Selected Publications
(A list of all publications appear Here )
Translation Complex Profile Sequencing (TCP-seq),
a protocol that was developed and implemented on
Saccharomyces cerevisiae, provides the footprints
of the small subunit (SSU) of the ribosome (with additional
factors) across the entire transcriptome of
the analyzed organism. In this study, based on the
TCP-seq data, we developed for the first-time a predictive
model of the SSU density and analyzed the
effect of transcript features on the dynamics of the
SSU scan in the 5UTR. Among others, our model is
based on novel tools for detecting complex statistical
relations tailored to TCP-seq. We quantitatively estimated
the effect of several important features, including
the context of the upstream AUG, the upstream
ORF length and the mRNA folding strength. Specifically,
we suggest that around 50% of the variance related
to the read counts (RC) distribution near a start
codon can be attributed to the AUG context score.
We provide the first large scale direct quantitative
evidence that shows that indeed AUG context affects
the small sub-unitmovement. In addition, we suggest
that strong folding may cause the detachment of the
SSU from the mRNA. We also identified a number of
novel sequence motifs that can affect the SSU scan;
some of these motifs affect transcription factors and
RNA binding proteins. The results presented in this
study provide a better understanding of the biophysical
aspects related to the SSU scan along the 5UTR
and of translation initiation in S. cerevisiae, a fundamental
step toward a comprehensive modeling of
initiation.
David Shallom, Danny Naiger, Shlomo Weiss, and Tamir Tuller. Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware. ACS synthetic biology . 2021.
In recent years, intracellular biophysical simulations
have been used with increasing frequency not only for answering
basic scientific questions but also in the field of synthetic biology.
However, since these models include networks of interaction
between millions of components, they are extremely timeconsuming
and cannot run easily on parallel computers. In this
study, we demonstrate for the first time a novel approach addressing this challenge by using a dedicated hardware designed
specifically to simulate such processes. As a proof of concept, we specifically focus on mRNA translation, which is the process
consuming most of the energy in the cell. We design a hardware that simulates translation in Escherichia coli and Saccharomyces
cerevisiae for thousands of mRNAs and ribosomes, which is in orders of magnitude faster than a similar software solution. With the
sharp increase in the amount of genomic data available today and the complexity of the corresponding models inferred from them,
we believe that the strategy suggested here will become common and can be used among others for simulating entire cells with all
gene expression steps.
Tal Gutman, Guy Goren, Omri Efroni, and Tamir Tuller. Estimating the predictive power of silent mutations on cancer classification and prognosis. NPJ Genom Med. 2021.
In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be
related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and
prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three
million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and nonsilent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the
equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial
diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the
cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold
considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is
highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and
its ramifications on cancer fitness.
Hadas Zur, Rachel Cohen-Kupiec, Sophie Vinokour, Tamir Tuller. Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate. scientific reports. 2020.
mRNA translation is a fundamental cellular process consuming most of the intracellular energy;
thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the
host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any
organism by introducing synonymous mutations based on comprehensive computational models.
The algorithms introduce silent mutations that may improve the allocation of ribosomes in the
cells via the decreasing of their traffic jams during translation respectively. As a result, resources
availability in the cell changes leading to improved growth-rate. We demonstrate experimentally
the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few
mutations in two computationally selected genes the mutant's titer increased. Our approach can be
employed for improving the growth rate of any organism providing the existence of data for inferring
models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful
in biotechnology, medicine, and agriculture.
Michael Peeri, Tamir Tuller. High-resolution modeling of the selection on local mRNA folding strength in coding sequences across the tree of life. Genome Biology. 2020.
Background: mRNA can form local secondary structure within the
protein-coding sequence, and the strength of this structure is
thought to influence gene expression regulation. Previous studies
suggest that secondary structure strength may be maintained under
selection, but the details of this phenomenon are not well
understood.
Results: We perform a comprehensive study of the selection on local
mRNA folding strengths considering variation between species across
the tree of life. We show for the first time that local folding
strength selection tends to follow a conserved characteristic
profile in most phyla, with selection for weak folding at the two
ends of the coding region and for strong folding elsewhere in the
coding sequence, with an additional peak of selection for strong
folding located downstream of the start codon. The strength of this
pattern varies between species and organism groups, and we
highlight contradicting cases.
To better understand the underlying evolutionary process, we show
that selection strengths in the different regions are strongly
correlated, and report four factors which have a clear predictive
effect on local mRNA folding selection within the coding sequence
in different species.
Conclusions: The correlations observed between selection for local
secondary structure strength in the different regions and with the
four genomic and environmental factors suggest that they are shaped
by the same evolutionary process throughout the coding sequence,
and might be maintained under direct selection related to
optimization of gene expression and specifically translation
regulation.
Iddo Weiner, Noam Shahar, Pini Marcu, Iftach Yacoby, Tamir Tuller. Solving the riddle of the evolution of Shine-Dalgarno based translation in chloroplasts. Molecular Biology and Evolution. 2019.
.Cover figure:
Chloroplasts originated from an ancient cyanobacterium and still harbor a bacterial-like genome. However, the centrality of Shine-Dalgarno ribosome binding, which predominantly regulates proteobacterial translation initiation, is significantly decreased in chloroplasts. As plastid ribosomal RNA anti-Shine-Dalgarno elements are similar to their bacterial counterparts, these sites alone cannot explain this decline. By computational simulation we show that upstream point mutations modulate the local structure of ribosomal RNA in chloroplasts, creating significantly tighter structures around the anti-Shine-Dalgarno locus, which in-turn reduce the probability of ribosome binding. To validate our model, we expressed two reporter genes (mCherry, hydrogenase) harboring a Shine-Dalgarno motif in the Chlamydomonas reinhardtii chloroplast. Co-expressing them with a 16S ribosomal RNA, modified according to our model, significantly enhances mCherry and hydrogenase expression compared to co-expression with an endogenous 16S gene.
Renana Sabi, Tamir Tuller. Novel Insights into Gene Expression Regulation during Meiosis Revealed by Translation Elongation Dynamics. Nature Systems Biology and Applications. 2019.
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The ability to dynamically control mRNA translation has a great impact on many intracellular processes. Whereas it is believed that translational control in eukaryotes occurs mainly at initiation, the condition-specific changes at the elongation level, and their potential regulatory role remain unclear. Using computational approaches applied to ribosome profiling data, we show that elongation rate is dynamic and can change considerably during the yeast meiosis to facilitate the selective translation of stage-specific transcripts. We observed unique elongation changes during meiosis II including a global inhibition of translation elongation mainly at the onset of anaphase II accompanied by a sharp shift toward increased elongation for genes required at this meiotic stage. We also show that ribosomal proteins counteract the global decreased elongation by maintaining high initiation rates. Our findings provide new insights into gene expression regulation during meiosis and demonstrate that codon usage evolved, among others, to optimize timely translation.
Alon Diament, Iddo Weiner, Noam Shahar, Shira Landman, Yael Feldman, Shimshi Atar, Meital Avitan, Shira Schweitzer, Iftach Yacoby, and Tamir Tuller. ChimeraUGEM: unsupervised gene expression modeling in any given organism. Bioinformatics. 2019.
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Motivation: Regulation of the amount of protein that is synthesized from genes has proved to be a serious challenge in terms of analysis and prediction, and in terms of engineering and optimization, due to the large diversity in expression machinery across species.
Results: To address this challenge, we developed a methodology and a software tool (ChimeraUGEM) for predicting gene expression as well as adapting the coding sequence of a target gene to any host organism. We demonstrate these methods by predicting protein levels in 7 organisms, in 7 human tissues, and by increasing in vivo the expression of a synthetic gene up to 26-fold in the single-cell green alga C. reinhardtii. The underlying model is designed to capture sequence patterns and regulatory signals with minimal prior knowledge on the host organism and can be applied to a multitude of species and applications.
Availability: Source code (MATLAB, C) and binaries are freely available for download for non-commercial use at http://www.cs.tau.ac.il/~tamirtul/ChimeraUGEM, and supported on macOS, Linux, and Windows.
Eli Goz, Zohar Zafrir, and Tamir Tuller. Universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code. Bioinformatics. 2018.
