Agent-Based Modeling of Endotoxin-Induced Acute Inflammatory Response in Human Blood Leukocytes

Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions.An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades.The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system

In Silico Simulation of Corticosteroids Effect on an NFkB- Dependent Physicochemical Model of Systemic Inflammation

During the onset of an inflammatory response signaling pathways are activated for "translating" extracellular signals into intracellular responses converging to the activation of nuclear factor (NF)-kB, a central transcription factor in driving the inflammatory response. An inadequate control of its transcriptional activity is associated with the culmination of a hyper-inflammatory response making it a desired therapeutic target. Predicated upon the nature of the response, a systems level analysis might provide rational leads for the development of strategies that promote the resolution of the response.A physicochemical host response model is proposed to integrate biological information in the form of kinetic rules and signaling cascades with pharmacokinetic models of drug action for the modulation of the response. The unifying hypothesis is that the response is triggered by the activation of the NFkB signaling module and corticosteroids serve as a template for assessing anti-inflammatory strategies. The proposed in silico model is evaluated through its ability to predict and modulate uncontrolled responses. The pre-exposure of the system to hypercortisolemia, i.e. 6 hr before or simultaneously with the infectious challenge "reprograms" the dynamics of the host towards a balanced inflammatory response. However, if such an intervention occurs long before the inflammatory insult a symptomatic effect is observed instead of a protective relief while a steroid infusion after inducing inflammation requires much higher drug doses.We propose a reversed engineered inflammation model that seeks to describe how the system responds to a multitude of external signals. Timing of intervention and dosage regimes appears to be key determinants for the protective or symptomatic effect of exogenous corticosteroids. Such results lie in qualitative agreement with in vivo human studies exposed both to LPS and corticosteroids under various time intervals thus improving our understanding of how interacting modules generate a behavior

Kallikrein-related peptidase 6 regulates epithelial-to-mesenchymal transition and serves as prognostic biomarker for head and neck squamous cell carcinoma patients

Background: Dysregulated expression of Kallikrein-related peptidase 6 (KLK6) is a common feature for many human malignancies and numerous studies evaluated KLK6 as a promising biomarker for early diagnosis or unfavorable prognosis. However, the expression of KLK6 in carcinomas derived from mucosal epithelia, including head and neck squamous cell carcinoma (HNSCC), and its mode of action has not been addressed so far. Methods: Stable clones of human mucosal tumor cell lines were generated with shRNA-mediated silencing or ectopic overexpression to characterize the impact of KLK6 on tumor relevant processes in vitro. Tissue microarrays with primary HNSCC samples from a retrospective patient cohort (n = 162) were stained by immunohistochemistry and the correlation between KLK6 staining and survival was addressed by univariate Kaplan-Meier and multivariate Cox proportional hazard model analysis. Results: KLK6 expression was detected in head and neck tumor cell lines (FaDu, Cal27 and SCC25), but not in HeLa cervix carcinoma cells. Silencing in FaDu cells and ectopic expression in HeLa cells unraveled an inhibitory function of KLK6 on tumor cell proliferation and mobility. FaDu clones with silenced KLK6 expression displayed molecular features resembling epithelial-to-mesenchymal transition, nuclear β-catenin accumulation and higher resistance against irradiation. Low KLK6 protein expression in primary tumors from oropharyngeal and laryngeal SCC patients was significantly correlated with poor progression-free (p = 0.001) and overall survival (p < 0.0005), and served as an independent risk factor for unfavorable clinical outcome. Conclusions: In summary, detection of low KLK6 expression in primary tumors represents a promising tool to stratify HNSCC patients with high risk for treatment failure. These patients might benefit from restoration of KLK6 expression or pharmacological targeting of signaling pathways implicated in EMT

Computational Identification of Transcriptional Regulators in Human Endotoxemia

One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes

Investigation of the reaction Ge-74(p,gamma)As-75 using the in-beam method to improve reaction network predictions for p nuclei

Background: Astrophysical models studying the origin of the neutron-deficient p nuclides require knowledge of proton capture cross sections at low energy. The production site of the p nuclei is still under discussion but a firm basis of nuclear reaction rates is required to address the astrophysical uncertainties. Data at astrophysically relevant interaction energies are scarce. Problems with the prediction of charged particle capture cross sections at low energy were found in the comparisons between previous data and calculations in the Hauser-Feshbach statistical model of compound reactions. Purpose: A measurement of Ge-74(p,gamma)As-75 at low proton energies, inside the astrophysically relevant energy region, is important in several respects. The reaction is directly important because it is a bottleneck in the reaction flow which produces the lightest p nucleus Se-74. It is also an important addition to the data set required to test reaction-rate predictions and to allow an improvement in the global p + nucleus optical potential required in such calculations. Method: An in-beam experiment was performed, making it possible to measure in the range 2.1 Results: The resulting cross sections were compared to Hauser-Feshbach calculations using the code SMARAGD. Only a constant renormalization factor of the calculated proton widths allowed a good reproduction of both total and partial cross sections. The accuracy of the calculation made it possible to check the spin assignment of some states in As-75. In the case of the 1075-keV state, a double state with spins and parities of 3/2- and 5/2- is needed to explain the experimental partial cross sections. A change in parity from 5/2(+) to 5/2(-) is required for the state at 401 keV. Furthermore, in the case of Ge-74, studying the combination of total and partial cross sections made it possible to test the gamma width, which is essential in the calculation of the astrophysical As-74(n,gamma)As-75 rate. Conclusions: Between data and statistical model prediction a factor of about two was found. Nevertheless, the improved astrophysical reaction rate of Ge-74(p,gamma) (and its reverse reaction) is only 28% larger than the previous standard rate. The prediction of the As-74(n,gamma)As-75 rate (and its reverse) was confirmed, the newly calculated rate differs only by a few percent from the previous prediction. The in-beam method with high-efficiency detectors proved to be a powerful tool for studies in nuclear astrophysics and nuclear structure.Peer reviewe

I.P.: A mixed-integer optimization framework for the synthesis and analysis of regulatory networks

ABSTRACT Motivation: A novel mixed-integer optimization framework is proposed for the design and analysis of regulatory networks. The model combines gene expression data and prior biological knowledge regarding the potential for regulatory interactions between genes and their corresponding transcription factors. The formalism provides significant advantages over available modeling methodologies in that the complexity of the regulatory network can be explicitly taken into account, multiple alternative structures can be systematically generated and finally robust and biological significant regulators can be rigorously identified. The original non-convex mixed integer reformulation is appropriately linearized and the resulting MILP is effectively optimized using standard solvers. The versatility is demonstrated using gene expression and binding data from an E.coli case study during transition from glucose to acetate as the sole carbon source