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Donovan GM. Pillars of Theoretical Biology: Airway stability and heterogeneity in the constricted lung. J Theor Biol 2023; 575:111628. [PMID: 37778618 DOI: 10.1016/j.jtbi.2023.111628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023]
Affiliation(s)
- Graham M Donovan
- Department of Mathematics, University of Auckland, Auckland, 1142, New Zealand.
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2
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Massing JC, Gross T. Generalized Structural Kinetic Modeling: A Survey and Guide. Front Mol Biosci 2022; 9:825052. [PMID: 35573734 PMCID: PMC9098827 DOI: 10.3389/fmolb.2022.825052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their numerical exploration suffers both from computational and data limitations. Here we review generalized modeling, an alternative approach for formulating dynamical models to gain insights into dynamics and bifurcations of uncertain systems. We argue that this approach deals elegantly with the uncertainties that exist in real world data and enables analytical insight or highly efficient numerical investigation. We provide a survey of recent successes of generalized modeling and a guide to the application of this modeling approach in future studies such as complex integrative ecological models.
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Affiliation(s)
- Jana C. Massing
- Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, Germany
- Helmholtz Centre for Marine and Polar Research, Alfred-Wegener-Institute, Bremerhaven, Germany
- Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl-von-Ossietzky University, Oldenburg, Germany
- *Correspondence: Jana C. Massing,
| | - Thilo Gross
- Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, Germany
- Helmholtz Centre for Marine and Polar Research, Alfred-Wegener-Institute, Bremerhaven, Germany
- Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl-von-Ossietzky University, Oldenburg, Germany
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Davis S, Milechin L, Patel T, Hernandez M, Ciccarelli G, Samsi S, Hensley L, Goff A, Trefry J, Johnston S, Purcell B, Cabrera C, Fleischman J, Reuther A, Claypool K, Rossi F, Honko A, Pratt W, Swiston A. Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates. Front Physiol 2021; 12:691074. [PMID: 34552498 PMCID: PMC8451540 DOI: 10.3389/fphys.2021.691074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/05/2021] [Indexed: 12/15/2022] Open
Abstract
Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices. Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device. Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001. Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.
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Affiliation(s)
- Shakti Davis
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Lauren Milechin
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Tejash Patel
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Mark Hernandez
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Greg Ciccarelli
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Siddharth Samsi
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Lisa Hensley
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - Arthur Goff
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - John Trefry
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - Sara Johnston
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - Bret Purcell
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - Catherine Cabrera
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Jack Fleischman
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Albert Reuther
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Kajal Claypool
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Franco Rossi
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - Anna Honko
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - William Pratt
- US Army Medical Research Institute of Infectious Diseases, Ft. Detrick, MD, United States
| | - Albert Swiston
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
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Pietrzak M. Adhesive capsulitis: An age related symptom of metabolic syndrome and chronic low-grade inflammation? Med Hypotheses 2016; 88:12-7. [PMID: 26880627 DOI: 10.1016/j.mehy.2016.01.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Revised: 11/29/2015] [Accepted: 01/01/2016] [Indexed: 02/08/2023]
Abstract
Adhesive capsulitis (AC) is very poorly understood, particularly it's underlying etiology. Obesity and metabolic syndrome, which are strongly associated with chronic low grade inflammation, are becoming increasingly understood to underlie a raft of morbid states including upper limb pain syndromes, diabetes (DM), cardiovascular disease (CVD), cancer and central nervous system dysfunction and degeneration. Notwithstanding age, two of the strongest established risk factors for AC are DM and CVD. The hypothesis argues that similar to DM and CVD, the inflammation and capsular fibrosis seen in AC is precipitated by metabolic syndrome and chronic low grade inflammation. These pathophysiological mechanisms are highly likely to be perpetuated by upregulation of pro-inflammatory cytokine production, sympathetic dominance of autonomic balance, and neuro-immune activation. The hypothesis predicts and describes how these processes may etiologically underpin and induce each sub-classification of AC. An improved understanding of the etiology of AC may lead to more accurate diagnosis, improved management, treatment outcomes, and reduce or prevent pain, disability and suffering associated with the disease. The paper follows on with a discussion of similarities between the pathophysiology of AC to general systemic inflammatory control mechanisms whereby connective tissue (CT) fibrosis is induced as a storage depot for leukocytes and chronic inflammatory cells. The potential role of hyaluronic acid (HA), the primary component of the extracellular matrix (ECM) and CT, in the pathophysiology of AC is also discussed with potential treatment implications. Lastly, a biochemical link between physical and mental health through the ECM is described and the concept of a periventricular-limbic central driver of CT dysfunction is introduced.
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Affiliation(s)
- Max Pietrzak
- University of Bath, Claverton Down Road, Bath, North East Somerset, BA2 7AY, United Kingdom.
