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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 56071

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Special Issue Editors

Dr. Neda Bagheri

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Guest Editor

Chemical & Biological Engineering, McCormick School of Engineering Northwestern University, Evanston, IL 60208, USA
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
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Dr. Jason Shoemaker

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Guest Editor

1. Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA
2. Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
Interests: systems biology; biotechnology and biochemical engineering; immunology; machine learning; biological network; influenza a virus; Orthomyxoviridae; human influenza; bioinformatics; gene expression data; dynamic bayesian networks; virus–host interaction mechanisms
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2400 CHF (Swiss Francs). 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

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Published Papers (10 papers)

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Research

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19 pages, 2419 KiB

Open AccessFeature PaperArticle

Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles

by Muying Wang, Satoshi Fukuyama, Yoshihiro Kawaoka and Jason E. Shoemaker

Processes 2019, 7(5), 301; https://doi.org/10.3390/pr7050301 - 21 May 2019

Viewed by 4048

Abstract

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R

^{2}

) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests. Full article

(This article belongs to the Special Issue Modeling & Control of Disease States)

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15 pages, 4667 KiB

Open AccessFeature PaperArticle

Mapping Tyrosine Kinase Receptor Dimerization to Receptor Expression and Ligand Affinities

by Spencer B. Mamer, Alexandra A. Palasz and P. I. Imoukhuede

Processes 2019, 7(5), 288; https://doi.org/10.3390/pr7050288 - 15 May 2019

Cited by 3 | Viewed by 5424

Abstract

Tyrosine kinase receptor (RTK) ligation and dimerization is a key mechanism for translating external cell stimuli into internal signaling events. This process is critical to several key cell and physiological processes, such as in angiogenesis and embryogenesis, among others. While modulating RTK activation is a promising therapeutic target, RTK signaling axes have been shown to involve complicated interactions between ligands and receptors both within and across different protein families. In angiogenesis, for example, several signaling protein families, including vascular endothelial growth factors and platelet-derived growth factors, exhibit significant cross-family interactions that can influence pathway activation. Computational approaches can provide key insight to detangle these signaling pathways but have been limited by the sparse knowledge of these cross-family interactions. Here, we present a framework for studying known and potential non-canonical interactions. We constructed generalized models of RTK ligation and dimerization for systems of two, three and four receptor types and different degrees of cross-family ligation. Across each model, we developed parameter-space maps that fully determine relative pathway activation for any set of ligand-receptor binding constants, ligand concentrations and receptor concentrations. Therefore, our generalized models serve as a powerful reference tool for predicting not only known ligand: Receptor axes but also how unknown interactions could alter signaling dimerization patterns. Accordingly, it will drive the exploration of cross-family interactions and help guide therapeutic developments across processes like cancer and cardiovascular diseases, which depend on RTK-mediated signaling. Full article

(This article belongs to the Special Issue Modeling & Control of Disease States)

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24 pages, 2972 KiB

Open AccessArticle

Optimal Strategies for Dengue Prevention and Control during Daily Commuting between Two Residential Areas

by Daniel Lasluisa, Edwin Barrios and Olga Vasilieva

Processes 2019, 7(4), 197; https://doi.org/10.3390/pr7040197 - 4 Apr 2019

Cited by 10 | Viewed by 4222

Abstract

In this paper, we report an application for the mathematical theory of dynamic optimization for design of optimal strategies that account for daily commuting of human residents, aiming to reduce vector-borne infections (dengue) among human populations. Our analysis is based on a two-patch dengue transmission model amended with control variables that represent personal protection measures aimed at reduction of the number of contacts between mosquitoes and human hosts (e.g., the use of repellents, mosquito nets, or insecticide-treated clothing). As a result, we have proposed and numerically solved an optimal control problem to minimize the costs associated with the application of control measures, while also minimizing the total number of dengue-infected people in both residential areas. Our principal goal was to identify an optimal strategy for personal protection that renders the maximal number of averted human infections per unit of invested cost, and this goal has been accomplished on the grounds of cost-effectiveness analysis. Full article

(This article belongs to the Special Issue Modeling & Control of Disease States)

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29 pages, 1367 KiB

Open AccessFeature PaperArticle

Mathematical Modeling of RBC Count Dynamics after Blood Loss

by Manuel Tetschke, Patrick Lilienthal, Torben Pottgiesser, Thomas Fischer, Enrico Schalk and Sebastian Sager

Processes 2018, 6(9), 157; https://doi.org/10.3390/pr6090157 - 5 Sep 2018

Cited by 9 | Viewed by 6640

Abstract

The regeneration of red blood cells (RBCs) after blood loss is an individual complex process. We present a novel simple compartment model which is able to capture the most important features and can be personalized using parameter estimation. We compare predictions of the proposed and personalized model to a more sophisticated state-of-the-art model for erythropoiesis, and to clinical data from healthy subjects. We discuss the choice of model parameters with respect to identifiability. We give an outlook on how extensions of this novel mathematical model could have an important impact for personalized clinical decision support in the case of polycythemia vera (PV). PV is a slow-growing type of blood cancer, where especially the production of RBCs is increased. The principal treatment targeting the symptoms of PV is bloodletting (phlebotomy), at regular intervals that are based on personal experiences of the physicians. Model-based decision support might help to identify optimal and individualized phlebotomy schedules. Full article

(This article belongs to the Special Issue Modeling & Control of Disease States)

