Bayesian Network Inference Research Articles

Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death.

Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1) and 26 were survivors (GOS > 1). A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS]) vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α) were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.

Bayesian-network-based system identification of spatial distribution of structural parameters

Structural deterioration or damage in civil infrastructures may results in severe losses of properties and human lives. To prevent such situations, structure health monitoring (SHM) technologies have been developed in recent decades. As one of the SHM technologies, so-called system identification (SI) methods aim to estimate structural parameters by minimizing an error function consisting of measured and calculated responses of a structure under the same loading condition. Such optimization-based SI algorithms may suffer from ill-posedness of the inverse problem, which may result in non-uniqueness of solutions or non-stability of the optimization process. In this paper, in order to avoid issues related to ill-posedness, an SI method based on the Bayesian Network (BN) technology is developed, especially for probabilistic identification of spatial distribution of structural parameters. Utilizing graph theories, a BN describes random variables by nodes connected by links, which represent conditional probability tables (CPT) explaining the probabilistic relationship between the linked nodes. For effective SI using BN, this study proposes a BN graph model, which employs a bi-variate Gaussian function as a shape function describing the spatial distribution of a structural parameter with a small number of parameters. The parameters of the Gaussian (shape) function are considered as nodes in the BN to describe various spatial distribution patterns using a small number of parent nodes. Using this modeling approach, the number of the BN nodes is not affected by the size of the finite element (FE) mesh. Thus, the approach prevents the BN graph from growing to an intractable size even if the FE analysis requires smaller meshes to improve the accuracy of the structural analysis. Using the constructed BN, information on applied loads, and observed structural responses, a BN inference algorithm effectively updates the prior distribution of spatial distribution parameters to the posterior distribution. The proposed method is tested and demonstrated by numerical examples. Using a variety of structural deterioration scenarios, the SI results by the proposed method are compared with those by maximum likelihood estimation (MLE), and an FE-updating method. During the test, the influences of measurement errors, and incomplete/missing data on the performance of the methods are also investigated. The results show that the proposed SI approach is more stable and robust than the other tested methods, and potentially has more merits that are worth investigating in future research.

Large-scale inference of conjunctive Bayesian networks.

The continuous time conjunctive Bayesian network (CT-CBN) is a graphical model for analyzing the waiting time process of the accumulation of genetic changes (mutations). CT-CBN models have been successfully used in several biological applications such as HIV drug resistance development and genetic progression of cancer. However, current approaches for parameter estimation and network structure learning of CBNs can only deal with a small number of mutations (<20). Here, we address this limitation by presenting an efficient and accurate approximate inference algorithm using a Monte Carlo expectation-maximization algorithm based on importance sampling. The new method can now be used for a large number of mutations, up to one thousand, an increase by two orders of magnitude. In simulation studies, we present the accuracy as well as the running time efficiency of the new inference method and compare it with a MLE method, expectation-maximization, and discrete time CBN model, i.e. a first-order approximation of the CT-CBN model. We also study the application of the new model on HIV drug resistance datasets for the combination therapy with zidovudine plus lamivudine (AZT + 3TC) as well as under no treatment, both extracted from the Swiss HIV Cohort Study database. The proposed method is implemented as an R package available at https://github.com/cbg-ethz/MC-CBN CONTACT: niko.beerenwinkel@bsse.ethz.ch Supplementary data are available at Bioinformatics online.

Genetic variation and phylogenetic relationship analysis of Jatropha curcas L. inferred from nrDNA ITS sequences

Genetic variation and phylogenetic relationships among 102 Jatropha curcas accessions from Asia, Africa, and the Americas were assessed using the internal transcribed spacer region of nuclear ribosomal DNA (nrDNA ITS). The average G+C content (65.04%) was considerably higher than the A+T (34.96%) content. The estimated genetic diversity revealed moderate genetic variation. The pairwise genetic divergences (GD) between haplotypes were evaluated and ranged from 0.000 to 0.017, suggesting a higher level of genetic differentiation in Mexican accessions than those of other regions. Phylogenetic relationships and intraspecific divergence were inferred by Bayesian inference (BI), maximum parsimony (MP), and median joining (MJ) network analysis and were generally resolved. The J. curcas accessions were consistently divided into three lineages, groups A, B, and C, which demonstrated distant geographical isolation and genetic divergence between American accessions and those from other regions. The MJ network analysis confirmed that Central America was the possible center of origin. The putative migration route suggested that J. curcas was distributed from Mexico or Brazil, via Cape Verde and then split into two routes. One route was dispersed to Spain, then migrated to China, eventually spreading to southeastern Asia, while the other route was dispersed to Africa, via Madagascar and migrated to China, later spreading to southeastern Asia.

