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PubMed2026

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of β = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-κB, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 ± 0.83 kcal·mol-¹) and HCAR3 with 3-Indolepropionic Acid (-6.35 ± 0.70 kcal·mol-¹), both below -5.0 kcal·mol-¹. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗

PubMed2005

An algorithmic framework for genome-wide modeling and analysis of translation networks.

The sequencing of genomes of several organisms and advances in high throughput technologies for transcriptome and proteome analysis has allowed detailed mechanistic studies of transcription and translation using mathematical frameworks that allow integration of both sequence-specific and kinetic properties of these fundamental cellular processes. To understand how perturbations in mRNA levels affect the synthesis of individual proteins within a large protein synthesis network, we consider here a genome-scale codon-wide model of the translation machinery with explicit description of the processes of initiation, elongation, and termination. The mechanistic codon-wide description of the translation process and the large number of mRNAs competing for resources, such as ribosomes, requires the use of novel efficient algorithmic approaches. We have developed such an efficient algorithmic framework for genome-scale models of protein synthesis. The mathematical and computational framework was applied to the analysis of the sensitivity of a translation network to perturbation in the rate constants and in the mRNA levels in the system. Our studies suggest that the highest specific protein synthesis rate (protein synthesis rate per mRNA molecule) is achieved when translation is elongation-limited. We find that the mRNA species with the highest number of actively translating ribosomes exerts maximum control on the synthesis of every protein, and the response of protein synthesis rates to mRNA expression variation is a function of the strength of initiation of translation at different mRNA species. Such quantitative understanding of the sensitivity of protein synthesis to the variation of mRNA expression can provide insights into cellular robustness mechanisms and guide the design of protein production systems.

Algorithms↗

PubMed2006

Reproducible statistical analysis in microarray profiling studies.

OBJECTIVES: Microarrays are a recent biotechnology that offers the hope of improved cancer classification. A number of publications presented clinically promising results by combining this new kind of biological data with specifically designed algorithmic approaches. But, reproducing published results in this domain is harder than it may seem. METHODS: This paper presents examples, discusses the problems hidden in the published analyses and demonstrates a strategy to improve the situation which is based on the vignette technology available from the R and Bioconductor projects. RESULTS: The tool of a compendium is discussed to achieve reproducible calculations and to offer an extensible computational framework. A compendium is a document that bundles primary data, processing methods (computational code), derived data, and statistical output with textual documentation and conclusions. It is interactive in the sense that it allows for the modification of the processing options, plugging in new data, or inserting further algorithms and visualizations. CONCLUSIONS: Due to the complexity of the algorithms, the size of the data sets, and the limitations of the medium printed paper it is usually not possible to report all the minutiae of the data processing and statistical computations. The technique of a compendium allows a complete critical assessment of a complex analysis.

Gene Expression Profiling↗

PubMed2003

Computational approach to quantifying hemodynamic forces in giant cerebral aneurysms.

BACKGROUND AND PURPOSE: The options for treating giant fusiform basilar aneurysms are limited, and the potential impact of planned interventions is difficult to assess. We developed a computational framework to evaluate the impact that interventions might have on hemodynamic conditions. METHODS: A computational fluid dynamics approach was used to determine the velocity field, wall shear stress, and pressure distribution within a model of a basilar artery before and after a simulated occlusion of one vertebral artery. The vascular geometry in a patient with a giant fusiform basilar artery aneurysm was determined by using contrast-enhanced MR angiography, and the numerical simulation approach was used to calculate the flow fields in the presenting geometry and to predict the flow field that would occur if a vertebral artery were occluded. RESULTS: In the model geometry, computational fluid dynamics indicated that there would be a symmetric flow pattern with a strong central stream and large recirculation zones at the walls. After simulated occlusion of one vertebral artery, the primary stream was diverted to one side, resulting in high pressure and increased wall shear stress. For the patient-specific geometry, flow patterns were shown to depend strongly on how much flow there was in each vertebral artery. CONCLUSION: Contrast-enhanced MR angiography is an effective tool for demonstrating the luminal boundaries of large intracranial aneurysms. Computational fluid dynamics is a powerful tool for determining the prevailing flow conditions in vascular territories and for modeling the possible alterations of the flow field that would result from interventional treatments.

Basilar Artery↗

PubMed2005

In silico microdissection of microarray data from heterogeneous cell populations.

