Dynamic Bayesian Network Inference Research Articles

Digital twin-based probabilistic prediction of microcrack initiation and propagation in the weld zone of orthotropic steel deck

Orthotropic steel deck (OSD) has become the primary bridge deck structure for steel bridges, but it is vulnerable to fatigue cracks in the weld zone of OSD under traffic loading. This study proposes a digital twin (DT)-based framework to give the probabilistic prediction of microcrack initiation and propagation in the weld zone of OSD. The DT-based framework couples crystal plasticity finite element model (CPFEM) simulations with fatigue tests through Dynamic Bayesian Network (DBN). The fatigue tests of the specimen (physical entity) cut from the weld zone of OSD and sliced to the centimeter scale are carried out using the standard dynamic test system together with optical microscope. The CPFEM simulations are used to develop a multiscale virtual entity to map the physical entity. The role of the DBN is to integrate the uncertainties into a hierarchical network. After completing the updating of uncertainties in the virtual entity via DBN, the microcrack propagation is predicted probabilistically by the DBN inference, and the microcrack initiation is predicted probabilistically by the inverse DBN inference. The results demonstrate the feasibility and accuracy of the DT-based framework for a full prediction of microcrack initiation and propagation in the weld zone of OSD.

Digital twin-driven framework for fatigue life prediction of welded structures considering residual stress

The welding of steel generates substantial welding residual stress (WRS), which exerts a significant impact on the fatigue life of steel bridges. In this study, a physical model for calculating the fatigue crack growth (FCG) life of welded specimens in the WRS field is established based on the weight function method. Experimental data validates the reliability and precision of the proposed physical model. The impact of WRS on the fatigue life of structural components is scrutinized and analyzed. On this basis, a digital twin (DT) framework driven by a physical-data model is proposed to consider the inherent parameter uncertainty in FCG behavior within the WRS domain. A dynamic Bayesian network (DBN) is used to characterize the evolution characteristics of fatigue crack states over time in the digital space. Particle filter algorithm is used as DBN inference method. The results show that the calculation of the physical model is in good agreement with the experimental values. Neglecting the influence of WRS distribution may lead to an overestimation of fatigue life in welded structures. The DT framework can update uncertain parameters online and realize the accurate prediction of the FCG life in the WRS field.

Dynamic risk assessment of deepwater drilling using data-based and probabilistic approach

Risks associated with deepwater drilling are dynamic because of various accident-causing factors such as equipment failures, abnormal processes, and operator errors. All these factors should be considered during the implementation of dynamic and quantitative risk assessment (DQRA) for drilling operations. Dynamic Bayesian network (DBN) can graphically present the cause-effect relationships among different types of risk influencing factors (RIFs). Hence, this study developed a four-step DBN model for the DQRA of deepwater drilling. Firstly, multi-type contributing RIFs were identified according to the process flow. Subsequently, a network structure was developed to present the potential accident scenarios and capture the interdependencies among the RIFs. Thereafter, the probabilities of equipment failures, abnormal processes, and operator errors were determined using the probabilistic, data-based, and Standardized Plant Analysis Risk—Human Reliability Analysis (SPAR-H) approach, respectively. Finally, DBN inference was performed to evaluate the probabilistic risk of drilling operations. The model was applied to a case study of DQRA for managed pressure drilling (MPD), where the calculated initial blowout probability was 9.30 × 10−5, whereafter it was updated dynamically. This case study demonstrates the practicability of the proposed approach. This study contributes to a systematic investigation of the role of multisource data in DQRA using a full DBN approach. The assessment results can provide early warnings for practitioners to implement risk elimination or mitigation measures in real time.

Structural fatigue life prediction considering model uncertainties through a novel digital twin-driven approach

In this work, a digital twin (DT)-driven approach is proposed to accurately predict structural fatigue life by establishing effective dual-information communication between a DT virtual model and a physical model of the structure of interest. The proposed DT virtual model consists of three modules (namely one crack tracking model, one high-precision approximating model and one dynamic Bayesian network (DBN) inference model) and operates in offline and online stages. The offline stage employs the extended finite element method (XFEM) to establish the crack tracking model, which will generate sufficient labeled datasets to train the high-precision approximating model. In the online stage, the model parameters are updated by the DBN inference model based on the well-trained approximating model, where real-time information exchange from the physical model of the structure is performed. As a result, unexpected uncertainties of the model parameters can be significantly reduced. Numerical examples are carried out to evaluate the performance of the proposed DT-driven approach and the analysis results demonstrate that the fatigue crack growth can be efficiently and accurately predicted.

