In silico simulation of corticosteroids effect on an NFkB- dependent physicochemical model of systemic inflammation

The Physiological and Pharmacological Significance of the Circadian Timing of the HPA Axis: A Mathematical Modeling Approach

As a potent endogenous regulator of homeostasis, the circadian time-keeping system synchronizes internal physiology to periodic changes in the external environment to enhance survival. Adapting endogenous rhythms to the external time is accomplished hierarchically with the central pacemaker located in the suprachiasmatic nucleus (SCN) signaling the hypothalamus-pituitary-adrenal (HPA) axis to release hormones, notably cortisol, which help maintain the body's circadian rhythm. Given the essential role of HPA-releasing hormones in regulating physiological functions, including immune response, cell cycle, and energy metabolism, their daily variation is critical for the proper function of the circadian timing system. In this review, we focus on cortisol and key fundamental properties of the HPA axis and highlight their importance in controlling circadian dynamics. We demonstrate how systems-driven, mathematical modeling of the HPA axis complements experimental findings, enhances our understanding of complex physiological systems, helps predict potential mechanisms of action, and elucidates the consequences of circadian disruption. Finally, we outline the implications of circadian regulation in the context of personalized chronotherapy. Focusing on the chrono-pharmacology of synthetic glucocorticoids, we review the challenges and opportunities associated with moving toward personalized therapies that capitalize on circadian rhythms.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression

COVID-19 is a disease that manifests itself in a multitude of ways across a wide range of tissues. Many factors are involved, and though impressive strides have been made in studying this novel disease in a very short time, there is still a great deal that is unknown about how the virus functions. Clinical data has been crucial for providing information on COVID-19 progression and determining risk factors. However, the mechanisms leading to the multi-tissue pathology are yet to be fully established. Although insights from SARS-CoV-1 and MERS-CoV have been valuable, it is clear that SARS-CoV-2 is different and merits its own extensive studies. In this review, we highlight unresolved questions surrounding this virus including the temporal immune dynamics, infection of non-pulmonary tissue, early life exposure, and the role of circadian rhythms. Risk factors such as sex and exposure to pollutants are also explored followed by a discussion of ways in which bioengineering approaches can be employed to help understand COVID-19. The use of sophisticated in vitro models can be employed to interrogate intercellular interactions and also to tease apart effects of the virus itself from the resulting immune response. Additionally, spatiotemporal information can be gleaned from these models to learn more about the dynamics of the virus and COVID-19 progression. Application of advanced tissue and organ system models into COVID-19 research can result in more nuanced insight into the mechanisms underlying this condition and elucidate strategies to combat its effects.

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

Multiscale modeling methods in biomechanics

More and more frequently, computational biomechanics deals with problems where the portion of physical reality to be modeled spans over such a large range of spatial and temporal dimensions, that it is impossible to represent it as a single space-time continuum. We are forced to consider multiple space-time continua, each representing the phenomenon of interest at a characteristic space-time scale. Multiscale models describe a complex process across multiple scales, and account for how quantities transform as we move from one scale to another. This review offers a set of definitions for this emerging field, and provides a brief summary of the most recent developments on multiscale modeling in biomechanics. Of all possible perspectives, we chose that of the modeling intent, which vastly affect the nature and the structure of each research activity. To the purpose we organized all papers reviewed in three categories: 'causal confirmation,' where multiscale models are used as materializations of the causation theories; 'predictive accuracy,' where multiscale modeling is aimed to improve the predictive accuracy; and 'determination of effect,' where multiscale modeling is used to model how a change at one scale manifests in an effect at another radically different space-time scale. Consistent with how the volume of computational biomechanics research is distributed across application targets, we extensively reviewed papers targeting the musculoskeletal and the cardiovascular systems, and covered only a few exemplary papers targeting other organ systems. The review shows a research subdomain still in its infancy, where causal confirmation papers remain the most common. WIREs Syst Biol Med 2017, 9:e1375. doi: 10.1002/wsbm.1375 For further resources related to this article, please visit the WIREs website.

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels

In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.

