Biomedical subjects

Arren Bar-Even

Publications and source records attributed to Arren Bar-Even.

4 recordsLinked to original sources

PubMed2006

Noise in protein expression scales with natural protein abundance.

Noise in gene expression is generated at multiple levels, such as transcription and translation, chromatin remodeling and pathway-specific regulation. Studies of individual promoters have suggested different dominating noise sources, raising the question of whether a general trend exists across a large number of genes and conditions. We examined the variation in the expression levels of 43 Saccharomyces cerevisiae proteins, in cells grown under 11 experimental conditions. For all classes of genes and under all conditions, the expression variance was approximately proportional to the mean; the same scaling was observed at steady state and during the transient responses to the perturbations. Theoretical analysis suggests that this scaling behavior reflects variability in mRNA copy number, resulting from random 'birth and death' of mRNA molecules or from promoter fluctuations. Deviation of coexpressed genes from this general trend, including high noise in stress-related genes and low noise in proteasomal genes, may indicate fluctuations in pathway-specific regulators or a differential activation pattern of the underlying gene promoters.

Flow Cytometry↗

PubMed2005

Transcription control reprogramming in genetic backup circuits.

A key question in molecular genetics is why severe mutations often do not result in a detectably abnormal phenotype. This robustness was partially ascribed to redundant paralogs that may provide backup for one another in case of mutation. Mining mutant viability and mRNA expression data in Saccharomyces cerevisiae, we found that backup was provided predominantly by paralogs that are expressed dissimilarly in most growth conditions. We considered that this apparent inconsistency might be resolved by a transcriptional reprogramming mechanism that allows the intact paralog to rescue the organism upon mutation of its counterpart. We found that in wild-type cells, partial coregulation across growth conditions predicted the ability of paralogs to alter their transcription patterns and to provide backup for one another. Notably, the sets of regulatory motifs that controlled the paralogs with the most efficient backup activity deliberately overlapped only partially; paralogs with highly similar or dissimilar sets of motifs had suboptimal backup activity. Such an arrangement of partially shared regulatory motifs reconciles the differential expression of paralogs with their ability to back each other up.

Kinetics↗

PubMed2005

Polymer GARD: computer simulation of covalent bond formation in reproducing molecular assemblies.

The basic Graded Autocatalysis Replication Domain (GARD) model consists of a repertoire of small molecules, typically amphiphiles, which join and leave a non-covalent micelle-like assembly. Its replication behavior is due to occasional fission, followed by a homeostatic growth process governed by the assembly's composition. Limitations of the basic GARD model are its small finite molecular repertoire and the lack of a clear path from a 'monomer world' towards polymer-based living entities. We have now devised an extension of the model (polymer GARD or P-GARD), where a monomer-based GARD serves as a 'scaffold' for oligomer formation, as a result of internal chemical rules. We tested this concept with computer simulations of a simple case of monovalent monomers, whereby more complex molecules (dimers) are formed internally, in a manner resembling biosynthetic metabolism. We have observed events of dimer 'take-over' - the formation of compositionally stable, replication-prone quasi stationary states (composomes) that have appreciable dimer content. The appearance of novel metabolism-like networks obeys a time-dependent power law, reminiscent of evolution under punctuated equilibrium. A simulation under constant population conditions shows the dynamics of takeover and extinction of different composomes, leading to the generation of different population distributions. The P-GARD model offers a scenario whereby biopolymer formation may be a result of rather than a prerequisite for early life-like processes.

Computer Simulation↗

PubMed2004

Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification.

MOTIVATION: Genes are often characterized dichotomously as either housekeeping or single-tissue specific. We conjectured that crucial functional information resides in genes with midrange profiles of expression. RESULTS: To obtain such novel information genome-wide, we have determined the mRNA expression levels for one of the largest hitherto analyzed set of 62 839 probesets in 12 representative normal human tissues. Indeed, when using a newly defined graded tissue specificity index tau, valued between 0 for housekeeping genes and 1 for tissue-specific genes, genes with midrange profiles having 0.15< tau<0.85 were found to constitute >50% of all expression patterns. We developed a binary classification, indicating for every gene the I(B) tissues in which it is overly expressed, and the 12-I(B) tissues in which it shows low expression. The 85 dominant midrange patterns with I(B)=2-11 were found to be bimodally distributed, and to contribute most significantly to the definition of tissue specification dendrograms. Our analyses provide a novel route to infer expression profiles for presumed ancestral nodes in the tissue dendrogram. Such definition has uncovered an unsuspected correlation, whereby de novo enhancement and diminution of gene expression go hand in hand. These findings highlight the importance of gene suppression events, with implications to the course of tissue specification in ontogeny and phylogeny. AVAILABILITY: All data and analyses are publically available at the GeneNote website, http://genecards.weizmann.ac.il/genenote/ and, GEO accession GSE803. CONTACT: doron.lancet@weizmann.ac.il SUPPLEMENTARY INFORMATION: Four tables available at the above site.

Algorithms↗