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1.
Plant Cell ; 25(9): 3175-85, 2013 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-24014549

RESUMEN

Traditional genetic analysis relies on mutants with observable phenotypes. Mutants lacking visible abnormalities may nevertheless exhibit molecular differences useful for defining gene function. To examine this, we analyzed tissue-specific transcript profiles from Arabidopsis thaliana transcription factor gene mutants with known roles in root epidermis development, but lacking a single-gene mutant phenotype due to genetic redundancy. We discovered substantial transcriptional changes in each mutant, preferentially affecting root epidermal genes in a manner consistent with the known double mutant effects. Furthermore, comparing transcript profiles of single and double mutants, we observed remarkable variation in the sensitivity of target genes to the loss of one or both paralogous genes, including preferential effects on specific branches of the epidermal gene network, likely reflecting the pathways of paralog subfunctionalization during evolution. In addition, we analyzed the root epidermal transcriptome of the transparent testa glabra2 mutant to clarify its role in the network. These findings provide insight into the molecular basis of genetic redundancy and duplicate gene diversification at the level of a specific gene regulatory network, and they demonstrate the usefulness of tissue-specific transcript profiling to define gene function in mutants lacking informative visible changes in phenotype.


Asunto(s)
Proteínas de Arabidopsis/genética , Arabidopsis/genética , Regulación de la Expresión Génica de las Plantas , Transcriptoma , Arabidopsis/anatomía & histología , Arabidopsis/crecimiento & desarrollo , Diferenciación Celular , Perfilación de la Expresión Génica , Redes Reguladoras de Genes , Genes Reporteros , Mutación , Análisis de Secuencia por Matrices de Oligonucleótidos , Especificidad de Órganos , Fenotipo , Epidermis de la Planta/anatomía & histología , Epidermis de la Planta/genética , Epidermis de la Planta/crecimiento & desarrollo , Raíces de Plantas/anatomía & histología , Raíces de Plantas/genética , Raíces de Plantas/crecimiento & desarrollo , Factores de Transcripción/genética
2.
PLoS Genet ; 8(1): e1002446, 2012 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-22253603

RESUMEN

The root epidermis of Arabidopsis provides an exceptional model for studying the molecular basis of cell fate and differentiation. To obtain a systems-level view of root epidermal cell differentiation, we used a genome-wide transcriptome approach to define and organize a large set of genes into a transcriptional regulatory network. Using cell fate mutants that produce only one of the two epidermal cell types, together with fluorescence-activated cell-sorting to preferentially analyze the root epidermis transcriptome, we identified 1,582 genes differentially expressed in the root-hair or non-hair cell types, including a set of 208 "core" root epidermal genes. The organization of the core genes into a network was accomplished by using 17 distinct root epidermis mutants and 2 hormone treatments to perturb the system and assess the effects on each gene's transcript accumulation. In addition, temporal gene expression information from a developmental time series dataset and predicted gene associations derived from a Bayesian modeling approach were used to aid the positioning of genes within the network. Further, a detailed functional analysis of likely bHLH regulatory genes within the network, including MYC1, bHLH54, bHLH66, and bHLH82, showed that three distinct subfamilies of bHLH proteins participate in root epidermis development in a stage-specific manner. The integration of genetic, genomic, and computational analyses provides a new view of the composition, architecture, and logic of the root epidermal transcriptional network, and it demonstrates the utility of a comprehensive systems approach for dissecting a complex regulatory network.


Asunto(s)
Arabidopsis/crecimiento & desarrollo , Factores de Transcripción con Motivo Hélice-Asa-Hélice Básico/genética , Diferenciación Celular/genética , Redes Reguladoras de Genes , Reguladores del Crecimiento de las Plantas/genética , Raíces de Plantas/crecimiento & desarrollo , Raíces de Plantas/genética , Arabidopsis/genética , Regulación del Desarrollo de la Expresión Génica , Regulación de la Expresión Génica de las Plantas , Genes de Plantas/genética , Mutación , Análisis de Secuencia por Matrices de Oligonucleótidos , Epidermis de la Planta/citología , Epidermis de la Planta/crecimiento & desarrollo , Epidermis de la Planta/metabolismo , Raíces de Plantas/citología , Transcriptoma/genética
3.
PLoS Genet ; 7(8): e1002234, 2011 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-21901105

RESUMEN

Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza.