Computational identification and analysis of signaling subnetworks with distinct functional roles in the regulation of TNF production

Inflammation is a complex process driven by the coordinated action of a vast number of pro- and anti-inflammatory molecular mediators. While experimental studies have provided an abundance of information about the properties and mechanisms of action of individual mediators, essential system-level regulatory patterns that determine the time-course of inflammation are not sufficiently understood. In particular, it is not known how the contributions from distinct signaling pathways involved in cytokine regulation combine to shape the overall inflammatory response over different time scales. We investigated the kinetics of the intra- and extracellular signaling network controlling the production of the essential pro-inflammatory cytokine, tumor necrosis factor (TNF), and its anti-inflammatory counterpart, interleukin 10 (IL-10), in a macrophage culture. To tackle the intrinsic complexity of the network, we employed a computational modeling approach using the available literature data about specific molecular interactions. Our computational model successfully captured experimentally observed short- and long-term kinetics of key inflammatory mediators. Subsequent model analysis showed that distinct subnetworks regulate IL-10 production by impacting different temporal phases of the cAMP response element-binding protein (CREB) phosphorylation. Moreover, the model revealed that functionally similar inhibitory control circuits regulate the early and late activation phases of nuclear factor κB and CREB. Finally, we identified and investigated distinct signaling subnetworks that independently control the peak height and tail height of the TNF temporal trajectories. The knowledge of such subnetwork-specific regulatory effects may facilitate therapeutic interventions aimed at precise modulation of the inflammatory response.

Chronic Nonbacterial Osteomyelitis: Pathophysiological Concepts and Current Treatment Strategies

Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder, covering a clinical spectrum with asymptomatic inflammation of single bones at the one end, and chronic recurrent multifocal osteomyelitis (CRMO) at the other end. The exact molecular pathophysiology of CNO remains largely unknown. Provided familial clusters and the association with inflammatory disorders of the skin and intestine suggest a genetic predisposition. Recently, profound dysregulation of cytokine responses was demonstrated in CRMO. Failure to produce antiinflammatory cytokines interleukin (IL)-10 and IL-19 contributes to activation of inflammasomes and subsequent IL-1β release. In IL-10-deficient and in CNO-prone chronic multifocal osteomyelitis mice, IL-1β was linked to bone inflammation. Further, alterations to the gut microbiome were suggested in contributing to IL-1β release from innate immune cells in mice, offering an interesting target in the search for molecular mechanisms in CNO. Here, we summarize clinical presentation and treatment options in CNO/CRMO, current pathophysiological concepts, available mouse models, and promising future scientific directions.

Systems Biology of Immunomodulation for Post-Stroke Neuroplasticity: Multimodal Implications of Pharmacotherapy and Neurorehabilitation

AIMS

Recent studies indicate that anti-inflammatory drugs, act as a double-edged sword, not only exacerbating secondary brain injury but also contributing to neurological recovery after stroke. Our aim is to explore whether there is a beneficial role for neuroprotection and functional recovery using anti-inflammatory drug along with neurorehabilitation therapy using transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), so as to improve functional recovery after ischemic stroke.

METHODS

We develop a computational systems biology approach from preclinical data, using ordinary differential equations, to study the behavior of both phenotypes of microglia, such as M1 type (pro-inflammatory) vis-à-vis M2 type (anti-inflammatory) under anti-inflammatory drug action (minocycline). We explore whether pharmacological treatment along with cerebral stimulation using tDCS and rTMS is beneficial or not. We utilize the systems pathway analysis of minocycline in nuclear factor kappa beta (NF-κB) signaling and neurorehabilitation therapy using tDCS and rTMS that act through brain-derived neurotrophic factor (BDNF) and tropomyosin-related kinase B (TrkB) signaling pathways.

RESULTS

We demarcate the role of neuroinflammation and immunomodulation in post-stroke recovery, under minocycline activated-microglia and neuroprotection together with improved neurogenesis, synaptogenesis, and functional recovery under the action of rTMS or tDCS. We elucidate the feasibility of utilizing rTMS/tDCS to increase neuroprotection across the reperfusion stage during minocycline administration. We delineate that the signaling pathways of minocycline by modulation of inflammatory genes in NF-κB and proteins activated by tDCS and rTMS through BDNF, TrkB, and calmodulin kinase (CaMK) signaling. Utilizing systems biology approach, we show that the activation pathways for pharmacotherapy (minocycline) and neurorehabilitation (rTMS applied to ipsilesional cortex and tDCS) results into increased neuronal and synaptic activity that commonly occur through activation of N-methyl-d-aspartate receptors. We construe that considerable additive neuroprotection effect would be obtained and delayed reperfusion injury can be remedied, if one uses multimodal intervention of minocycline together with tDCS and rTMS.

CONCLUSION

Additive beneficial effect is, thus, noticed for pharmacotherapy along with neurorehabilitation therapy, by maneuvering the dynamics of immunomodulation using anti-inflammatory drug and cerebral stimulation for augmenting the functional recovery after stroke, which may engender clinical applicability for enhancing plasticity, rehabilitation, and neurorestoration.

