Challenges for modeling and interpreting the complex biology of severe injury and inflammation

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.

Curcumin Protects Against Intestinal Origin Endotoxemia in Rat Liver Cirrhosis by Targeting PCSK9

Intestinal origin endotoxemia always occurs in severe liver injury. The aim of the current study was to test antiendotoxemia effect of curcumin on tetrachloride (CCl₄ )-induced liver cirrhosis rats, and to elucidate the underlying molecular mechanism. Rat cirrhosis models were constructed with CCl₄ subcutaneous injections with curcumin (200 mg/kg/d) administered via gavages for 12 wk until the rats were sacrificed. We found that the administration of curcumin improved the physiological condition pertaining to activity index and temperature, and ameliorated the liver injury in CCl₄ -induced cirrhosis rats. Enzyme-linked immunosorbent assay (ELISA) and real-time quantitative polymerase chain reaction (qRT-PCR) showed that curcumin could reduce c-reaction protein levels and inflammatory cytokine (TNF-α, IL-1β, IL-6, and CINC-1/IL-8) concentrations in peripheral serum and liver tissue. Furthermore, curcumin treatment decreased lipopolysaccharide (LPS) levels in peripheral vein, but not in portal vein. As low-density lipoprotein receptor (LDLR) is the important receptor on the surface of hepatocyte during LPS detoxification process, we used qRT-PCR, western blot, and immunohistochemistry (IHC), finding that curcumin significantly increased LDLR protein levels, but not gene levels in the liver tissues. We also tested proprotein convertase subtilisin/kexin type 9 (PCSK9), one negative regulator of LDLR, by qRT-PCR, western blot, and IHC. The results showed that PCSK9 significantly decreased both gene and protein levels in the rat liver tissues of curcumin treatment. Thus, we concluded that curcumin could function to protect against intestinal origin endotoxemia by inhibiting PCSK9 to promote LDLR expression, thereby enhancing LPS detoxification as one pathogen lipid through LDLR in the liver.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

A Chemical Engineer's Perspective on Health and Disease

Chemical process systems engineering considers complex supply chains which are coupled networks of dynamically interacting systems. The quest to optimize the supply chain while meeting robustness and flexibility constraints in the face of ever changing environments necessitated the development of theoretical and computational tools for the analysis, synthesis and design of such complex engineered architectures. However, it was realized early on that optimality is a complex characteristic required to achieve proper balance between multiple, often competing, objectives. As we begin to unravel life's intricate complexities, we realize that that living systems share similar structural and dynamic characteristics; hence much can be learned about biological complexity from engineered systems. In this article, we draw analogies between concepts in process 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.

Unraveling tissue repair immune responses in flies

Drosophila melanogaster has emerged as a powerful model to understand innate immune responses to infection (note the 2011 Nobel Prize in Physiology or Medicine), and in recent years this system has begun to inform on the role and regulation of immune responses during tissue injury. Due to the speed and complexity of inflammation signals upon damage, a complete understanding of the immune responses during repair requires a combination of live imaging at high temporal resolution and genetic dissection, which is possible in a number of different injury models in the fly. Here we discuss the range of wound-induced immune responses that can be modeled in flies. These wound models have revealed the most immediate signals leading to immune cell activation, and highlighted a number of complex signaling cascades required for subsequent injury-associated inflammatory responses. What has emerged from this system are a host of both local acting signals, and surprisingly, more systemic tissue repair immune responses.

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.

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.

Genetic evidence of a redox-dependent systemic wound response via Hayan protease-phenoloxidase system in Drosophila

Systemic wound response (SWR) through intertissue communication in response to local wounds is an essential biological phenomenon that occurs in all multicellular organisms from plants to animals. However, our understanding of SWR has been greatly hampered by the complexity of wound signalling communication operating within the context of an entire organism. Here, we show genetic evidence of a redox-dependent SWR from the wound site to remote tissues by identifying critical genetic determinants of SWR. Local wounds in the integument rapidly induce activation of a novel circulating haemolymph serine protease, Hayan, which in turn converts pro-phenoloxidase (PPO) to phenoloxidase (PO), an active form of melanin-forming enzyme. The Haemolymph Hayan-PO cascade is required for redox-dependent activation of the c-Jun N-terminal kinase (JNK)-dependent cytoprotective program in neuronal tissues, thereby achieving organism level of homeostasis to resist local physical trauma. These results imply that the PO-activating enzyme cascade, which is a prominent defense system in humoral innate immunity, also mediates redox-dependent SWR, providing a novel link between wound response and the nervous system.

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.

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.

