Greg Constantine

Bio: Greg Constantine is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Downregulation and upregulation & Hypoxia-inducible factors. The author has an hindex of 5, co-authored 6 publications receiving 332 citations.

In silico design of clinical trials: a method coming of age.

Abstract: Objective:To determine the feasibility and potential usefulness of mathematical models in evaluating immunomodulatory strategies in clinical trials of severe sepsis.Design:Mathematical modeling of immunomodulation in simulated patients.Setting:Computer laboratory.Measurements and Main Results:We int

In silico models of acute inflammation in animals

Abstract: Trauma and hemorrhagic shock elicit an acute inflammatory response, predisposing patients to sepsis, organ dysfunction, and death. Few approved therapies exist for these acute inflammatory states, mainly due to the complex interplay of interacting inflammatory and physiological elements working at multiple levels. Various animal models have been used to simulate these phenomena, but these models often do not replicate the clinical setting of multiple overlapping insults. Mathematical modeling of complex systems is an approach for understanding the interplay among biological interactions. We constructed a mathematical model using ordinary differential equations that encompass the dynamics of cells and cytokines of the acute inflammatory response, as well as global tissue dysfunction. The model was calibrated in C57Bl/6 mice subjected to (1) various doses of lipopolysaccharide (LPS) alone, (2) surgical trauma, and (3) surgery + hemorrhagic shock. We tested the model's predictive ability in scenarios on which it had not been trained, namely, (1) surgery +/- hemorrhagic shock + LPS given at times after the beginning of surgical instrumentation, and (2) surgery + hemorrhagic shock + bilateral femoral fracture. Software was created that facilitated fitting of the mathematical model to experimental data, as well as for simulation of experiments with various inflammatory challenges and associated variations (gene knockouts, inhibition of specific cytokines, etc.). Using this software, the C57Bl/6-specific model was recalibrated for inflammatory analyte data in CD14-/- mice and was used to elucidate altered features of inflammation in these animals. In other experiments, rats were subjected to surgical trauma +/- LPS or to bacterial infection via fibrin clots impregnated with various inocula of Escherichia coli. Mathematical modeling may provide insights into the complex dynamics of acute inflammation in a manner that can be tested in vivo using many fewer animals than has been possible previously.

A conceptual time window‐based model for the early stratification of trauma patients

Abstract: Progress in the testing of therapies targeting the immune response following trauma, a leading cause of morbidity and mortality worldwide, has been slow. We propose that the design of interventional trials in trauma would benefit from a scheme or platform that could support the identification and implementation of prognostic strategies for patient stratification. Here, we propose a stratification scheme based on defined time periods or windows following the traumatic event. This 'time-window' model allows for the incorporation of prognostic variables ranging from circulating biomarkers and clinical data to patient-specific information such as gene variants to predict adverse short- or long-term outcomes. A number of circulating biomarkers, including cell injury markers and damage-associated molecular patterns (DAMPs), and inflammatory mediators have been shown to correlate with adverse outcomes after trauma. Likewise, several single nucleotide polymorphisms (SNPs) associate with complications or death in trauma patients. This review summarizes the status of our understanding of the prognostic value of these classes of variables in predicting outcomes in trauma patients. Strategies for the incorporation of these prognostic variables into schemes designed to stratify trauma patients, such as our time-window model, are also discussed.

Hypoxia-induced overexpression of BNIP3 is not dependent on hypoxia-inducible factor 1α in mouse hepatocytes.