Reliability analysis based on dynamic Bayesian networks: A case study of an unmanned surface vessel

Unmanned surface vessels (USVs) have been widely used in various activities to date. Reliability is particularly significant for USVs because of the increasing complexity of USV tasks and their characteristic that cannot be repaired during task execution. This study aims to analyse the reliability of USVs' overall system for providing an optimisation reference for their reliability improvement in the design phase. The method for modelling and analysing the reliability of complex systems based on Dynamic Bayesian Networks (DBNs) is studied. The reliability of a USV for seafloor topography survey is analysed. The reliability model of the USV overall system is established by mapping the Dynamic Fault Trees (DFTs) into DBNs considering the system logical structure, redundant configuration and dynamic behaviour. Reliability analysis, including reliability evaluation, importance measure and sensitivity analysis, are conducted on the basis of DBN inference. The overall reliability parameters of the USV are obtained to determine the critical subsystem and components. This research may provide a reference for the reliability design and optimisation of USVs.

The Effects of Tacrolimus on Tissue-Specific, Protein-Level Inflammatory Networks in Vascularized Composite Allotransplantation.

Systems-level insights into inflammatory events after vascularized composite allotransplantation (VCA) are critical to the success of immunomodulatory strategies of these complex procedures. To date, the effects of tacrolimus (TAC) immunosuppression on inflammatory networks in VCA, such as in acute rejection (AR), have not been investigated. We used a systems biology approach to elucidate the effects of tacrolimus on dynamic networks and principal drivers of systemic inflammation in the context of dynamic tissue-specific immune responses following VCA. Lewis (LEW) rat recipients received orthotopic hind limb VCA from fully major histocompatibility complex-mismatched Brown Norway (BN) donors or matched LEW donors. Group 1 (syngeneic controls) received LEW limbs without TAC, and Group 2 (treatment group) received BN limbs with TAC. Time-dependent changes in 27 inflammatory mediators were analyzed in skin, muscle, and peripheral blood using Principal Component Analysis (PCA), Dynamic Bayesian Network (DyBN) inference, and Dynamic Network Analysis (DyNA) to define principal characteristics, central nodes, and putative feedback structures of systemic inflammation. Analyses were repeated on skin + muscle data to construct a “Virtual VCA”, and in skin + muscle + peripheral blood data to construct a “Virtual Animal.” PCA, DyBN, and DyNA results from individual tissues suggested important roles for leptin, VEGF, various chemokines, the NLRP3 inflammasome (IL-1β, IL-18), and IL-6 after TAC treatment. The chemokines MCP-1, MIP-1α; and IP-10 were associated with AR in controls. Statistical analysis suggested that 24/27 inflammatory mediators were altered significantly between control and TAC-treated rats in peripheral blood, skin, and/or muscle over time. “Virtual VCA” and “Virtual Animal” analyses implicated the skin as a key control point of dynamic inflammatory networks, whose connectivity/complexity over time exhibited a U-shaped trajectory and was mirrored in the systemic circulation. Our study defines the effects of TAC on complex spatiotemporal evolution of dynamic inflammation networks in VCA. We also demonstrate the potential utility of computational analyses to elucidate nonlinear, cross-tissue interactions. These approaches may help define precision medicine approaches to better personalize TAC immunosuppression in VCA recipients.

Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference. However, no reviews of the literature have been found that chronicle their importance and development over time. The aim of this study is to provide a systematic review of the literature that details the evolution and advancement of DBNs, focusing in the period 1997–2019 that emphasize the aspects of modeling, learning and inference. While the literature presents temporal event networks, knowledge encapsulation, relational and time varying representations as the four predominant DBN modeling approaches, this work groups them as essential techniques within DBNs and help practitioners by associating each to various challenge that arise in pattern discovery and prediction in dynamic processes. Regarding learning, the predominant methods mainly focus on scoring with greedy search. Finally, our study suggests that the main methods used in DBN inference extend or adapt those used in static BNs, and are oriented to either optimize processing time or error rate.

