Browsing by Author "Hoops, Stefan"

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    Algebraic Methods for Modeling Gene Regulatory Networks
    Murrugarra Tomairo, David M. (Virginia Tech, 2012-07-18)
    So called discrete models have been successfully used in engineering and computational systems biology. This thesis discusses algebraic methods for modeling and analysis of gene regulatory networks within the discrete modeling context. The first chapter gives a background for discrete models and put in context some of the main research problems that have been pursued in this field for the last fifty years. It also outlines the content of each subsequent chapter. The second chapter focuses on the problem of inferring dynamics from the structure (topology) of the network. It also discusses the characterization of the attractor structure of a network when a particular class of functions control the nodes of the network. Chapters~3 and 4 focus on the study of multi-state nested canalyzing functions as biologically inspired functions and the characterization of their dynamics. Chapter 5 focuses on stochastic methods, specifically on the development of a stochastic modeling framework for discrete models. Stochastic discrete modeling is an alternative approach from the well-known mathematical formalizations such as stochastic differential equations and Gillespie algorithm simulations. Within the discrete setting, a framework that incorporates propensity probabilities for activation and degradation is presented. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations. Finally, Chapter 6 discusses future research directions inspired by the work presented here.
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    Analysis and Application of Haseltine and Rawlings's Hybrid Stochastic Simulation Algorithm
    Wang, Shuo (Virginia Tech, 2016-10-06)
    Stochastic effects in cellular systems are usually modeled and simulated with Gillespie's stochastic simulation algorithm (SSA), which follows the same theoretical derivation as the chemical master equation (CME), but the low efficiency of SSA limits its application to large chemical networks. To improve efficiency of stochastic simulations, Haseltine and Rawlings proposed a hybrid of ODE and SSA algorithm, which combines ordinary differential equations (ODEs) for traditional deterministic models and SSA for stochastic models. In this dissertation, accuracy analysis, efficient implementation strategies, and application of of Haseltine and Rawlings's hybrid method (HR) to a budding yeast cell cycle model are discussed. Accuracy of the hybrid method HR is studied based on a linear chain reaction system, motivated from the modeling practice used for the budding yeast cell cycle control mechanism. Mathematical analysis and numerical results both show that the hybrid method HR is accurate if either numbers of molecules of reactants in fast reactions are above certain thresholds, or rate constants of fast reactions are much larger than rate constants of slow reactions. Our analysis also shows that the hybrid method HR allows for a much greater region in system parameter space than those for the slow scale SSA (ssSSA) and the stochastic quasi steady state assumption (SQSSA) method. Implementation of the hybrid method HR requires a stiff ODE solver for numerical integration and an efficient event-handling strategy for slow reaction firings. In this dissertation, an event-handling strategy is developed based on inverse interpolation. Performances of five wildly used stiff ODE solvers are measured in three numerical experiments. Furthermore, inspired by the strategy of the hybrid method HR, a hybrid of ODE and SSA stochastic models for the budding yeast cell cycle is developed, based on a deterministic model in the literature. Simulation results of this hybrid model match very well with biological experimental data, and this model is the first to do so with these recently available experimental data. This study demonstrates that the hybrid method HR has great potential for stochastic modeling and simulation of large biochemical networks.
