A Neutrophil Phenotype Model for Extracorporeal Treatment of Sepsis

A data-driven model of the role of energy in sepsis

Exposure to pathogens elicits a complex immune response involving multiple interdependent pathways. This response may mitigate detrimental effects and restore health but, if imbalanced, can lead to negative outcomes including sepsis. This complexity and need for balance pose a challenge for clinicians and have attracted attention from modelers seeking to apply computational tools to guide therapeutic approaches. In this work, we address a shortcoming of such past efforts by incorporating the dynamics of energy production and consumption into a computational model of the acute immune response. With this addition, we performed fits of model dynamics to data obtained from non-human primates exposed to Escherichia coli. Our analysis identifies parameters that may be crucial in determining survival outcomes and also highlights energy-related factors that modulate the immune response across baseline and altered glucose conditions.

Dynamical modeling of pro- and anti-inflammatory cytokines in the early stage of septic shock

A dynamical model of the pathophysiological behaviors of IL18 and IL10 cytokines with their receptors is tested against data for the case of early sepsis. The proposed approach considers the surroundings (organs and bone marrow) and the different subsystems (cells and cyctokines). The interactions between blood cells, cytokines and the surroundings are described via mass balances. Cytokines are adsorbed onto associated receptors at the cell surface. The adsorption is described by the Langmuir model and gives rise to the production of more cytokines and associated receptors inside the cell. The quantities of pro and anti-inflammatory cytokines present in the body are combined to give global information via an inflammation level function which describes the patient’s state. Data for parameter estimation comes from the Sepsis 48 H database. Comparisons between patient data and simulations are presented and are in good agreement. For the IL18/IL10 cytokine pair, 5 key parameters have been found. They are linked to pro-inflammatory IL18 cytokine and show that the early sepsis is driven by components of inflammatory character.

Long noncoding RNA and messenger RNA abnormalities in pediatric sepsis: a preliminary study

Background

Sepsis represents a complex disease with dysregulated inflammatory response and high mortality rate. Long noncoding RNAs (lncRNAs) have been reported to play regulatory roles in a variety of biological processes. However, studies evaluating the function of lncRNAs in pediatric sepsis are scarce, and current knowledge of the role of lncRNAs in pediatric sepsis is still limited. The present study explored the expression patterns of both lncRNAs and mRNAs between pediatric sepsis patients and healthy controls based on a comprehensive microarray analysis.

Methods

LncRNA and mRNA microarray was used to detect the expression of lncRNAs and mRNAs in the septic and control groups. Aberrantly expressed mRNAs and lncRNAs identified were further interpreted by enrichment analysis, receiver operating characteristic (ROC) curve analysis, co-expression network analysis, and quantitative real-time PCR (qPCR).

Results

A total of 1488 differetially expressed lncRNAs and 1460 differentially expressed mRNAs were identified. A co-expression network of the identified lncRNAs and mRNAs was constructed. In this network, lncRNA lnc-RP11-1220 K2.2.1–7 is correlated with mRNA CXCR1 and CLEC4D; lncRNA lnc-ANXA3–2 is correlated with mRNA CLEC4D; lncRNA lnc-TRAPPC5–1 is correlated with mRNA DYSF and HLX; lncRNA lnc-ZNF638–1 is correlated with mRNA DYSF and HLX. Significantly different expressions between pediatric sepsis patients and controls were validated by qPCR for the 4 lncRNAs and 4 co-expressed mRNAs, validating the microarray results.

Conclusions

Our study contributes to a comprehensive understading of the involvment of lncRNAs and mRNAs in pediatric sepsis, which may guide subsequent experimental research. Furthermore, our study may also provide potential candidate lncRNAs and mRNAs for the diagnosis and treatment of pediatric sepsis.

Parallel Tempering with Lasso for model reduction in systems biology

Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization to identify minimal subsets of reactions in a signaling network that are sufficient to reproduce experimentally observed data. The Bayesian approach finds distinct reduced models that fit data equivalently. A variant of this approach that uses Lasso to perform selection at the level of reaction modules is applied to the NF-κB signaling network to test the necessity of feedback loops for responses to pulsatile and continuous pathway stimulation. Taken together, our results demonstrate that Bayesian parameter estimation combined with regularization can isolate and reveal core motifs sufficient to explain data from complex signaling systems.

Cells respond to diverse environmental cues using complex networks of interacting proteins and other biomolecules. Mathematical and computational models have become invaluable tools to understand these networks and make informed predictions to rationally perturb cell behavior. However, the complexity of detailed models that try to capture all known biochemical elements of signaling networks often makes it difficult to determine the key regulatory elements that are responsible for specific cell behaviors. Here, we present a Bayesian computational approach, PTLasso, to automatically extract minimal subsets of detailed models that are sufficient to explain experimental data. The method simultaneously calibrates and reduces models, and the Bayesian approach samples globally, allowing us to find alternate mechanistic explanations for the data if present. We demonstrate the method on both synthetic and real biological data and show that PTLasso is an effective method to isolate distinct parts of a larger signaling model that are sufficient for specific data.

Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children

This paper uses computer technology combined with regression model to analyze the risk factors of children with sepsis, determine the relevant factors and establish a corresponding early warning model of sepsis, and then verify the clinical application value of the regression model. The paper collected severe infections and sepsis in children who came to our hospital from 2014 to 2018, including 129 patients with infection and 86 patients with sepsis. The general conditions, clinical symptoms, laboratory tests and other factors were used. Analysis, to identify the risk of infection development into sepsis, and use Logistic regression model combined with computer technology to construct an early warning model of sepsis. The experimental results show that early warning of sepsis is closely related to skin spots, platelets, procalcitonin, creatinine and international normalized ratio. The experiment demonstrates that the early warning model has higher sensitivity and specificity, and has higher accuracy for predicting whether infection develops into sepsis in advance, and has certain clinical value.

Inflammation and Disease: Modelling and Modulation of the Inflammatory Response to Alleviate Critical Illness

Critical illness, a constellation of interrelated inflammatory and physiological derangements occurring subsequent to severe infection or injury, affects a large number of individuals in both developed and developing countries. The prototypical complex system embodied in critical illness has largely defied therapy beyond supportive care. We have focused on the utility of data-driven and mechanistic computational modelling to help address the complexity of critical illness and provide pathways towards discovering potential therapeutic options and combinations. Herein, we review recent progress in this field, with a focus on both animal and computational models of critical illness. We suggest that therapy for critical illness can be posed as a model-based dynamic control problem, and discuss novel theoretical and experimental approaches involving biohybrid devices aimed at reprogramming inflammation dynamically. Together, these advances offer the potential for Model-based Precision Medicine for critical illness.

Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology

Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://github.com/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.