Abstract 3376: Molecular drivers of endothelial hypoxic adaption during angiogenesis deciphered using the Berg Interrogative Biology® platform

Abstract Angiogenesis is a key feature of tumor progression providing oxygen and nutrients required for tumor cell growth, hypoxia being a major driver of this phenomenon. A systems biology approach using the Berg Interrogative Biology® platform was implemented to identify drivers of the endothelial cell (EC) angiogenic response to hypoxia. To determine the proteomic profile of proliferating ECs and non-proliferating confluent ECs exposed to normoxia or hypoxia, a functional proteomic approach employing activity-based probes and phosphorylation analysis was utilized. Kinases and ATPases were labeled with ATP-binding domain enrichment probes and titanium dioxide enrichment of phosphopeptides was employed for capture of protein phosphorylation events. Phenotypic assays including proliferation rates, apoptosis, mitochondrial superoxide and ROS/NO signaling were included as a measure of EC phenotype. Comparative proteomics, kinase activity, phosphoproteomics and assay data were integrated using an AI based Bayesian Network Inference approach to investigate causal signaling networks in order to elucidate the complexity and dynamics of angiogenesis and more specifically, the role of hypoxia in driving intracellular signaling in response to changes in oxygen tension. High confidence causal networks identified novel proteins that modulate the EC hypoxic response, validation of which are in process. Previously characterized proteins that are responsive to hypoxia were also identified in the hypoxic, but not normoxic signaling networks, namely Aldoc, Rac1, mTor, Cav-1 and Bax. Endothelial proliferation rate is closely related to both hydrogen peroxide and nitric oxide signaling, as has previously been reported. Novel regulatory networks that determine these interactions were identified. Using the Berg Interrogative Biology® platform, we are deciphering both the effects of the hypoxic microenvironment, and the unique characteristics of proliferating ECs by applying integrated functional proteomic assays and a systems approach to determine global changes in intracellular signaling in response to hypoxia. Citation Format: Tony E. Walshe, Justin Bourdelais, Viatcheslav R. Akmaev, Leonardo O. Rodrigues, Socheata Lao, Stephane Gesta, Michael A. Kiebish, Vivek K. Vishnudas, Rangaprasad Sarangarajan, Niven R. Narain. Molecular drivers of endothelial hypoxic adaption during angiogenesis deciphered using the Berg Interrogative Biology® platform. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3376.

Active inference for dynamic Bayesian networks with an application to tissue engineering

In temporal domains, agents need to actively gather information to make more informed decisions about both the present and the future. When such a domain is modeled as a temporal graphical model, what the agent observes can be incorporated into the model by setting the respective random variables as evidence. Motivated by a tissue engineering application where the experimenter needs to decide how early a laboratory experiment can be stopped so that its possible future outcomes can be predicted within an acceptable uncertainty, we first present a dynamic Bayesian network (DBN) model of vascularization in engineered tissues and compare it with both real-world experimental data and agent-based simulations. We then formulate the question of "how early an experiment can be stopped to guarantee an acceptable uncertainty about the final expected outcome" as an active inference problem for DBNs and empirically and analytically evaluate several search algorithms that aim to find the ideal time to stop a tissue engineering laboratory experiment.

Variational Message Passing

Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniques have been developed, one of the most widely used being a deterministic framework called variational inference. In this paper we introduce Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to Bayesian Networks. Like belief propagation, VMP proceeds by sending messages between nodes in the network and updating posterior beliefs using local operations at each node. Each such update increases a lower bound on the log evidence (unless already at a local maximum). In contrast to belief propagation, VMP can be applied to a very general class of conjugate-exponential models because it uses a factorised variational approximation. Furthermore, by introducing additional variational parameters, VMP can be applied to models containing non-conjugate distributions. The VMP framework also allows the lower bound to be evaluated, and this can be used both for model comparison and for detection of convergence. Variational message passing has been implemented in the form of a general purpose inference engine called VIBES ('Variational Inference for BayEsian networkS') which allows models to be specified graphically and then solved variationally without recourse to coding.

Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum.

Summary Head blight caused by Fusarium graminearum threatens world‐wide wheat production, resulting in both yield loss and mycotoxin contamination.We reconstructed the global F. graminearum gene regulatory network (GRN) from a large collection of transcriptomic data using Bayesian network inference, a machine‐learning algorithm. This GRN reveals connectivity between key regulators and their target genes. Focusing on key regulators, this network contains eight distinct but interwoven modules. Enriched for unique functions, such as cell cycle, DNA replication, transcription, translation and stress responses, each module exhibits distinct expression profiles.Evolutionarily, the F. graminearum genome can be divided into core regions shared with closely related species and variable regions harboring genes that are unique to F. graminearum and perform species‐specific functions. Interestingly, the inferred top regulators regulate genes that are significantly enriched from the same genomic regions (P < 0.05), revealing a compartmentalized network structure that may reflect network rewiring related to specific adaptation of this plant pathogen.This first‐ever reconstructed filamentous fungal GRN primes our understanding of pathogenicity at the systems biology level and provides enticing prospects for novel disease control strategies involving the targeting of master regulators in pathogens. The program can be used to construct GRNs of other plant pathogens.

Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016.

Characterization of Genetic Networks Associated with Alzheimer's Disease.

At the molecular level, the genetics of complex disease such as Alzheimer's disease (AD) manifests itself as series of alterations in the molecular interactions in pathways and networks that define biological processes underlying the pathophysiological states of disease. While large-scale genome-wide association (GWA) studies of late-onset alzheimer's disease (LOAD) have uncovered prominent genomic regions linked to the disease, the cause for the vast majority of LOAD cases still remains unknown. Increasingly available large-scale genomic and genetic data related to LOAD has made it possible to comprehensively uncover the mechanisms causally lined to LOAD in a completely data-driven manner. Here we review the various aspects of systems/network biology approaches and methodology in constructing genetic networks associated with AD from large sampling of postmortem brain tissues. We describe in detail a multiscale network modeling approach (MNMA) that integrates interaction and causal gene networks to analyze large-scale DNA, gene expression and pathophysiological data from multiple post-mortem brain regions of LOAD patients as well non-demented normal controls. MNMA first employs weighted gene co-expression network analysis (WGCNA) to construct multi-tissue networks that simultaneously capture intra-tissue and inter-tissue gene-gene interactions and then quantifies the change in connectivity among highly co-expressed genes in LOAD with respect to the normal state. Co-expressed gene modules are then rank ordered by relevance to pathophysiological traits and enrichment of genes differentially expressed in LOAD. Causal regulatory relationships among the genes in each module are then determined by a Bayesian network inference framework that is used to formally integrate genetic and gene expression information. MNMA has uncovered a massive remodeling of network structures in LOAD and identified novel subnetworks and key regulators that are causally linked to LOAD. In the end, we will outline the challenges in systems/network approaches to LOAD.

Joint probability inference algorithms of a Bayesian network and reliability analysis of electronic products

Considering the tedious and complex problem of solving joint probability with the bucket elimination algorithm, a parallel joint probability inference algorithm was proposed. The logical relation was analysed among each node variable and the inverse order method of the bucket elimination was used to introduce the mathematical expressions of joint probability and calculate the probabilities of one or more node failures when the network fails. The method is applied into the example of reliable analysis of electronic products and is demonstrated the simplicity and effectiveness of the method in the process of two-way Bayesian network inference. The results can be used to identify the weak links of electronic products and also provide a theoretical basis for reliability and maintenance strategies for electronic products.

Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure.

Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.