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Motivation: Understanding how viruses co-evolve with their hosts and adapt various genomic level strategies in order to ensure their fitness may have essential implications in unveiling the secrets of viral evolution, and in developing new vaccines and therapeutic approaches. Here, based on a novel genomic analysis of 2,625 different viruses and 439 corresponding host organisms, we provide evidence of universal evolutionary selection for high dimensional 'silent' patterns of information hidden in the redundancy of viral genetic code. Results: Our model suggests that long substrings of nucleotides in the coding regions of viruses from all classes, often also repeat in the corresponding viral hosts from all domains of life. Selection for these substrings cannot be explained only by such phenomena as codon usage bias, horizontal gene transfer, and the encoded proteins. Genes encoding structural proteins responsible for building the core of the viral particles were found to include more host-repeating substrings, and these substrings tend to appear in the middle parts of the viral coding regions. In addition, in human viruses these substrings tend to be enriched with motives related to transcription factors and RNA binding proteins. The host-repeating substrings are possibly related to the evolutionary pressure on the viruses to effectively interact with host's intracellular factors and to efficiently escape from the host's immune system.
Alon Diament*, Anna Feldman*, Elisheva Schochet, Martin Kupiec, Yoav Arava, and Tamir Tuller. The extent of ribosome queuing in budding yeast. PLoS Comput Biol. 2018.
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Ribosome queuing is a fundamental phenomenon suggested to be related to topics such as genome evolution, synthetic biology, gene expression regulation, intracellular biophysics, and more. However, this phenomenon hasn't been quantified yet at a genomic level. Nevertheless, methodologies for studying translation (e.g. ribosome footprints) are usually calibrated to capture only single ribosome protected footprints (mRPFs) and thus limited in their ability to detect ribosome queuing. On the other hand, most of the models in the field assume and analyze a certain level of queuing. Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of ribosomes (dRPFs) using a modified ribosome profiling protocol. We combine our approach with traditional ribosome profiling to generate a detailed profile of ribosome traffic. The data are analyzed using computational models of translation dynamics. The approach was implemented on the Saccharomyces cerevisiae transcriptome. Our data shows that ribosome queuing is more frequent than previously thought: the measured ratio of ribosomes within dRPFs to mRPFs is 0.2-0.35, suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods. We found that specific regions are enriched with queued ribosomes, such as the 5'-end of ORFs, and regions upstream to mRPF peaks, among others. While queuing is related to higher density of ribosomes on the transcript (characteristic of highly translated genes), we report cases where traffic jams are relatively more severe in lowly expressed genes and possibly even selected for. In addition, our analysis demonstrates that higher adaptation of the coding region to the intracellular tRNA levels is associated with lower queuing levels. Our analysis also suggests that the Saccharomyces cerevisiae transcriptome undergoes selection for eliminating traffic jams. Thus, our proposed approach is an essential tool for high resolution analysis of ribosome traffic during mRNA translation and understanding its evolution.
Alon Diament and Tamir Tuller. Tracking the evolution of 3D gene organization demonstrates its connection to phenotypic divergence. Nucleic Acids Res. 2017.
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It has recently been shown that the organization of genes in eukaryotic genomes, and specifically in 3D, is strongly related to gene expression and function and partially conserved between organisms. However, previous studies of 3D genomic organization analyzed each organism independently from others. Here, we propose an approach for unified inter-organismal analysis of gene organization based on a network representation of Hi-C data. We define and detect four classes of spatially co-evolving orthologous modules (SCOMs), i.e. gene families that co-evolve in their 3D organization, based on patterns of divergence and conservation of distances. We demonstrate our methodology on Hi-C data from Saccharomyces cerevisiae and Schizosaccharomyces pombe, and identify, among others, modules relating to RNA splicing machinery and chromatin silencing by small RNA which are central to S. pombe's lifestyle. Our results emphasize the importance of 3D genomic organization in eukaryotes and suggest that the evolutionary mechanisms that shape gene organization affect the organism fitness and phenotypes. The proposed algorithms can be utilized in future studies of genome evolution and comparative analysis of spatial genomic organization in different tissues, conditions and single cells.
Alon Raveh, Michael Margaliot, Eduardo D. Sontag, Tamir Tuller. A model for competition for ribosomes in the cell. J. R. Soc. Interface. 2016 Mar;13(116). .
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A single mammalian cell includes an order of 10^4 -- 10^5 mRNA molecules and as many as 10^5 -- 10^6 ribosomes. Large-scale simultaneous mRNA translation induces correlations between the mRNA molecules, as they all compete for the finite pool of available ribosomes. This has important implications for the cell's functioning and evolution. Developing a better understanding of the intricate correlations between these simultaneous processes, rather than focusing on the translation of a single isolated transcript, should help in gaining a better understanding of mRNA translation regulation and the way elongation rates affect organismal fitness. A model of simultaneous translation is specifically important when dealing with highly expressed genes, as these consume more resources. In addition, such a model can lead to more accurate predictions that are needed in the interconnection of translational modules in synthetic biology. We develop and analyse a general dynamical model for large-scale simultaneous mRNA translation and competition for ribosomes. This is based on combining several ribosome flow models (RFMs) interconnected via a pool of free ribosomes. We use this model to explore the interactions between the various mRNA molecules and ribosomes at steady state. We show that the compound system always converges to a steady state and that it always entrains or phase locks to periodically time-varying transition rates in any of the mRNA molecules. We then study the effect of changing the transition rates in one mRNA molecule on the steady-state translation rates of the other mRNAs that results from the competition for ribosomes. We show that increasing any of the codon translation rates in a specific mRNA molecule yields a local effect, an increase in the translation rate of this mRNA, and also a global effect, the translation rates in the other mRNA molecules all increase or all decrease. These results suggest that the effect of codon decoding rates of endogenous and heterologous mRNAs on protein production is more complicated than previously thought.
Tuval Ben-Yehezkel*, Shimshi Atar*, Hadas Zur, Alon Diament, Eli Goz, Tzipy Marx, Rafael Cohen, Alex Dana, Anna Feldman, Ehud Shapiro, Tamir Tuller. Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants. RNA Biol. 2015 Sep 2;12(9):972-84.
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Deducing generic causal relations between RNA transcript features and protein expression profiles from endogenous gene expression data remains a major unsolved problem in biology. The analysis of gene expression from heterologous genes contributes significantly to solving this problem, but has been heavily biased toward the study of the effect of 5' transcript regions and to prokaryotes. Here, we employ a synthetic biology driven approach that systematically differentiates the effect of different regions of the transcript on gene expression up to 240 nucleotides into the ORF. This enabled us to discover new causal effects between features in previously unexplored regions of transcripts, and gene expression in natural regimes. We rationally designed, constructed, and analyzed 383 gene variants of the viral HRSVgp04 gene ORF, with multiple synonymous mutations at key positions along the transcript in the eukaryote S. cerevisiae. Our results show that a few silent mutations at the 5'UTR can have a dramatic effect of up to 15 fold change on protein levels, and that even synonymous mutations in positions more than 120 nucleotides downstream from the ORF 5'end can modulate protein levels up to 160%-300%. We demonstrate that the correlation between protein levels and folding energy increases with the significance of the level of selection of the latter in endogenous genes, reinforcing the notion that selection for folding strength in different parts of the ORF is related to translation regulation. Our measured protein abundance correlates notably(correlation up to r = 0.62 (p=0.0013)) with mean relative codon decoding times, based on ribosomal densities (Ribo-Seq) in endogenous genes, supporting the conjecture that translation elongation and adaptation to the tRNA pool can modify protein levels in a causal/direct manner. This report provides an improved understanding of transcript evolution, design principles of gene expression regulation, and suggests simple rules for engineering synthetic gene expression in eukaryotes.
Tamir Tuller, Hadas Zur. Multiple roles of the coding sequence 5' end in gene expression regulation. Nucleic Acids Res. 2015 Jan;43(1):13-28.
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The codon composition of the coding sequence's (ORF) 5' end first few dozen codons is known to be distinct to that of the rest of the ORF. Various explanations for the unusual codon distribution in this region have been proposed in recent years, and include, among others, novel regulatory mechanisms of translation initiation and elongation. However, due to the fact that many overlapping regulatory signals are suggested to be associated with this relatively short region, its research is challenging. Here, we review the currently known signals that appear in this region, the theories related to the way they regulate translation and affect the organismal fitness, and the debates they provoke.