Asunto(s)
Infecciones Asintomáticas , Interacciones Huésped-Patógeno , Subtipo H3N2 del Virus de la Influenza A , Gripe Humana/metabolismo , Adolescente , Adulto , Citocinas/biosíntesis , Citocinas/metabolismo , Perfilación de la Expresión Génica , Humanos , Gripe Humana/genética , Gripe Humana/virología , Persona de Mediana Edad , Estrés Oxidativo/genética , Proteínas Ribosómicas/genética , Proteínas Ribosómicas/metabolismo , Estrés Fisiológico
4.
BMC Bioinformatics ; 12: 243, 2011 Jun 17.
Artículo en Inglés | MEDLINE | ID: mdl-21682879

RESUMEN

BACKGROUND: A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression. RESULTS: The top regulatory relationships identified by RegNetB include the regulation of RLN1, RLN2, by PAX4, the regulation of ACPP (PAP) by JUN, BACH1 and BACH2, and the co-regulation of PGC and GDF15 by MAZ and TAF8. These target genes are known to participate in tumor progression, but the suggested regulatory roles of PAX4, BACH1, BACH2, MAZ and TAF8 in the process is new. CONCLUSION: Integrating gene expression data and regulatory topologies can aid in identifying potentially causal mechanisms for observed changes in gene expression.


Asunto(s)
Teorema de Bayes , Regulación Neoplásica de la Expresión Génica , Redes Reguladoras de Genes , Neoplasias de la Próstata/genética , Fosfatasa Ácida , Carcinoma/genética , Carcinoma/patología , Proteínas de Unión al ADN/metabolismo , Proteínas de Homeodominio/metabolismo , Humanos , Masculino , Factores de Transcripción Paired Box/metabolismo , Próstata/metabolismo , Próstata/patología , Neoplasias de la Próstata/metabolismo , Neoplasias de la Próstata/patología , Proteínas Tirosina Fosfatasas/metabolismo , Factor de Transcripción TFIID/metabolismo , Factores de Transcripción/metabolismo
5.
J Mol Diagn ; 23(5): 532-540, 2021 05.
Artículo en Inglés | MEDLINE | ID: mdl-33549858

RESUMEN

Routine testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in health care workers (HCWs) is critical. Group testing strategies to increase capacity facilitate mass population testing but do not prioritize turnaround time, an important consideration for HCW screening. We propose a nonadaptive combinatorial (NAC) group testing strategy to increase throughput while facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350, and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1% to 10%. NAC matrices were compared versus Dorfman sequential (DS) group testing approaches. NAC matrices performed well at low prevalence levels, with an average of 97% of samples resolved after a single round of testing via the n = 700 matrix at a prevalence of 1%. In simulations of low to medium (0.1% to 3%) prevalence, all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%), the DS matrix was marginally superior, although both group testing approaches performed poorly at high prevalence levels. This strategy maximizes the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. This methodology may allow laboratories to adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.


Asunto(s)
COVID-19/diagnóstico , SARS-CoV-2/aislamiento & purificación , Prueba de COVID-19/economía , Prueba de COVID-19/métodos , Brotes de Enfermedades , Personal de Salud , Humanos , Tamizaje Masivo/economía , Tamizaje Masivo/métodos
6.
BMC Bioinformatics ; 11: 299, 2010 Jun 02.
Artículo en Inglés | MEDLINE | ID: mdl-20525223

RESUMEN

BACKGROUND: Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements. RESULTS: A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Differential-expression analysis of the smart pooled data was performed and compared against the unpooled control experiment. CONCLUSIONS: The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Although the smart pooled experiment did not compare favorably with the control, the experiment highlighted important conditions for the successful implementation of smart pooling - linearity of measurements, sparsity in data, and large experiment size.