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    Computational modeling-based discovery of novel classes of anti-inflammatory drugs that  target lanthionine synthetase C-like protein 2
    Lu, Pinyi (Virginia Tech, 2015-12-15)
    Lanthionine synthetase C-like protein 2 (LANCL2) is a member of the LANCL protein family, which is broadly expressed throughout the body. LANCL2 is the molecular target of abscisic acid (ABA), a compound with insulin-sensitizing and immune modulatory actions. LANCL2 is required for membrane binding and signaling of ABA in immune cells. Direct binding of ABA to LANCL2 was predicted in silico using molecular modeling approaches and validated experimentally using ligand-binding assays and kinetic surface plasmon resonance studies. The therapeutic potential of the LANCL2 pathway ranges from increasing cellular sensitivity to anticancer drugs, insulin-sensitizing effects and modulating immune and inflammatory responses in the context of immune-mediated and infectious diseases. A case for LANCL2-based drug discovery and development is also illustrated by the anti-inflammatory activity of novel LANCL2 ligands such as NSC61610 against inflammatory bowel disease in mice. This dissertation discusses the value of LANCL2 as a novel therapeutic target for the discovery and development of new classes of orally active drugs against chronic metabolic, immune-mediated and infectious diseases and as a validated target that can be used in precision medicine. Specifically, in Chapter 2 of the dissertation, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of LANCL1 as a template. Our molecular docking studies predicted that ABA and other PPAR - agonists share a binding site on the surface of LANCL2. In Chapter 3 of the dissertation, structure-based virtual screening was performed. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. In Chapter 4 of the dissertation, we developed a novel integrated approach for creating a synthetic patient population and testing the efficacy of the novel pre-clinical stage LANCL2 therapeutic for Crohn's disease in large clinical cohorts in silico. Efficacy of treatments on Crohn's disease was evaluated by analyzing predicted changes of Crohn's disease activity index (CDAI) scores and correlations with immunological variables were evaluated. The results from our placebo-controlled, randomized, Phase III in silico clinical trial at 6 weeks following the treatment shows a positive correlation between the initial disease activity score and the drop in CDAI score. This observation highlights the need for precision medicine strategies for IBD.
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    Condor-COPASI: high-throughput computing for biochemical networks
    Kent, Edward; Hoops, Stefan; Mendes, Pedro (2012-07-26)
    Background Mathematical modelling has become a standard technique to improve our understanding of complex biological systems. As models become larger and more complex, simulations and analyses require increasing amounts of computational power. Clusters of computers in a high-throughput computing environment can help to provide the resources required for computationally expensive model analysis. However, exploiting such a system can be difficult for users without the necessary expertise. Results We present Condor-COPASI, a server-based software tool that integrates COPASI, a biological pathway simulation tool, with Condor, a high-throughput computing environment. Condor-COPASI provides a web-based interface, which makes it extremely easy for a user to run a number of model simulation and analysis tasks in parallel. Tasks are transparently split into smaller parts, and submitted for execution on a Condor pool. Result output is presented to the user in a number of formats, including tables and interactive graphical displays. Conclusions Condor-COPASI can effectively use a Condor high-throughput computing environment to provide significant gains in performance for a number of model simulation and analysis tasks. Condor-COPASI is free, open source software, released under the Artistic License 2.0, and is suitable for use by any institution with access to a Condor pool. Source code is freely available for download at http://code.google.com/p/condor-copasi/, along with full instructions on deployment and usage.
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    COPASI and its applications in biotechnology
    Bergmann, Frank T.; Hoops, Stefan; Klahn, Brian; Kummer, Ursula; Mendes, Pedro; Pahle, Juergen; Sahle, Sven (2017-11-10)
    COPASI is software used for the creation, modification, simulation and computational analysis of kinetic models in various fields. It is open-source, available for all major platforms and provides a user-friendly graphical user interface, but is also controllable via the command line and scripting languages. These are likely reasons for its wide acceptance. We begin this review with a short introduction describing the general approaches and techniques used in computational modeling in the biosciences. Next we introduce the COPASI package, and its capabilities, before looking at typical applications of COPASI in biotechnology.
