the field of Network Physiology provide the building blocks of a first atlas of dynamic organ interactions, the Human Physiolome (Ivanov, 2021).
Establishing Network Physiology in space: a new frontier in Network Physiology
Spaceflight uniquely exposes humans to microgravity, radiation, isolation, and circadian disruption, challenging the physiological networks that sustain life. Building on developments in the multidisciplinary field of Network Physiology over the last 15 years, there is a clear necessity, vision, and perspective for the development of a new research area, Network Physiology in Space, that would focus on applying network physiology approaches to multi-scale data from space and space-analogue environments to elucidate mechanisms of adaptation, system vulnerability, and recovery. By combining multi-omics integration, advanced physiological monitoring, data-driven computational and network modeling, Network Physiology in Space would study how physiological networks across systems and levels of biological organization reorganize in space, thereby improving our understanding of adverse space effects, including muscle and bone loss, cardio-vascular and cardio-respiratory deconditioning, and immune and neuro-vestibular adaptations. These investigations would also inform diagnostics and therapies relevant to aging, metabolic disorders, and rehabilitation on Earth. Thus, space serves as a model for resilience, advancing both human spaceflight and terrestrial medicine. The importance of research on physiological networks is also evidenced by the recent ASTROAIMED 2025 Consortium recommendations (Goswami et al., 2026b) that advocate for the establishment of Network Physiology in Space as a new research area to investigate human or animal spaceflight data, analogues of spaceflight (e.g., bed rest, dry immersion, isolation) (Pišot et al., 2016; Marusic et al., 2021; Ritzmann et al., 2026), ground-based platforms (e.g., parabolic flights, artificial gravity), or countermeasures such as lower body negative pressure (; ; Goswami et al., 2019; ).
Network Physiology in Space will play essential role in addressing fundamental questions related to basic physiology and clinical medicine on Earth and in space, including: Molecular and cellular networks: Effects of space stressors on gene expression, epigenetics, proteomics, metabolism, and cell-signaling networks; Organ and system adaptations: Network-level coordination among muscle and bone loss, cardiovascular and respiratory changes, immune dysregulation, and neuro-vestibular modulation; Novel Methodological approaches: Systems biology, multi-omics integration, network synchronization, adaptive networks, information theory of multivariate signals, causality, time-varying interactions, time delay stability, computational modeling, and biomarker discovery; Translational applications: Space-derived diagnostics and interventions for aging, metabolic disease, neurodegeneration, rehabilitation, and resilience; Multidisciplinary studies: Integrating physiology and space medicine with bioinformatics, network science, engineering, and comparative analyses across spaceflight and terrestrial health; Modeling and AI: Use of mechanistic models and AI (e.g., machine learning, network analysis) to capture complex physiological interactions, infer network dynamics, fill knowledge gaps, and create hybrid predictive models. Integrating AI with mechanistic modeling enables simulation of system-wide responses, personalized countermeasures, optimized treatment strategies, and improved mission planning. Together, these approaches provide a powerful framework for understanding human physiology in extreme environments and for developing safer, more effective countermeasures for long-duration space missions.
Summary of key aspects, novel concepts, and methodology for Networks Physiology in Space Applications:
Integrated Physiology Across Scales: Physiological function emerges from both vertical integration (molecules → cells → organs) and horizontal integration (dynamic communication between organs), jointly maintaining health and driving disease mechanisms.
Network Physiology Paradigm: Reframes the body as a dynamic, adaptive network of interactions among systems across levels of organization (from sub-cellular to organs and organism level), where physiological states and functions are defined by network hierarchal organization across systems and sub-systems and by time-varying interactions, not just individual components.
Organ-System Connectivity: Distinct physiological states are associated with specific network topology, interaction patterns and systems cross-communication channels (e.g., frequency bands) between organs, including brain–heart, cardio-respiratory, cortico-muscular, cardio-muscular, inter-muscular networks.