The role of the hypothalamic-pituitary-adrenal axis in modulating seasonal changes in immunity

Seasonal changes in environmental conditions are accompanied by significant adjustment of multiple biological processes. In temperate regions, the day fraction, or photoperiod, is a robust environmental cue that synchronizes seasonal variations in neuroendocrine and metabolic function. In this work, we propose a semimechanistic mathematical model that considers the influence of seasonal photoperiod changes as well as cellular and molecular adaptations to investigate the seasonality of immune function. Our model predicts that the circadian rhythms of cortisol, our proinflammatory mediator, and its receptor exhibit seasonal differences in amplitude and phase, oscillating at higher amplitudes in the winter season with peak times occurring later in the day. Furthermore, the reduced photoperiod of winter coupled with seasonal alterations in physiological activity induces a more exacerbated immune response to acute stress, simulated in our studies as the administration of an acute dose of endotoxin. Our findings are therefore in accordance with experimental data that reflect the predominance of a proinflammatory state during the winter months. These changes in circadian rhythm dynamics may play a significant role in the seasonality of disease incidence and regulate the diurnal and seasonal variation of disease symptom severity.

A Biomathematical Model of Pneumococcal Lung Infection and Antibiotic Treatment in Mice

Pneumonia is considered to be one of the leading causes of death worldwide. The outcome depends on both, proper antibiotic treatment and the effectivity of the immune response of the host. However, due to the complexity of the immunologic cascade initiated during infection, the latter cannot be predicted easily. We construct a biomathematical model of the murine immune response during infection with pneumococcus aiming at predicting the outcome of antibiotic treatment. The model consists of a number of non-linear ordinary differential equations describing dynamics of pneumococcal population, the inflammatory cytokine IL-6, neutrophils and macrophages fighting the infection and destruction of alveolar tissue due to pneumococcus. Equations were derived by translating known biological mechanisms and assuming certain response kinetics. Antibiotic therapy is modelled by a transient depletion of bacteria. Unknown model parameters were determined by fitting the predictions of the model to data sets derived from mice experiments of pneumococcal lung infection with and without antibiotic treatment. Time series of pneumococcal population, debris, neutrophils, activated epithelial cells, macrophages, monocytes and IL-6 serum concentrations were available for this purpose. The antibiotics Ampicillin and Moxifloxacin were considered. Parameter fittings resulted in a good agreement of model and data for all experimental scenarios. Identifiability of parameters is also estimated. The model can be used to predict the performance of alternative schedules of antibiotic treatment. We conclude that we established a biomathematical model of pneumococcal lung infection in mice allowing predictions regarding the outcome of different schedules of antibiotic treatment. We aim at translating the model to the human situation in the near future.

Regulation of signal transduction by spatial parameters: a case in NF-κB oscillation

NF-κB is a transcription factor regulating expression of more than 500 genes, and its dysfunction leads to the autoimmune and inflammatory diseases. In malignant cancer cells, NF-κB is constitutively activated. Thus the elucidation of mechanisms for NF-κB regulation is important for the establishment of therapeutic treatment caused by incorrect NF-κB responses. Cytoplasmic NF-κB translocates to the nucleus by the application of extracellular stimuli such as cytokines. Nuclear NF-κB is known to oscillate with the cycle of 1.5-4.5 h, and it is thought that the oscillation pattern regulates the expression profiles of genes. In this review, first we briefly describe regulation mechanisms of NF-κB. Next, published computational simulations on the oscillation of NF-κB are summarised. There are at least 60 reports on the computational simulation and analysis of NF-κB oscillation. Third, the importance of a 'space' for the regulation of oscillation pattern of NF-κB is discussed, showing altered oscillation pattern by the change in spatial parameters such as diffusion coefficient, nuclear to cytoplasmic volume ratio (N/C ratio), and transport through nuclear membrane. Finally, simulations in a true intracellular space (TiCS), which is an intracellular 3D space reconstructed in a computer with organelles such as nucleus and mitochondria are discussed.