Abstract: We sought to investigate the expression of the cell death protein BNIP3 in hypoxic hepatocytes, as well as the role that hypoxia-inducible factor 1 (HIF-1α) plays in the upregulation of BNIP3 in hypoxic primary mouse hepatocytes and in the livers of mice subjected to ischemia-reperfusion. Freshly isolated mouse hepatocytes were exposed to 1% hypoxia for 1, 3, 6, 24, and 48 h, and the RNA and protein were isolated for reverse transcriptase-polymerase chain reaction and Western blot analysis. Similarly, livers from mice subjected to segmental (70%) hepatic warm ischemia for 30 min or 1 h, or to 1-h ischemia followed by 0.5- to 4-h reperfusion, were collected and subjected to Western blot analysis for HIF-1α protein. We showed that hypoxic stress increases the formation of the BNIP3 homodimer while decreasing the amount of the monomeric form of BNIP3 in primary mouse hepatocytes. In contrast to RAW264.7 macrophages, there is a basal expression of HIF-α protein in normoxic primary mouse hepatocytes that does not change significantly upon exposure to hypoxia. Using siRNA technology, we demonstrated that reduced HIF-1α protein levels did not block the hypoxia-induced overexpression of BNIP3. In contrast to the effect on BNIP3 expression reported previously, livers from ischemic animals demonstrated only a modest increase in HIF-1α protein as compared with resting livers from control animals; and this expression was not statistically different from sham controls. These results suggest that HIF-1α does not mediate the hypoxia-induced upregulation of BNIP3 in mouse hepatocytes in vitro and possibly in the liver in vivo.

Conceptualizing a Model A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–2

Abstract: The appropriate development of a model begins with understanding the problem that is being represented. The aim of this article is to provide a series of consensus-based best practices regarding the process of model conceptualization. For the purpose of this series of papers, the authors consider the development of models whose purpose is to inform medical decisions and health-related resource allocation questions. They specifically divide the conceptualization process into two distinct components: the conceptualization of the problem, which converts knowledge of the health care process or decision into a representation of the problem, followed by the conceptualization of the model itself, which matches the attributes and characteristics of a particular modeling type to the needs of the problem being represented. Recommendations are made regarding the structure of the modeling team, agreement on the statement of the problem, the structure, perspective and target population of the model, and the interventions and outcomes represented. Best practices relating to the specific characteristics of model structure, and which characteristics of the problem might be most easily represented in a specific modeling method, are presented. Each section contains a number of recommendations that were iterated among the authors, as well as the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

Agent-based models in translational systems biology

Abstract: Agent-based modeling is an object-oriented, discrete event, population-focused method for the computational representation of dynamic systems. Agent-based models (ABMs) treat systems as aggregates of populations of interacting components governed by rules. This means of system representation allows ABMs to map well to how biological knowledge is represented and communicated. As a result, agent-based modeling is an intuitive means by which biomedical researchers can represent their knowledge in a dynamic computational form and in so doing can lower the threshold for the general biological researcher to engage in computational modeling. ABMs are particularly suited for representing the behavior of populations of cells (i.e., “cell-as-agents”) but have also been used to model lower level processes, such as molecular interactions when spatial and structural properties are involved, as well as higher level systems, such as in human populations in epidemiological studies. For purposes of its use in translational systems biology, we focus on the use of cell/tissue-as-agent ABMs and demonstrate how agent-based modeling can serve as an integrating framework for dynamic knowledge representation of biological systems.

Translational Systems Biology of Inflammation

Abstract: Inflammation is a complex, multi-scale biologic response to stress that is also required for repair and regeneration after injury. Despite the repository of detailed data about the cellular and molecular processes involved in inflammation, including some understanding of its pathophysiology, little progress has been made in treating the severe inflammatory syndrome of sepsis. To address the gap between basic science knowledge and therapy for sepsis, a community of biologists and physicians is using systems biology approaches in hopes of yielding basic insights into the biology of inflammation. “Systems biology” is a discipline that combines experimental discovery with mathematical modeling to aid in the understanding of the dynamic global organization and function of a biologic system (cell to organ to organism). We propose the term translational systems biology for the application of similar tools and engineering principles to biologic systems with the primary goal of optimizing clinical practice. We describe the efforts to use translational systems biology to develop an integrated framework to gain insight into the problem of acute inflammation. Progress in understanding inflammation using translational systems biology tools highlights the promise of this multidisciplinary field. Future advances in understanding complex medical problems are highly dependent on methodological advances and integration of the computational systems biology community with biologists and clinicians.