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    Evaluation of Word and Paragraph Embeddings and Analogical Reasoning as an  Alternative to Term Frequency-Inverse Document Frequency-based Classification in Support of Biocuration
    Sullivan, Daniel Edward (Virginia Tech, 2016-06-07)
    This research addresses the problem, can unsupervised learning generate a representation that improves on the commonly used term frequency-inverse document frequency (TF-IDF ) representation by capturing semantic relations? The analysis measures the quality of sentence classification using term TF-IDF representations, and finds a practical upper limit to precision and recall in a biomedical text classification task (F1-score of 0.85). Arguably, one could use ontologies to supplement TF-IDF, but ontologies are sparse in coverage and costly to create. This prompts a correlated question: can unsupervised learning capture semantic relations at least as well as existing ontologies, and thus supplement existing sparse ontologies? A shallow neural network implementing the Skip-Gram algorithm is used to generate semantic vectors using a corpus of approximately 2.4 billion words. The ability to capture meaning is assessed by comparing semantic vectors generated with MESH. Results indicate that semantic vectors trained by unsupervised methods capture comparable levels of semantic features in some cases, such as amino acid (92% of similarity represented in MESH), but perform substantially poorer in more expansive topics, such as pathogenic bacteria (37.8% similarity represented in MESH). Possible explanations for this difference in performance are proposed along with a method to combine manually curated ontologies with semantic vector spaces to produce a more comprehensive representation than either alone. Semantic vectors are also used as representations for paragraphs, which, when used for classification, achieve an F1-score of 0.92. The results of classification and analogical reasoning tasks are promising but a formal model of semantic vectors, subject to the constraints of known linguistic phenomenon, is needed. This research includes initial steps for developing a formal model of semantic vectors based on a combination of linear algebra and fuzzy set theory subject to the semantic molecularism linguistic model. This research is novel in its analysis of semantic vectors applied to the biomedical domain, analysis of different performance characteristics in biomedical analogical reasoning tasks, comparison semantic relations captured by between vectors and MESH, and the initial development of a formal model of semantic vectors.
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    High-resolution computational modeling of immune responses in the gut
    Verma, Meghna; Bassaganya-Riera, Josep; Leber, Andrew; Tubau-Juni, Nuria; Hoops, Stefan; Abedi, Vida; Chen, Xi; Hontecillas, Raquel (Oxford University Press, 2019-06-01)
    Background: Helicobacter pylori causes gastric cancer in 1-2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori-colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing-driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations. Results: The outputs were analyzed using 2 sequential stages: The first used a partial rank correlation coefficient regression-based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells. Conclusions: The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.
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    Ironing Out the Host-fungal Interaction in Airway Epithelial Cells
    Lee, Shernita (Virginia Tech, 2014-04-10)
    Aspergillus fumigatus is a ubiquitous fungus associated with several airway complications and diseases including asthma, allergies, cystic fibrosis, and most commonly invasive aspergillosis. The airway epithelium, a protective barrier, is the first anatomical site to interact with A. fumigatus. Although this host-fungal interaction is often asymptomatic for immunocompetent individuals, for immunocompromised persons, due to a weakened competence of the immune system, they have an increased likelihood of fungal infection. This dissertation aims to investigate the effect of A. fumigatus on the transcriptional response of human airway epithelial cells, focusing on the relationship between innate immunity and iron regulation from the host perspective. The trace element iron is needed by both the fungus and the host for cellular maintenance and survival, but tightly controlled iron regulation in the host is required to prevent oxidative stress and cell death. The research methods in this dissertation employ a systems biology approach, by incorporating mathematical modeling, RNA-seq analysis, and experimental biology techniques to assess the role of airway epithelial cells in the host-fungal interaction. Both the quantitative and qualitative research design allows for characterization of airway epithelial cells and the downstream changes in iron importer genes. This study addresses literature gaps through analysis of the host transcriptome using multiple time points, by performing an extensive evaluation of the effect of cytokines on iron importer genes, and conceptualization of a comprehensive mathematical model of the airway epithelial cell. The major findings suggest the following: 1) airway epithelial cells avidly respond to A. fumigatus through modification of the expression of immune response related genes at different infection stages, 2) during A. fumigatus co-incubation with airway epithelial cells, the iron importers genes respond in strikingly different ways, and 3) cytokines have a significant effect on the increase in expression of an iron importer gene. We illuminated the role of airway epithelial cells in fungal recognition and activation of the immune response in signaling cascades that consequently modify iron importer genes and hope to use this information as a platform to discover potential therapeutic targets.