An Aging-Related Single-Nucleotide Polymorphism is Associated With Altered Clinical Outcomes and Distinct Inflammatory Profiles in Aged Blunt Trauma Patients.

The contribution of individual genetic determinants of aging to the adverse clinical outcomes and altered inflammation mediator networks characteristic of aged trauma patients is unknown. The AA genotype of the aging-related single-nucleotide polymorphism (SNP) rs2075650 in TOMM40 has been associated with longevity, while the AG and GG genotypes are associated with an increased risk of Alzheimer disease. Here, we studied the effect of rs2075650 on clinical outcomes and dynamic biomarker patterns after traumatic injury. Genomic DNA was obtained from blunt trauma patients admitted to the ICU and examined for 551,839 SNPs using an Illumina microarray kit. Plasma was sampled from each patient three times within the first 24 h and daily from day 1 to 7 then assayed for 31 biomarkers using Luminex. Aged patients (65-90 years) were segregated into AA (n = 77) and AG/GG (n = 17) genotypes. Additional comparisons were made with matched groups of young patients (18-30 years), controlling for injury severity score (ISS) and sex ratio, and also segregated into AA (n = 56) and AG/GG (n = 19) genotypes. Aged patients with the AA genotype had a significantly lower requirement for ventilation and fewer days on mechanical ventilation, as well as significantly higher levels of one mediator and lower levels of two mediators. Dynamic Bayesian Network inference revealed IL-23 as a central node in each network regardless of age or genotype, with MIG and IP-10 also as key mediators in the networks of the aged patients. These findings suggest that an aging-related SNP, rs2075650, may influence clinical outcomes and inflammation networks in aged patients following blunt trauma, and thus may serve as a predictive outcome biomarker in the setting of polytrauma.

Tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks.

Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat2+cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network(DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

Diurnal Variation in Systemic Acute Inflammation and Clinical Outcomes Following Severe Blunt Trauma.

Animal studies suggest that the time of day is a determinant of the immunological response to both injury and infection. We hypothesized that due to this diurnal variation, time of injury could affect the systemic inflammatory response and outcomes post-trauma and tested this hypothesis by examining the dynamics of circulating inflammatory mediators in blunt trauma patients injured during daytime vs. nighttime. From a cohort of 472 blunt trauma survivors, two stringently matched sub-cohorts of moderately/severely injured patients [injury severity score (ISS) >20] were identified. Fifteen propensity-matched, daytime-inured (“mDay”) patients (age 43.6 ± 5.2, M/F 11/4, ISS 22.9 ± 0.7) presented during the shortest local annual period (8:00 am−5:00 pm), and 15 propensity-matched “mNight” patients (age 43 ± 4.3, M/F 11/4, ISS 24.5 ± 2.5) presented during the shortest night period (10:00 pm−5:00 am). Serial blood samples were obtained (3 samples within the first 24 h and daily from days 1–7) from all patients. Thirty-two plasma inflammatory mediators were assayed. Two-way Analysis of Variance (ANOVA) was used to compare groups. Dynamic Network Analysis (DyNA) and Dynamic Bayesian Network (DyBN) inference were utilized to infer dynamic interrelationships among inflammatory mediators. Both total hospital and intensive care unit length of stay were significantly prolonged in the mNight group. Circulating IL-17A was elevated significantly in the mNight group from 24 h to 7 days post-injury. Circulating MIP-1α, IL-7, IL-15, GM-CSF, and sST2 were elevated in the mDay group. DyNA demonstrated elevated network complexity in the mNight vs. the mDay group. DyBN suggested that cortisol and sST2 were central nodes upstream of TGF-β1, chemokines, and Th17/protective mediators in both groups, with IL-6 being an additional downstream node in the mNight group only. Our results suggest that time of injury affects clinical outcomes in severely injured patients in a manner associated with an altered systemic inflammation program, possibly implying a role for diurnal or circadian variation in the response to traumatic injury.

