Biomedical subjects

Nir Friedman

Publications and source records attributed to Nir Friedman.

At least 19 recordsLinked to original sources

PubMed2006

Linking stochastic dynamics to population distribution: an analytical framework of gene expression.

We present an analytical framework describing the steady-state distribution of protein concentration in live cells, considering that protein production occurs in random bursts with an exponentially distributed number of molecules. We extend this framework for cases of transcription autoregulation and noise propagation in a simple genetic network. This model allows for the extraction of kinetic parameters of gene expression from steady-state distributions of protein concentration in a cell population, which are available from single cell data obtained by flow cytometry or fluorescence microscopy.

DNA↗

PubMed2006

Stochastic protein expression in individual cells at the single molecule level.

In a living cell, gene expression--the transcription of DNA to messenger RNA followed by translation to protein--occurs stochastically, as a consequence of the low copy number of DNA and mRNA molecules involved. These stochastic events of protein production are difficult to observe directly with measurements on large ensembles of cells owing to lack of synchronization among cells. Measurements so far on single cells lack the sensitivity to resolve individual events of protein production. Here we demonstrate a microfluidic-based assay that allows real-time observation of the expression of beta-galactosidase in living Escherichia coli cells with single molecule sensitivity. We observe that protein production occurs in bursts, with the number of molecules per burst following an exponential distribution. We show that the two key parameters of protein expression--the burst size and frequency--can be either determined directly from real-time monitoring of protein production or extracted from a measurement of the steady-state copy number distribution in a population of cells. Application of this assay to probe gene expression in individual budding yeast and mouse embryonic stem cells demonstrates its generality. Many important proteins are expressed at low levels, and are thus inaccessible by current genomic and proteomic techniques. This microfluidic single cell assay opens up possibilities for system-wide characterization of the expression of these low copy number proteins.

Animals↗

PubMed2006

Towards an integrated protein-protein interaction network: a relational Markov network approach.

Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.

Algorithms↗

PubMed2006

Multivariate information bottleneck.

The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural code, and spectral analysis. Here, we introduce a general principled framework for multivariate extensions of the IB method. This allows us to consider multiple systems of data partitions that are interrelated. Our approach utilizes Bayesian networks for specifying the systems of clusters and which information terms should be maintained. We show that this construction provides insights about bottleneck variations and enables us to characterize the solutions of these variations. We also present four different algorithmic approaches that allow us to construct solutions in practice and apply them to several real-world problems.

Algorithms↗

PubMed2005

A gamma mixture model better accounts for among site rate heterogeneity.

MOTIVATION: Variation of substitution rates across nucleotide and amino acid sites has long been recognized as a characteristic of molecular sequence evolution. Evolutionary models that account for this rate heterogeneity usually use a gamma density function to model the rate distribution across sites. This density function, however, may not fit real datasets, especially when there is a multimodal distribution of rates. Here, we present a novel evolutionary model based on a mixture of gamma density functions. This model better describes the among-site rate variation characteristic of molecular sequence evolution. The use of this model may improve the accuracy of various phylogenetic methods, such as reconstructing phylogenetic trees, dating divergence events, inferring ancestral sequences and detecting conserved sites in proteins. RESULTS: Using diverse sets of protein sequences we show that the gamma mixture model better describes the stochastic process underlying protein evolution. We show that the proposed gamma mixture model fits protein datasets significantly better than the single-gamma model in 9 out of 10 datasets tested. We further show that using the gamma mixture model improves the accuracy of model-based prediction of conserved residues in proteins. AVAILABILITY: C++ source codes are available from the authors upon request.

Chromosome Mapping↗

PubMed2005

Single-nucleosome mapping of histone modifications in S. cerevisiae.

Covalent modification of histone proteins plays a role in virtually every process on eukaryotic DNA, from transcription to DNA repair. Many different residues can be covalently modified, and it has been suggested that these modifications occur in a great number of independent, meaningful combinations. Published low-resolution microarray studies on the combinatorial complexity of histone modification patterns suffer from confounding effects caused by the averaging of modification levels over multiple nucleosomes. To overcome this problem, we used a high-resolution tiled microarray with single-nucleosome resolution to investigate the occurrence of combinations of 12 histone modifications on thousands of nucleosomes in actively growing S. cerevisiae. We found that histone modifications do not occur independently; there are roughly two groups of co-occurring modifications. One group of lysine acetylations shows a sharply defined domain of two hypo-acetylated nucleosomes, adjacent to the transcriptional start site, whose occurrence does not correlate with transcription levels. The other group consists of modifications occurring in gradients through the coding regions of genes in a pattern associated with transcription. We found no evidence for a deterministic code of many discrete states, but instead we saw blended, continuous patterns that distinguish nucleosomes at one location (e.g., promoter nucleosomes) from those at another location (e.g., over the 3' ends of coding regions). These results are consistent with the idea of a simple, redundant histone code, in which multiple modifications share the same role.