Asunto(s)
Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , ARN Mensajero/química , Programas Informáticos , Bases de Datos Genéticas , Perfilación de la Expresión Génica
7.
Proteomics ; 9(23): 5371-83, 2009 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-19834887

RESUMEN

Fluorescence resonance energy transfer (FRET) microscopy can measure the spatial distribution of protein interactions inside live cells. Such experiments give rise to complex data sets with many images of single cells, motivating data reduction and abstraction. In particular, determination of the value of the equilibrium dissociation constant (K(d)) will provide a quantitative measure of protein-protein interactions, which is essential to reconstructing cellular signaling networks. Here, we investigate the feasibility of using quantitative FRET imaging of live cells to estimate the local value of K(d) for two interacting labeled molecules. An algorithm is developed to infer the values of K(d) using the intensity of individual voxels of 3-D FRET microscopy images. The performance of our algorithm is investigated using synthetic test data, both in the absence and in the presence of endogenous (unlabeled) proteins. The influence of optical blurring caused by the microscope (confocal or wide field) and detection noise on the accuracy of K(d) inference is studied. We show that deconvolution of images followed by analysis of intensity data at local level can improve the estimate of K(d). Finally, the performance of this algorithm using cellular data on the interaction between yellow fluorescent protein-Rac and cyan fluorescent protein-PBD in mammalian cells is shown.


Asunto(s)
Algoritmos , Transferencia Resonante de Energía de Fluorescencia/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Microscopía Confocal/métodos , Mapeo de Interacción de Proteínas/métodos , Proteínas/análisis , Animales , Células COS , Chlorocebus aethiops , Imagenología Tridimensional/métodos , Unión Proteica , Proteínas/metabolismo
8.
BMC Bioinformatics ; 10: 433, 2009 Dec 18.
Artículo en Inglés | MEDLINE | ID: mdl-20021670

RESUMEN

BACKGROUND: The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge. RESULTS: We introduce a new general-purpose analytic method called Mechanistic Bayesian Networks (MBNs) that allows for the integration of gene expression data and known constraints within a signal or regulatory pathway to predict new downstream pathway targets. The MBN framework is implemented in an open-source Bayesian network learning package, the Python Environment for Bayesian Learning (PEBL). We demonstrate how MBNs can be used by modeling the early steps of the sonic hedgehog pathway using gene expression data from different developmental stages and genetic backgrounds in mouse. Using the MBN approach we are able to automatically identify many of the known downstream targets of the hedgehog pathway such as Gas1 and Gli1, along with a short list of likely targets such as Mig12. CONCLUSIONS: The MBN approach shown here can easily be extended to other pathways and data types to yield a more mechanistic framework for learning genetic regulatory models.


Asunto(s)
Teorema de Bayes , Biología Computacional/métodos , Redes Reguladoras de Genes , Proteínas Hedgehog/metabolismo , Perfilación de la Expresión Génica , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Transducción de Señal
9.
BMC Bioinformatics ; 10: 161, 2009 May 27.
Artículo en Inglés | MEDLINE | ID: mdl-19473525

RESUMEN

BACKGROUND: Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. RESULTS: To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature. CONCLUSION: GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php.


Asunto(s)
Perfilación de la Expresión Génica/métodos , Redes Reguladoras de Genes , Modelos Estadísticos , Transducción de Señal , Programas Informáticos , Algoritmos , Proteína Morfogenética Ósea 6/genética , Proteína Morfogenética Ósea 6/metabolismo , Simulación por Computador , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/metabolismo , Análisis de Secuencia por Matrices de Oligonucleótidos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad
10.
BMC Bioinformatics ; 9: 256, 2008 May 30.
Artículo en Inglés | MEDLINE | ID: mdl-18513431

