An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data

Citation: Shi, Z. Z., Chapes, S. K., Ben-Arieh, D., & Wu, C. H. (2016). An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data. Plos One, 11(8), 39. doi:10.1371/journal.pone.0161131We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-a ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies

Closed-Loop Fluid Resuscitation Control Via Blood Volume Estimation

This paper presents a closed-loop control of fluid resuscitation to overcome hypovolemia based on model-based estimation of relative changes in blood volume (BV). In this approach, the control system consists of a model-based relative BV (RBV) estimator and a feedback controller. The former predicts relative changes in the BV response to augmented fluid by analyzing an arterial blood pressure (BP) waveform and the electrocardiogram (ECG). Then, the latter determines the amount of fluid to be augmented by comparing target versus predicted relative changes in BV. In this way, unlike many previous methods for fluid resuscitation based on controlled variable(s) nonlinearly correlated with the changes in BV, fluid resuscitation can be guided by a controlled variable linearly correlated with the changes in BV. This paper reports initial design of the closed-loop fluid resuscitation system and its in silico evaluation in a wide range of hypovolemic scenarios. The results suggest that closed-loop fluid resuscitation guided by a controlled variable linearly correlated with the changes in BV can be effective in overcoming hypovolemia: across 100 randomly produced hypovolemia cases, it resulted in the BV regulation error of 7.98 6 171.6 ml, amounting to 0.18 6 3.04% of the underlying BV. When guided by pulse pressure (PP), a classical controlled variable nonlinearly correlated with the changes in BV; the same closed-loop fluid resuscitation system resulted in persistent under-resuscitation with the BV regulation error of À779.1 6 147.4 ml, amounting to À13.9 6 2.65% of the underlying BV

Deep learning methods for improving diabetes management tools

Diabetes is a chronic disease that is characterised by a lack of regulation of blood glucose concentration in the body, and thus elevated blood glucose levels. Consequently, affected individuals can experience extreme variations in their blood glucose levels with exogenous insulin treatment. This has associated debilitating short-term and long-term complications that affect quality of life and can result in death in the worst instance. The development of technologies such as glucose meters and, more recently, continuous glucose monitors have offered the opportunity to develop systems towards improving clinical outcomes for individuals with diabetes through better glucose control. Data-driven methods can enable the development of the next generation of diabetes management tools focused on i) informativeness ii) safety and iii) easing the burden of management. This thesis aims to propose deep learning methods for improving the functionality of the variety of diabetes technology tools available for self-management. In the pursuit of the aforementioned goals, a number of deep learning methods are developed and geared towards improving the functionality of the existing diabetes technology tools, generally classified as i) self-monitoring of blood glucose ii) decision support systems and iii) artificial pancreas. These frameworks are primarily based on the prediction of glucose concentration levels. The first deep learning framework we propose is geared towards improving the artificial pancreas and decision support systems that rely on continuous glucose monitors. We first propose a convolutional recurrent neural network (CRNN) in order to forecast the glucose concentration levels over both short-term and long-term horizons. The predictive accuracy of this model outperforms those of traditional data-driven approaches. The feasibility of this proposed approach for ambulatory use is then demonstrated with the implementation of a decision support system on a smartphone application. We further extend CRNNs to the multitask setting to explore the effectiveness of leveraging population data for developing personalised models with limited individual data. We show that this enables earlier deployment of applications without significantly compromising performance and safety. The next challenge focuses on easing the burden of management by proposing a deep learning framework for automatic meal detection and estimation. The deep learning framework presented employs multitask learning and quantile regression to safely detect and estimate the size of unannounced meals with high precision. We also demonstrate that this facilitates automated insulin delivery for the artificial pancreas system, improving glycaemic control without significantly increasing the risk or incidence of hypoglycaemia. Finally, the focus shifts to improving self-monitoring of blood glucose (SMBG) with glucose meters. We propose an uncertainty-aware deep learning model based on a joint Gaussian Process and deep learning framework to provide end users with more dynamic and continuous information similar to continuous glucose sensors. Consequently, we show significant improvement in hyperglycaemia detection compared to the standard SMBG. We hope that through these methods, we can achieve a more equitable improvement in usability and clinical outcomes for individuals with diabetes.Open Acces