Chromatin Immunoprecipitation↗

PubMed2005

Ab initio prediction of transcription factor targets using structural knowledge.

Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable to transcription factors with no prior binding data. Our approach combines sequence data and structural information to infer context-specific amino acid-nucleotide recognition preferences. These are used to predict binding sites for novel transcription factors from the same structural family. We demonstrate our approach on the Cys(2)His(2) Zinc Finger protein family, and show that the learned DNA-recognition preferences are compatible with experimental results. We use these preferences to perform a genome-wide scan for direct targets of Drosophila melanogaster Cys(2)His(2) transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins.

Journal Article↗

PubMed2005

Precise temporal modulation in the response of the SOS DNA repair network in individual bacteria.

The SOS genetic network is responsible for the repair/bypass of DNA damage in bacterial cells. While the initial stages of the response have been well characterized, less is known about the dynamics of the response after induction and its shutoff. To address this, we followed the response of the SOS network in living individual Escherichia coli cells. The promoter activity (PA) of SOS genes was monitored using fluorescent protein-promoter fusions, with high temporal resolution, after ultraviolet irradiation activation. We find a temporal pattern of discrete activity peaks masked in studies of cell populations. The number of peaks increases, while their amplitude reaches saturation, as the damage level is increased. Peak timing is highly precise from cell to cell and is independent of the stage in the cell cycle at the time of damage. Evidence is presented for the involvement of the umuDC operon in maintaining the pattern of PA and its temporal precision, providing further evidence for the role UmuD cleavage plays in effecting a timed pause during the SOS response, as previously proposed. The modulations in PA we observe share many features in common with the oscillatory behavior recently observed in a mammalian DNA damage response. Our results, which reveal a hitherto unknown modulation of the SOS response, underscore the importance of carrying out dynamic measurements at the level of individual living cells in order to unravel how a natural genetic network operates at the systems level.

Bacterial Proteins↗

PubMed2005

Quantitative kinetic analysis of the bacteriophage lambda genetic network.

The lysis-lysogeny decision of bacteriophage lambda has been a paradigm for a developmental genetic network, which is composed of interlocked positive and negative feedback loops. This genetic network is capable of responding to environmental signals and to the number of infecting phages. An interplay between CI and Cro functions suggested a bistable switch model for the lysis-lysogeny decision. Here, we present a real-time picture of the execution of lytic and lysogenic pathways with unprecedented temporal resolution. We monitor, in vivo, both the level and function of the CII and Q gene regulators. These activators are cotranscribed yet control opposite developmental pathways. Conditions that favor the lysogenic response show severe delay and down-regulation of Q activity, in both CII-dependent and CII-independent ways. Whereas CII activity correlates with its protein level, Q shows a pronounced threshold before its function is observed. Our quantitative analyses suggest that by regulating CII and CIII, Cro plays a key role in the ability of the lambda genetic network to sense the difference between one and more than one phage particles infecting a cell. Thus, our results provide an improved framework to explain the longstanding puzzle of the decision process.

Bacteriophage lambda↗

PubMed2005

From signatures to models: understanding cancer using microarrays.

Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higher-level modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.

Algorithms↗

PubMed2004

A module map showing conditional activity of expression modules in cancer.

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor; for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.

Databases, Genetic↗

PubMed2004

Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression.

Yeast cells modulate their protein synthesis capacity in response to physiological needs through the transcriptional control of ribosomal protein (RP) genes. Here we demonstrate that the transcription factor Sfp1, previously shown to play a role in the control of cell size, regulates RP gene expression in response to nutrients and stress. Under optimal growth conditions, Sfp1 is localized to the nucleus, bound to the promoters of RP genes, and helps promote RP gene expression. In response to inhibition of target of rapamycin (TOR) signaling, stress, or changes in nutrient availability, Sfp1 is released from RP gene promoters and leaves the nucleus, and RP gene transcription is down-regulated. Additionally, cells lacking Sfp1 fail to appropriately modulate RP gene expression in response to environmental cues. We conclude that Sfp1 integrates information from nutrient- and stress-responsive signaling pathways to help control RP gene expression.

Cyclic AMP-Dependent Protein Kinases↗

PubMed2004

Modulation of DNA conformations through the formation of alternative high-order HU-DNA complexes.

HU is an abundant, highly conserved protein associated with the bacterial chromosome. It belongs to a small class of proteins that includes the eukaryotic proteins TBP, SRY, HMG-I and LEF-I, which bind to DNA non-specifically at the minor groove. HU plays important roles as an accessory architectural factor in a variety of bacterial cellular processes such as DNA compaction, replication, transposition, recombination and gene regulation. In an attempt to unravel the role this protein plays in shaping nucleoid structure, we have carried out fluorescence resonance energy transfer measurements of HU-DNA oligonucleotide complexes, both at the ensemble and single-pair levels. Our results provide direct experimental evidence for concerted DNA bending by HU, and the abrogation of this effect at HU to DNA ratios above about one HU dimer per 10-12 bp. These findings support a model in which a number of HU molecules form an ordered helical scaffold with DNA lying in the periphery. The abrogation of these nucleosome-like structures for high HU to DNA ratios suggests a unique role for HU in the dynamic modulation of bacterial nucleoid structure.