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    JigCell Model Connector: Building Large Molecular Network Models from Components
    Jones, Thomas Carroll Jr. (Virginia Tech, 2017-06-28)
    The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist.
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    JigCell Run Manager (JC-RM): a tool for managing large sets of biochemical model parametrizations
    Palmisano, Alida; Hoops, Stefan; Watson, Layne T.; Jones, Thomas C.; Tyson, John J.; Shaffer, Clifford A. (Biomed Central, 2015-12-24)
    Background Most biomolecular reaction modeling tools allow users to build models with a single list of parameter values. However, a common scenario involves different parameterizations of the model to account for the results of related experiments, for example, to define the phenotypes for a variety of mutations (gene knockout, over expression, etc.) of a specific biochemical network. This scenario is not well supported by existing model editors, forcing the user to manually generate, store, and maintain many variations of the same model. Results We developed an extension to our modeling editor called the JigCell Run Manager (JC-RM). JC-RM allows the modeler to define a hierarchy of parameter values, simulations, and plot settings, and to save them together with the initial model. JC-RM supports generation of simulation plots, as well as export to COPASI and SBML (L3V1) for further analysis. Conclusions Developing a model with its initial list of parameter values is just the first step in modeling a biological system. Models are often parameterized in many different ways to account for mutations of the organism and/or for sets of related experiments performed on the organism. JC-RM offers two critical features: it supports the everyday management of a large model, complete with its parameterizations, and it facilitates sharing this information before and after publication. JC-RM allows the modeler to define a hierarchy of parameter values, simulation, and plot settings, and to maintain a relationship between this hierarchy and the initial model. JC-RM is implemented in Java and uses the COPASI API. JC-RM runs on all major operating systems, with minimal system requirements. Installers, source code, user manual, and examples can be found at the COPASI website (http://www.copasi.org/Projects).
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    Minimum Information About a Simulation Experiment (MIASE)
    Waltemath, Dagmar; Adams, Richard; Beard, Daniel A.; Bergmann, Frank T.; Bhalla, Upinder S.; Britten, Randall; Chelliah, Vijayalakshmi; Cooling, Michael T.; Cooper, Jonathan; Crampin, Edmund J.; Garny, Alan; Hoops, Stefan; Hucka, Michael; Hunger, Peter; Klipp, Edda; Laibe, Camille; Miller, Andrew K.; Moraru, Ion; Nickerson, David; Nielsen, Poul; Nikolski, Macha; Sahle, Sven; Sauro, Herbert M.; Schmidt, Henning; Snoep, Jacky L.; Tolle, Dominic; Wolkenhauer, Olaf; Le Novère, Nicolas (Public Library of Science, 2011-04-28)
    Reproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment (MIASE, Glossary in Box 1) describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.