An integrated mathematical model of cellular cholesterol biosynthesis and lipoprotein metabolism

Cholesterol regulation is an important aspect of human health. In this work we bring together and extend two recent mathematical models describing cholesterol biosynthesis and lipoprotein endocytosis to create an integrated model of lipoprotein metabolism in the context of a single hepatocyte. The integrated model includes a description of low density lipoprotein (LDL) receptor and cholesterol synthesis, delipidation of very low density lipoproteins (VLDLs) to LDLs and subsequent lipoprotein endocytosis. Model analysis shows that cholesterol biosynthesis produces the majority of intracellular cholesterol. The availability of free receptors does not greatly effect the concentration of intracellular cholesterol, but has a detrimental effect on extracellular VLDL and LDL levels. We test our model by considering its ability to reproduce the known biology of Familial Hypercholesterolaemia and statin therapy. In each case the model reproduces the known biological behaviour. Quantitative differences in response to statin therapy are discussed in the context of the need to extend the work to a more {\it in vivo} setting via the incorporation of more dietary lipoprotein related processes and the need for further testing and parameterisation of {\it in silico} models of lipoprotein metabolism

Silibinin and SARS-CoV-2: Dual Targeting of Host Cytokine Storm and Virus Replication Machinery for Clinical Management of COVID-19 Patients

COVID-19, the illness caused by infection with the novel coronavirus SARS-CoV-2, is a rapidly spreading global pandemic in urgent need of effective treatments. Here we present a comprehensive examination of the host- and virus-targeted functions of the flavonolignan silibinin, a potential drug candidate against COVID-19/SARS-CoV-2. As a direct inhibitor of STAT3-a master checkpoint regulator of inflammatory cytokine signaling and immune response-silibinin might be expected to phenotypically integrate the mechanisms of action of IL-6-targeted monoclonal antibodies and pan-JAK1/2 inhibitors to limit the cytokine storm and T-cell lymphopenia in the clinical setting of severe COVID-19. As a computationally predicted, remdesivir-like inhibitor of RNA-dependent RNA polymerase (RdRp)-the central component of the replication/transcription machinery of SARS-CoV-2-silibinin is expected to reduce viral load and impede delayed interferon responses. The dual ability of silibinin to target both the host cytokine storm and the virus replication machinery provides a strong rationale for the clinical testing of silibinin against the COVID-19 global public health emergency. A randomized, open-label, phase II multicentric clinical trial (SIL-COVID19) will evaluate the therapeutic efficacy of silibinin in the prevention of acute respiratory distress syndrome in moderate-to-severe COVID-19-positive onco-hematological patients at the Catalan Institute of Oncology in Catalonia, Spain

Strategies mitigating hypoxaemia in high-risk populations during anaesthesia and respiratory critical care: computational modelling studies