Decoding the secreted inflammatory response of primary human hepatocytes to hypoxic stress in vitro

The cellular and molecular response of liver cells to hypoxic stress is not fully understood. We used computational modeling to gain insights into the inflammatory response of primary human hepatocytes (HC) to hypoxic stress in vitro. Primary HC from cancer patients were exposed to hypoxia (1% O2) or normoxia (21% O2) for 1-48 h, and the cell supernatants were assayed for 21 inflammatory mediators. Data were analyzed by Two-Way ANOVA, Dynamic Bayesian Network (DBN) inference, Dynamic Network Analysis (DyNA), and Time-interval Principal Component Analysis (TI-PCA). The chemokines MCP-1/CCL2 and IP-10/CXCL10, along with the cytokines interleukin (IL)-2 and IL-15 were altered significantly over time in hypoxic vs. normoxic HC. DBN inference suggested central, coordinating roles for MCP-1 and IL-8 in regulating a largely conserved inflammatory program in both hypoxic and normoxic HC. DyNA likewise suggested similar network trajectories of decreasing complexity over time in both hypoxic and normoxic HC, though with differential connectivity of MCP-1, IP-10, IL-8, and Eotaxin. TI-PCA pointed to IL-1β as a central characteristic of inflammation in hypoxic HC across all time intervals, along with IL-15 and IL-10, vs. Eotaxin, IL-7, IL-10, IL-15, and IL-17A in normoxic HC. Thus, diverse human HC appear to respond in a largely conserved fashion to cell culture stress, with distinct characteristics based on the presence or absence of hypoxia.

Advanced hybrid stochastic dynamic Bayesian network inference algorithm development in the context of the web applications test execution

The article is devoted to the application of dynamic Bayesian networks models for fuzzing web applications and development of effective hybrid algorithms for probabilistic inference based on particle filter algorithm. Dynamic Bayesian networks models allow to simulate the dynamic process transformation of web applications associated with the process of their constant instrumental and logical updates, and create a probabilistic structure required for learning process of testing the top web applications vulnerabilities, that able to use the evidence and inference results obtained in the retrospective and current testing slices and improve testing mechanisms in new time slices. The hybrid probabilistic inference algorithm for dynamic Bayesian networks models for testing web-applications, proposed in the current research, significantly increase the efficiency of the classical approximate probabilistic inference algorithms, well reflect the features of the temporary testing links formation and adapted to the detection of anomalous errors.

Dynamic risk assessment in healthcare based on Bayesian approach

Risks related with healthcare are always dynamic, and they are affected by situations of patients, human errors in treatment and even the states of medical devices. This paper proposes a dynamic medical risk assessment model, for capturing the impacts of factors on the occurrence of adverse events. In this model, a static fault tree is established to show risk scenarios. Dynamic Bayesian network and Bayesian inference are introduced to analyze the operations of medical devices, in consideration of their failures, repairs, and human errors over time. Hemodialysis infection is taken as the case to verify that the proposed method is helpful to demonstrate the changes of medical risks with time, and to identify the critical events contributing to the occurrence of the adverse event at different moments. These findings can act as the basis to assign and adjust safety measures.

Restricted-derestricted dynamic Bayesian Network inference of transcriptional regulatory relationships among genes in cancer

Understanding transcriptional regulatory relationships among genes is important for gaining etiological insights into diseases such as cancer. To this end, high-throughput biological data have been generated through advancements in a variety of technologies. These rely on computational approaches to discover underlying structures in such data. Among these computational approaches, Bayesian networks (BNs) stand out because their probabilistic nature enables them to manage randomness in the dynamics of gene regulation and experimental data. Feedback loops inherent in networks of regulatory relationships are more tractable when enhancements to BNs are applied to them. Here, we propose Restricted-Derestricted dynamic BNs with a novel search technique, Restricted-Derestricted Greedy Method, for such tasks. This approach relies on the Restricted-Derestricted Greedy search technique to infer transcriptional regulatory networks in two phases: restricted inference and derestricted inference. An application of this approach to real data sets reveals it performs favourably well compared to other existing well performing dynamic BN approaches in terms of recovering true relationships among genes. In addition, it provides a balance between searching for optimal networks and keeping biologically relevant regulatory interactions among variables.

Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference.