Toward the Human Physiolome: Ongoing work is building a comprehensive dynamic atlas of physiological systems interactions, a new-kind of BigData comprising thousands (possibly millions) of blueprint reference maps representing physiological networks and their reorganization with transition across states and conditions in health and disease.
New Frontier: Network Physiology in Space: Spaceflight introduces extreme stressors (microgravity, radiation, isolation), making it a unique model to study system-wide physiological adaptation, vulnerability, and recovery.
Research Focus Areas: Multi-scale networks: molecular → cellular → organ systems → organism
System adaptations: central autonomic and peripheral nervous system, cardio-vascular, cardio-respiratory, musculoskeletal, neuro-vestibular, sleep/circadian systems, immune, metabolic circuitry
Advanced methods: theory of adaptive dynamic networks, synchronization, time delay stability, cross-frequency coupling, higher-order network interactions, information theory, causality inference, multi-omics, computational modeling, next-generation bio-inspired AI
Translational impact: aging, neurodegeneration, metabolic disease, rehabilitation, resilience, sleep and circadian disorders
AI & Systems Modelling: Integration of AI with mechanistic models enables prediction of complex physiological responses based on individual systems dynamics and their adaptive network interactions, personalized interventions, and optimized strategies for both space missions and terrestrial medicine.
Broader Impact: Space-based research in Network Physiology will advance precision medicine, improve diagnostics and therapies on Earth, and support safe long-duration human spaceflight.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
Author contributions
NG: Investigation, Methodology, Project administration, Supervision, Writing – original draft, Funding acquisition, Writing – review and editing, Resources. JB: Investigation, Writing – review and editing, Visualization, Methodology. YM: Writing – original draft, Visualization, Methodology, Writing – review and editing, Investigation. PF: Methodology, Investigation, Writing – review and editing. DG: Methodology, Investigation, Writing – review and editing. PI: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review and editing, Project administration.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The project is funded by the Dubai Future Foundation, the UAE RDI program, the W. M. Keck Foundation, United States, and the US-Israel Binational Science Foundation (BSF Grant 2020020). NG, JJB, PMF, and DG are members of the ASTROAIMED Consortium. PChI is the Director of the Keck Laboratory of Network Physiology, Boston University. YJXM and PChI are ASTROAIMED international collaborators.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author PI declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. Generative AI was partially used to assist in the creation of illustrative figures 2, 3 and 4. These figures are conceptual schematics rather than representations of computational analyses or experimental results. The overall structure, scientific content, and conceptual design of the figures were developed by the authors, who substantially modified the AI-assisted outputs to ensure accuracy and consistency with the manuscript. Note, the manuscript text was written solely by the authors, without any AI inputs.
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Summary
Keywords
artificial intelligence (AI), human physiolome, microgravity, multi-omics, network physiology, physiological deconditioning, spaceflight, systems biology
Citation
Goswami N, Batzel JJ, Ma YJX, Fredriksen PM, Green DA and Ivanov PC (2026) Network physiology in space: vision and perspectives on exploring physiological networks during spaceflight for the benefit of life on earth. Front. Netw. Physiol. 6:1817815. doi: 10.3389/fnetp.2026.1817815
Received
26 February 2026
Revised
02 April 2026
Accepted
03 April 2026
Published
24 April 2026
Volume
6 - 2026
Edited by
Marko Gosak, University of Maribor, Slovenia
Reviewed by
Luka Šlosar, Scientific Research Center Koper, Slovenia
Sandra Postic, University of Gothenburg, Sweden
Updates
Copyright
© 2026 Goswami, Batzel, Ma, Fredriksen, Green and Ivanov.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nandu Goswami, nandu.goswami@medunigraz.at, nandu.goswami@dubaihealth.ae; Per Morten Fredriksen, permorten.fredriksen@inn.no; Plamen Ch. Ivanov, plamen@buphy.bu.edu
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