Assessing interventions applied during clinically-encounterable extreme scenarios is vital to enhance the quality of care. However, the studies that examine these situations are rare, ethically and clinically challenging. Computational modelling offers a reliable, efficient and almost ethical-free approach to investigate high-risk scenarios. This thesis evaluated interventions applied during (i) prolonged apnoea in obesity, (ii) airway obstruction in emergency crises, and (iii) hyperbaric oxygen therapy in severe hypoxaemic respiratory failure patients through a series of high-fidelity computational modelling studies. Worldwide, there are more than 650 million obese individuals and anticipated to increase. In the context of anaesthesia and critical care, obese subjects are at increased risks during general anaesthesia, such as airway difficulties and apnoea intolerance (rapid occurrence of hypoxaemia). Developing and quantifying methods to extend the safe (non-hypoxaemic) apnoea time would increase their safety remarkably during this procedure. The thesis showed that the use of high-flow nasal oxygen significantly delayed the safe apnoea time in a bank of obese virtual subjects. Persistent airway obstruction is not common in anaesthesia practice, but it could lead to catastrophic outcomes. Complete blockage of the upper airway was simulated until life-threatening hypoxaemia occurred, followed by relieving the obstruction and delivery of multiple patterns of tidal ventilation. Larger tidal volume did not achieve faster re-oxygenation compared to lower tidal volume. Globally, up to 20 million acute respiratory failure patients receive mechanical ventilation annually. The mortality of acute respiratory distress syndrome (ARDS) remains considerably high despite the implementation of the lung-protective ventilation strategy. A bank of severe ARDS virtual patients was configured and underwent maximum lung-protective ventilation strategy at atmospheric pressure (with high positive end-expiratory pressure [PEEP]) and hyperbaric pressure (with low PEEP). The hyperbaric oxygen significantly increased the oxygen delivery to tissues even with a low fraction of inspired oxygen. The thesis’s original contributions to knowledge are: first, it quantified the impact of airway obstruction and patency, high oxygen concentration and high-flow nasal oxygen, applied during apnoea, on the safe apnoea time in obesity. Second, it demonstrated that larger tidal ventilation during airway rescue is not necessary. Finally, it highlighted that hyperbaric oxygen therapy could provide adequate tissue oxygen delivery and may be considered as a rescue option for severe ARDS patients who remain hypoxaemic despite maximum lung-protective ventilation strategy

Biosimulation of Vocal Fold Inflammation and Healing

Personalized, pre-emptive and predictive medicine is the capstone of contemporary medical care. The central aim of this dissertation is to address clinical challenges in prescribing personalized therapy to patients with acute phonotrauma. Inflammation and healing, which are innate tissue responses to mechanical stress/ trauma, are regulated by a complex dynamic system. A systems biology approach, which combines empirical, mathematical and computational tools, was taken to study the biological complexity of this dynamic system in vocal fold injury.Computational agent-based models (ABMs) were developed to quantitatively characterize multiple cellular and molecular interactions around inflammation and healing. The models allowed for tests of various hypothetical effects of motion-based treatments in individuals with acute phonotrauma. A phonotrauma ABM was calibrated and verified with empirical data of a panel of inflammatory mediators, obtained from laryngeal secretions in individuals following experimentally induced phonotrauma and a randomly assigned motion-based treatment. A supplementary ABM of surgically induced vocal fold trauma was developed and subsequently calibrated and verified with empirical data of inflammatory mediators and extracellular matrix substances from rat studies, for the purpose of gaining insight into the &ldquo net effect &rdquo of cellular and molecular responses at the tissue level.ABM simulations reproduced and predicted trajectories of inflammatory mediators and extracellular matrix as seen in empirical data of phonotrauma and surgical vocal fold trauma. The simulation results illustrated a spectrum of inflammatory responses to phonotrauma, surgical trauma and motion-based treatments. The results suggested that resonant voice exercise may optimize the combination of para- and anti-inflammatory responses to accelerate healing. Moreover, the ABMs suggested that hyaluronan fragments might be an early molecular index of tissue damage that is sensitive to varying stress levels - from relatively low phonatory stress to high surgical stress.We propose that this translational application of biosimulation can be used to quantitatively chart individual healing trajectories, test the effects of different treatment options and most importantly provide new understanding of laryngeal health and healing. By placing biology on a firm mathematical foundation, this line of research has potential to influence the contour of scientific thinking and clinical care of vocal fold injury

Large–scale data–driven network analysis of human–plasmodium falciparum interactome: extracting essential targets and processes for malaria drug discovery