Voluntarily produced increases in heart rate variability modulate autonomic effects of endotoxin induced systemic inflammation: an exploratory study

Exposure of healthy people to lipopolysaccharide (LPS; endotoxin) produces a pro-inflammatory response, subjective symptoms, and decreased heart rate variability (HRV). Given the efficacy of HRV biofeedback (BF) for treating asthma, the large autonomic effects of HRV BF, and the link between vagus nerve activity and inflammation, we hypothesized that HRV BF would dampen the acute manifestations of systemic inflammation induced by LPS challenge. Healthy participants age 18-40 were randomly assigned to four-one-hour training sessions of either HRV BF (n = 6) or a control 15/min paced breathing condition (n = 5) prior to acute experimentally induced LPS exposure. Participants were coached to do the procedures for 10 min each at five hourly time points after LPS injection, and then 2 h later. Subjective symptoms, HRV parameters, and plasma cytokine levels were measured at each time point, 2 h afterward, and the following morning. Participants were able to perform the procedures both during four pre-exposure training sessions and while experiencing LPS-induced symptoms. The HRV BF group showed significant attenuation of the LPS-induced decline in HRV for the 6 h following LPS exposure, suggesting that HRV BF decreased autonomic dysfunction produced by LPS-induced inflammation. HRV BF also reduced symptoms of headache and eye sensitivity to light, but did not affect LPS-induced levels of pro-inflammatory cytokines or symptoms of nausea, muscle aches, or feverishness. Further evaluation of HRV BF appears to be warranted among patients with inflammatory conditions.

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.

The evolution of an inflammatory response

BACKGROUND

The past 25 years have witnessed immense progress in our understanding of the systemic, tissue-specific, and cellular consequences of severe injury and infection. Despite such insights, considerable controversy remains regarding appropriate biologic and management interventions to prevent or ameliorate the associated adverse outcomes.

METHODS

A review of several scientific developments arising from studies initiated at Cornell University Medical College during the tenure of Dr. G. Tom Shires. The implications of those and subsequent studies are discussed.

RESULTS

An understanding of patient-specific variation and adaptability could direct individualized biologic and management interventions for severe injury and infection.

CONCLUSION

Despite more detailed appreciation of the molecular mechanisms of danger and pathogen recognition and response biology, we have much to learn about the complexity of severe injury and infection. There is a great need to extend our investigation of these mechanisms to experimental and stress-modified clinical scenarios.

Hematopoiesis and its disorders: a systems biology approach

Scientists have traditionally studied complex biologic systems by reducing them to simple building blocks. Genome sequencing, high-throughput screening, and proteomics have, however, generated large datasets, revealing a high level of complexity in components and interactions. Systems biology embraces this complexity with a combination of mathematical, engineering, and computational tools for constructing and validating models of biologic phenomena. The validity of mathematical modeling in hematopoiesis was established early by the pioneering work of Till and McCulloch. In reviewing more recent papers, we highlight deterministic, stochastic, statistical, and network-based models that have been used to better understand a range of topics in hematopoiesis, including blood cell production, the periodicity of cyclical neutropenia, stem cell production in response to cytokine administration, and the emergence of imatinib resistance in chronic myeloid leukemia. Future advances require technologic improvements in computing power, imaging, and proteomics as well as greater collaboration between experimentalists and modelers. Altogether, systems biology will improve our understanding of normal and abnormal hematopoiesis, better define stem cells and their daughter cells, and potentially lead to more effective therapies.

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.

The stressed host response to infection: the disruptive signals and rhythms of systemic inflammation

The cognate signals from sterile or pathogen-induced sources converge on the same recognition or response pathways. In the surgical patient, a systemic response to infection most often occurs in the context of ongoing inflammatory stress. Such an inflammatory response is modulated initially by the magnitude of injury and by patient-specific (endogenous) factors, such as confounding illness, age, and genetic variation. Over an extended period of stress, treatmentrelated (exogenous) factors add unpredictability to host responses to subsequent challenges, such as acquired infection. The host response is discussed in the context of how existing sterile stressors may modify the response to acquired infection in surgical patients.

Activation of endoplasmic reticulum stress response following trauma-hemorrhage

Hemorrhagic trauma leads to organ dysfunction, sepsis and death. There is abnormal production of proinflammatory cytokines by Kupffer cells, tissue hypoxia and liver injury following trauma-hemorrhage. The physiological conditions consequent to trauma-hemorrhage are consistent with factors necessary to initiate endoplasmic reticulum (ER) stress and unfolded protein response. However, the contribution of ER stress to apoptosis and liver injury after trauma-hemorrhage is not known. In the present study ER stress was investigated in mice that underwent trauma-hemorrhage or sham operation. Expressions of endoplasmic reticulum stress proteins Bip, ATF6, PERK, IRE1alpha, and PDI were significantly elevated in the liver after trauma-hemorrhage compared to the controls. The ER stress associated proapoptotic transcription factor CHOP protein expression was also significantly elevated in trauma-hemorrhage group. Consistent with this, enhanced DNA fragmentation was observed, confirming apoptosis, in the liver following trauma-hemorrhage. These results demonstrate the initiation of ER stress and its role in apoptosis and liver injury, subsequent to hemorrhagic trauma.