Circadian characteristics of permissive and suppressive effects of cortisol and their role in homeostasis and the acute inflammatory response

In this work we explore a semi-mechanistic model that considers cortisol's permissive and suppressive effects through the regulation of cytokine receptors and cytokines respectively. Our model reveals the proactive role of cortisol during the resting period and its reactive character during the body's activity phase. Administration of an acute LPS dose during the night, when cortisol's permissive effects are higher than suppressive, leads to increased cytokine levels compared to LPS administration at morning when cortisol's suppressive effects are higher. Interestingly, our model presents a hysteretic behavior where the relative predominance of permissive or suppressive effects results not only from cortisol levels but also from the previous states of the model. Therefore, for the same cortisol levels, administration of an inflammatory stimulus at cortisol's ascending phase, that follows a time period where cytokine receptor expression is elevated ultimately sensitizing the body for the impending stimulus, leads to higher cytokine expression compared to administration of the same stimulus at cortisol's descending phase.

Bestrophin 3 ameliorates TNFα-induced inflammation by inhibiting NF-κB activation in endothelial cells

Increasing evidences have suggested vascular endothelial inflammatory processes are the initiator of atherosclerosis. Bestrophin 3 (Best-3) is involved in the regulation of cell proliferation, apoptosis and differentiation of a variety of physiological functions, but its function in cardiovascular system remains unclear. In this study, we investigated the effect of Best-3 on endothelial inflammation. We first demonstrated that Best-3 is expressed in endothelial cells and decreased after tumor necrosis factor-α (TNFα) challenge. Overexpression of Best-3 significantly attenuated TNFα-induced expression of adhesion molecules and chemokines, and subsequently inhibited the adhesion of monocytes to human umbilical vein endothelial cells (HUVECs). Conversely, knockdown of Best-3 with siRNA resulted in an enhancement on TNFα-induced expression of adhesion molecules and chemokines and adhesion of monocytes to HUVECs. Furthermore, overexpression of Best-3 with adenovirus dramatically ameliorated inflammatory response in TNFα-injected mice. Mechanistically, we found up-regulation of Best-3 inhibited TNFα-induced IKKβ and IκBα phosphorylation, IκBα degradation and NF-κB translocation. Our results demonstrated that Best-3 is an endogenous inhibitor of NF-κB signaling pathway in endothelial cells, suggesting that forced Best-3 expression may be a novel approach for the treatment of vascular inflammatory diseases.

Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

A pharma perspective on the systems medicine and pharmacology of inflammation

Biological systems are complex and comprehend multiple scales of organisation. Hence, holistic approaches are necessary to capture the behaviour of these entities from the molecular and cellular to the whole organism level. This also applies to the understanding and treatment of different diseases. Traditional systems biology has been successful in describing different biological phenomena at the cellular level, but it still lacks of a holistic description of the multi-scale interactions within the body. The importance of the physiological context is of particular interest in inflammation. Regulatory agencies have urged the scientific community to increase the translational power of bio-medical research and it has been recognised that modelling and simulation could be a path to follow. Interestingly, in pharma R&D, modelling and simulation has been employed since a long time ago. Systems pharmacology, and particularly physiologically based pharmacokinetic/pharmacodynamic models, serve as a suitable framework to integrate the available and emerging knowledge at different levels of the drug development process. Systems medicine and pharmacology of inflammation will potentially benefit from this framework in order to better understand inflammatory diseases and to help to transfer the vast knowledge on the molecular and cellular level into a more physiological context. Ultimately, this may lead to reliable predictions of clinical outcomes such as disease progression or treatment efficacy, contributing thereby to a better care of patients.

Integrating gene expression and protein interaction data for signaling pathway prediction of Alzheimer's disease

Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer's disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF-κB (nuclear factor-kappa B), as a significant inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF-κB and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD.

In silico modeling: methods and applications to trauma and sepsis

OBJECTIVES

To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis.

DATA SOURCES

PubMed search and review of relevant medical literature.

SUMMARY

Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed.

CONCLUSIONS

Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.

A Systems Engineering Perspective on Homeostasis and Disease

Engineered systems are coupled networks of interacting sub-systems, whose dynamics are constrained to requirements of robustness and flexibility. They have evolved by design to optimize function in a changing environment and maintain responses within ranges. Analysis, synthesis, and design of complex supply chains aim to identify and explore the laws governing optimally integrated systems. Optimality expresses balance between conflicting objectives while resiliency results from dynamic interactions among elements. Our increasing understanding of life’s multi-scale architecture suggests that living systems share similar characteristics with much to be learned about biological complexity from engineered systems. If health reflects a dynamically stable integration of molecules, cell, tissues, and organs; disease indicates displacement compensated for and corrected by activation and combination of feedback mechanisms through interconnected networks. In this article, we draw analogies between concepts in systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.