RESUMEN

BACKGROUND: A key goal of drug discovery is to increase the throughput of small molecule screens without sacrificing screening accuracy. High-throughput screening (HTS) in drug discovery involves testing a large number of compounds in a biological assay to identify active compounds. Normally, molecules from a large compound library are tested individually to identify the activity of each molecule. Usually a small number of compounds are found to be active, however the presence of false positive and negative testing errors suggests that this one-drug one-assay screening strategy can be significantly improved. Pooling designs are testing schemes that test mixtures of compounds in each assay, thereby generating a screen of the whole compound library in fewer tests. By repeatedly testing compounds in different combinations, pooling designs also allow for error-correction. These pooled designs, for specific experiment parameters, can be simply and efficiently created using the Shifted Transversal Design (STD) pooling algorithm. However, drug screening contains a number of key constraints that require specific modifications if this pooling approach is to be useful for practical screen designs. RESULTS: In this paper, we introduce a pooling strategy called poolHiTS (Pooled High-Throughput Screening) which is based on the STD algorithm. In poolHiTS, we implement a limit on the number of compounds that can be mixed in a single assay. In addition, we show that the STD-based pooling strategy is limited in the error-correction that it can achieve. Due to the mixing constraint, we show that it is more efficient to split a large library into smaller blocks of compounds, which are then tested using an optimized strategy repeated for each block. We package the optimal block selection algorithm into poolHiTS. The MATLAB codes for the poolHiTS algorithm and the corresponding decoding strategy are also provided. CONCLUSION: We have produced a practical version of STD algorithm for pooled drug screens. This pooling strategy provides both assay compression and error-correction capabilities that can both accelerate and reduce the overall cost of HTS in drug discovery.


Asunto(s)
Algoritmos , Bioensayo/métodos , Fenómenos Fisiológicos Celulares/efectos de los fármacos , Evaluación Preclínica de Medicamentos/métodos , Preparaciones Farmacéuticas/administración & dosificación , Programas Informáticos
11.
BMC Bioinformatics ; 9: 467, 2008 Nov 03.
Artículo en Inglés | MEDLINE | ID: mdl-18980677

RESUMEN

BACKGROUND: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative biological data. In this work we introduce a new statistical learning strategy, MI3 that addresses three common issues in previous methods simultaneously: (1) handling of continuous variables, (2) detection of more complex three-way relationships and (3) better differentiation of causal versus confounding relationships. With these improvements, we provide a more realistic representation of the underlying biological system. RESULTS: We test the MI3 algorithm using both synthetic and experimental data. In the synthetic data experiment, MI3 achieved an absolute sensitivity/precision of 0.77/0.83 and a relative sensitivity/precision both of 0.99. In addition, MI3 significantly outperformed the control methods, including Bayesian networks, classical two-way mutual information and a discrete version of MI3. We then used MI3 and control methods to infer a regulatory network centered at the MYC transcription factor from a published microarray dataset. Models selected by MI3 were numerically and biologically distinct from those selected by control methods. Unlike control methods, MI3 effectively differentiated true causal models from confounding models. MI3 recovered major MYC cofactors, and revealed major mechanisms involved in MYC dependent transcriptional regulation, which are strongly supported by literature. The MI3 network showed that limited sets of regulatory mechanisms are employed repeatedly to control the expression of large number of genes. CONCLUSION: Overall, our work demonstrates that MI3 outperforms the frequently used control methods, and provides a powerful method for inferring mechanistic relationships underlying biological and other complex systems. The MI3 method is implemented in R in the "mi3" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php and from the R package archive CRAN.