Alon Diament, Ron Y. Pinter, Tamir Tuller. Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function. Nature Communications. 16 Dec 2014.
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We show, via a novel codon-based metric, that genes with shared function and similar expression levels tend to be close in the three-dimensional conformation of the yeast, a model plant species, mouse and human genomes.
Hadas Zur, Tamir Tuller. Exploiting Hidden Information Interleaved in the Redundancy of the Genetic Code without Prior Knowledge. Bioinformatics. 2014 Nov 29. pii: btu797.
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We suggest Chimera - an unsupervised computationally efficient approach for exploiting hidden high dimensional information related to the way gene expression is encoded in the ORF, based solely on the genome of the analysed organism. One version of the approach, named Chimera Average Repetitive Substring (ChimeraARS), estimates the adaptability of an ORF to the intracellular gene expression machinery of a genome (host), by computing its tendency to include long substrings that appear in its coding sequences; the second version, named ChimeraMap, engineers the codons of a protein such that it will include long substrings of codons that appear in the host coding sequences, improving its adaptation to a new host's gene expression machinery. We demonstrate the applicability of the new approach for analyzing and engineering heterologous genes and for analyzing endogenous genes. Specifically, focusing on E. coli, we show that it can exploit information that cannot be detected by conventional approaches (e.g. the CAI - Codon Adaptation Index), which only consider single codon distributions; for example, we report correlations of up to 0.67 for the ChimeraARS measure with heterologous gene expression, when the CAI yielded no correlation.
Alexandra Dana, Tamir Tuller. The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res. 2014 Jul 23.
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The possible effect of transfer ribonucleic acid (tRNA) concentrations on codons decoding time is a fundamental biomedical research question; however, due to a large number of variables affecting this process and the non-direct relation between them, a conclusive answer to this question has eluded so far researchers in the field. In this study, we perform a novel analysis of the ribosome profiling data of four organisms which enables ranking the decoding times of different codons while filtering translational phenomena such as experimental biases, extreme ribosomal pauses and ribosome traffic jams. Based on this filtering, we show for the first time that there is a significant correlation between tRNA concentrations and the codons estimated decoding time both in prokaryotes and in eukaryotes in natural conditions (-0.38 to -0.66, all P values lower than 0.006); in addition, we show that when considering tRNA concentrations, codons decoding times are not correlated with aminoacyl-tRNA levels. The reported results support the conjecture that translation efficiency is directly influenced by the tRNA levels in the cell. Thus, they should help to understand the evolution of synonymous aspects of coding sequences via the adaptation of their codons to the tRNA pool.
Hadas Zur, Tamir Tuller. New universal rules of eukaryotic translation initiation fidelity. PLoS Comput Biol. 9(7), 2013.
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We uncovering a new set of initiation rules for improving the cost of translation and its efficiency. Analyzing dozens of eukaryotic genomes, we find that in all frames there is a universal trend of selection for low numbers of ATG codons; specifically, 16-27 codons upstream, but also 5-11 codons downstream of the START ATG, include less ATG codons than expected. We further suggest that there is selection for anti optimal ATG contexts in the vicinity of the START ATG. Thus, the efficiency and fidelity of translation initiation is encoded in the 5'UTR as required by the scanning model, but also at the beginning of the ORF. The observed nt patterns suggest that in all the analyzed organisms the pre-initiation complex often misses the START ATG of the ORF, and may start translation from an alternative initiation start-site. Thus, to prevent the translation of undesired proteins, there is selection for nucleotide sequences with low affinity to the pre-initiation complex near the beginning of the ORF.
Hadas Zur, Tamir Tuller. Strong association between mRNA folding strength and protein abundance in S. cerevisiae. EMBO Rep. 13(3), 2012.
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One of the open questions in regulatory genomics is how the efficiency of gene translation is encoded in the coding sequence. Here we analyse recently generated measurements of folding energy in Saccharomyces cerevisiae, showing that genes with high protein abundance tend to have strong mRNA folding (mF; R=0.68). mF strength also strongly correlates with ribosomal density and mRNA levels, suggesting that this relation at least partially pertains to the efficiency of translation elongation, presumably by preventing aggregation of mRNA molecules.
Shlomi Reuveni*, Isaac Meilijson, Martin Kupiec, Eytan Ruppin, Tamir Tuller*. Genome-scale analysis of translation elongation with a ribosome flow model. PLoS Comput Biol. 2011 Sep;7(9):e1002127.
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We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative ('non-physical') approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs.
Tamir Tuller, Shimshi Atar, Eytan Ruppin, Michael Gurevich, and Anat Achiron. Global map of physical interactions among differentially expressed genes in multiple sclerosis relapses and remissions. Hum Mol Genet. 2011 Sep 15;20(18):3606-19.
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In this study, we performed a high-resolution systems biology analysis of gene expression and physical interactions in MS relapse and remission. To this end, we integrated 164 large-scale measurements of gene expression in peripheral blood mononuclear cells of MS patients in relapse or remission and healthy subjects, with large-scale information about the physical interactions between these genes obtained from public databases. These data were analyzed with a variety of computational methods. We find that there is a clear and significant global network-level signal that is related to the changes in gene expression of MS patients in comparison to healthy subjects. However, despite the clear differences in the clinical symptoms of MS patients in relapse versus remission, the network level signal is weaker when comparing patients in these two stages of the disease.
Tamir Tuller*, Asaf Carmi*, Kalin Vestsigain, Sivan Navon, Yuval Dorfan, John Zaborske, Tao Pan, Orna Dahan, Itay Furman, Yitzhak Pilpel. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell. 2010 Apr 16;141(2):344-54.
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We identify a universally conserved profile of translation efficiency along mRNAs based on adaptation between coding sequences and the tRNA pool. In this profile, the first ~30-50 codons are, on average, translated with a low efficiency. It serves as a late stage of translation initiation, forming an optimal and robust means to reduce ribosomal traffic jams, thus minimizing the cost of protein expression.
Tamir Tuller, Yedael Y. Waldman, Martin Kupiec, Eytan Ruppin. Translation Efficiency Is Determined By Both Codon Bias and Folding Energy.Proc. Natl. Acad. Sci. USA. 2010 Feb 2.
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We find a strong correlation between the genomic profiles of ribosomal density and genomic profiles of folding energy across mRNA, suggesting that stronger mRNA folding slows down the ribosomes. We find that selection forces act to decrease the folding energy at the beginning of genes to improve ribosome binding.
Tamir Tuller*, Hadas Birin*, Uri Gophna, Martin Kupiec and Eytan Ruppin. Reconstructing Ancestral Gene content by Co-Evolution. Genome Research. 2009 Nov 30.
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Inferring the gene content of ancestral genomes is a fundamental challenge in molecular evolution. Due to the statistical nature of this problem, ancestral genomes inferred by the maximum likelihood (ML) or the maximum-parsimony (MP) methods are prone to considerable error rates. We describe a simple appraoch for improving the reconstruction of ancestral gene content based on co-evolutionary relations between genes. Our appraoch yields a marked decrease in error rate compared with ML or MP.
Yedael Y. Waldman*, Tamir Tuller*, Roded Sharan and Eytan Ruppin. TP53 cancerous mutations exhibit selection for translation efficiency. Cancer Research. 2009 Nov 15;69(22):8807-13.
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The tumor suppressor gene TP53 is known to be a key regulator in cancer. Point mutations in TP53 not only disrupt its function but also possess gain-of-function and dominant negative effects on wild-type copies, thus making the mutated gene an oncogene. Our analysis suggest that TP53 mutations may be under selection for increasing the overall translation efficiency of defected TP53 in cancerous cells.
Tamir Tuller, Martin Kupiec, Eytan Ruppin. Co-evolutionary Networks of Genes and Cellular Processes Across Fungal Species. Genome Biol. 2009;10(5):R48.
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We present two new measures, the 'relative evolutionary rate pattern' (rERP), which records the relative evolutionary rates of conserved genes across the different branches of a species' phylogenetic tree, and the 'copy number pattern' (CNP) which quantifies the rate of gene loss of less conserved genes. We show that genes with similar cellular functions tend to have similar pattern of evolution.