Predicting critical transitions in a model of systemic inflammation

The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed for this purpose, but lack of data has limited applications in physiology. Here, we propose a model-based approach towards identifying critical transitions in systemic inflammation based on a minimal amount of assumptions about the availability of data and the structure of the system. We derived a warning signal metric to identify forthcoming abrupt transitions occurring in a mathematical model of systemic inflammation with a gradually increasing bacterial load. Intervention to remove the inflammatory stimulus was successful in restoring homeostasis if undertaken when the warning signal was elevated rather than waiting for the state variables of the system themselves to begin moving to a new steady state. The proposed combination of data and model-based analysis for predicting physiological transitions represents a step forward towards the quantitative study of complex biological systems.

Lipopolysaccharide-induced inhibition of transcription of tlr4 in vitro is reversed by dexamethasone and correlates with presence of conserved NFκB binding sites

Engagement of Toll-like receptor 4 (TLR4) by lipopolysaccharide (LPS) is a master trigger of the deleterious effects of septic shock. Horses and humans are considered the most sensitive species to septic shock, but the mechanisms explaining these phenomena remain elusive. Analysis of tlr4 promoters revealed high similarity among LPS-sensitive species (human, chimpanzee, and horse) and low similarity with LPS-resistant species (mouse and rat). Four conserved nuclear factor kappa B (NFκB) binding sites were found in the tlr4 promoter and two in the md2 promoter sequences that are likely to be targets for dexamethasone regulation. In vitro treatment of equine peripheral blood mononuclear cells (eqPBMC) with LPS decreased transcripts of tlr4 and increased transcription of md2 (myeloid differentiation factor 2) and cd14 (cluster of differentiation 14). Treatment with dexamethasone rescued transcription of tlr4 after LPS inhibition. LPS-induced transcription of md2 was inhibited in the presence of dexamethasone. Dexamethasone alone did not affect transcription of tlr4 and md2.

An agent-based model of cellular dynamics and circadian variability in human endotoxemia

As cellular variability and circadian rhythmicity play critical roles in immune and inflammatory responses, we present in this study an agent-based model of human endotoxemia to examine the interplay between circadian controls, cellular variability and stochastic dynamics of inflammatory cytokines. The model is qualitatively validated by its ability to reproduce circadian dynamics of inflammatory mediators and critical inflammatory responses after endotoxin administration in vivo. Novel computational concepts are proposed to characterize the cellular variability and synchronization of inflammatory cytokines in a population of heterogeneous leukocytes. Our results suggest that there is a decrease in cell-to-cell variability of inflammatory cytokines while their synchronization is increased after endotoxin challenge. Model parameters that are responsible for IκB production stimulated by NFκB activation and for the production of anti-inflammatory cytokines have large impacts on system behaviors. Additionally, examining time-dependent systemic responses revealed that the system is least vulnerable to endotoxin in the early morning and most vulnerable around midnight. Although much remains to be explored, proposed computational concepts and the model we have pioneered will provide important insights for future investigations and extensions, especially for single-cell studies to discover how cellular variability contributes to clinical implications.

Modeling physiologic variability in human endotoxemia

The control and management of inflammation is a key aspect of clinical care for critical illnesses such as sepsis. In an ideal reaction to injury, the inflammatory response provokes a strong enough response to heal the injury and then restores homeostasis. When inflammation becomes dysregulated, a persistent inflammatory state can lead to significant deleterious effects and clinical challenges. Thus, gaining a better biological understanding of the mechanisms driving the inflammatory response is of the utmost importance. In this review, we discuss our work with the late Stephen F. Lowry to investigate systemic inflammation through systems biology of human endotoxemia. We present our efforts in modeling the human endotoxemia response with a particular focus on physiologic variability. Through modeling, with a focus ultimately on translational applications, we obtain more fundamental understanding of relevant physiological processes. And by taking advantage of the information embedded in biological rhythms, ranging in time scale from high-frequency autonomic oscillations reflected in heart rate variability to circadian rhythms in inflammatory mediators, we gain insight into the underlying physiology.

Translational applications of evaluating physiologic variability in human endotoxemia

Dysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and the disruption of these rhythms in inflammation may be applied in a translational context. By leveraging the information content embedded in physiologic variability, ranging in scale from oscillations in autonomic activity driving short-term heart rate variability to circadian rhythms in immunomodulatory hormones, there is significant potential to gain insight into the underlying physiology.