Structural health monitoring (SHM) has been widely used in all kinds of bridges. It is significant to accurately assess the serviceability and reliability of bridge subjected to severe conditions by SHM technique. Bridge deflection as an essential evaluation index can reflect structural condition perfectly. In this study, an approach for deflection calculation and reliability assessment of simply supported bridge is presented. Firstly, a bridge deflection calculation method is proposed based on modal flexibility and Kriging method improved by artificial bee colony algorithm. Secondly, a dynamic Bayesian network is employed to evaluate the deflection reliability combined with monitoring results which include modal frequency, mode shape, environmental temperature, and humidity. A linear regression model is established to analyze the relationship between modal parameters and environmental factors. Thirdly, a simply supported bridge is constructed and monitored to verify the effectiveness of the proposed method. The results reveal that the proposed method can precisely calculate the bridge deflection. Finally, the time-dependent reliabilities of two cases are computed and the effects of monitoring factors on bridge deflection reliability are analyzed by sensitivity parameter. It indicates that the reliability is negatively correlated with temperature and more sensitive to mode shape than other three factors.

Young and Aged Blunt Trauma Patients Display Major Differences in Circulating Inflammatory Mediator Profiles after Severe Injury

Aging is accompanied by alterations in immune functions. How these changes translate into levels of circulating inflammatory mediators and network expression after severe trauma is not well characterized. To address this, we compared time-dependent changes in the levels of an extensive biomarker panel in cohorts of severely injured young and aged adults. Cohorts of young (18 to 30 years old, n= 115) and aged (65 to 90 years old, n= 101) blunt trauma patients admitted to the ICU with plasma sampled 3 times within the first 24 hours and daily from day 1 to day 7 were assayed for 30 inflammatory biomarkers using Luminex analyzer. Stringently matched groups controlling for sex ratio and Injury Severity Score (n=56 young vs n= 56 aged) were generated. Data were analyzed using 2-way ANOVA, area under the curve analysis, Dynamic Bayesian Network inference, and Dynamic Network Analysis. In the overall cohorts, the young group had a significantly higher Injury Severity Score, which was associated with higher circulating levels of 18 inflammatory mediators from admission to day 7. The aged group had higher levels of C-X-C motif chemokine ligand 10/interferon gamma-induced protein 10 and C-X-C motif chemokine ligand 9/monokine induced by gamma interferon. In groups that were matched for Injury Severity Score, the significantly higher levels of interferon gamma-induced protein 10 and monokine induced by gamma interferon persisted in the aged. Dynamic Bayesian Network revealed interferon gamma-induced protein 10 and monokine induced by gamma interferon as key mediators in the aged, and Dynamic Network Analysis revealed higher network complexity in the aged. These findings indicate that differences in the early inflammatory networks between young and aged trauma patients are not simply a suppression of pro-inflammatory responses in the aged, but are characterized by a major shift in the mediator profile patterns with high levels of CXC chemokines in the aged.

A dynamic and quantitative risk assessment method with uncertainties for offshore managed pressure drilling phases

Drilling into offshore oil and gas fields often meets many challenges and uncertainties, such as a narrow window of drilling fluid density and shallow gas zones. Managed pressure drilling (MPD) techniques are increasingly used as alternatives to conventional drilling operations to manage such extreme conditions and reduce drilling costs and risks. Many safety and operational issues related to MPD process need to be investigated more thoroughly. Well kick is considered a typical hazardous event that may occur at different drilling phases, and such an event is prone to develop into a blowout. During offshore drilling phases, the risk of accidents may change with time, and such a dynamic characteristic should be recorded in risk assessment. This study presents a method for the application of dynamic Bayesian networks (DBNs) in conducting accident scenario analysis and dynamic quantitative risk assessment for MPD safety. This method can model the influence of uncertain risk factors, which have been ignored in existing research, by introducing an additional probability parameter. The effects of degradation are also taken into account. DBN inference is adopted to perform quantitative risk analysis and dynamic risk evolution. Then, the vulnerable root causes are identified by sensitivity analysis for accident prevention and mitigation measures. Well kick for four drilling cases is analyzed as a case study to demonstrate the feasibility of the proposed method. Three-step analysis partially validates the correctness and rationality of the proposed DBN model.

Toll-Like Receptor 4 on both Myeloid Cells and Dendritic Cells Is Required for Systemic Inflammation and Organ Damage after Hemorrhagic Shock with Tissue Trauma in Mice.