Guohua Jin, Luay Nakhleh, Sagi Snir, and Tamir Tuller. Maximum likelihood of phylogenetic networks. Bioinformatics 22 (21): 2604-2611, 2006.
(supplementary meterial ).
Horizontal gene transfer (HGT) is believed to be ubiquitous among bacteria, and plays a major role in their genome diversification as well as their ability to develop resistance to antibiotics. We provide a new HGT-oriented likelihood framework for many problems that involve phylogeny-based HGT detection and reconstruction.
Benny Chor, Tamir Tuller. Finding a maximum likelihood tree is hard. J. ACM 53(5):722-744, 2006.
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We solve a 20 years old problem proving that one of the most popular optimality criterion for selecting evolutionary trees (Maximum Likelihood) is NP-hard.
All Publications
Journals
193. Tal Gutman and Tamir Tuller. Evidence that MDR1 Variants affect cancer cells via their effect on mRNA folding. PLoS CB. 2024.
192. Shaked Bergman and Tamir Tuller. Codon usage and expression-based features significantly improve prediction of CRISPR efficiency. npj systems biology and applications. 2024.
191. Tali Haviv Reingewertz, Zohar Zafrir, Tamir Tuller, and Amnon Horovitz. Synonymous and non-synonymous codon substitutions can alleviate dependence on GroEL for folding. To appear in Protein Science 2024.
190. Shaked Bergman and Tamir Tuller. Strong association between genomic 3D structure and CRISPR cleavage efficiency. PLoS-CB. 2024.
189. Sharon Bader and Tamir Tuller. Advanced Computational Predictive Models of miRNA-mRNA Interaction Efficiency. To appear in Computational and Structural Biotechnology Journal 2024.
188. Nicolas Lynn and Tamir Tuller. Detecting and understanding meaningful cancerous mutations based on computational models of mRNA splicing. npj systems biology and applications. 2023.
187. Matan Arbel, Itamar Menuhin-Gruman, Hader Benshoshan, Doron Naki, Shaked Bergman, Yarin Udi ,and Tamir Tuller. The causes for genomic instability and how to try and reduce them through rational design of synthetic DNA. Methods in Molecular Biology. 2023.
186. Matan Arbel-Groissman, Itamar Menuhin-Gruman, Doron Naki, Shaked Bergman, and Tamir Tuller. Fighting the battle against evolution: designing genetically modified organisms for evolutionary stability. To appear in Trends in Biotechnology. 2023.
185. Haoran Duan, Siqiong Zhang, Yoram Zarai, Rupert Ollinger, Yanmeng Wu, Li Sun, Cheng Hu, Yaohui He, Guiyou Tian, Roland Rad, Xiangquan Kong, Yabin Cheng, Tamir Tuller, Dieter A. Wolf. eIF3 mRNA selectivity profiling reveals eIF3k as a cancer-relevant regulator of ribosome content. To appear in EMBO J. 2023.
184. Modi Safra, Zvi Tamari, Pazit Polak, Shachaf Shiber, Moshe Matan, Hani Karameh, Yigal Helviz, Adva Barda, Vered Yahalom, Avi Peretz, Eli Ben-Chetrit, Baruch Brenner, Tamir Tuller, Meital Gal Tanamy, Gur Yaari. Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity. Frontiers in Immunology. 2023
183. Rami Katz, Elad Attias, Tamir Tuller& and Michael Margaliot&. Translation in the cell under fierce competition for shared resources: a mathematical model. To appear in Journal of the Royal Society Interface 2022
182. Efrat Leopold, Tamir Tuller&, Mickey Scheinowitz&. A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test. To appear in PLoS-1 2022
181. Liyam Chitayat Levi, Ido Rippin. Moran Ben Tulila, Rotem Galron, Tamir Tuller. Modulating gene expression within a microbiome based on computational models. To appear in Biology 2022
180. Stav Carmel Ezra, Tamir Tuller. Modeling the effect of rRNA-mRNA interactions and mRNA folding on mRNA translation in chloroplasts. To appear in Computational and Structural Biotechnology Journal. 2022.
179. Arup Panda, Tamir Tuller. Determinants of associations between codon and amino acid usage patterns of microbial communities and the environment inferred based on a cross-biome metagenomic analysis. To appear in npj biofilms and microbiomes 2022
178. MPEPE, a predictive approach to improve protein expression in E. coli based on deep learning. Zundan Ding, Feifei Guan, Guoshun Xu, Yuchen Wang, Yaru Yan, Wei Zhang, Ningfeng Wu, Bin Yao, Huoqing Huang*, Tamir Tuller*, Jian Tian* Computational and Structural Biotechnology Journal. 2022.
177. Tamar Neumann, Tamir Tuller. Modeling the ribosomal small subunit dynamic in S. cerevisiae based on TCP-seq data. To appear in Nucleic Acids Research. 2021.
176. Sophie Vinokour, Tamir Tuller. Determinants of Efficient Modulation of Ribosomal Traffic Jams. To appear in Computational and Structural Biotechnology Journal. 2021.
175. Itamar Menuhin-Gruman, Matan Arbel, Niv Amitay, Karin Sionov, Doron Naki, Itai Katzir, Omer Edgar, Shaked Bergman, Tamir Tuller. Evolutionary Stability Optimizer (ESO): A Novel Approach to Identify and Avoid Mutational Hotspots in DNA Sequences while maintaining High Expression Levels. To appear in ACS Synthetic Biology. 2021. Cover figure:
174. David Shallom, Danny Naiger, Shlomo Weiss, Tamir Tuller. Accelerating whole cell simulations of mRNA translation using a dedicated hardware. To appear in ACS Synthetic Biology. 2021.
173. Rami Zakh, Alexander Churkin, Franziska Totzeck, Marina Parr, Tamir Tuller, Ohad Etzion, Harel Dahari, Dmitrij Frishman, and Danny Barash. A mathematical analysis of HDV genotypes: from molecules to cells. To appear in Mathematics. 2021.
172. Shir Bahiri, Rachel Cohen-Kupiec, Dana Yacobi, Larissa Fine, Boaz Apt, Alon Diament, and Tamir Tuller. Prokaryotic rRNA-mRNA interactions are involved in all translation steps and shape bacterial transcripts. To appear in RNA biol. 2021.
171. Tal Gutman, Guy Goren, Omri Efroni, Tamir Tuller. Estimating the Predictive Power of Silent Mutations on Cancer Classification and Prognosis. To appear in nature npj Genome Medicine. 2021.
170. Shir Bahiri and Tamir Tuller. Codon-based indexes for modeling gene expression and transcript evolution. To appear in Computational and Structural Biotechnology Journal. 2021.
169. Alexander Churkin, Franziska Totzeck, Marina Parr, Tamir Tuller, Dmitrij Frishman, and Danny Barash. A mathematical analysis of the RNA structure in viruses (RNASIV) database. To appear in Mathematics. 2021.
168.Michael Margaliot, Wasim Huleihel, Tamir Tuller. Variability in mRNA Translation: A Random Matrix Theory Approach . To appear in Scientific Reports 2021
167. Hadas Zur, Rachel Cohen-Kupiec, Sophie Vinokour, Tamir Tuller. Algorithms for Ribosome Traffic Engineering and their Potential in Improving Host Cells' Titer and Growth Rate. To appear in Scientific Reports 2020
166. Shaked Bergman , Alon Diamant, Tamir Tuller. New computational model for miRNA-mediated repression reveals novel regulatory roles of miRNA bindings inside the coding region. To appear in Bioinformatics 2020
165. Michal Perach#, Zohar Zafrir#, Tamir Tuller*, Oded Lewinson*. Identifying evolutionarily selected slow codons in gene families from the entire genomes of E. coli and B. subtilis. To appear in RNA Biology 2020
164. Lia Baron*, Shimshi Atar*, Hadas Zur*, Modi Roopin*, Eli Goz, Tamir Tuller. Computational based design, generation, and tracking of 500 synthetic silent variants of Porcine circovirus reveals the relations between silent genomic information and viral fitness. To appear in Scientific Reports. 2020.
163. Lena Danielli, Ximing Li, Tamir Tuller, Ramiz Daniel. Quantifying distribution of protein oligomerization degree reflects cellular information capacity. To appear in Scientific Reports. 2020.