Computational and systems biology in trauma and sepsis: current state and future perspectives

Trauma, often accompanied by hemorrhage, is a leading cause of death worldwide, often leading to inflammation-related late complications that include sepsis and multiple organ failure. These secondary complications are a manifestation of the complexity of biological responses elicited by trauma/hemorrhage, responses that span most, if not all, cell types, tissues, and organ systems. This daunting complexity at the patient level is manifest by the near total dearth of available therapeutics, and we suggest that this dire condition is due in large part to the lack of a rational, systems-oriented framework for drug development, clinical trial design, in-hospital diagnostics, and post-hospital care. We have further suggested that mechanistic computational modeling can form the basis of such a rational framework, given the maturity of systems biology/computational biology. Herein, we briefly summarize the state of the art of these approaches, and highlight the biological insights and novel hypotheses derived from these approaches. We propose a rational framework for transitioning through the currently fragmented process from identification of biological networks that are potential therapeutic targets, through clinical trial design, to personalized diagnosis and care. Insights derived from systems and computational biology in trauma and sepsis include the centrality of Damage-Associated Molecular Pattern molecules as drivers of both beneficial and detrimental inflammation, along with a novel view of multiple organ dysfunction as a cascade of containment failures with distinct implications for therapy. Finally, we suggest how these insights might be best implemented to drive transformational change in the fields of trauma and sepsis.

Sepsis: from pattern to mechanism and back

Sepsis is a clinical entity in which complex inflammatory and physiological processes are mobilized, not only across a range of cellular and molecular interactions, but also in clinically relevant physiological signals accessible at the bedside. There is a need for a mechanistic understanding that links the clinical phenomenon of physiologic variability with the underlying patterns of the biology of inflammation, and we assert that this can be facilitated through the use of dynamic mathematical and computational modeling. An iterative approach of laboratory experimentation and mathematical/computational modeling has the potential to integrate cellular biology, physiology, control theory, and systems engineering across biological scales, yielding insights into the control structures that govern mechanisms by which phenomena, detected as biological patterns, are produced. This approach can represent hypotheses in the formal language of mathematics and computation, and link behaviors that cross scales and domains, thereby offering the opportunity to better explain, diagnose, and intervene in the care of the septic patient.

Pharmacokinetic/pharmacodynamic modeling in inflammation

Inflammation is an array of immune responses to infection and injury. It results from a complex immune cascade and is the basis of many chronic diseases such as arthritis, diabetes, and cancer. Numerous mathematical models have been developed to describe the disease progression and effects of anti-inflammatory drugs. This review illustrates the state of the art in modeling the effects of diverse drugs for treating inflammation, describes relevant biomarkers amenable to modeling, and summarizes major advantages and limitations of the published pharmacokinetic/ pharmacodynamic (PK/PD) models. Simple direct inhibitory models are often used to describe in vitro effects of anti-inflammatory drugs. Indirect response models are more mechanism based and have been widely applied to the turnover of symptoms and biomarkers. These, along with target-mediated and transduction models, have been successfully applied to capture the PK/PD of many anti-inflammatory drugs and describe disease progression of inflammation. Biologics have offered opportunities to address specific mechanisms of action, and evolve small systems models to quantitatively capture the underlying physiological processes. More advanced mechanistic models should allow evaluation of the roles of some key mediators in disease progression, assess drug interactions, and better translate drug properties from in vitro and animal data to patients.

A physiological model for autonomic heart rate regulation in human endotoxemia

The systemic inflammatory response syndrome often accompanies critical illnesses and can be an important cause of morbidity and mortality. Marked abnormalities in cardiovascular function accompany acute illnesses manifested as sustained tachyarrhythmias, which are but one component of systemic dysregulation. The realization that cardiac pacemaker activity is under control of the autonomic nervous system has promoted the analysis of heart rate (HR) variation for assessing autonomic activities. In acute illnesses, autonomic imbalance manifesting in part as parasympathetic attenuation is associated with increased morbidity in patients who manifest systemic inflammatory response syndrome phenotype. Driven by the premise that biological phenotypes emerge as the outcome of the coordinated action of network elements across the host, a multiscale model of human endotoxemia, as a prototype model of systemic inflammation in humans, is developed that quantifies critical aspects of the complex relationship between inflammation and autonomic HR regulation. In the present study, changes in HR response to acute injury, phenotypically expressed as tachycardia, are simulated as a result of autonomic imbalance that reflects sympathetic activity excess and parasympathetic attenuation. The proposed model assesses both the anti-inflammatory and cardiovascular effects of antecedent stresses upon the systemic inflammatory manifestations of human endotoxemia as well as a series of nonlinear inflammatory relevant scenarios. Such a modeling approach provides a comprehensive conceptual framework linking inflammation and physiological complexity via a multiscale model that may advance the translational potential of systems modeling in clinical research.