BACKGROUND: Very few analytical approaches have been reported to resolve the variability in microarray measurements stemming from sample heterogeneity. For example, tissue samples used in cancer studies are usually contaminated with the surrounding or infiltrating cell types. This heterogeneity in the sample preparation hinders further statistical analysis, significantly so if different samples contain different proportions of these cell types. Thus, sample heterogeneity can result in the identification of differentially expressed genes that may be unrelated to the biological question being studied. Similarly, irrelevant gene combinations can be discovered in the case of gene expression based classification. RESULTS: We propose a computational framework for removing the effects of sample heterogeneity by "microdissecting" microarray data in silico. The computational method provides estimates of the expression values of the pure (non-heterogeneous) cell samples. The inversion of the sample heterogeneity can be facilitated by providing accurate estimates of the mixing percentages of different cell types in each measurement. For those cases where no such information is available, we develop an optimization-based method for joint estimation of the mixing percentages and the expression values of the pure cell samples. We also consider the problem of selecting the correct number of cell types. CONCLUSION: The efficiency of the proposed methods is illustrated by applying them to a carefully controlled cDNA microarray data obtained from heterogeneous samples. The results demonstrate that the methods are capable of reconstructing both the sample and cell type specific expression values from heterogeneous mixtures and that the mixing percentages of different cell types can also be estimated. Furthermore, a general purpose model selection method can be used to select the correct number of cell types.

Algorithms↗

PubMed2005

TAMO: a flexible, object-oriented framework for analyzing transcriptional regulation using DNA-sequence motifs.

SUMMARY: TAMO (Tools for Analysis of MOtifs) is an object-oriented computational framework for interpreting transcriptional regulation using DNA-sequence motifs. To simplify the application of multiple motif discovery programs to genome-wide data, TAMO provides a sophisticated motif object with interfaces to several popular programs. In addition, TAMO provides modules for integrating motif analysis with diverse data sources including genomic sequences, microarrays and various databases. Finally, TAMO includes tools for sequence analysis, algorithms for scoring, comparing and clustering motifs, and several useful statistical tests. Recently, we have applied these tools to analyze tens of thousands of motifs derived from hundreds of microarray experiments.

Algorithms↗

PubMed2003

3-D structural modeling of humic acids through experimental characterization, computer assisted structure elucidation and atomistic simulations. 1. Chelsea soil humic acid.

This paper describes an integrated experimental and computational framework for developing 3-D structural models for humic acids (HAs). This approach combines experimental characterization, computer assisted structure elucidation (CASE), and atomistic simulations to generate all 3-D structural models or a representative sample of these models consistent with the analytical data and bulk thermodynamic/structural properties of HAs. To illustrate this methodology, structural data derived from elemental analysis, diffuse reflectance FT-IR spectroscopy, 1-D/2-D 1H and 13C solution NMR spectroscopy, and electrospray ionization quadrupole time-of-flight mass spectrometry (ESI QqTOF MS) are employed as input to the CASE program SIGNATURE to generate all 3-D structural models for Chelsea soil humic acid (HA). These models are subsequently used as starting 3-D structures to carry out constant temperature-constant pressure molecular dynamics simulations to estimate their bulk densities and Hildebrand solubility parameters. Surprisingly, only a few model isomers are found to exhibit molecular compositions and bulk thermodynamic properties consistent with the experimental data. The simulated 13C NMR spectrum of an equimolar mixture of these model isomers compares favorably with the measured spectrum of Chelsea soil HA.

Humic Substances↗

PubMed2006

Agents in bioinformatics, computational and systems biology.

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

Artificial Intelligence↗

PubMed2006

Modeling driver behavior in a cognitive architecture.

OBJECTIVE: This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system. BACKGROUND: Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. METHOD: An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment. RESULTS: This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. CONCLUSION: The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks. APPLICATION: The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.

Automobile Driving↗

PubMed2003

Meshfree representation and computation: applications to cardiac motion analysis.

