Biomedical subjects

Tsvi Tlusty

Publications and source records attributed to Tsvi Tlusty.

7 recordsLinked to original sources

PubMed2026

The genetic code at the balance point of error and demand.

The origin and organizing principles of the genetic code remain central problems in molecular evolution. The low probability of the natural codon-to-amino acid mapping arising by chance has spurred the hypothesis that its structure is optimized for robustness to mutations and translational errors. For the construction of effective molecular machines, the repertoire of encoded amino acids must also be diverse enough in physicochemical features. Here, we examine whether the standard genetic code can be understood as a near-optimal solution balancing these two objectives: minimizing error load and aligning codon assignments with the naturally occurring amino acid composition. Using simulated annealing, we explore this trade-off across a broad range of parameters. We find that the standard genetic code resides near an optimum in the fitness landscape of possible genetic codes. The degeneracy of the code plays a dual role, minimizing mistranslation errors while matching codon multiplicity to amino acid usage frequencies. As a result, uniform codon usage alone is sufficient to recover the empirical amino acid composition, without any additional bias. It is a highly effective solution that balances fidelity against resource availability constraints. A comparative analysis of natural variants also reveals a functional decoupling: error robustness acts as a rigid global constraint determined by code topology, whereas compositional alignment serves as a more flexible variable that adapts to lineage-specific demands. These results support a multi-objective optimization framework in which the genetic code reflects a balance between translational fidelity and proteomic demand.

Genetic Code↗

PubMed2006

Percolation in living neural networks.

We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component disintegrates, characterized by a power law with an exponent beta approximately or = 0.65. Beta is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is Gaussian rather than scale free.

Animals↗

PubMed2006

High fidelity of RecA-catalyzed recombination: a watchdog of genetic diversity.

Homologous recombination plays a key role in generating genetic diversity, while maintaining protein functionality. The mechanisms by which RecA enables a single-stranded segment of DNA to recognize a homologous tract within a whole genome are poorly understood. The scale by which homology recognition takes place is of a few tens of base pairs, after which the quest for homology is over. To study the mechanism of homology recognition, RecA-promoted homologous recombination between short DNA oligomers with different degrees of heterology was studied in vitro, using fluorescence resonant energy transfer. RecA can detect single mismatches at the initial stages of recombination, and the efficiency of recombination is strongly dependent on the location and distribution of mismatches. Mismatches near the 5' end of the incoming strand have a minute effect, whereas mismatches near the 3' end hinder strand exchange dramatically. There is a characteristic DNA length above which the sensitivity to heterology decreases sharply. Experiments with competitor sequences with varying degrees of homology yield information about the process of homology search and synapse lifetime. The exquisite sensitivity to mismatches and the directionality in the exchange process support a mechanism for homology recognition that can be modeled as a kinetic proofreading cascade.

Adenosine Triphosphate↗

PubMed2006

Coding limits on the number of transcription factors.

BACKGROUND: Transcription factor proteins bind specific DNA sequences to control the expression of genes. They contain DNA binding domains which belong to several super-families, each with a specific mechanism of DNA binding. The total number of transcription factors encoded in a genome increases with the number of genes in the genome. Here, we examined the number of transcription factors from each super-family in diverse organisms. RESULTS: We find that the number of transcription factors from most super-families appears to be bounded. For example, the number of winged helix factors does not generally exceed 300, even in very large genomes. The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family. Coding theory predicts that such upper bounds on the number of transcription factors should exist, in order to minimize cross-binding errors between transcription factors. This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal. We present evidence that transcription factors with similar binding sequences tend to regulate genes with similar biological functions, supporting this prediction. CONCLUSION: The present study suggests limits on the transcription factor repertoire of cells, and suggests coding constraints that might apply more generally to the mapping between binding sites and biological function.

Amino Acid Motifs↗

PubMed2006

Rules for biological regulation based on error minimization.

The control of gene expression involves complex mechanisms that show large variation in design. For example, genes can be turned on either by the binding of an activator (positive control) or the unbinding of a repressor (negative control). What determines the choice of mode of control for each gene? This study proposes rules for gene regulation based on the assumption that free regulatory sites are exposed to nonspecific binding errors, whereas sites bound to their cognate regulators are protected from errors. Hence, the selected mechanisms keep the sites bound to their designated regulators for most of the time, thus minimizing fitness-reducing errors. This offers an explanation of the empirically demonstrated Savageau demand rule: Genes that are needed often in the natural environment tend to be regulated by activators, and rarely needed genes tend to be regulated by repressors; in both cases, sites are bound for most of the time, and errors are minimized. The fitness advantage of error minimization appears to be readily selectable. The present approach can also generate rules for multi-regulator systems. The error-minimization framework raises several experimentally testable hypotheses. It may also apply to other biological regulation systems, such as those involving protein-protein interactions.

Binding Sites↗

PubMed2004

High-fidelity DNA sensing by protein binding fluctuations.

One of the major functions of RecA protein in the cell is to bind single-stranded DNA exposed upon damage, thereby triggering the SOS repair response. We present fluorescence anisotropy measurements at the binding onset, showing enhanced DNA length discrimination induced by adenosine triphosphate consumption. Our model explains the observed DNA length sensing as an outcome of out-of-equilibrium binding fluctuations, reminiscent of microtubule dynamic instability. The cascade architecture of the binding fluctuations is a generalization of the kinetic proofreading mechanism. Enhancement of precision by an irreversible multistage pathway is a possible design principle in the noisy biological environment.

Adenosine Triphosphate↗

PubMed2002

Protein-DNA computation by stochastic assembly cascade.

The assembly of RecA on single-stranded DNA is measured and interpreted as a stochastic finite-state machine that is able to discriminate fine differences between sequences, a basic computational operation. RecA filaments efficiently scan DNA sequence through a cascade of random nucleation and disassembly events that is mechanistically similar to the dynamic instability of microtubules. This iterative cascade is a multistage kinetic proofreading process that amplifies minute differences, even a single base change. Our measurements suggest that this stochastic Turing-like machine can compute certain integral transforms.

Computational Biology↗