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    Modeling Host Immune Responses in Infectious Diseases
    Verma, Meghna (Virginia Tech, 2019-12-17)
    Infectious diseases caused by bacteria, fungi, viruses and parasites have affected humans historically. Infectious diseases remain a major cause of premature death and a public health concern globally with increased mortality and significant economic burden. Unvaccinated individuals, people with suppressed and compromised immune systems are at higher risk of suffering from infectious diseases. In spite of significant advancements in infectious diseases research, the control or treatment process faces challenges. The mucosal immune system plays a crucial role in safeguarding the body from harmful pathogens, while being constantly exposed to the environment. To develop treatment options for infectious diseases, it is vital to understand the immune responses that occur during infection. The two infectious diseases presented here are: i) Helicobacter pylori infection and ii) human immunodeficiency (HIV) and human papillomavirus (HPV) co-infection. H pylori, is a bacterium that colonizes the stomach and causes gastric cancer in 1-2% but is beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H pylori colonized individuals show no detrimental effects. HIV is a virus that causes AIDS, one of the deadliest and most persistent epidemics. HIV-infected patients are at an increased risk of co-infection with HPV, and report an increased incidence of oral cancer. The goal of this thesis is to elucidate the host immune responses in infectious diseases via the use of computational and mathematical models. First, the thesis reviews the need for computational and mathematical models to study the immune responses in the course of infectious diseases. Second, it presents a novel sensitivity analysis method that identifies important parameters in a hybrid (agent-based/equation-based) model of H. pylori infection. Third, it introduces a novel model representing the HIV/HPV coinfection and compares the simulation results with a clinical study. Fourth, it discusses the need of advanced modeling technologies to achieve a personalized systems wide approach and the challenges that can be encountered in the process. Taken together, the work in this dissertation presents modeling approaches that could lead to the identification of host immune factors in infectious diseases in a predictive and more resource-efficient manner.
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    Modeling the Mechanisms by Which HIV Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa
    Verma, Meghna; Erwin, Samantha; Abedi, Vida; Hontecillas, Raquel; Hoops, Stefan; Leber, Andrew; Bassaganya-Riera, Josep; Ciupe, Stanca M. (PLOS, 2017-01-06)
    Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.
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    Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection
    Philipson, Casandra W.; Bassaganya-Riera, Josep; Viladomiu, Monica; Kronsteiner, Barbara; Abedi, Vida; Hoops, Stefan; Michalak, Pawel; Kang, Lin; Girardin, Stephen E.; Hontecillas, Raquel (PLOS, 2015-09-14)
    Helicobacter pylori colonizes half of the world’s population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. Global transcriptomic analysis on bone marrow-derived macrophages (BMDM) in a gentamycin protection assay at six time points unveiled the presence of three sequential host response waves: an early transient regulatory gene module followed by sustained and late effector responses. Kinetic behaviors of pattern recognition receptors (PRRs) are linked to differential expression of spatiotemporal response waves and function to induce effector immunity through extracellular and intracellular detection of H. pylori. We report that bacterial interaction with the host intracellular environment caused significant suppression of regulatory NLRC3 and NLRX1 in a pattern inverse to early regulatory responses. To further delineate complex immune responses and pathway crosstalk between effector and regulatory PRRs, we built a computational model calibrated using time-series RNAseq data. Our validated computational hypotheses are that: 1) NLRX1 expression regulates bacterial burden in macrophages; and 2) early host response cytokines down-regulate NLRX1 expression through a negative feedback circuit. This paper applies modeling approaches to characterize the regulatory role of NLRX1 in mechanisms of host tolerance employed by macrophages to respond to and/or to co-exist with intracellular H. pylori.
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    Modeling the Role of Peroxisome Proliferator-Activated Receptor c and MicroRNA-146 in Mucosal Immune Responses to Clostridium difficile
    Viladomiu, Monica; Hontecillas, Raquel; Pedragosa, Mireia; Carbo, Adria; Hoops, Stefan; Michalak, Pawel; Michalak, Katarzyna; Guerrant, Richard L.; Roche, James K.; Warren, Cirle A.; Bassaganya-Riera, Josep (Public Library of Science, 2012-10-11)
    Clostridium difficile is an anaerobic bacterium that has re-emerged as a facultative pathogen and can cause nosocomial diarrhea, colitis or even death. Peroxisome proliferator-activated receptor (PPAR) c has been implicated in the prevention of inflammation in autoimmune and infectious diseases; however, its role in the immunoregulatory mechanisms modulating host responses to C. difficile and its toxins remains largely unknown. To characterize the role of PPARc in C. difficileassociated disease (CDAD), immunity and gut pathology, we used a mouse model of C. difficile infection in wild-type and T cell-specific PPARc null mice. The loss of PPARc in T cells increased disease activity and colonic inflammatory lesions following C. difficile infection. Colonic expression of IL-17 was upregulated and IL-10 downregulated in colons of T cellspecific PPARc null mice. Also, both the loss of PPARc in T cells and C. difficile infection favored Th17 responses in spleen and colonic lamina propria of mice with CDAD. MicroRNA (miRNA)-sequencing analysis and RT-PCR validation indicated that miR-146b was significantly overexpressed and nuclear receptor co-activator 4 (NCOA4) suppressed in colons of C. difficile infected mice. We next developed a computational model that predicts the upregulation of miR-146b, downregulation of the PPARc co-activator NCOA4, and PPARc, leading to upregulation of IL-17. Oral treatment of C. difficile-infected mice with the PPARc agonist pioglitazone ameliorated colitis and suppressed pro-inflammatory gene expression. In conclusion, our data indicates that miRNA-146b and PPARc activation may be implicated in the regulation of Th17 responses and colitis in C. difficile-infected mice.
