Blog
About

6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation

      * ,

      PLoS Computational Biology

      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Sepsis, a manifestation of the body’s inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical “sepsis,” and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true “precision control” of sepsis.

          Author summary

          Sepsis, characterized by the body’s inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process. In this work, we utilize a computational model of the human immune response to infectious injury to offer an explanation as to why previously attempted treatment strategies are inadequate and why the current approach to drug/therapy-development is inadequate. We then use evolutionary computation algorithms to explore drug-intervention space using this same computational model as a surrogate system for human sepsis to identify the scale and scope of interventions to successfully control sepsis, as well as the types of data needed to derive these interventions. We demonstrate that multi-point and time-dependent varying controls are necessary and able to control the cytokine network dynamics of the immune system.

          Related collections

          Most cited references 26

          • Record: found
          • Abstract: not found
          • Article: not found

          Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            miRWalk--database: prediction of possible miRNA binding sites by "walking" the genes of three genomes.

            MicroRNAs are small, non-coding RNA molecules that can complementarily bind to the mRNA 3'-UTR region to regulate the gene expression by transcriptional repression or induction of mRNA degradation. Increasing evidence suggests a new mechanism by which miRNAs may regulate target gene expression by binding in promoter and amino acid coding regions. Most of the existing databases on miRNAs are restricted to mRNA 3'-UTR region. To address this issue, we present miRWalk, a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat. All mRNAs, mitochondrial genes and 10 kb upstream flanking regions of all known genes of human, mouse and rat were analyzed by using a newly developed algorithm named 'miRWalk' as well as with eight already established programs for putative miRNA binding sites. An automated and extensive text-mining search was performed on PubMed database to extract validated information on miRNAs. Combined information was put into a MySQL database. miRWalk presents predicted and validated information on miRNA-target interaction. Such a resource enables researchers to validate new targets of miRNA not only on 3'-UTR, but also on the other regions of all known genes. The 'Validated Target module' is updated every month and the 'Predicted Target module' is updated every 6 months. miRWalk is freely available at http://mirwalk.uni-hd.de/. Copyright © 2011 Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Agent-based modeling: methods and techniques for simulating human systems.

               E Bonabeau (2002)
              Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                15 February 2018
                February 2018
                : 14
                : 2
                Affiliations
                Department of Surgery, University of Chicago, Chicago, Illinois, United States of America
                University of California San Francisco, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-17-01068
                10.1371/journal.pcbi.1005876
                5813897
                29447154
                © 2018 Cockrell, An

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 6, Tables: 1, Pages: 17
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100006227, Lawrence Livermore National Laboratory;
                Award ID: B616283
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 1RO1GM115839-01
                Award Recipient :
                This research used high performance computing resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Additionally, this research was supported in part by NIH through resources provided by the University of Chicago Computation Institute (Beagle2) and the Biological Sciences Division of the University of Chicago and Argonne National Laboratory, under grant 1S10OD018495-01. This work was also supported by funds from Lawrence Livermore National Laboratory under Award #B616283. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Physiology
                Immune Physiology
                Cytokines
                Medicine and Health Sciences
                Physiology
                Immune Physiology
                Cytokines
                Biology and Life Sciences
                Immunology
                Immune System
                Innate Immune System
                Cytokines
                Medicine and Health Sciences
                Immunology
                Immune System
                Innate Immune System
                Cytokines
                Biology and Life Sciences
                Developmental Biology
                Molecular Development
                Cytokines
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Medicine and Health Sciences
                Diagnostic Medicine
                Signs and Symptoms
                Sepsis
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Signs and Symptoms
                Sepsis
                Biology and Life Sciences
                Immunology
                Immune Response
                Inflammation
                Medicine and Health Sciences
                Immunology
                Immune Response
                Inflammation
                Medicine and Health Sciences
                Diagnostic Medicine
                Signs and Symptoms
                Inflammation
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Signs and Symptoms
                Inflammation
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Genetic Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Genetic Algorithms
                Computer and Information Sciences
                Systems Science
                Dynamical Systems
                Physical Sciences
                Mathematics
                Systems Science
                Dynamical Systems
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Epithelial Cells
                Endothelial Cells
                Biology and Life Sciences
                Anatomy
                Biological Tissue
                Epithelium
                Epithelial Cells
                Endothelial Cells
                Medicine and Health Sciences
                Anatomy
                Biological Tissue
                Epithelium
                Epithelial Cells
                Endothelial Cells
                Custom metadata
                All relevant data are within the paper and its supporting information file.

                Quantitative & Systems biology

                Comments

                Comment on this article