Background: Plasmodium falciparum malaria is an infectious disease considered to have great impact on public health due to its associated high mortality rates especially in sub Saharan Africa. Falciparum drugresistant strains, notably, to chloroquine and sulfadoxine-pyrimethamine in Africa is traced mainly to Southeast Asia where artemisinin resistance rate is increasing. Although careful surveillance to monitor the emergence and spread of artemisinin-resistant parasite strains in Africa is on-going, research into new drugs, particularly, for African populations, is critical since there is no replaceable drug for artemisinin combination therapies (ACTs) yet. Objective: The overall objective of this study is to identify potential protein targets through host–pathogen protein–protein functional interaction network analysis to understand the underlying mechanisms of drug failure and identify those essential targets that can play their role in predicting potential drug candidates specific to the African populations through a protein-based approach of both host and Plasmodium falciparum genomic analysis. Methods: We leveraged malaria-specific genome wide association study summary statistics data obtained from Gambia, Kenya and Malawi populations, Plasmodium falciparum selective pressure variants and functional datasets (protein sequences, interologs, host-pathogen intra-organism and host-pathogen inter-organism protein-protein interactions (PPIs)) from various sources (STRING, Reactome, HPID, Uniprot, IntAct and literature) to construct overlapping functional network for both host and pathogen. Developed algorithms and a large-scale data-driven computational framework were used in this study to analyze the datasets and the constructed networks to identify densely connected subnetworks or hubs essential for network stability and integrity. The host-pathogen network was analyzed to elucidate the influence of parasite candidate key proteins within the network and predict possible resistant pathways due to host-pathogen candidate key protein interactions. We performed biological and pathway enrichment analysis on critical proteins identified to elucidate their functions. In order to leverage disease-target-drug relationships to identify potential repurposable already approved drug candidates that could be used to treat malaria, pharmaceutical datasets from drug bank were explored using semantic similarity approach based of target–associated biological processes Results: About 600,000 significant SNPs (p-value< 0.05) from the summary statistics data were mapped to their associated genes, and we identified 79 human-associated malaria genes. The assembled parasite network comprised of 8 clusters containing 799 functional interactions between 155 reviewed proteins of which 5 clusters contained 43 key proteins (selective variants) and 2 clusters contained 2 candidate key proteins(key proteins characterized by high centrality measure), C6KTB7 and C6KTD2. The human network comprised of 32 clusters containing 4,133,136 interactions between 20,329 unique reviewed proteins of which 7 clusters contained 760 key proteins and 2 clusters contained 6 significant human malaria-associated candidate key proteins or genes P22301 (IL10), P05362 (ICAM1), P01375 (TNF), P30480 (HLA-B), P16284 (PECAM1), O00206 (TLR4). The generated host-pathogen network comprised of 31,512 functional interactions between 8,023 host and pathogen proteins. We also explored the association of pfk13 gene within the host-pathogen. We observed that pfk13 cluster with host kelch–like proteins and other regulatory genes but no direct association with our identified host candidate key malaria targets. We implemented semantic similarity based approach complemented by Kappa and Jaccard statistical measure to identify 115 malaria–similar diseases and 26 potential repurposable drug hits that can be 3 appropriated experimentally for malaria treatment. Conclusion: In this study, we reviewed existing antimalarial drugs and resistance–associated variants contributing to the diminished sensitivity of antimalarials, especially chloroquine, sulfadoxine-pyrimethamine and artemisinin combination therapy within the African population. We also described various computational techniques implemented in predicting drug targets and leads in drug research. In our data analysis, we showed that possible mechanisms of resistance to artemisinin in Africa may arise from the combinatorial effects of many resistant genes to chloroquine and sulfadoxine–pyrimethamine. We investigated the role of pfk13 within the host–pathogen network. We predicted key targets that have been proposed to be essential for malaria drug and vaccine development through structural and functional analysis of host and pathogen function networks. Based on our analysis, we propose these targets as essential co-targets for combinatorial malaria drug discovery