Sepsis: Something old, something new, and a systems view

Sepsis is a clinical syndrome characterized by a multisystem response to a microbial pathogenic insult consisting of a mosaic of interconnected biochemical, cellular, and organ-organ interaction networks. A central thread that connects these responses is inflammation that, while attempting to defend the body and prevent further harm, causes further damage through the feed-forward, proinflammatory effects of damage-associated molecular pattern molecules. In this review, we address the epidemiology and current definitions of sepsis and focus specifically on the biologic cascades that comprise the inflammatory response to sepsis. We suggest that attempts to improve clinical outcomes by targeting specific components of this network have been unsuccessful due to the lack of an integrative, predictive, and individualized systems-based approach to define the time-varying, multidimensional state of the patient. We highlight the translational impact of computational modeling and other complex systems approaches as applied to sepsis, including in silico clinical trials, patient-specific models, and complexity-based assessments of physiology.

Modeling autonomic regulation of cardiac function and heart rate variability in human endotoxemia

Heart rate variability (HRV), the quantification of beat-to-beat variability, has been studied as a potential prognostic marker in inflammatory diseases such as sepsis. HRV normally reflects significant levels of variability in homeostasis, which can be lost under stress. Much effort has been placed in interpreting HRV from the perspective of quantitatively understanding how stressors alter HRV dynamics, but the molecular and cellular mechanisms that give rise to both homeostatic HRV and changes in HRV have received less focus. Here, we develop a mathematical model of human endotoxemia that incorporates the oscillatory signals giving rise to HRV and their signal transduction to the heart. Connections between processes at the cellular, molecular, and neural levels are quantitatively linked to HRV. Rhythmic signals representing autonomic oscillations and circadian rhythms converge to modulate the pattern of heartbeats, and the effects of these oscillators are diminished in the acute endotoxemia response. Based on the semimechanistic model developed herein, homeostatic and acute stress responses of HRV are studied in terms of these oscillatory signals. Understanding the loss of HRV in endotoxemia serves as a step toward understanding changes in HRV observed clinically through translational applications of systems biology based on the relationship between biological processes and clinical outcomes.

Mechanism of regulation of Na-H exchanger in inflammatory bowel disease: role of TLR-4 signaling mechanism

OBJECTIVE

We investigated the role of toll-like receptor-4 (TLR-4) signal transduction in the regulation of Na-H exchanger-1 isoform (NHE-1) in ulcerative colitis (UC).

METHODS

Colonic biopsies from control and UC patients were selected from four groups: controls (group 1), untreated UC patients (group 2), UC patients treated with 5'-aminosalicylic acid (5'-ASA) plus steroid (group 3), and UC patients treated with 5'-ASA plus azathioprine (AZA) (group 4). Patients presenting with abdominal pain (n = 13) and a normal colon on endoscopy served as controls. NHE-1, TLR-4, MyD88, NFkB and actin protein levels were estimated using Western blot analysis and sodium pump activity (PNPase) by a spectrophotometeric method. Myeloperoxidase (MPO) activity and histologic evaluation confirmed inflammation.

RESULTS

PNPase activity decreased significantly (P < 0.05) in the untreated UC patients as compared to the controls or treated UC groups 3 and 4. There was a significant decrease of NHE-1 and a significant increase (P < 0.05) of TLR-4, MyD88 and NFkB protein levels in the untreated UC or 5'-ASA plus steroid treated UC patients as compared to the controls. NHE-1, TLR-4, MyD88 and NFkB protein levels were not significantly different in 5'-ASA plus AZA treated biopsies as compared to controls. The level of actin remained unaltered. Inflammatory cells' infiltration and MPO activity increased significantly in the untreated UC, but was significantly lower in the treated UC groups 3 and 4 (P < 0.05).

CONCLUSIONS

These findings suggest that NHE-1 in UC is regulated by NFkB induced through TLR-4 and MyD88 signaling mechanism. These findings identify TLR-4 as a putative therapeutic target for IBD.

A dual negative regulation model of Toll-like receptor 4 signaling for endotoxin preconditioning in human endotoxemia

We discuss a model illustrating how the outcome of repeated endotoxin administration experiments can emerge as a natural consequence of the tightly regulated signaling pathways and also highlight the importance of a dual negative feedback regulation including PI3K/Akt and IRAK-M (IRAK3). We identify the relative time scales of the onset and the magnitude of the stimulus as key determinants of outcome in repeated administration experiments. The results of our simulations involve potentiated response, tolerance, and protective tolerance. Moreover, the knockout of negative regulators shows that IRAK-M is a necessary and sufficient factor for generation of endotoxin tolerance (ET). The effects of the knockout of IRAK-M gene or administration of PI3K inhibitor do yield predictions that have been verified experimentally. Finally, the pretreatment with PI3K inhibitor reveals the interaction between these two negative regulations.