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    Multistate Model Builder (MSMB): a flexible editor for compact biochemical models
    Palmisano, Alida; Hoops, Stefan; Watson, Layne T.; Jones, Thomas C, Jr.; Tyson, John J.; Shaffer, Clifford A. (Biomed Central, 2014-04-04)
    Background Building models of molecular regulatory networks is challenging not just because of the intrinsic difficulty of describing complex biological processes. Writing a model is a creative effort that calls for more flexibility and interactive support than offered by many of today’s biochemical model editors. Our model editor MSMB -- Multistate Model Builder -- supports multistate models created using different modeling styles. Results MSMB provides two separate advances on existing network model editors. (1) A simple but powerful syntax is used to describe multistate species. This reduces the number of reactions needed to represent certain molecular systems, thereby reducing the complexity of model creation. (2) Extensive feedback is given during all stages of the model creation process on the existing state of the model. Users may activate error notifications of varying stringency on the fly, and use these messages as a guide toward a consistent, syntactically correct model. MSMB default values and behavior during model manipulation (e.g., when renaming or deleting an element) can be adapted to suit the modeler, thus supporting creativity rather than interfering with it. MSMB’s internal model representation allows saving a model with errors and inconsistencies (e.g., an undefined function argument; a syntactically malformed reaction). A consistent model can be exported to SBML or COPASI formats. We show the effectiveness of MSMB’s multistate syntax through models of the cell cycle and mRNA transcription. Conclusions Using multistate reactions reduces the number of reactions need to encode many biochemical network models. This reduces the cognitive load for a given model, thereby making it easier for modelers to build more complex models. The many interactive editing support features provided by MSMB make it easier for modelers to create syntactically valid models, thus speeding model creation. Complete information and the installation package can be found at http://www.copasi.org/SoftwareProjects. MSMB is based on Java and the COPASI API.
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    PlantSimLab - a modeling and simulation web tool for plant biologists
    Ha, Sook; Dimitrova, ‪Elena; Hoops, Stefan; Altarawy, Doaa; Ansariola, Mitra; Deb, Devdutta; Glazebrook, Jane; Hillmer, Rachel; Shahin, Hossameldin L.; Katagiri, Fumiaki; McDowell, John M.; Megraw, Molly; Setubal, João C.; Tyler, Brett M.; Laubenbacher, Reinhard C. (2019-10-21)
    Background At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. Results This paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. Conclusions Mathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise.
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    Predictive Computational Modeling of the Mucosal Immune Responses during Helicobacter pylori Infection
    Carbo, Adria; Bassaganya-Riera, Josep; Pedragosa, Mireia; Viladomiu, Monica; Marathe, Madhav; Eubank, Stephen; Wendesdorf, Katherine; Bisset, Keith R.; Hoops, Stefan; Deng, Xinwei; Alam, Maksudul; Kronsteiner, Barbara; Mei, Yongguo; Hontecillas, Raquel (Public Library of Science, 2013-09-05)
    T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levelｙs of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor γ (PPARγ) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPARγ knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. These integrated immunoinformatics approaches characterized the induction of mucosal effector and regulatory pathways controlled by PPARγ during H. pylori infection affecting disease outcomes.