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18 pages, 8972 KiB

Open AccessArticle

Agent-Based Modeling of Immune Response to Study the Effects of Regulatory T Cells in Type 1 Diabetes

by Qian Xu, Mustafa Cagdas Ozturk and Ali Cinar

Processes 2018, 6(9), 141; https://doi.org/10.3390/pr6090141 - 27 Aug 2018

Cited by 2 | Viewed by 3615

Abstract

Regulatory T cells (Tregs) have an important role in self-tolerance. Understanding the functions of Tregs is important for preventing or slowing the progress of Type 1 Diabetes. We use a two-dimensional (2D) agent-based model to simulate immune response in mice and test the effects of Tregs in tissue protection. We compared the immune response with and without Tregs, and also tested the effects of Tregs from different sources or with different functions. The results show that Tregs can inhibit the proliferation of effector T cells by inhibiting antigens presenting via dendritic cells (DCs). Although the number and function of Tregs affect the inhibition, a small number of Tregs compared to CD4⁺ T cells can effectively protect islets in pancreatic tissue. Finally, we added Tregs to the system in the middle phase of the immune response. The simulation results show that Tregs can inhibit the production of effector CD8⁺ T cells and maintain a good environment for β cell regeneration. Full article

(This article belongs to the Special Issue Modeling & Control of Disease States)

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16 pages, 1153 KiB

Open AccessArticle

The Role of Immune Response in Optimal HIV Treatment Interventions

by Hernán Darío Toro-Zapata, Angélica Caicedo-Casso and Sunmi Lee

Processes 2018, 6(8), 102; https://doi.org/10.3390/pr6080102 - 26 Jul 2018

Cited by 3 | Viewed by 4541

Abstract

A mathematical model for the transmission dynamics of human immunodeficiency virus (HIV) within a host is developed. Our model focuses on the roles of immune response cells or cytotoxic lymphocytes (CTLs). The model includes active and inactive cytotoxic immune cells. The basic reproduction number and the global stability of the virus free equilibrium is carried out. The model is modified to include anti-retroviral treatment interventions and the controlled reproduction number is explored. Their effects on the HIV infection dynamics are investigated. Two different disease stage scenarios are assessed: early-stage and advanced-stage of the disease. Furthermore, optimal control theory is employed to enhance healthy CD4

^{+}

T cells, active cytotoxic immune cells and minimize the total cost of anti-retroviral treatment interventions. Two different anti-retroviral treatment interventions (RTI and PI) are incorporated. The results highlight the key roles of cytotoxic immune response in the HIV infection dynamics and corresponding optimal treatment strategies. It turns out that the combined control (both RTI and PI) and stronger immune response is the best intervention to maximize healthy CD4

^{+}

T cells at a minimal cost of treatments. Full article

(This article belongs to the Special Issue Modeling & Control of Disease States)

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22 pages, 714 KiB

Open AccessFeature PaperArticle

EPO Dosage Optimization for Anemia Management: Stochastic Control under Uncertainty Using Conditional Value at Risk

by Jayson McAllister, Zukui Li, Jinfeng Liu and Ulrich Simonsmeier

Processes 2018, 6(5), 60; https://doi.org/10.3390/pr6050060 - 21 May 2018

Cited by 2 | Viewed by 4984

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 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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16 pages, 6176 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Mathematical Modeling of Metastatic Cancer Migration through a Remodeling Extracellular Matrix

by Yen T. Nguyen Edalgo and Ashlee N. Ford Versypt

Processes 2018, 6(5), 58; https://doi.org/10.3390/pr6050058 - 16 May 2018

Cited by 15 | Viewed by 9655

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 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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25 pages, 1231 KiB

Open AccessArticle

Optimal Control Strategy for TB-HIV/AIDS Co-Infection Model in the Presence of Behaviour Modification

by Temesgen Debas Awoke and Semu Mitiku Kassa

Processes 2018, 6(5), 48; https://doi.org/10.3390/pr6050048 - 1 May 2018

Cited by 24 | Viewed by 7208

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 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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Review

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19 pages, 6567 KiB

Open AccessFeature PaperReview

A Systems and Treatment Perspective of Models of Influenza Virus-Induced Host Responses

by Ericka Mochan, Emily E. Ackerman and Jason E. Shoemaker

Processes 2018, 6(9), 138; https://doi.org/10.3390/pr6090138 - 23 Aug 2018

Cited by 6 | Viewed by 4299

Abstract

Severe influenza infections are often characterized as having unique host responses (e.g., early, severe hypercytokinemia). Neuraminidase inhibitors can be effective in controlling the severe symptoms of influenza but are often not administered until late in the infection. Several studies suggest that immune modulation may offer protection to high risk groups. Here, we review the current state of mathematical models of influenza-induced host responses. Selecting three models with conserved immune response components, we determine if the immune system components which most affect virus replication when perturbed are conserved across the models. We also test each model’s response to a pre-induction of interferon before the virus is administered. We find that each model emphasizes the importance of controlling the infected cell population to control viral replication. Moreover, our work shows that the structure of current models does not allow for significant responses to increased interferon concentrations. These results suggest that the current library of available published models of influenza infection does not adequately represent the complex interactions of the virus, interferon, and other aspects of the immune response. Specifically, the method used to model virus-resistant cells may need to be adapted in future work to more realistically represent the immune response to viral infection. Full article

(This article belongs to the Special Issue Modeling & Control of Disease States)

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