Asunto(s)
Algoritmos , Inteligencia Artificial , Expresión Génica , Redes Reguladoras de Genes/genética , Biología de Sistemas/métodos
12.
Bioinformatics ; 23(18): 2423-32, 2007 Sep 15.
Artículo en Inglés | MEDLINE | ID: mdl-17644819

RESUMEN

MOTIVATION: Many biomedical and clinical research problems involve discovering causal relationships between observations gathered from temporal events. Dynamic Bayesian networks are a powerful modeling approach to describe causal or apparently causal relationships, and support complex medical inference, such as future response prediction, automated learning, and rational decision making. Although many engines exist for creating Bayesian networks, most require a local installation and significant data manipulation to be practical for a general biologist or clinician. No software pipeline currently exists for interpretation and inference of dynamic Bayesian networks learned from biomedical and clinical data. RESULTS: miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. AVAILABILITY: miniTUBA is available at http://www.minituba.org.


Asunto(s)
Algoritmos , Inteligencia Artificial , Sistemas de Apoyo a Decisiones Clínicas , Diagnóstico por Computador/métodos , Informática Médica/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Programas Informáticos , Teorema de Bayes , Bases de Datos Factuales , Redes Neurales de la Computación , Integración de Sistemas , Factores de Tiempo
13.
J Mol Histol ; 35(7): 667-77, 2004 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-15614622

RESUMEN

Many receptor-level processes involve the diffusion and reaction of receptors with other membrane-localized molecules. Monte Carlo simulation is a powerful technique that allows us to track the motions and discrete reactions of individual receptors, thus simulating receptor dynamics and the early events of signal transduction. In this paper, we discuss simulations of two receptor processes, receptor dimerization and G-protein activation. Our first set of simulations demonstrates how receptor dimerization can create clusters of receptors via partner switching and the relevance of this clustering for receptor cross-talk and integrin signaling. Our second set of simulations investigates the activation and desensitization of G-protein coupled receptors when either a single agonist or both an agonist and an antagonist are present. For G-protein coupled receptor systems in the presence of an agonist alone, the dissociation rate constant of agonist is predicted to affect the ratio of G-protein activation to receptor phosphorylation. Similarly, this ratio is affected by the antagonist dissociation rate constant when both agonist and antagonist are present. The relationship of simulation predictions to experimental findings and potential applications of our findings are also discussed.


Asunto(s)
Método de Montecarlo , Receptores de Superficie Celular/metabolismo , Sistemas de Mensajero Secundario/fisiología , Adhesión Celular , Dimerización , Receptores de Superficie Celular/agonistas
14.
Biophys Chem ; 104(1): 217-27, 2003 May 01.
Artículo en Inglés | MEDLINE | ID: mdl-12834840

RESUMEN

Protein-protein dimerization is ubiquitous in biology, but its role in self-organization remains unexplored. Here we use Monte Carlo simulations to demonstrate that under diffusion-limited conditions, reversible dimerization alone can cause membrane proteins to cluster into oligomer-like structures. When multiple distinct protein species are able to form dimers, then heterodimerization and homodimerization can organize proteins into structured clusters that can affect cellular physiology. As an example, we demonstrate how receptor dimerization could provide a physical mechanism for regulating information flow by controlling receptor-receptor cross talk. These results are physically realistic for some membrane proteins, including members of the G-protein coupled receptor family, and may provide a physiological reason as to why many proteins dimerize.


Asunto(s)
Proteínas de Unión al GTP/química , Receptores Acoplados a Proteínas G/química , Dimerización , Proteínas de Unión al GTP/metabolismo , Método de Montecarlo , Tamaño de la Partícula , Conformación Proteica , Receptores Acoplados a Proteínas G/metabolismo , Transducción de Señal , Relación Estructura-Actividad
15.
Methods Mol Biol ; 876: 189-94, 2012.
Artículo en Inglés | MEDLINE | ID: mdl-22576096

RESUMEN

Gene expression profiling studies are commonly used to study signaling pathways and their impact on transcriptional regulation in plants. In some cases, a profiling study results in expression profiles in which most genes exhibit a small number of differentially expressed states among a large number of samples. In such instances, a pooling approach would help improve the efficiency of the profiling effort by employing fewer microarray chips and ensuring more robust measurement of transcript levels. Smart pooling involves pooling of mRNA samples in an information-efficient manner such that each sample is tested multiple times but always in pools with other samples. The resulting pooled measurements are then decoded to recover the expression profile of all samples in the study. In this protocol, we describe in detail the process of designing smart pooling experiments and decoding their results, which have been used for studying signaling in Arabidopsis root development. Heuristics are provided to select the design parameters that would ensure successful execution of smart pooling.


