Efficient Simulation and Parametrization of Stochastic Petri Nets in SystemC: A Case study from Systems Biology

Stochastic Petri nets (SPN) are a form of Petri net where the transitions fire after a probabilistic and randomly determined delay. They are adopted in a wide range of appli- cations thanks to their capability of incorporating randomness in the models and taking into account possible fluctuations and environmental noise. In Systems Biology, they are becoming a reference formalism to model metabolic networks, in which the noise due to molecule interactions in the environment plays a crucial role. Some frameworks have been proposed to implement and dynamically simulate SPN. Nevertheless, they do not allow for automatic model parametrization, which is a crucial task to identify the network configurations that lead the model to satisfy temporal properties of the model. This paper presents a framework that synthesizes the SPN models into SystemC code. The framework allows the user to formally define the network properties to be observed and to automatically extrapolate, thorough Assertion-based Verification (ABV), the parameter configurations that lead the network to satisfy such properties. We applied the framework to implement and simulate a complex biological network, i.e., the purine metabolism, with the aim of reproducing the metabolomics data obtained in-vitro from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders

Leishmania and other intracellular pathogens: selectivity, drug distribution and PK-PD.

New drugs and treatments for diseases caused by intracellular pathogens, such as leishmaniasis and the Leishmania species, have proved to be some of the most difficult to discover and develop. The focus of discovery research has been on the identification of potent and selective compounds that inhibit target enzymes (or other essential molecules) or are active against the causative pathogen in phenotypic in vitro assays. Although these discovery paradigms remain an essential part of the early stages of the drug R & D pathway, over the past two decades additional emphasis has been given to the challenges needed to ensure that the potential anti-infective drugs distribute to infected tissues, reach the target pathogen within the host cell and exert the appropriate pharmacodynamic effect at these sites. This review will focus on how these challenges are being met in relation to Leishmania and the leishmaniases with lessons learned from drug R & D for other intracellular pathogens

Coupling of Petri Net Models of the Mycobacterial Infection Process and Innate Immune Response

Algorithms and the Foundations of Software technologyComputer Systems, Imagery and Medi

Dynamic balance of pro‐ and anti‐inflammatory signals controls disease and limits pathology

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long‐term control of infection while also preventing an over‐zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro‐ and anti‐inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host‐directed therapies.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146448/1/imr12671.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146448/2/imr12671_am.pd

Computational Techniques for the Structural and Dynamic Analysis of Biological Networks

The analysis of biological systems involves the study of networks from different omics such as genomics, transcriptomics, metabolomics and proteomics. In general, the computational techniques used in the analysis of biological networks can be divided into those that perform (i) structural analysis, (ii) dynamic analysis of structural prop- erties and (iii) dynamic simulation. Structural analysis is related to the study of the topology or stoichiometry of the biological network such as important nodes of the net- work, network motifs and the analysis of the flux distribution within the network. Dy- namic analysis of structural properties, generally, takes advantage from the availability of interaction and expression datasets in order to analyze the structural properties of a biological network in different conditions or time points. Dynamic simulation is useful to study those changes of the biological system in time that cannot be derived from a structural analysis because it is required to have additional information on the dynamics of the system. This thesis addresses each of these topics proposing three computational techniques useful to study different types of biological networks in which the structural and dynamic analysis is crucial to answer to specific biological questions. In particu- lar, the thesis proposes computational techniques for the analysis of the network motifs of a biological network through the design of heuristics useful to efficiently solve the subgraph isomorphism problem, the construction of a new analysis workflow able to integrate interaction and expression datasets to extract information about the chromo- somal connectivity of miRNA-mRNA interaction networks and, finally, the design of a methodology that applies techniques coming from the Electronic Design Automation (EDA) field that allows the dynamic simulation of biochemical interaction networks and the parameter estimation