162. Yonatan Chemla#, Michael Peeri#, Mathias Heltberg, Mogens HOgh Jensen, Tamir Tuller*, Lital Alfonta*. mRNA secondary structure stability regulates bacterial translation insulation and re-initiation. To appear in Nature communication. 2020.
161. Yoram Zarai, Zohar Zafrir, Bunpote Siridechadilok, Amporn Suphatrakul, Modi Ruppin, Justin Julander and Tamir Tuller. Evolutionary Selection against Short Nucleotide Sequences in Viruses and their Related Hosts. To appear in DNA Research. 2020.
160. Doron Levin, Tamir Tuller. Whole cell biophysical modeling of codon-tRNA competition reveals novel insights related to translation dynamics. To appear in PLoS Compt. Biol. 2020.
159. Shir Bahiri-Elitzur and Tamir Tuller. Computational discovery and modeling of novel gene expression rules encoded in the mRNA. To Appear in Biochemical Society Transactions. 2020. Cover figure:
158. Maya Galili, Tamir Tuller. CSN: Unsupervised Approach for Inferring Biological Networks based on the Genome Alone. To Appear in BMC Bioinformatics. 2020.
157. Noam Shahar, Shira Landman, Iddo Weiner*, Tamar Elman, Eyal Dafni, Yael Feldman, Tamir Tuller, Iftach Yacoby. The integration of multiple nuclear-encoded transgenes in the green alga Chlamydomonas reinhardtii results in higher transcription levels. To Appear in Frontiers in Plant Science. 2020.
156. Michael Peeri, Tamir Tuller. High resolution modeling of the selection on local mRNA folding strength in coding sequences across the tree of life. To appear in Genome Biology. 2020.
155. Shaked Bergman, Tamir Tuller. Widespread non-modular overlapping codes in the coding regions. To appear in Physical Biology. 2019.
154. Iddo Weiner, Noam Shahar, Pini Marcu, Iftach Yacoby, Tamir Tuller. Solving the riddle of the evolution of Shine-Dalgarno based translation in chloroplasts. To appear in Molecular Biology and Evolution. 2019.
153. Iddo Weiner#, Yael Feldman#, Noam Shahar, Iftach Yacoby, Tamir Tuller. CSO -- a sequence optimization software for engineering chloroplast expression in Chlamydomonas reinhardtii. To appear in Algal Research. 2019.
152. Renana Sabi, Tamir Tuller. Novel Insights into Gene Expression Regulation during Meiosis Revealed by Translation Elongation Dynamics. To appear in Nature Systems Biology and Applications. 2019.
151. Noam Shahar#, Iddo Weiner#, Lior Stotsky, Tamir Tuller*, Iftach Yacoby*. Prediction and large-scale analysis of primary operons in plastids reveals unique genetic features in the evolution of chloroplasts. To appear in Nucleic Acids Res. 2019.
150. Arup Panda, Tamir Tuller. Exploring the Evidences of Local Selection in Naturally Occurring Intrinsically Disordered Proteins. to appear in Genomics Proteomics and Bioinformatics. 2019
149. A. Diament, I. Weiner, N. Shahar, S. Landman, Y. Feldman, S. Atar, M. Avitan, S. Schweitzer, I. Yacoby, T. Tuller. ChimeraUGEM: unsupervised gene expression modeling in any given organism. to appear in Bioinformatics. 2019
148. T. Tuller, A. Diament, A. Yahalom, A. Zemach, S. Atar, D.A. Chamovitz . The COP9 signalosome influences the epigenetic landscape of Arabidopsis thaliana. to appear in Bioinformatics. 2018
147. Eyal Dafni, Iddo Weiner, Tamir Tuller*, Iftach Yacoby* . Image processing algorithm for high throughput quantification of colony luminescence. to appear in mSphere. 2018
146. R. Sabi and T. Tuller. Modeling and measuring intracellular competition for finite resources during gene expression. To appear in J. R. Soc. Interface. 2018.
145. I. Nanikashvili, Y. Zarai, A. Ovseevich, T. Tuller* and M. Margaliot. Networks of ribosome flow models for modeling and analyzing intracellular traffic. To appear in Scientific Rep. 2018.
144. E. Leopold, T. Tuller and M. Scheinowitz. Prediction of the Wingate anaerobic mechanical power outputs from a maximal incremental cardiopulmonary exercise stress test using machine-learning approach. To appear in PLoS-1. 2018.
143. A. Panda, M. Drancourt, T. Tuller and P. Pontarotti. Genome wide analysis of horizontally acquired genes in the genus Mycobacterium. To appear in Scientific Rep. 2018.
142. D. Levin, T. Tuller. Genome-Scale Analysis of Perturbations in Translation Elongation Based on a Computational Model. To appear in Scientific Rep. 2018.
141. I. Weiner, N. Shahar, Y. Feldman, S. Landman, Y. Milrad, O. Ben-Zvi, M. Avitan, E. Dafni, S. Schweitzer, H. Eilenberg, S. Atar, A. Diament, T. Tuller*, I. Yacoby*. Overcoming the expression barrier of the ferredoxin-hydrogenase chimera in Chlamydomonas reinhardtii supports a linear increment in photosynthetic hydrogen output. To appear in Algal Research. 2018.
140. E. Goz, Z. Zafrir, T. Tuller. Universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code. To appear in Bioinformatics. 2018.
139. A. Diament and T. Tuller. Modeling three-dimensional genomic organization in evolution and pathogenesis. To appear in Seminars in Cell and Developmental Biology. 2018.
138. Y. Zarai and T. Tuller. Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources. To appear in PLoS Comput Biol. 2018.
137. E. Goz*, Y. Tsalenchuck*, R. Oren Benaroya, Z. Zafrir, S. Atar, T. Altman , J. Julander, T. Tuller. Generation of a synthetic Dengue virus and comparison to the wild type strains. To appear in BMC Bioinformatics. 2018.
136. I. Weiner#, S. Atar#, S. Schweitzer#, H. Eilenberg, Y. Feldman, M. Avitan, A. Danon, T. Tuller*, I. Yacoby*. Enhancing heterologous expression in Chlamydomonas reinhardtii by transcript sequence optimization. To appear in The plant journal. 2017.
135. A. Diament*, A. Feldman*, E. Schochet, M. Kupiec, Y. Arava, and T. Tuller. The extent of ribosome queuing in budding yeast. To appear in PLoS Comput Biol. 2017.
134. G. Shaham and T. Tuller. Genome scale analysis of E. coli with a comprehensive prokaryotic sequence-based biophysical model of translation initiation and elongation. To appear in DNA Research. 2017.
133. E. Cohen* Z. Zafrir* and T. Tuller. A Code for Transcription Elongation Speed. To appear in RNA Biology. 2017.
132. O. Mioduser*, E. Goz*, T. Tuller. Significant differences in terms of codon usage bias between bacteriophage early and late genes: A comparative genomics analysis. To appear in BMC Genomics. 2017.
131. Y. Zarai, M. Margaliot and T. Tuller. Ribosome Flow Model with Extended Objects. To appear in J. R. Soc. Interface. 2017.
130. Y. Zarai, M. Margaliot and T. Tuller. A Deterministic Mathematical Model for Bidirectional Excluded Flow with Langmuir Kinetics . To appear in PLoS-1. 2017.
129. A. Diament and T. Tuller. Tracking the evolution of 3D gene organization demonstrates its connection to phenotypic divergence. To appear in Nucleic Acids Res. 2017.
128. R. Sabi and T. Tuller. Nascent peptide-mediated ribosome stalling and its evolution. To appear in RNA. 2017.
127. Y. Zarai, M. Margaliot, E.D. Sontag, T. Tuller. Controlling mRNA Translation. To appear IEEE/ACM Trans Comput Biol Bioinform. 2017.
126. Y. Zarai, M. Margaliot, T. Tuller. Optimal Down Regulation of mRNA Translation. To appear in Scientific reports. 2016.
125. E. Goz*, O. Mioduser*, A. Diament, T. Tuller. Evidence of Translation Efficiency Adaptation of the Coding Regions of the Bacteriophage Lambda. To appear in DNA Research. 2016.
124. Z. Zafrir and T. Tuller. Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression prediction. To appear in BMC Bioinformatics. 2016.
