BACKGROUND: HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens. METHODS: In a longitudinal study of 10 untreated HIV-1-infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction. At each sampling time, a statistical analysis inferred the optimal transcriptional network that best predicted viral set point. We then assessed the accuracy of this transcriptional model by predicting viral set point in an independent cohort of 10 untreated HIV-1-infected patients from Malawi. RESULTS: The gene network inferred at time of enrollment predicted viral set point 24 weeks later in the independent Malawian cohort with an accuracy of 87.5%. As expected, the predictive accuracy of the networks inferred at later time points was even greater, exceeding 90% after week 4. The composition of the inferred networks was largely conserved between time points. The 12 genes comprising this dynamic signature of viral set point implicated the involvement of two major canonical pathways: interferon signaling (p<0.0003) and membrane fraction (p<0.02). A silico knockout study showed that HLA-DRB1 and C4BPA may contribute to restricting HIV-1 replication. CONCLUSIONS: Longitudinal gene expression profiling of peripheral blood mononuclear cells from patients with acute HIV-1 infection can be used to create transcriptional network models to early predict viral set point with a high degree of accuracy.

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.

BACKGROUND: Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. RESULTS: We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. CONCLUSIONS: The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Information theory--a field at the intersection of applied mathematics and electrical engineering--was primarily developed for the purpose of addressing problems arising in data storage and data transmission over (noisy) communication media. Consequently, information theory provides the formal basis for much of today’s storage and communication infrastructure.

This paper develops a Bayesian network (BN) predictor to profile cross-race gene expression data. Cross-race studies face more data variability than single-lab studies. Our design handles this problem by using the BN framework. In addition, unlike existing methods that unrealistically assume independent genes, our BN approach can capture the dependencies among genes. Existing BN algorithms in biomedicine applications quantize data, leading to information loss; we adopt linear Gaussian model to keep the data intact, so our resulting model is more reliable. The application of our BN predictor to a lung adenocarcinoma study shows high prediction accuracy, and performance evaluation demonstrates our gene signature agreeable with those reported in the literature. Our tool has a promising potential in finding disease biomarkers common to multiple races.

BACKGROUND: Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods. RESULTS: Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature. CONCLUSIONS: The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis.

OBJECTIVE: Identify clinical factors that modulate the risk of progression to COPD among asthma patients using data extracted from electronic medical records. DESIGN: Demographic information and comorbidities from adult asthma patients who were observed for at least 5 years with initial observation dates between 1988 and 1998, were extracted from electronic medical records of the Partners Healthcare System using tools of the National Center for Biomedical Computing "Informatics for Integrating Biology to the Bedside" (i2b2). MEASUREMENTS: A predictive model of COPD was constructed from a set of 9,349 patients (843 cases, 8,506 controls) using Bayesian networks. The model's predictive accuracy was tested using it to predict COPD in a future independent set of asthma patients (992 patients; 46 cases, 946 controls), who had initial observation dates between 1999 and 2002. RESULTS: A Bayesian network model composed of age, sex, race, smoking history, and 8 comorbidity variables is able to predict COPD in the independent set of patients with an accuracy of 83.3%, computed as the area under the Receiver Operating Characteristic curve (AUROC). CONCLUSIONS: Our results demonstrate that data extracted from electronic medical records can be used to create predictive models. With improvements in data extraction and inclusion of more variables, such models may prove to be clinically useful.

UNLABELLED: Many bioinformatics solutions suffer from the lack of usable interface/platform from which results can be analyzed and visualized. Overcoming this hurdle would allow for more widespread dissemination of bioinformatics algorithms within the biological and medical communities. The algorithms should be accessible without extensive technical support or programming knowledge. Here, we propose a dynamic wizard platform that provides users with a Graphical User Interface (GUI) for most Java bioinformatics library toolkits. The application interface is generated in real-time based on the original source code. This platform lets developers focus on designing algorithms and biologists/physicians on testing hypotheses and analyzing results. AVAILABILITY: The open source code can be downloaded from: http://bcl.med.harvard.edu/proteomics/proj/APBA/.