Interspecific networks in ground beetle (Coleoptera: Carabidae) assemblages

Although changes to interspecific relationships can significantly alter the composition of insect assemblages, they are often ignored when assessing impacts of environmental change. Long-term ground beetle data were used in this study to analyse ecological networks from three habitats at two sites in Scotland. A Bayesian Network inference algorithm was used to reveal interspecific relationships. The significance and strength of relationships between species (nodes) were estimated along with other network properties. Links were identified as positive relationships if co-occurrences of beetles correlated positively, and as negatives relationships if there was a negative correlation between the occurrences of the species. Most of the species had few links and only 10% of the nodes were connected with several links. Calathus fuscipes, a common carabid in the samples, was the most connected, with nine links to other species. More interspecific relationships were found to be positive than negative, with 48 and 23 links, respectively. The modular structure of the network was assessed and eight separate sub-networks were found. Habitat preferences of the species were clearly represented in the structure of the sets of those five sub-networks containing more than one species and were in line with the findings of the indicator species analysis. In our study, we showed that generated Bayesian networks can model interspecific relationships between carabid species. Due to the relative ease of the collection of field data and the high information content of the results, this method could be incorporated into everyday ecological analysis.

Exploiting local and repeated structure in Dynamic Bayesian Networks

We introduce the structural interface algorithm for exact probabilistic inference in Dynamic Bayesian Networks. It unifies state-of-the-art techniques for inference in static and dynamic networks, by combining principles of knowledge compilation with the interface algorithm. The resulting algorithm not only exploits the repeated structure in the network, but also the local structure, including determinism, parameter equality and context-specific independence. Empirically, we show that the structural interface algorithm speeds up inference in the presence of local structure, and scales to larger and more complex networks.

Inference in hybrid Bayesian networks with large discrete and continuous domains

Inference in Bayesian networks with large domain of discrete variables requires significant computational effort. In order to reduce the computational effort, current approaches often assume that discrete variables have some bounded number of values or are represented at an appropriate size of clusters. In this paper, we introduce decision-tree structured conditional probability representations that can efficiently handle a large domain of discrete and continuous variables. These representations can partition the large number of values into some reasonable number of clusters and lead to more robust parameter estimation. Very rapid computation and ability to treat both discrete and continuous variables are accomplished via modified belief propagation algorithm. Being able to compute various types of reasoning from a single Bayesian network eliminates development and maintenance issues associated with the use of distinct models for different types of reasoning. Application to real-world steel production process data is presented.

An Overview of Bayesian Network Applications in Uncertain Domains

 Abstract—Uncertainty is a major barrier in knowledge discovery from complex problem domains. Knowledge discovery in such domains requires qualitative rather than quantitative analysis. Therefore, the quantitative measures can be used to represent uncertainty with the integration of various models. The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains. Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association rule mining and medical domain analysis. BN techniques can be used in these domains for prediction and decision support. In this article, a discussion on general BN representation, draw inferences, learning and prediction is followed by applications of BN in some specific domains. Domain specific BN representation, inferences and learning process are also presented. Building upon the knowledge presented, some future research directions are also highlighted.

Self-Similar Magneto-Electric Nanocircuit Technology for Probabilistic Inference Engines

Probabilistic graphical models are powerful mathematical formalisms for machine learning and reasoning under uncertainty that are widely used for cognitive computing. However, they cannot be employed efficiently for large problems (with variables in the order of 100K or larger) on conventional systems, due to inefficiencies resulting from layers of abstraction and separation of logic and memory in CMOS implementations. In this paper, we present a magnetoelectric probabilistic technology framework for implementing probabilistic reasoning functions. The technology leverages straintronic magneto-tunneling junction (S-MTJ) devices in a novel mixed-signal circuit framework for direct computations on probabilities while enabling in-memory computations with persistence. Initial evaluations of the Bayesian likelihood estimation operation occurring during Bayesian Network inference indicate up to 127× lower area, 214× lower active power, and 70× lower latency compared to an equivalent 45-nm CMOS Boolean implementation.