Asunto(s)
Perfilación de la Expresión Génica/métodos , ARN Mensajero/genética , Programas Informáticos , Transcripción Genética/genética
16.
BMC Syst Biol ; 5: 86, 2011 May 27.
Artículo en Inglés | MEDLINE | ID: mdl-21619639

RESUMEN

BACKGROUND: Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful. RESULTS: Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using E. coli expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for E. coli was integrated with 266 E. coli gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data. CONCLUSION: POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.


Asunto(s)
Biología Computacional/métodos , Redes Reguladoras de Genes , Algoritmos , Teorema de Bayes , ARN Polimerasas Dirigidas por ADN/metabolismo , Escherichia coli/genética , Escherichia coli/metabolismo , Perfilación de la Expresión Génica , Regulación Bacteriana de la Expresión Génica , Genes Bacterianos , Humanos , Modelos Biológicos , Modelos Genéticos , ARN Mensajero/metabolismo , Factor sigma/metabolismo , Biología de Sistemas
17.
PLoS One ; 6(4): e18836, 2011 Apr 22.
Artículo en Inglés | MEDLINE | ID: mdl-21526128

RESUMEN

BACKGROUND: Indispensible amino acids (IAAs) are used by the body in different proportions. Most animal-based foods provide these IAAs in roughly the needed proportions, but many plant-based foods provide different proportions of IAAs. To explore how these plant-based foods can be better used in human nutrition, we have created the computational tool vProtein to identify optimal food complements to satisfy human protein needs. METHODS: vProtein uses 1251 plant-based foods listed in the United States Department of Agriculture standard release 22 database to determine the quantity of each food or pair of foods required to satisfy human IAA needs as determined by the 2005 daily recommended intake. The quantity of food in a pair is found using a linear programming approach that minimizes total calories, total excess IAAs, or the total weight of the combination. RESULTS: For single foods, vProtein identifies foods with particularly balanced IAA patterns such as wheat germ, quinoa, and cauliflower. vProtein also identifies foods with particularly unbalanced IAA patterns such as macadamia nuts, degermed corn products, and wakame seaweed. Although less useful alone, some unbalanced foods provide unusually good complements, such as Brazil nuts to legumes. Interestingly, vProtein finds no statistically significant bias toward grain/legume pairings for protein complementation. These analyses suggest that pairings of plant-based foods should be based on the individual foods themselves instead of based on broader food group-food group pairings. Overall, the most efficient pairings include sweet corn/tomatoes, apple/coconut, and sweet corn/cherry. The top pairings also highlight the utility of less common protein sources such as the seaweeds laver and spirulina, pumpkin leaves, and lambsquarters. From a public health perspective, many of the food pairings represent novel, low cost food sources to combat malnutrition. Full analysis results are available online at http://www.foodwiki.com/vprotein.


Asunto(s)
Aminoácidos/análisis , Biología Computacional/métodos , Análisis de los Alimentos , Plantas Comestibles/química , Programas Informáticos , Humanos
18.
BMC Syst Biol ; 5: 82, 2011 May 23.
Artículo en Inglés | MEDLINE | ID: mdl-21605425