123. Y. Zarai, M. Margaliot, T. Tuller. On the Ribosomal Density that Maximizes Protein Translation Rate. To appear in PLoS-1. 2016.
122. R. Sabi, R. Volvovich, T. Tuller. stAIcalc: tRNA Adaptation Index Calculator based on Species-Specific weights. To appear in Bioinformatics. 2016.
121. H. Zur, T. Tuller. Biophysical Modeling and Understanding of the Dynamics of mRNA Translation and its Evolution. To appear in Nucleic Acids Res. 2016.
120. E. Goz, T. Tuller. Evidence of a direct evolutionary selection for strong folding and mutational robustness within HIV coding regions. To appear in Journal of Computational Biology. 2016.
119. A. Diament, T. Tuller. Estimation of ribosome profiling performance and reproducibility at various levels of resolution. Biology Direct.2016, 11:24.
118. A. Raveh, M. Margaliot, Eduardo D. Sontag, and T. Tuller. A Model for Competition for Ribosomes in the Cell. To appear in Journal of the Royal Society Interface. 2016.
117. Z. Zafrir, H. Zur, T. Tuller. Selection for Reduced Intronic Translation Costs in Fungi. To appear in DNA Research. 2016.
116. H. Zur*, R. Aviner* and T. Tuller. Complementary Post Transcriptional Regulatory Information is Detected by PUNCH-P and Ribosome Profiling. To appear in Scientific reports. 2016.
115. R. Sabi, and T. Tuller. A comparative genomics study on the effect of individual amino acids on ribosome stalling. BMC Genomics. 2015 Oct 2;16 Suppl 10:S5 (special issue of RECOMB-CG 2015).
114. E. Goz, T. Tuller. Widespread Signatures of Local mRNA Folding Structure Selection in Four Dengue Serotypes. BMC Genomics. 2015 Oct 2;16 Suppl 10:S4(special issue of RECOMB-CG 2015)
113. E. D. Sontag, M. Margaliot and T. Tuller. Contraction After Small Transients. To appear in Automatica 2015.
112. T. Ben-Yehezkel*, S. Atar*, H. Zur, A. Diament, E. Goz, T. Marx, R. Cohen A. Dana, A. Feldman, E. Shapiro, T. Tuller. Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants. RNA Biol. 2015;12(9):972-84.
111. Z. Zafrir and T. Tuller. Nucleotide Sequences Composition Adjacent to Intronic Splice Sites Improves Splicing Efficiency via its Effect on pre-mRNA Local Folding in Fungi. RNA. 2015 Oct;21(10):1704-18.
110. A. Diament and T. Tuller. Improving 3D Genome Reconstructions Using Orthologous and Functional Constraints. PLoS Comput Biol. 2015 May 22;11(5):e1004298.
109. A. Raveh, Y. Zarai, M. Margaliot, and T. Tuller. Ribosome Flow Model on a Ring. IEEE/ACM Trans Comput Biol Bioinform. 2015 Nov-Dec;12(6):1429-39.
108. G. Poker, M. Margaliot, T. Tuller. Sensitivity of mRNA Translation. Sci Rep. 2015 Aug 4;5:12795.
107. T. Tuller, H. Zur. Multiple Roles of the Coding Sequence 5' End in Gene Expression Regulation. Nucleic Acids Res. 2015 Jan;43(1):13-28.
106. H. Zur and T. Tuller. Exploiting Hidden Information Interleaved in the Redundancy of the Genetic Code without Prior Knowledge. Bioinformatics. 2015 Apr 15;31(8):1161-8.
105. A. Dana and T. Tuller. The Mean of the typical decoding rates: a new translation efficiency index based on ribosome analysis data. G3: GENES, GENOMES, GENETICS. 2014. 2014 Dec 1;5(1):73-80.
104. A. Diament, R. Pinter, and T. Tuller. Three Dimensional Genomic Organization of Eukaryotic Genes Is Strongly Correlated With Their Codon Usage, Expression And Function. Nature communications. 2014 Dec 16;5:5876.
103. G. Poker, Y. Zarai, M. Margaliot, and T. Tuller. A Convex Approach for Optimizing Protein. Translation Rate in the Inhomogeneous Ribosome Flow Model. J R Soc Interface. 2014 Nov 6;11(100):20140713.
102. A. Dana and T. Tuller. Properties and Determinants of Codons Decoding Time Distributions. BMC Genomics. 2014;15 Suppl 6:S13 (special issue of RECOMB-CG 2014).
101. R. Singer, S. Atar, O. Atias, E. Oron, D. Segal, J. Hirsch, T. Tuller, A. Orian, D. Chamovitz. Drosophila COP9 Signalosome Subunit 7 interacts with multiple genomic loci to regulate development. Nucleic Acids Res. 2014 Sep;42(15):9761-70.
100. A. Dana and T. Tuller. The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res. 2014 Aug;42(14):9171-81.
99. G. Pelchovich, N. Sigal, A. Dana, T. Tuller, I. G. Bravo, and U. Gophna. Ribosomal mutations affecting the translation of genes that use non-optimal codons. FEBS J. 2014 Aug;281(16):3701-18.
98. Y. Zarai, M. Margaliot and T. Tuller. Maximizing protein translation rate in the homogeneous ribosome flow model. to appear in IEEE/ACM Trans Comput Biol Bioinform. 2014.
97. R. Sabi and T. Tuller. Modeling the efficiency of codon-tRNA interactions based on codon usage bias. DNA Res. 2014 Oct;21(5):511-26.
96. I. Yofe*, Z. Zafrir* , R. Blau, M. Schuldiner*, T. Tuller *, E. Shapiro*, T. Ben Yehezkel*. Accurate, model-based tuning of synthetic gene expression using introns in S.cerevisiae. PLoS Genet. 2014 Jun 26;10(6):e1004407.
95. M. Margaliot, E. D. Sontag and T. Tuller. Entrainment to Periodic Initiation and Transition Rates in the Ribosome Flow Model. PLoS One. 2014 May 6;9(5):e96039.
94. S. Edri, T. Tuller. Quantifying the Effect of Ribosomal Density on mRNA Stability. PLoS One. 2014 Jul 14;9(7):e102308.
93. G. Shaham T. Tuller. Most Associations between Transcript Features Gene Expression are Monotone. Mol Biosyst. 2014 Jun;10(6):1426-40.
92. G. Daras, S. Rigas, D. Tsitsekian, H. Zur, T. Tuller, and P. Hatzopoulos. Alternative transcription initiation and the AUG context configuration control dual-organellar targeting and functional competence of Arabidopsis Lon1 protease. Mol Plant. 2014 Jun;7(6):989-1005.
91. S. Edri, E. Gazit, E. Cohen,T. Tuller. The RNA Polymerase Flow Model of Gene Transcription. IEEE Trans Biomed Circuits Syst. 2014 Feb;8(1):54-64.
90. R. Norel, E. Bilal, N. Conrad-Chemineau, R. Bonneau, A. de la Fuente, I. Jurisica, D. Marbach, P. Meyer, J. J. Rice, T. Tuller and G. Stolovitzky.Sbv IMPROVER Diagnostics Signature Challenge Scoring Strategies. Systems Biomedicine 1:4, 1–9; October/November/December 2013
89. Y. Zarai, M. Margaliot and T. Tuller. Explicit expression for the steady state translation rate in the infinite-dimensional homogeneous ribosome flow model. IEEE/ACM Trans Comput Biol Bioinform. 2013 Sep-Oct;10(5):1322-8
88. H. Zur and T. Tuller. Transcript Features Alone Enable Accurate Prediction and Understanding of Gene Expression Evolution in S. cerevisiae. BMC Bioinformatics. 2013;14 Suppl 15:S1 (special issue of RECOMB-CG 2013).
87. H. Zur and T. Tuller. New Universal Rules of Eukaryotic Translation Initiation Fidelity. PLoS Comput Biol. 2013 Jul;9(7):e1003136.
86. T Tuller. Challenges and Obstacles Related to Solving the Codon Bias Riddles. Biochem Soc Trans. 2014 Feb;42(1):155-9.
85. M. Margaliot and T. Tuller . Ribosome Flow Model with Positive Feedback. J R Soc Interface. 2013 May 29;10(85):20130267.