For medical image analysis issues where the domain mappings between images involve large geometrical shape changes, such as the cases of nonrigid motion recovery and inter-object image registration, the finite element methods exhibit considerable loss of accuracy when the elements in the mesh become extremely skewed or compressed. Therefore, algorithmically difficult and computationally expensive remeshing procedures must be performed in order to alleviate the problem. We present a general representation and computation framework which is purely based on the sampling nodal points and does not require the construction of mesh structure of the analysis domain. This meshfree strategy can more naturally handle very large object deformation and domain discontinuity problems. Because of its intrinsic h-p adaptivity, the meshfree framework can achieve desired numerical accuracy through adaptive node and polynomial shape function refinement with minimum extra computational expense. We focus on one of the more robust meshfree efforts, the element free Galerkin method, through the moving least square approximation and the Galerkin weak form formulation, and demonstrate its relevancy to medical image analysis problems. Specifically, we show the results of applying this strategy to physically motivated multiframe motion analysis, using synthetic data for accuracy assessment and for comparison to finite element results, and using canine magnetic resonance tagging and phase contrast images for cardiac kinematics recovery.

Algorithms↗

PubMed2003

Accounting for nonignorable verification bias in assessment of diagnostic tests.

A "gold" standard test, providing definitive verification of disease status, may be quite invasive or expensive. Current technological advances provide less invasive, or less expensive, diagnostic tests. Ideally, a diagnostic test is evaluated by comparing it with a definitive gold standard test. However, the decision to perform the gold standard test to establish the presence or absence of disease is often influenced by the results of the diagnostic test, along with other measured, or not measured, risk factors. If only data from patients who received the gold standard test were used to assess the test performance, the commonly used measures of diagnostic test performance--sensitivity and specificity--are likely to be biased. Sensitivity would often be higher, and specificity would be lower, than the true values. This bias is called verification bias. Without adjustment for verification bias, one may possibly introduce into the medical practice a diagnostic test with apparent, but not truly, high sensitivity. In this article, verification bias is treated as a missing covariate problem. We propose a flexible modeling and computational framework for evaluating the performance of a diagnostic test, with adjustment for nonignorable verification bias. The presented computational method can be utilized with any software that can repetitively use a logistic regression module. The approach is likelihood-based, and allows use of categorical or continuous covariates. An explicit formula for the observed information matrix is presented, so that one can easily compute standard errors of estimated parameters. The methodology is illustrated with a cardiology data example. We perform a sensitivity analysis of the dependency of verification selection process on disease.

Aged↗

PubMed1998

DoEpi. Computer-assisted instruction in epidemiology and computing and a framework for creating new exercises.

DoEpi is a series of computer exercises and a framework for making new exercises based on the Epi Info programs for epidemiologic computing. The system contains three outbreak investigations, a research survey, four exercises in advanced Epi Info programming, and four exercises in public health surveillance. The exercises are available via the Internet (www.cdc.gov, under "Publications, Products, and Software") with provision for CME and CEU credit from the Centers for Disease Control and Prevention. They can serve as a useful adjunct to lectures and textbooks in teaching epidemiology or epidemiologic computing. A new DoEpi exercise with hypertext, low-resolution photographs, questions, answers, and an examination can be constructed in hours rather than weeks or months using an Exercise Development "wizard" provided as part of the instructor's module. Epi Info exercises with data files and customized programs require more work to construct but can be added by those with the necessary skills. DoEpi exercises can be used in a variety of ways for different curricula and students of different background levels, including those with English as a second language. Translation of DoEpi exercises into other languages is facilitated by the instructor's module, and construction of new exercises with locally suitable materials is encouraged. DoEpi is based on DOS programs to allow the widest use. The format lends itself to conversion to hypertext programs in the Microsoft Windows and Internet formats at a future date.

Centers for Disease Control and Prevention, U.S.↗

PubMed2026

Decoding spatiotemporal fibrotic and cellular immunosuppression of therapeutic T cells in live pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDA) is profoundly immunosuppressive. To help define this behavior, we present integrated experimental and computational frameworks to elucidate therapeutic T cell dynamics. Through the development of TME-CARTographer (TME-CART), a computational pipeline integrating high-dimensional data, graph theory, behavior analysis, and deep learning (DL), we present quantitative insights on 4D T cell-TME interactions in live PDA tumors. Mapping physical immunosuppression demonstrates that collagen fiber architectures direct migration while concomitantly limiting off-axis movement, creating immune exclusion zones. Expanding these findings, we establish that the collagen matrix harbors and spatially organizes immunosuppressive myeloid cells to serve as cooperative co-modulators of T cell behaviors, including migration, sampling, repulsion, and sequestration. Consistent with these findings, DL defines both linear and nonlinear collagen matrix and cellular neighborhood interactions as drivers of T cell behavior. The TME-CART DL framework also accurately predicts shifts in immunosuppression following depletion of myeloid cells. Overall, we identify synergistic barriers impeding anti-tumor T cell behaviors and present TME-CART as a discovery platform for interpreting complex 4D data to enhance the understanding and design of immunotherapies.

