Special Issue "Modeling & Control of Disease States"

A special issue of Processes (ISSN 2227-9717).

Deadline for manuscript submissions: 30 June 2018

Special Issue Editors

Guest Editor
Dr. Neda Bagheri

Chemical & Biological Engineering, McCormick School of Engineering Northwestern University, Evanston, IL 60208, USA
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Interests: The Bagheri Lab operates at the evolving interface between engineering and biology, promoting a diverse, creative research environment consisting of engineers and basic scientists that share the common mission of advancing medicine and biology. Through this collective effort, the lab aim to identify design principles that underlie complex biological function, and modulate extrinsic factors to optimize therapeutic interventions
Guest Editor
Dr. Jason E. Shoemaker

Chemical & Petroleum Engineering, University of Pittsburgh, 940 Benedum Hall Pittsburgh, PA 15261, USA
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Interests: The Shoemaker Lab focuses on systems engineering applications in medicine. We focus on developing a variety of techniques for designing closed-loop, patient-specific therapeutics with current applications in immunopathology, respiratory infection and hemodynamics

Special Issue Information

Dear Colleagues,

Computational models of human physiology are fundamental to advancing the basic understanding and prediction of disease states. Informative models have the unmatched potential to enhance therapeutic strategies and offer more effective, personalized medicine. To realize such impact, dynamical systems models must explain experimental observations and provide accurate representations of the relevant biology in a control theoretic framework. Closed-loop control of blood insulin levels in diabetics is a notable example of successful integration of process modeling in medicine, however many other diseases have yet to be appropriately managed through tight integration/iteration of experimental design with process modeling and control. Tumorigenesis, tumor maintenance, and immune-response associated pathology (i.e., immunopathology) are examples of physiological contexts that remain particularly challenging to characterize. Single-scale and multi-scale models spanning differential equation and stochastic systems to rule-based models are needed to address these complex medical challenges. Validated models can reveal dynamic indicators of disease states, identify new drug targets, reveal medical contexts for repurposing current drugs, minimize negative consequences of targeted interventions, and refine treatment schedules for more personalized medicine.

This Special Issue on "Modeling and Control of Disease States" aims to curate novel advances in the development and application of computational modeling to address longstanding challenges in translational medicine and disease treatment. Topics include, but are not limited to:

  • Development of new disease-specific models to guide therapy;
  • Diagnosis, or design of therapeutic strategies, that makes use of model predictions;
  • Integration of open-loop or closed-loop control to drive disease responses toward healthy ones; and
  • The development of species-specific or patient-specific models to guide translation and personalization.

Dr. Neda Bagheri
Dr. Jason E. Shoemaker
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Please note that for papers submitted after 1 July 2018 an APC of 850 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computational systems biology
  • Modeling
  • Control
  • Prediction of disease
  • Translational medicine

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle EPO Dosage Optimization for Anemia Management: Stochastic Control under Uncertainty Using Conditional Value at Risk
Processes 2018, 6(5), 60; https://doi.org/10.3390/pr6050060
Received: 8 April 2018 / Revised: 3 May 2018 / Accepted: 15 May 2018 / Published: 21 May 2018
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Abstract
Due to insufficient endogenous production of erythropoietin, chronic kidney disease patients with anemia are often treated by the administration of recombinant human erythropoietin (EPO). The target of the treatment is to keep the patient’s hemoglobin level within a normal range. While conventional methods
[...] Read more.
Due to insufficient endogenous production of erythropoietin, chronic kidney disease patients with anemia are often treated by the administration of recombinant human erythropoietin (EPO). The target of the treatment is to keep the patient’s hemoglobin level within a normal range. While conventional methods for guiding EPO dosing used by clinicians normally rely on a set of rules based on past experiences or retrospective studies, model predictive control (MPC) based dosage optimization is receiving attention recently. The objective of this paper is to incorporate the hemoglobin response model uncertainty into the dosage optimization decision making. Two methods utilizing Conditional Value at Risk (CVaR) are proposed for hemoglobin control in chronic kidney disease under model uncertainty. The first method includes a set-point tracking controller with the addition of CVaR constraints. The second method involves the use of CVaR directly in the cost function of the optimal control problem. The methods are compared to set-point tracking MPC and Zone-tracking MPC through computer simulations. Simulation results demonstrate the benefits of utilizing CVaR in stochastic predictive control for EPO dosage optimization. Full article
(This article belongs to the Special Issue Modeling & Control of Disease States)
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Open AccessFeature PaperArticle Mathematical Modeling of Metastatic Cancer Migration through a Remodeling Extracellular Matrix
Processes 2018, 6(5), 58; https://doi.org/10.3390/pr6050058
Received: 22 April 2018 / Revised: 12 May 2018 / Accepted: 14 May 2018 / Published: 16 May 2018
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Abstract
The spreading of cancer cells, also known as metastasis, is a lethal hallmark in cancer progression and the primary cause of cancer death. Recent cancer research has suggested that the remodeling of collagen fibers in the extracellular matrix (ECM) of the tumor microenvironment
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The spreading of cancer cells, also known as metastasis, is a lethal hallmark in cancer progression and the primary cause of cancer death. Recent cancer research has suggested that the remodeling of collagen fibers in the extracellular matrix (ECM) of the tumor microenvironment facilitates the migration of cancer cells during metastasis. ECM remodeling refers to the following two procedures: the ECM degradation caused by enzyme matrix metalloproteinases and the ECM alignment due to the cross-linking enzyme lysyl oxidase (LOX). Such modifications of ECM collagen fibers result in changes of ECM physical and biomechanical properties that affect cancer cell migration through the ECM. However, the mechanism of such cancer migration through a remodeling ECM remains not well understood. A mathematical model is proposed in this work to better describe and understand cancer migration by means of ECM remodeling. Effects of LOX are considered to enable transport of enzymes and migration of cells through a dynamic, reactive tumor microenvironment that is modulated during cell migration. For validation cases, the results obtained show comparable trends to previously established models. In novel test cases, the model predicts the impact on ECM remodeling and the overall migration of cancer cells due to the inclusion of LOX, which has not yet been included in previous cancer invasion models. Full article
(This article belongs to the Special Issue Modeling & Control of Disease States)
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Open AccessArticle Optimal Control Strategy for TB-HIV/AIDS Co-Infection Model in the Presence of Behaviour Modification
Processes 2018, 6(5), 48; https://doi.org/10.3390/pr6050048
Received: 15 March 2018 / Revised: 25 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
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Abstract
A mathematical model for a transmission of TB-HIV/AIDS co-infection that incorporates prevalence dependent behaviour change in the population and treatment for the infected (and infectious) class is formulated and analyzed. The two sub-models, when each of the two diseases are considered separately are
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A mathematical model for a transmission of TB-HIV/AIDS co-infection that incorporates prevalence dependent behaviour change in the population and treatment for the infected (and infectious) class is formulated and analyzed. The two sub-models, when each of the two diseases are considered separately are mathematically analyzed. The theory of optimal control analysis is applied to the full model with the objective of minimizing the aggregate cost of the infections and the control efforts. In the numerical simulation section, various combinations of the controls are also presented and it has been shown in this part that the optimal combination of both prevention and treatment controls will suppress the prevalence of both HIV and TB to below 3% within 10 years. Moreover, it is found that the treatment control is more effective than the preventive controls. Full article
(This article belongs to the Special Issue Modeling & Control of Disease States)
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