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    Reconstruction of endosomal organization and function by a combination of ODE and agent-based modeling strategies
    Mayorga, Luis S.; Cebrian, Ignacio; Verma, Meghna; Hoops, Stefan; Bassaganya-Riera, Josep (2018-11-23)
    Background Reproducing cell processes using an in silico system is an essential tool for understanding the underlying mechanisms and emergent properties of this extraordinary complex biological machine. However, computational models are seldom applied in the field of intracellular trafficking. In a cell, numerous molecular interactions occur on the surface or in the interior of membrane-bound compartments that continually change position and undergo dynamic processes of fusion and fission. At present, the available simulation tools are not suitable to develop models that incorporate the dynamic evolution of the cell organelles. Results We developed a modeling platform combining Repast (Agent-Based Modeling, ABM) and COPASI (Differential Equations, ODE) that can be used to reproduce complex networks of molecular interactions. These interactions occur in dynamic cell organelles that change position and composition over the course of time. These two modeling strategies are fundamentally different and comprise of complementary capabilities. The ODEs can easily model the networks of molecular interactions, signaling cascades, and complex metabolic reactions. On the other hand, ABM software is especially suited to simulate the movement, interaction, fusion, and fission of dynamic organelles. We used the combined ABM-ODE platform to simulate the transport of soluble and membrane-associated cargoes that move along an endocytic route composed of early, sorting, recycling and late endosomes. We showed that complex processes that strongly depend on transport can be modeled. As an example, the hydrolysis of a GM2-like glycolipid was programmed by adding a trans-Golgi network compartment, lysosomal enzyme trafficking, endosomal acidification, and cholesterol processing to the simulation model. Conclusions The model captures the highly dynamic nature of cell compartments that fuse and divide, creating different conditions for each organelle. We expect that this modeling strategy will be useful to understand the logic underlying the organization and function of the endomembrane system. Reviewers This article was reviewed by Drs. Rafael Fernández-Chacón, James Faeder, and Thomas Simmen.
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    The Role of the Alternaria Secondary Metabolite Alternariol in Inflammation
    Grover, Shivani (Virginia Tech, 2016-01-10)
    Allergic inflammatory disorders of the airway like asthma and atopic asthma are complex, often long-term diseases that generate large public health and socioeconomic footprints especially in developed countries like US, UK and Australia. In 2009, approximately 8.2%, 24.6 million people in United States were affected by asthma. Currently 235 million people are affected by asthma worldwide and about 90% of those have allergic (atopic) asthma. An important factor in patients with allergic respiratory tract diseases is sensitization to fungi. Other risk factors for asthma include inhaled allergens that irritate the airways. Up to 70% of mold allergic patients have skin test reactivity to Alternaria. Alta1, an allergen produced by A. alternata also produces a prolonged and intense IgE mediated reaction in sensitized patients. Therefore A. alternata is not only a risk factor in development of asthma but also can lead to exacerbation of severe and potentially lethal asthma than any other fungus. Despite the well-documented clinical importance of Alternaria in allergic airway diseases, little knowledge exists about the role of individual fungal genes and gene products in theses pathological states besides a small repertoire of allergens and proteolytic enzymes. Moreover, the importance of small, secreted molecules of fungal origin has not been explored whatsoever in regards to immune responses triggered by Alternaria. This study addresses the hypothesis that Alternaria derived small molecule's have immune modulatory properties. A major thrust of this project was to assess the role of Alternaria secondary metabolites that are synthesized by genes called polyketide synthases (PKS) in immune responses of lung epithelial cells.