Biomedical subjects

Naama Barkai

Publications and source records attributed to Naama Barkai.

26 records · Page 2Linked to original sources

PubMed2003

Similarities and differences in genome-wide expression data of six organisms.

Comparing genomic properties of different organisms is of fundamental importance in the study of biological and evolutionary principles. Although differences among organisms are often attributed to differential gene expression, genome-wide comparative analysis thus far has been based primarily on genomic sequence information. We present a comparative study of large datasets of expression profiles from six evolutionarily distant organisms: S. cerevisiae, C. elegans, E. coli, A. thaliana, D. melanogaster, and H. sapiens. We use genomic sequence information to connect these data and compare global and modular properties of the transcription programs. Linking genes whose expression profiles are similar, we find that for all organisms the connectivity distribution follows a power-law, highly connected genes tend to be essential and conserved, and the expression program is highly modular. We reveal the modular structure by decomposing each set of expression data into coexpressed modules. Functionally related sets of genes are frequently coexpressed in multiple organisms. Yet their relative importance to the transcription program and their regulatory relationships vary among organisms. Our results demonstrate the potential of combining sequence and expression data for improving functional gene annotation and expanding our understanding of how gene expression and diversity evolved.

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PubMed2003

Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae.

Cellular networks are subject to extensive regulation, which modifies the availability and efficiency of connections between components in response to external conditions. Thus far, studies of large-scale networks have focused on their connectivity, but have not considered how the modulation of this connectivity might also determine network properties. To address this issue, we analyzed how the coordinated expression of enzymes shapes the metabolic network of Saccharomyces cerevisiae. By integrating large-scale expression data with the structural description of the metabolic network, we systematically characterized the transcriptional regulation of metabolic pathways. The analysis revealed recurrent patterns, which may represent design principles of metabolic gene regulation. First, we find that transcription regulation biases metabolic flow toward linearity by coexpressing only distinct branches at metabolic branchpoints. Second, individual isozymes were often separately coregulated with distinct processes, providing a means of reducing crosstalk between pathways using a common reaction. Finally, transcriptional regulation defined a hierarchical organization of metabolic pathways into groups of varying expression coherence. These results emphasize the utility of incorporating regulatory information when analyzing properties of large-scale cellular networks.

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PubMed2003

Iterative signature algorithm for the analysis of large-scale gene expression data.

We present an approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, which searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of singular value decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast Saccharomyces cerevisiae.

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PubMed2003

Self-enhanced ligand degradation underlies robustness of morphogen gradients.

Morphogen gradients provide long-range positional information by extending across a developing field. To ensure reproducible patterning, their profile is invariable despite genetic or environmental fluctuations. Common models assume a morphogen profile that decays exponentially. Here, we show that exponential profiles cannot, at the same time, buffer fluctuations in morphogen production rate and define long-range gradients. To comply with both requirements, morphogens should decay rapidly close to their source but at a significantly slower rate over most of the field. Numerical search revealed two network designs that support robustness to fluctuations in morphogen production rate. In both cases, morphogens enhance their own degradation, leading to a higher degradation rate close to their source. This is achieved through reciprocal interactions between the morphogen and its receptor. The two robust networks are consistent with properties of the Wg and Hh morphogens in the Drosophila wing disc and provide novel insights into their function.

Animals↗

PubMed2002

Robustness of the BMP morphogen gradient in Drosophila embryonic patterning.

Developmental patterning relies on morphogen gradients, which generally involve feedback loops to buffer against perturbations caused by fluctuations in gene dosage and expression. Although many gene components involved in such feedback loops have been identified, how they work together to generate a robust pattern remains unclear. Here we study the network of extracellular proteins that patterns the dorsal region of the Drosophila embryo by establishing a graded activation of the bone morphogenic protein (BMP) pathway. We find that the BMP activation gradient itself is robust to changes in gene dosage. Computational search for networks that support robustness shows that transport of the BMP class ligands (Scw and Dpp) into the dorsal midline by the BMP inhibitor Sog is the key event in this patterning process. The mechanism underlying robustness relies on the ability to store an excess of signalling molecules in a restricted spatial domain where Sog is largely absent. It requires extensive diffusion of the BMP-Sog complexes, coupled with restricted diffusion of the free ligands. We show experimentally that Dpp is widely diffusible in the presence of Sog but tightly localized in its absence, thus validating a central prediction of our theoretical study.

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PubMed2002

Modeling pattern formation: counting to two in the Drosophila egg.

The EGF receptor pathway patterns the Drosophila egg and specifies the position of its dorsal appendages. A new mathematical analysis of this patterning network has highlighted its crucial features and provided novel insights into the spatial and temporal kinetics controlling patterning.

Animals↗

PubMed2002

Revealing modular organization in the yeast transcriptional network.

Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.

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PubMed2002

Mechanisms of noise-resistance in genetic oscillators.

A wide range of organisms use circadian clocks to keep internal sense of daily time and regulate their behavior accordingly. Most of these clocks use intracellular genetic networks based on positive and negative regulatory elements. The integration of these "circuits" at the cellular level imposes strong constraints on their functioning and design. Here, we study a recently proposed model [Barkai, N. & Leibler, S. (2000) Nature (London), 403, 267-268] that incorporates just the essential elements found experimentally. We show that this type of oscillator is driven mainly by two elements: the concentration of a repressor protein and the dynamics of an activator protein forming an inactive complex with the repressor. Thus, the clock does not need to rely on mRNA dynamics to oscillate, which makes it especially resistant to fluctuations. Oscillations can be present even when the time average of the number of mRNA molecules goes below one. Under some conditions, this oscillator is not only resistant to but, paradoxically, also enhanced by the intrinsic biochemical noise.

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