Journal Article↗

PubMed2025

Iterative, multimodal, and scalable single-cell profiling for discovery and characterization of signaling regulators.

Cell signaling plays a critical role in regulating cellular state, yet uncovering regulators of signaling pathways and understanding their molecular consequences remains challenging. Here, we present an iterative experimental and computational framework to identify and characterize regulators of signaling proteins, using the mTOR marker phosphorylated RPS6 (pRPS6) as a case study. We present a customized workflow that uses the 10x Flex assay to jointly profile intracellular protein levels, transcriptomes, and CRISPR perturbations in single cells. We use this to generate a "glossary" dataset of paired protein-RNA measurements across targeted perturbations, which we leverage to train a predictive model of pRPS6 levels based solely on transcriptomic data. Applying this model to a genome-wide Perturb-seq dataset enables in silico screening for pRPS6 and nominates novel regulators of mTOR signaling. Experimental validation confirms these predictions and reveals mechanistic diversity among hits, including changes in signaling output driven by anabolic activity, cellular proliferation and multiple stress pathways. Our work demonstrates how integrated experimental and computational approaches provide a scalable framework for multimodal phenotyping and discovery.

Journal Article↗

PubMed1999

A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery.

Recent advances in the field of stereotactic neurosurgery have made it possible to coregister preoperative computed tomography (CT) and magnetic resonance (MR) images with instrument locations in the operating field. However, accounting for intraoperative movement of brain tissue remains a challenging problem. While intraoperative CT and MR scanners record concurrent tissue motion, there is motivation to develop methodologies which would be significantly lower in cost and more widely available. The approach we present is a computational model of brain tissue deformation that could be used in conjunction with a limited amount of concurrently obtained operative data to estimate subsurface tissue motion. Specifically, we report on the initial development of a finite element model of brain tissue adapted from consolidation theory. Validations of the computational mathematics in two and three dimensions are shown with errors of 1%-2% for the discretizations used. Experience with the computational strategy for estimating surgically induced brain tissue motion in vivo is also presented. While the predicted tissue displacements differ from measured values by about 15%, they suggest that exploiting a physics-based computational framework for updating preoperative imaging databases during the course of surgery has considerable merit. However, additional model and computational developments are needed before this approach can become a clinical reality.

Animals↗

PubMed2005

An efficient Monte Carlo method for estimating Ne from temporally spaced samples using a coalescent-based likelihood.

This article presents an efficient importance-sampling method for computing the likelihood of the effective size of a population under the coalescent model of Berthier et al. Previous computational approaches, using Markov chain Monte Carlo, required many minutes to several hours to analyze small data sets. The approach presented here is orders of magnitude faster and can provide an approximation to the likelihood curve, even for large data sets, in a matter of seconds. Additionally, confidence intervals on the estimated likelihood curve provide a useful estimate of the Monte Carlo error. Simulations show the importance sampling to be stable across a wide range of scenarios and show that the N(e) estimator itself performs well. Further simulations show that the 95% confidence intervals around the N(e) estimate are accurate. User-friendly software implementing the algorithm for Mac, Windows, and Unix/Linux is available for download. Applications of this computational framework to other problems are discussed.

Algorithms↗

PubMed2003

Computational strategies for analyzing data in gene expression microarray experiments.

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Algorithms↗

PubMed2006

Framework models of ion permeation through membrane channels and the generalized King-Altman method.

A modern approach to studying the detailed dynamics of biomolecules is to simulate them on computers. Framework models have been developed to incorporate information from these simulations in order to calculate properties of the biomolecules on much longer time scales than can be achieved by the simulations. They also provide a simple way to think about the simulated dynamics. This article develops a method for the solution of framework models, which generalizes the King-Altman method of enzyme kinetics. The generalized method is used to construct solutions of two framework models which have been introduced previously, the single-particle and Grotthuss (proton conduction) models. The solution of the Grotthuss model is greatly simplified in comparison with direct integration. In addition, a new framework model is introduced, generalizing the shaking stack model of ion conduction through the potassium channel.

Algorithms↗