Trauma combined with hemorrhagic shock (HS/T) leads to systemic inflammation, which results in organ injury. Toll-like Receptor 4 (TLR4)-signaling activation contributes to the initiation of inflammatory pathways following HS/T but its cell-specific roles in this setting are not known. We assessed the importance of TLR4 on leukocytes of myeloid lineage and dendritic cells (DCs) to the early systemic inflammatory response following HS/T. Mice were subjected to HS/T and 20 inflammatory mediators were measured in plasma followed by Dynamic Bayesian Network (DBN) Analysis. Organ damage was assessed by histology and plasma ALT levels. The role of TLR4 was determined using TLR4−/−, MyD88−/−, and Trif−/− C57BL/6 (B6) mice, and by in vivo administration of a TLR4-specific neutralizing monoclonal antibody (mAb). The contribution of TLR4 expressed by myeloid leukocytes and DC was determined by generating cell-specific TLR4−/− B6 mice, including Lyz-Cre × TLR4loxP/loxP, and CD11c-Cre × TLR4loxP/loxP B6 mice. Adoptive transfer of bone marrow-derived TLR4+/+ or TLR4−/− DC into TLR4−/− mice confirmed the contribution of TLR4 on DC to the systemic inflammatory response after HS/T. Using both global knockout mice and the TLR4-blocking mAb 1A6 we established a central role for TLR4 in driving systemic inflammation. Using cell-selective TLR4−/− B6 mice, we found that TLR4 expression on both myeloid cells and CD11chigh DC is required for increases in systemic cytokine levels and organ damage after HS/T. We confirmed the capacity of TLR4 on CD11chigh DC to promote inflammation and liver damage using adoptive transfer of TLR4+/+ conventional (CD11chigh) DC into TLR4−/− mice. DBN inference identified CXC chemokines as proximal drivers of dynamic changes in the circulating levels of cytokines/chemokines after HS/T. TLR4 on DC was found to contribute selectively to the elevations in these proximal drivers. TLR4 on both myeloid cells and conventional DC is required for the initial systemic inflammation and organ damage in a mouse model of HS/T. This includes a role for TLR4 on DC in promoting increases in the early inflammatory networks identified in HS/T. These data establish DC along with macrophages as essential to the recognition of tissue damage and stress following tissue trauma with HS.

Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells.

Differential inflammatory networks distinguish responses to bone marrow-derived versus adipose-derived mesenchymal stem cell therapies in vascularized composite allotransplantation.

Vascularized composite allotransplantation (VCA) is aimed at enabling injured individuals to return to their previous lifestyles. Unfortunately, VCA induces an immune/inflammatory response, which mandates lifelong, systemic immunosuppression, with attendant detrimental effects. Mesenchymal stem cells (MSC)-both adipose-derived (AD-MSC) and bone marrow-derived (BM-MSC)-can reprogram inflammation and have been suggested as an alternative to immunosuppression, but their mechanism of action is as yet not fully elucidated. We sought to gain insights into these mechanisms using a systems biology approach. PKH26 (red) dye-labeled AD-MSC or BM-MSC were administered intravenously to Lewis rat recipients of mismatched Brown-Norway hindlimb transplants. Short course tacrolimus (FK-506) monotherapy was withdrawn at postoperative day 21. Sera were collected at 4 weeks, 6 weeks, and 18 weeks; assayed for 29 inflammatory/immune mediators; and the resultant data were analyzed using Dynamic Network Analysis (DyNA), Dynamic Bayesian Network (DyBN) inference, and Principal Component Analysis. DyNA network complexity decreased with time in AD-MSC rats, but increased in BM-MSC rats. DyBN and Principal Component Analysis suggested mostly different central nodes and principal characteristics, respectively, in AD-MSC versus BM-MSC rats. AD-MSC and BM-MSC are associated with both overlapping and distinct dynamic networks and principal characteristics of inflammatory/immune mediators in VCA grafts with short-course tacrolimus induction therapy. The decreasing inflammatory complexity of dynamic networks in the presence of AD-MSC supports the previously suggested role for T regulatory cells induced by AD-MSC. The finding of some overlapping and some distinct central nodes and principal characteristics suggests the role of key mediators in the response to VCA in general, as well as potentially differential roles for other mediators ascribed to the actions of the different MSC populations. Thus, combined in vivo/in silico strategies may yield novel means of optimizing MSC therapy for VCA.