Inference of regulatory networks with a convergence improved MCMC sampler

BackgroundOne of the goals of the Systems Biology community is to have a detailed map of all biological interactions in an organism. One small yet important step in this direction is the creation of biological networks from post-genomic data. Bayesian networks are a very promising model for the inference of regulatory networks in Systems Biology. Usually, Bayesian networks are sampled with a Markov Chain Monte Carlo (MCMC) sampler in the structure space. Unfortunately, conventional MCMC sampling schemes are often slow in mixing and convergence. To improve MCMC convergence, an alternative method is proposed and tested with different sets of data. Moreover, the proposed method is compared with the traditional MCMC sampling scheme.ResultsIn the proposed method, a simpler and faster method for the inference of regulatory networks, Graphical Gaussian Models (GGMs), is integrated into the Bayesian network inference, trough a Hierarchical Bayesian model. In this manner, information about the structure obtained from the data with GGMs is taken into account in the MCMC scheme, thus improving mixing and convergence. The proposed method is tested with three types of data, two from simulated models and one from real data. The results are compared with the results of the traditional MCMC sampling scheme in terms of network recovery accuracy and convergence. The results show that when compared with a traditional MCMC scheme, the proposed method presents improved convergence leading to better network reconstruction with less MCMC iterations.ConclusionsThe proposed method is a viable alternative to improve mixing and convergence of traditional MCMC schemes. It allows the use of Bayesian networks with an MCMC sampler with less iterations. The proposed method has always converged earlier than the traditional MCMC scheme. We observe an improvement in accuracy of the recovered networks for the Gaussian simulated data, but this improvement is absent for both real data and data simulated from ODE.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0734-6) contains supplementary material, which is available to authorized users.

Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans.

Microarray-based transcriptomic analysis has been demonstrated to hold the opportunity to study the effects of human exposure to, e.g., chemical carcinogens at the whole genome level, thus yielding broad-ranging molecular information on possible carcinogenic effects. Since genes do not operate individually but rather through concerted interactions, analyzing and visualizing networks of genes should provide important mechanistic information, especially upon connecting them to functional parameters, such as those derived from measurements of biomarkers for exposure and carcinogenic risk. Conventional methods such as hierarchical clustering and correlation analyses are frequently used to address these complex interactions but are limited as they do not provide directional causal dependence relationships. Therefore, our aim was to apply Bayesian network inference with the purpose of phenotypic anchoring of modified gene expressions. We investigated a use case on transcriptomic responses to cigarette smoking in humans, in association with plasma cotinine levels as biomarkers of exposure and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic risk. Many of the genes that appear in the Bayesian networks surrounding plasma cotinine, and to a lesser extent around aromatic DNA-adducts, hold biologically relevant functions in inducing severe adverse effects of smoking. In conclusion, this study shows that Bayesian network inference enables unbiased phenotypic anchoring of transcriptomics responses. Furthermore, in all inferred Bayesian networks several dependencies are found which point to known but also to new relationships between the expression of specific genes, cigarette smoke exposure, DNA damaging-effects, and smoking-related diseases, in particular associated with apoptosis, DNA repair, and tumor suppression, as well as with autoimmunity.

Impact of Injury Severity on Dynamic Inflammation Networks Following Blunt Trauma.

Clinical outcomes following trauma depend on the extent of injury and the host's response to injury, along with medical care. We hypothesized that dynamic networks of systemic inflammation manifest differently as a function of injury severity in human blunt trauma. From a cohort of 472 blunt trauma survivors studied following institutional review board approval, three Injury Severity Score (ISS) subcohorts were derived after matching for age and sex: mild ISS (49 patients [33 males and 16 females, aged 42 ± 1.9 years; ISS 9.5 ± 0.4]); moderate ISS (49 patients [33 males and 16 females, aged 42 ± 1.9; ISS 19.9 ± 0.4]), and severe ISS (49 patients [33 males and 16 females, aged 42 ± 2.5 years; ISS 33 ± 1.1]). Multiple inflammatory mediators were assessed in serial blood samples. Dynamic Bayesian Network inference was utilized to infer causal relationships based on probabilistic measures. Intensive care unit length of stay, total length of stay, days on mechanical ventilation, Marshall Multiple Organ Dysfunction score, prevalence of prehospital hypotension and nosocomial infection, and admission lactate and base deficit were elevated as a function of ISS. Multiple circulating inflammatory mediators were significantly elevated in severe ISS versus moderate or mild ISS over both the first 24 h and out to 7 days after injury. Dynamic Bayesian Network suggested that interleukin 6 production in severe ISS was affected by monocyte chemotactic protein 1/CCL2, monokine inducible by interferon γ (MIG)/CXCL9, and IP-10/CXCL10; by monocyte chemotactic protein 1/CCL2 and MIG/CXCL9 in moderate ISS; and by MIG/CXCL9 alone in mild ISS over 7 days after injury. Injury Severity Score correlates linearly with morbidity, prevalence of infection, and early systemic inflammatory connectivity of chemokines to interleukin 6.