RESUMEN

BACKGROUND: BMP6 mediated osteoblast differentiation plays a key role in skeletal development and bone disease. Unfortunately, the signaling pathways regulated by BMP6 are largely uncharacterized due to both a lack of data and the complexity of the response. RESULTS: To better characterize the signaling pathways responsive to BMP6, we conducted a time series microarray study to track BMP6 induced osteoblast differentiation and mineralization. These temporal data were analyzed using a customized gene set analysis approach to identify temporally coherent sets of genes that act downstream of BMP6. Our analysis identified BMP6 regulation of previously reported pathways, such as the TGF-beta pathway. We also identified previously unknown connections between BMP6 and pathways such as Notch signaling and the MYB and BAF57 regulatory modules. In addition, we identify a super-network of pathways that are sequentially activated following BMP6 induction. CONCLUSION: In this work, we carried out a microarray-based temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization using GAGE method. This novel temporal analysis is more informative and powerful than the classical static pathway analysis in that: (1) it captures the interconnections between signaling pathways or functional modules and demonstrates the even higher level organization of molecular biological systems; (2) it describes the temporal perturbation patterns of each pathway or module and their dynamic roles in osteoblast differentiation. The same set of experimental and computational strategies employed in our work could be useful for studying other complex biological processes.


Asunto(s)
Proteína Morfogenética Ósea 6/genética , Regulación de la Expresión Génica , Algoritmos , Diferenciación Celular , Biología Computacional/métodos , Perfilación de la Expresión Génica , Humanos , Modelos Biológicos , Modelos Genéticos , Modelos Estadísticos , Análisis de Secuencia por Matrices de Oligonucleótidos , Osteoblastos/citología , Osteoblastos/metabolismo , Receptores Notch/metabolismo , Transducción de Señal , Biología de Sistemas , Factores de Tiempo
19.
Methods Mol Biol ; 674: 401-18, 2010.
Artículo en Inglés | MEDLINE | ID: mdl-20827604

RESUMEN

Probabilistic methods such as mutual information and Bayesian networks have become a major category of tools for the reconstruction of regulatory relationships from quantitative biological data. In this chapter, we describe the theoretic framework and the implementation for learning gene regulatory networks using high-order mutual information via the MI3 method (Luo et al. (2008) BMC Bioinformatics 9, 467; Luo (2008) Gene regulatory network reconstruction and pathway inference from high throughput gene expression data. PhD thesis). We also cover the closely related Bayesian network method in detail.


Asunto(s)
Biología Computacional/métodos , Redes Reguladoras de Genes , Transcripción Genética , Algoritmos , Teorema de Bayes , Gráficos por Computador , Perfilación de la Expresión Génica , Modelos Genéticos , Programas Informáticos , Estadística como Asunto
20.
PLoS One ; 5(8): e12355, 2010 Aug 30.
Artículo en Inglés | MEDLINE | ID: mdl-20814578

RESUMEN

BACKGROUND: Quantitative PCR (qPCR) is a workhorse laboratory technique for measuring the concentration of a target DNA sequence with high accuracy over a wide dynamic range. The gold standard method for estimating DNA concentrations via qPCR is quantification cycle () standard curve quantification, which requires the time- and labor-intensive construction of a standard curve. In theory, the shape of a qPCR data curve can be used to directly quantify DNA concentration by fitting a model to data; however, current empirical model-based quantification methods are not as reliable as standard curve quantification. PRINCIPAL FINDINGS: We have developed a two-parameter mass action kinetic model of PCR (MAK2) that can be fitted to qPCR data in order to quantify target concentration from a single qPCR assay. To compare the accuracy of MAK2-fitting to other qPCR quantification methods, we have applied quantification methods to qPCR dilution series data generated in three independent laboratories using different target sequences. Quantification accuracy was assessed by analyzing the reliability of concentration predictions for targets at known concentrations. Our results indicate that quantification by MAK2-fitting is as reliable as standard curve quantification for a variety of DNA targets and a wide range of concentrations. SIGNIFICANCE: We anticipate that MAK2 quantification will have a profound effect on the way qPCR experiments are designed and analyzed. In particular, MAK2 enables accurate quantification of portable qPCR assays with limited sample throughput, where construction of a standard curve is impractical.


Asunto(s)
Modelos Teóricos , Reacción en Cadena de la Polimerasa/métodos , Calibración , ADN/genética , Cinética , Reacción en Cadena de la Polimerasa/normas , Estándares de Referencia
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