84. T. Ben-Yehezkel*, H. Zur*, T. Marx, E. Shapiro, T Tuller. Mapping the translation initiation landscape of an S. cerevisiae gene using fluorescent proteinse. Genomics. 2013 May 28. doi:pii: S0888-7543(13)00110-9.
83. T. Tuller, S. Atar, E. Ruppin, M. Gurevich, A. Achiron. Common and Specific Signatures of Gene Expression and Protein-Protein Interactions in Autoimmune Diseases. Gene and Immunity. 2012
82. T. Tuller. The effect of dysregulation of tRNA genes and translation efficiency mutations in cancer and neurodegeneration. Front Genet. 2012;3:201.
81. A. Dana and T. Tuller. Determinants of translation elongation speed and ribosomal profiling biases in mouse embryonic stem cells. PLoS Comput Biol. 2012;8(11):e1002755.
80. MN. Lurie-Weinberger, M. Peeri, T. Tuller, U. Gophna. Extensive inter-domain lateral gene transfer in the evolution of the human commensal Methanosphaera stadtmanae. Front Genet. 2012;3:182.
79. M. Margaliot and T. Tuller. On the Steady-State Distribution in the Homogeneous Ribosome Flow Model. IEEE/ACM Trans Comput Biol Bioinform. 2012 Nov-Dec;9(6):1724-36.
78. H. Zur and T. Tuller. RFMapp: Ribosome Flow Model Application. Bioinformatics. 2012 Jun 15;28(12):1663-4.
77. M. Margaliot and T. Tuller. Stability Analysis of the Ribosome Flow Model. IEEE/ACM Trans Comput Biol Bioinform. 2012 Sep-Oct;9(5):1545-52.
76. S. Mahlab, T. Tuller*, M. Linial*. Conservation of the relative tRNA compositions in healthy and cancerous tissues. RNA. 2012 Apr;18(4)640-52
75. H. Zur and T. Tuller. Strong association between mRNA folding strength and protein abundance in S. cerevisiae. EMBO-Rep. 2012 Mr 1; 13(3):272-7.
74. G. Lenz, A. Doron-Faigenboim, E. Z. Ron, T. Tuller, and U. Gophna. Sequence features of E. coli mRNAs affect their degradation. PLoS-one. 2011;6(12):e28544.
73. T. Tuller*, I. Veksler*, N. Gazit, M. Kupiec, E. Ruppin, M. Ziv . Composite Effects of the Coding Sequences Determinants on the Speed and Density of Ribosomes. Genome Biol. 2011 Nov 3; 12(11):R110.
72. A. Dana and T. Tuller. Efficient Manipulations of Synonymous Mutations for Controlling Translation Rate -- an Analytical Approach. J. Comput. Biol. (special issue of RECOMB-RG 2011). 2012 Feb; 19(2):200-31
71. H. Birin and T. Tuller. Efficient Algorithms for Reconstructing Genomic Sequences by Co-Evolution. BMC Bioinformatics (special issue of RECOMB-CG 2011). 2001 Oct 5; 12 Suppl 9:12.
70. Y. Y. Waldman, T. Tuller*, A. Keinan*, and E. Ruppin*. Selection for translation efficiency in human SNPs. Genome Biol. Evol. 2011;3:749-61.
69. T. Tuller, S. Atar, E. Ruppin, M. Gurevich, A. Achiron. Global Map of Physical Interactions among Differentially Expressed Genes in Multiple Sclerosis Relapses and Remissions. Hum. Mol. Gen. 2011 Sep 15;20(18):3606-19.
68. S. Reuveni*, I. Meilijson, M. Kupiec, E. Ruppin and T. Tuller*. Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model.PLoS Comput. Biol. Sep 2011;7(9):e1002127.
67. X. Zhang*, M. Kupiec, U. Gophna and T. Tuller*. Analysis of Co-evolving Gene Families Using Evolutionarily Reciprocal Orthologous Modules. Genome Biol. Evol. 2011;3:413-23.
66. T. Tuller. Codon bias, tRNA pools, and horizontal gene transfer. Mobile Genetic Elements 2011; 1(1):75 - 77.
65. Yizhak, T. Tuller, P. Bala'zs, E. Ruppin. Metabolic modeling of endosymbiont genome reduction on a temporal scale. Nature Mol. Sys. Biol. 2011 Mar 29;7:479.
64. Tamir Tuller, Yana Gir, Yael Sella, Avi Kreimer, Shiri Freilich, Martin Kupiec, Uri Gophna, Eytan Ruppin. Asso ciations Between Translation Efficiency and Horizontal Gene Transfer Within Microbial Communities. Nucleic Acids Res. 2011 Feb 22 [Epub ahead of print].
63. Tamir Tuller, Elchanan Mossel. Co-evolution is Incompatible with the Markov Assumption in Phylogenetics. IEEE/ACM Trans Comput Biol Bioinform. 2010 Nov 24. [Epub ahead of print].
62. Tamir Tuller*, Hadas Birin*, Martin Kupiec and Eytan Ruppin. Reconstructing Ancestral Genomic Sequences by Co-Evolution: Formal Definitions, Computational Issues, and Biological Examples. JCB 2010 (RECOMB-CG 2009 special issue).
61. Michael Gurevich, ,Tali Gritzman, Rotem Or-bach, Tamir Tuller,.Ana Feldman, Anat Achiron. Laquinimod suppress antigen presentation in relapsing-remitting multiple sclerosis: In-vitro high-throughput gene expression study. J Neuroimmunol. 2010 Apr 15;221(1-2):87-94.
60. Tamir Tuller*, Asaf Carmi*, Kalin Vestsigain, Sivan Navon, Yuval Dorfan, John Zaborske, Tao Pan, Orna Dahan, Itay Furman, Yitzhak Pilpel. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell. 2010 Apr 16;141(2):344-54.
59. Yedael Y. Waldman*, Tamir Tuller*, Tomer Shlomi, Roded Sharan, Eytan Ruppin. Translation efficiency in humans: tissue specificity, global optimization and differences between developmental stages. Nucleic Acid Res. 2010 Jan 21 [Epub ahead of print].
58. Tamir Tuller, Yedael Y. Waldman, Martin Kupiec, Eytan Ruppin. Translation Efficiency Is Determined By Both Codon Bias and Folding Energy.Proc. Natl. Acad. Sci. USA. 2010 Feb 2 [Epub ahead of print].
57. Tamir Tuller*, Yifat Felder*, Martin Kupiec. Discovering Local Patterns of Co - Evolution: Computational Aspects and Biological Examples.BMC Bioinformatics. 2010 Jan 22; 11(1). [Epub ahead of print].
56. Adi Mano, Tamir Tuller, Oded Beja, Ron Y. Pinter. Comparative Classification of Species and the Study of Pathway Evolution based on the Alignment of Metabolic Pathways. BMC Bioinformatics. 2010, 11(Suppl 1):S38 (APBC 2010 special issue).
55. Tamir Tuller*, Hadas Birin*, Uri Gophna, Martin Kupiec and Eytan Ruppin. Reconstructing Ancestral Gene content by Co-Evolution. Genome Research. 2009 Nov 30. [Epub ahead of print].
54. Yedael Y. Waldman*, Tamir Tuller*, Roded Sharan and Eytan Ruppin. TP53 cancerous mutations exhibit selection for translation efficiency. Cancer Research. 2009 Nov 15;69(22):8807-13.
53. Tamir Tuller, Martin Kupiec, Eytan Ruppin. Properties of untranslated regions of the S. cerevisiae genome. BMC Genomics. 2009 Aug 22;10:391.
52. Michael Gurevich*, Tamir Tuller*, Udi Rubinstein, Rotem Or-Bach and Anat Achiron. Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells. BMC Med. Genomics. 2009 Jul 22;2:46..
51. Tamir Tuller, Martin Kupiec, Eytan Ruppin. Co-evolutionary Networks of Genes and Cellular Processes Across Fungal Species. Genome Biol. 2009;10(5):R48.
50. Mathilda Mandel, Anat Achiron, Tamir Tuller, Tilda Barliya, Gideon Rechavi, Ninette Amariglio, Ron Loewenthal, Gad Lavie. Clone clusters in autoreactive T-cell lines from probable multiple sclerosis patients from disease characteristic signature. Immunology. 2009 Oct;128(2):287-300.