Bacterial Proteins↗

PubMed2004

Stress-related genomic responses during the course of heat acclimation and its association with ischemic-reperfusion cross-tolerance.

Acclimation to heat is a biphasic process involving a transient perturbed phase followed by a long lasting period during which acclimatory homeostasis is developed. In this investigation, we used cDNA stress microarray (Clontech Laboratory) to characterize the stress-related genomic response during the course of heat acclimation and to test the hypotheses that 1) heat acclimation influences the threshold of activation of protective molecular signaling, and 2) heat-acclimation-mediated ischemic-reperfusion (I/R) protection is coupled with reprogrammed gene expression leading to altered capacity or responsiveness of protective-signaling pathways shared by heat and I/R cytoprotective systems. Rats were acclimated at 34 degrees C for 0, 2, and 30 days. 32P-labeled RNA samples prepared from the left ventricles of rats before and after subjection to heat stress (HS; 2 h, 41 degrees C) or after I/R insult (ischemia: 75%, 45 min; reperfusion: 30 min) were hybridized onto the array membranes. Confirmatory RT-PCR of selected genes conducted on samples taken at 0, 30, and 60 min after HS or total ischemia was used to assess the promptness of the transcriptional response. Cluster analysis of the expressed genes indicated that acclimation involves a "two-tier" defense strategy: an immediate transient response peaking at the initial acclimating phase to maintain DNA and cellular integrity, and a sustained response, correlated with slowly developed adaptive, long-lasting cytoprotective signaling networks involving genes encoding proteins that are essential for the heat-shock response, antiapoptosis, and antioxidation. Gene activation was stress specific. Faster activation and suppression of signaling pathways shared by HS and I/R stressors probably contribute to heat-acclimation I/R cross-tolerance.

Acclimatization↗

PubMed2004

Inferring cellular networks using probabilistic graphical models.

High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.

Bayes Theorem↗

PubMed2004

Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays.

MOTIVATION: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. RESULTS: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.

Algorithms↗

PubMed2004

Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activity.

Multiple sclerosis (MS) is a central nervous system disease with an unpredictable course and outcome. Peripheral blood mononuclear cells (PBMCs) are involved in the disease pathogenesis and induce active demyelination. Using oligonucleotide microarrays, we identified a statistically significant transcriptional signature of 1,109 genes in PBMCs from 26 MS patients, irrespective of disease activation state or immunomodulatory treatment. This signature contains genes that implicate underlying processes involved in MS pathogenesis including T-cell activation and expansion, inflammation, and apoptosis. Another transcriptional signature of 721 genes involved in cellular recruitment, epitope spreading, and escape from regulatory immune surveillance identified MS patients in acute relapse compared with remission. Our results offer new opportunity for understanding the mechanisms involved in MS and indicate that gene expression patterns in PBMCs contain information about a remote-target disease process that may be useful for diagnosis and future tailoring of therapeutic strategies for MS.

Adjuvants, Immunologic↗

PubMed2004

Efficient exact p-value computation for small sample, sparse, and surprising categorical data.

A major obstacle in applying various hypothesis testing procedures to datasets in bioinformatics is the computation of ensuing p-values. In this paper, we define a generic branch-and-bound approach to efficient exact p-value computation and enumerate the required conditions for successful application. Explicit procedures are developed for the entire Cressie-Read family of statistics, which includes the widely used Pearson and likelihood ratio statistics in a one-way frequency table goodness-of-fit test. This new formulation constitutes a first practical exact improvement over the exhaustive enumeration performed by existing statistical software. The general techniques we develop to exploit the convexity of many statistics are also shown to carry over to contingency table tests, suggesting that they are readily extendible to other tests and test statistics of interest. Our empirical results demonstrate a speed-up of orders of magnitude over the exhaustive computation, significantly extending the practical range for performing exact tests. We also show that the relative speed-up gain increases as the null hypothesis becomes sparser, that computation precision increases with increase in speed-up, and that computation time is very moderately affected by the magnitude of the computed p-value. These qualities make our algorithm especially appealing in the regimes of small samples, sparse null distributions, and rare events, compared to the alternative asymptotic approximations and Monte Carlo samplers. We discuss several established bioinformatics applications, where small sample size, small expected counts in one or more categories (sparseness), and very small p-values do occur. Our computational framework could be applied in these, and similar cases, to improve performance.

Computational Biology↗