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

Translational systems biology of inflammation: potential applications to personalized medicine

A central goal of industrialized nations is to provide personalized, preemptive and predictive medicine, while maintaining healthcare costs at a minimum. To do so, we must confront and gain an understanding of inflammation, a complex, nonlinear process central to many diseases that affect both industrialized and developing nations. Herein, we describe the work aimed at creating a rational, engineering-oriented and evidence-based synthesis of inflammation geared towards rapid clinical application. This comprehensive approach, which we call 'Translational Systems Biology', to date has been utilized for in silico studies of sepsis, trauma/hemorrhage/traumatic brain injury, acute liver failure and wound healing. This framework has now allowed us to suggest how to modulate acute inflammation in a rational and individually optimized fashion using engineering principles applied to a biohybrid device. We suggest that we are on the cusp of fulfilling the promise of in silico modeling for personalized medicine for inflammatory disease.

A quantitative model of thermal injury-induced acute inflammation

Severe burns are among the most common causes of death from unintentional injury. The induction and resolution of the burn-induced systemic inflammatory response are mediated by a network of factors and regulatory proteins. Numerous mechanisms operate simultaneously, thus requiring a systems level approach to characterize their overall impact. Towards this goal, we propose an in silico semi-mechanistic model of burn-induced systemic inflammation using liver-specific gene expression from a rat burn model. Transcriptional responses are coupled with extracellular signals through a receptor mediated indirect response (IDR) and transit compartment model. The activation of the innate immune system in response to the burn stimulus involves the interaction between extracellular signals and critical receptors which triggers downstream signal transduction cascades leading to transcriptional changes. The resulting model consists of fifteen (15) coupled ordinary differential equations capturing key aspects of inflammation such as pro-inflammation, anti-inflammation and hypermetabolism. The model was then evaluated through a series of biologically relevant scenarios aiming at revealing the non-linear behavior of acute inflammation including: investigating the implication of effect of different severity of thermal injury; examining possible mechanistic dysregulation of IKK-NF-κB system which may reflect secondary effects that lead to potential malfunction of the response; and exploring the outcome of administration of receptor antagonist or anti-body to significant cytokines.

Multiscale model for the assessment of autonomic dysfunction in human endotoxemia

Severe injury and infection are associated with autonomic dysfunction. The realization that a dysregulation in autonomic function may predispose a host to excessive inflammatory processes has renewed interest in understanding the role of central nervous system (CNS) in modulating systemic inflammatory processes. Assessment of heart rate variability (HRV) has been used to evaluate systemic abnormalities and as a predictor of the severity of illness. Dissecting the relevance of neuroimmunomodulation in controlling inflammatory processes requires an understanding of the multiscale interplay between CNS and the immune response. A vital enabler in that respect is the development of a systems-based approach that integrates data across multiple scales, and models the emerging host response as the outcome of interactions of critical modules. Thus, a multiscale model of human endotoxemia, as a prototype model of systemic inflammation in humans, is proposed that integrates processes across the host from the cellular to the systemic host response level. At the cellular level interacting components are associated with elementary signaling pathways that propagate extracellular signals to the transcriptional response level. Further, essential modules associated with the neuroendocrine immune crosstalk are considered. Finally, at the systemic level, phenotypic expressions such as HRV are incorporated to assess systemic decomplexification indicative of the severity of the host response. Thus, the proposed work intends to associate acquired endocrine dysfunction with diminished HRV as a critical enabler for clarifying how cellular inflammatory processes and neural-based pathways mediate the links between patterns of autonomic control (HRV) and clinical outcomes.

Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes

BACKGROUND

Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions.

METHODOLOGY/PRINCIPAL FINDINGS

An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades.

CONCLUSIONS/SIGNIFICANCE

The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system.

NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION

Biological systems can be modeled as networks of interacting components across multiple scales. A central problem in computational systems biology is to identify those critical components and the rules that define their interactions and give rise to the emergent behavior of a host response. In this paper we will discuss two fundamental problems related to the construction of transcription factor networks and the identification of networks of functional modules describing disease progression. We focus on inflammation as a key physiological response of clinical and translational importance.