Dual RNA-Seq analysis of Mus musculus and Leishmania donovani transcriptomes

Parasitic protozoa of the genus Leishmania cause a spectrum of disease, affecting 12 million people worldwide. This project aimed to investigate the effect of Leishmania donovani infection on the gene expression of healthy/WT (Black 6) and immunocompromised (RAG2KO) mice. Differences in the gene expression of parasites in inoculum and tissue were also elucidated. WT and RAG mice were infected using an L. donovani inoculum, and were euthanised after 28 days. Harvested spleens (and the inoculum) were used to generate RNA samples, from which mRNA was isolated and purified. Transcriptome data was generated using dual RNA-Seq approaches from the mRNA samples. After appropriate pre-processing, data underwent a number of bioinformatic analyses to explore differential gene expression, such as Gene Ontology, Gene Set Enrichment, and KEGG Pathway analysis. Comparison of different mouse spleen transcriptomes revealed that even in uninfected mice, WT mice more highly express genes related to immunoglobulins when compared with their immunocompromised counterparts. Healthy mice were found to react to infection through the induction of inflammatory response, and the production of NOX generating species. RAG mice still upregulated immunoglobulin-related genes in response to infection, despite their inability to generate antibodies, T-cells, and B-cells. However, RAG modulation of haeme and iron metabolism may contribute to defence against the parasites despite a lack of acquired immunity. Differences in the amastin, the key glycoprotein on the surface on intracellular-stage parasites, are apparent between the inoculum and tissue parasites, which may reflect microenvironment adaptation. Additionally, tissue-derived parasites showed significant upregulation of genes related to gene expression control, such as histones and DNA-packing. These experiments are among the first attempts to in vivo transcriptome sequence mice and Leishmania simultaneously, a powerful approach giving insight to action and reaction. However, these techniques are not without challenge, such as low parasite read counts

Computational modelling of mycobacterium infection and innate immune response in zebrafish

In this thesis we provided a comprehensive overview on the steps that are involved in the modeling process and simulation of biological phenomena; from the choice of the method to the validation of the results. We gradually implemented a model with which we would be able to study the complex interplay of the components involved in the Mycobacterium marinum infection process and innate immune response in zebrafish embryos. In itself this process is a model for deeper understanding of tuberculosis infection in humans using zebrafish as model organism. Each chapter is a building block in the modeling process, which gradually forms a model that can represent cause-and-effect among these components involved in the biological behavior.Computer Systems, Imagery and Medi

Understanding and Treating Mycobacterium tuberculosis Infection: A Multi-Scale Modeling Approach.

Tuberculosis (TB), caused by the pathogen Mycobacterium tuberculosis (Mtb), remains a significant burden on global health. Central to both host immune responses and antibiotic treatment are structures known as granulomas. In this dissertation we used computational and experimental approaches at a single granuloma level to understand how immune responses to Mtb contribute to both bacterial control and persistence. In addition, we predicted the dynamics of antibiotics in granulomas and designed improved treatment strategies. We built a hybrid multi-scale model of Mtb infection that integrates the cytokines tumor necrosis factor-α (TNF) and interleukin-10 (IL-10). We predicted that a balance of TNF and IL-10 is essential to infection control with minimal host-induced tissue damage. We extended our description of TNF and IL-10 to include simplified models of intracellular signaling driving macrophage polarization, which suggests that the temporal dynamics of macrophage polarization in granulomas are predictive of granuloma outcome. Next, we focused on determining the role of IL-10 in controlling antimicrobial activity. We predicted a transient role for IL-10 in controlling a trade-off between early host immunity antimicrobial responses and tissue damage. This trade-off determines sterilization of granulomas. Lastly, using an experimental model of granuloma formation, we measured significant gradients of TNF in granulomas. xxii We developed a pharmacokinetic and pharmacodynamic model of oral dosing of rifampin and isoniazid used to treat Mtb and incorporated it into our computational model. We predicted that oral antibiotic strategies fail due to sub-optimal exposure in granulomas, which leads to bacterial regrowth between doses. We extended our platform to include a description of inhaled formulations dosed to the lungs with reduced frequencies. We predicted that dosing every two-weeks with an inhaled formulation of isoniazid is feasible with increased sterilization capabilities and reduced toxicity, while an inhaled formulation of rifampin has equivalent sterilization capabilities, but early associated toxicity and infeasible carrier loadings. The keys to understanding immune responses and successful antibiotic treatment of TB lie in the dynamics at the site of infection. Our results help identify the roles of cytokines during Mtb infection, provide new possibilities for immune related therapies, and guide design of better antibiotic strategies.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108883/1/ncilfone_1.pd

A petri net model of granulomatous inflammation

Leishmania donovani is an obligate intracellular parasite responsible for the systemic disease visceral leishmaniasis. During the course of the disease, the parasite is found in the spleen, liver and bone marrow. Characteristic of the liver immune response to leishmaniasis is a type of inflammation ("ggranulomatous inflammation") that results in the formation of granulomas, structures comprised of an infiltrate of mononuclear cells surrounding a core of infected macrophages. Granulomas help limit the spread of infection and facilitate the killing of parasites. Liver-resident macrophages (Kupffer cells) are able to spontaneously kill many infectious agents, but L. donovani is capable of reproducing inside these cells. Activation of Kupffer cells is required to turn them from host cell to a cell that is able to kill intracellular L. donovani . This process of activation is regulated by cytokines (notably IFNγ) produced by many different types of leukocytes, including natural killer (NK) cells ([1]), CD4+ and CD8+ T cells ([2]), and NKT cells ([3]).</p