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Nagaraja S, Reifman J, Mitrophanov AY. Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response. PLoS Comput Biol 2015; 11:e1004460. [PMID: 26633296 PMCID: PMC4669096 DOI: 10.1371/journal.pcbi.1004460] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 06/16/2015] [Indexed: 11/19/2022] Open
Abstract
Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators − tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation. A recent approach to quantitatively characterize the timing and intensity of the inflammatory response relies on the use of four quantities termed inflammation indices. The values of the inflammation indices may reflect the differences between normal and pathological inflammation, and may be used to gauge the effects of therapeutic interventions aimed to control inflammation. Yet, the specific inflammatory mechanisms that can be targeted to selectively control these indices remain unknown. Here, we developed and applied a computational strategy to identify potential target mechanisms to regulate such indices. We used our recently developed model of local inflammation to simulate thousands of inflammatory scenarios. We then subjected the corresponding inflammation index values to sensitivity and correlation analysis. We found that the inflammation indices may be significantly influenced by the macrophage influx and efflux rates, as well as by the degradation rates of three specific molecular mediators. These results suggested that the indices can be effectively regulated by individual or combined inhibition of those molecular mediators, which we confirmed by computational experiments. Taken together, our results highlight possible targets of therapeutic intervention that can be used to control both the timing and the intensity of the inflammatory response.
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Affiliation(s)
- Sridevi Nagaraja
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail:
| | - Alexander Y. Mitrophanov
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
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Trefois C, Antony PMA, Goncalves J, Skupin A, Balling R. Critical transitions in chronic disease: transferring concepts from ecology to systems medicine. Curr Opin Biotechnol 2014; 34:48-55. [PMID: 25498477 DOI: 10.1016/j.copbio.2014.11.020] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Revised: 11/18/2014] [Accepted: 11/19/2014] [Indexed: 01/01/2023]
Abstract
Ecosystems and biological systems are known to be inherently complex and to exhibit nonlinear dynamics. Diseases such as microbiome dysregulation or depression can be seen as complex systems as well and were shown to exhibit patterns of nonlinearity in their response to perturbations. These nonlinearities can be revealed by a sudden shift in system states, for instance from health to disease. The identification and characterization of early warning signals which could predict upcoming critical transitions is of primordial interest as prevention of disease onset is a major aim in health care. In this review, we focus on recent evidence for critical transitions in diseases and discuss the potential of such studies for therapeutic applications.
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Affiliation(s)
- Christophe Trefois
- Experimental Neurobiology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Paul M A Antony
- Experimental Neurobiology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Jorge Goncalves
- Systems Control Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Alexander Skupin
- National Center for Microscopy and Imaging Research, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, United States; Integrative Cell Signalling Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Rudi Balling
- Experimental Neurobiology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
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Abstract
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.
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Affiliation(s)
- Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854 ; Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854 ; Department of Surgery, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ 08901
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Mathew S, Bartels J, Banerjee I, Vodovotz Y. Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses. J Theor Biol 2014; 358:132-48. [PMID: 24909493 DOI: 10.1016/j.jtbi.2014.05.036] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2014] [Revised: 05/22/2014] [Accepted: 05/23/2014] [Indexed: 01/09/2023]
Abstract
The precise inflammatory role of the cytokine interleukin (IL)-6 and its utility as a biomarker or therapeutic target have been the source of much debate, presumably due to the complex pro- and anti-inflammatory effects of this cytokine. We previously developed a nonlinear ordinary differential equation (ODE) model to explain the dynamics of endotoxin (lipopolysaccharide; LPS)-induced acute inflammation and associated whole-animal damage/dysfunction (a proxy for the health of the organism), along with the inflammatory mediators tumor necrosis factor (TNF)-α, IL-6, IL-10, and nitric oxide (NO). The model was partially calibrated using data from endotoxemic C57Bl/6 mice. Herein, we investigated the sensitivity of the area under the damage curve (AUCD) to the 51 rate parameters of the ODE model for different levels of simulated LPS challenges using a global sensitivity approach called Random Sampling High Dimensional Model Representation (RS-HDMR). We explored sufficient parametric Monte Carlo samples to generate the variance-based Sobol' global sensitivity indices, and found that inflammatory damage was highly sensitive to the parameters affecting the activity of IL-6 during the different stages of acute inflammation. The AUCIL6 showed a bimodal distribution, with the lower peak representing healthy response and the higher peak representing sustained inflammation. Damage was minimal at low AUCIL6, giving rise to a healthy response. In contrast, intermediate levels of AUCIL6 resulted in high damage, and this was due to the insufficiency of damage recovery driven by anti-inflammatory responses from IL-10 and the activation of positive feedback sustained by IL-6. At high AUCIL6, damage recovery was interestingly restored in some population of simulated animals due to the NO-mediated anti-inflammatory responses. These observations suggest that the host's health status during acute inflammation depends in a nonlinear fashion on the magnitude of the inflammatory stimulus, on the host's propensity to produce IL-6, and on NO-mediated downstream responses.
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Affiliation(s)
- Shibin Mathew
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | | | - Ipsita Banerjee
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15219, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Yoram Vodovotz
- Immunetrics, Inc., Pittsburgh, PA 15203, USA; Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA.
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