With the human genome sequenced, attention has been shifting to proteins and their function. Several technologies including mass spectrometry and gel electrophoresis have traditionally been used to study proteins. These technologies rely on proteins' masses to characterize and/or identify them. Once identified, the discovered proteins' are often analyzed for their functional relevance. In this paper, we present an alternative approach to studying protein function directly, based on protein mass. By analyzing proteins with similar mass ranges (bins), we discovered that certain biological functions are associated with specific mass bins more frequently than would be expected by chance. Biological functional classes found in this manner could be seen across the three examined organisms: human, worm and fly. We found no sequence-based homology across significant mass-function proteins in the three different organisms. This investigation leads to useful property that the mass-based biological functional classes are preserved across the organisms. Thus, this paper describes a potential constraint for evolution of similar function based on mass constraints. Finally, this work yields a roadmap for experimental design for functional exploration of proteomes in mass-based technologies such as gel electrophoresis and mass spectrometry.

The accumulation of genomic and proteomic data of many organisms presents an opportunity to analyze entire phylogenetic trees in a systematic, quantified manner. The universal tree of life, constructed by genomic data, provides an evolutionary context for proteomic data of individual organisms. Using proteomic information mapped to biological functions, we survey the tree of life for evolutionary trends, where trends are low p-value linear regressions of relative protein diversity against an evolutionary timeline. These trends provide quantified perspectives across evolution that would be otherwise difficult to discern from the large amount of available bioinformatic data. We analyzed 22,762 functions across 167 proteomes for approximately 3.8 million information content calculations. Of particular interest is the rise of protein-protein interactions along the path of mammalian evolution. Of general interest are the numerous uncovered trends that open the door to future queries of focused scope.

BACKGROUND: Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS: We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION: The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.

Dynamic Bayesian networks offer a powerful modelling tool to unravel cellular mechanisms. In particular, Gaussian networks have recently been used to model gene expression data, thanks to their capability to avoid information loss associated with discretization and their good computational efficiency. Gaussian networks typically describe the conditional mean of a node as a linear regression of the parent variables. Such model can be generalized by using a linear regression of nonlinear transformations of the parent values. In this paper we investigate the use of both models and evaluate the performance of Gaussian networks in learning the complex dynamic interactions among genes and proteins. To this aim, we analyzed simulated data produced by a mathematical model of cell cycle control in budding yeast. The results obtained allowed us to appraise the performance of the different models and confirmed the suitability of dynamic Bayesian networks for a first level, genome-wide analysis of high throughput dynamic data

Current methods to learn Bayesian Networks from incomplete databases share the common assumption that the unreported data are missing at random. This paper describes a method—called Bound and Collapse (BC)—to learn Bayesian Networks from incomplete databases which allows the analyst to efficiently integrate information provided by the observed data and exogenous knowledge about the pattern of missing data. BC starts by bounding the set of estimates consistent with the available information and then collapses the resulting set to a point estimate via a convex combination of the extreme points, with weights depending on the assumed pattern of missing data. Experiments comparing BC to Gibbs Sampling are provided.

An abstraction paradigm for unifying different perspectives concerning the analysis and design of knowledge-based systems (KBSs) is presented. The model accounts for all of the conceptual features of knowledge-based systems, thus making clear which features are intrinsic to the problem and which are artifacts of the implementation. The proposal is based on a two-level analysis of knowledge-based systems: an epistemological and a computational level. At the first level, ontology and inference models of a knowledge-based system are defined. At the computational level, methods and formalisms are adopted after the epistemological analysis has been carried out. The study is confined to medicine with three generic tasks identified: diagnosis, therapy planning, and monitoring. The results of this analysis indicate that the generic tasks manage different ontologies, but can be executed exploiting a unique inference model. Computational issues are discussed to argue that the model provides a conceptual view on existing systems and some design insights for future ones.>