49. Sagi Snir and Tamir Tuller. The NET-HMM approach: Phylogenetic Network Inference by Combining Maximum Likelihood and Hidden Markov Models. J. Bioinform. Comput. Biol. 2009 Aug;7(4):625-44.
48. Tamir Tuller, Udi Rubinstein, Dani Bar, Michael Gurevitch, Eytan Ruppin and Martin Kupiec. Higher-order Genomic Organization of Cellular Functions in Yeast. J. Comput. Biol. 16(2):1-14, 2009 (RECOMB-SB-RG 2008 special issue).
47. Guohua Jin, Luay Nakhleh, Sagi Snir, and Tamir Tuller. Parsimony Score of Phylogenetic Networks: Hardness Results and a Linear-Time Heuristic. IEEE/ACM Trans. Comput. Biol. Bioinform. 2009 Jul-Sep;6(3):495-505.
46. Tamir Tuller, Martin Kupiec, Eytan Ruppin. Evolutionary Rate and Gene Expression Across Different Brain Regions. Genome Biol. 9(9):R142, 2008.
45. Hadas Birin, Zohar Gal-or, Isaac Elias and Tamir Tuller. Inferring Horizontal Transfers in the Presence of Rearrangements by the Minimum Evolution Criterion. Bioinformatics 2008 24(6):826-32.
44. Tamir Tuller, Martin Kupiec, Eytan Ruppin. Determinants of Protein Abundance and Translation Efficiency in S. cerevisiae. PLoS Comput. Biol. 3(12):e248, 2007.
43. Tamir Tuller, Benny Chor, Nathan Nelson. Forbidden Penta-Peptides. Prot. Sci. 16(10):2251-2259, 2007.
42. Efrat Oron, Tamir Tuller, Li Ling, Nina Rozovsky, Daniel Yekutieli, Daniel Segal, Benny Chor, Bruce Edgar, Sigal Rencus, Daniel Chamovitz. Genomic analysis of COP9 signalosome function in Drosophila melanogaster reveals a role in temporal regulation of gene expression. Mol. Syst. Biol. 3:108, 2007.
41. Isaac Elias and Tamir Tuller. Reconstruction of Ancestral Genomic Sequences Using Likelihood. J. Comput. Biol. 14(2):216-37, 2007.
40. Benny Chor and Tamir Tuller. Biological Networks: Comparison, Conservation, and Evolution via Relative Description Length. J. Comput. Biol. 14(6):617-38, 2007.
39. Guohua Jin, Luay Nakhleh, Sagi Snir, and Tamir Tuller. Efficient Parsimony-based Methods for Phylogenetic Network Reconstruction. Bioinformatics 23 (2):123-128, 2007.
38. Guohua Jin, Luay Nakhleh, Sagi Snir, and Tamir Tuller. Inferring Phylogenetic Networks by the Maximum Parsimony Criterion: A Case Study. Mol. Biol. Evol. 24 (1):324-37, 2007.
37. Guohua Jin, Luay Nakhleh, Sagi Snir, and Tamir Tuller. Maximum likelihood of phylogenetic networks. Bioinformatics 22 (21): 2604-2611, 2006.
36. Benny Chor, Tamir Tuller. Finding a maximum likelihood tree is hard. J. ACM 53(5):722-744, 2006.
35. Igor Ulitsky, David Burstein, Tamir Tuller, Benny Chor. The Average Common Substring Approach to Phylogenomics. Journal of Computational Biology (JCB) 13(2):336-50, 2006.
34. Benny Chor, Tamir Tuller. Maximum Likelihood of Evolutionary Trees: Hardness and Approximation. Bioinformatics 21(Suppl 1):i97-i106, 2005.
Peer-Reviewed Conference Proceedings
33. Tal Gutman and Tamir Tuller. Revisiting the effects of MDR1 polymorphisms using computational approaches. he 21th RECOMB Comparative Genomics Satellite Workshop (RECOMB-CG 2024). Stata Center, MIT. Boston, MA, United States. April 27-28, 2024
32. Alma Davidson, Marina Parr, Franziska Totzeck, Alexander Churkin, Dmitrij Frishman, Danny Barash, Tamir Tuller. Evidence of increased adaptation of Omicron SARS-CoV-2 codon to humans. The 21th RECOMB Comparative Genomics Satellite Workshop (RECOMB-CG 2024). Stata Center, MIT. Boston, MA, United States. April 27-28, 2024
31. L. Chitayat Levi, I. Rippin. M. Ben Tulila, R. Galron, T. Tuller. Using computational synthetic biology tools to modulate gene expression within a microbiome. The 19th RECOMB Comparative Genomics Satellite Workshop (RECOMB-CG 2022). La Jolla, USA, May 20-21, 2022.
30. O. Shami-Schnitzer, Z. Zafir, T. Tuller. Novel Driver Synonymous Mutations in the Coding Regions of GCB Lymphoma Patients Improve the Transcription Levels of BCL2. The 2ND INTERNATIONAL SYMPOSIUM ON MATHEMATICAL AND COMPUTATIONAL ONCOLOGY (ISMCO'20). October 8-9 2020.
29. E. Goz*, Y. Tsalenchuck*, R. O. Benaroya, S. Atar, T. Altman, J. Julander, T. Tuller. Generation and Comparative Genomics of Synthetic Dengue Viruses. To appear in the 15th RECOMB Comparative Genomics Satellite Workshop (RECOMB-CG 2017). Barcelona, Spain, October 4-6 2017.
28. Y. Zarai, M. Margaliot, E.D. Sontag and T. Tuller. Controlling the Ribosome Density Profile in mRNA Translation. the 55rd IEEE Conference on Decision and Control (CDC 2016). Las Vegas, USA on December 12-14, 2016.
27. E.D. Sontag, M. Margaliot and T. Tuller. On Three Generalizations of Contraction. The 53rd IEEE Conference on Decision and Control (CDC 2014). December 15-17, 2014, Los Angeles, CA, USA.
26. Shlomi Reuveni*, Isaac Meilijson, Martin Kupiec, Eytan Ruppin and Tamir Tuller* . Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model. Accepted to RECOMB 2011, Vancouver,BC, March 28-31, 2011.
25. Adi Mano, Tamir Tuller, Oded Beja, Ron Y. Pinter. Comparative Classification of Species and the Study of Pathway Evolution based on the Alignment of Metabolic Pathways. APBC 2010.
24. Tamir Tuller*, Hadas Birin*, Martin Kupiec and Eytan Ruppin. Co-evolutionary Models for Reconstructing Ancestral Genomic Sequences: Computational Issues and Biological examples. RECOMB-CG 2009.
23. Tamir Tuller, Udi Rubinstein, Dani Bar, Michael Gurevitch, Eytan Ruppin and Martin Kupiec. Higher-order Genomic Organization of Cellular Functions in Yeast. The 4th Annual RECOMB Satellite on Systems Biology, MIT, MA, USA, Oct 29-Nov 2, 2008.
22. Yedael Waldman*, Tamir Tuller*, Tomer Shlomi, Shaul Karni, Roded Sharan and Eytan Ruppin. Gene Translation Efficiency in Healthy and Cancerous Human Tissues. The 5th Annual RECOMB Satellite on Regulatory Genomics, MIT, MA, USA, Oct 29-Nov 2, 2008.
21. Udi Rubinstein, Yifat Felder, Nana Ginzbourg, Michael Gurevich, and Tamir Tuller. Editing Bayesian Networks: A New Approach for Combining Prior Knowledge and Gene Expression Measurements for Researching Diseases. The 2008 IEEE International Conference on Bioinformatics and Biomedicine. Philadelphia, PA, USA, Nov. 5-7, 2008.
20. Yifat Felder and Tamir Tuller. Discovering Local Patterns of Co - Evolution. Comparative Genomics, International Workshop, RECOMB-CG 2008, Paris, France, October 13-15, 2008.
19. Sagi Snir and Tamir Tuller. Novel Phylogenetic Network Inference by Combining Maximum Likelihood and Hidden Markov Models. The 8th workshop on Algorithms in Bioinformatics (WABI), Karlsruhe, Germany, September 15-19, 2008.
18. Hadas Birin, Zohar Gal-or, Isaac Elias and Tamir Tuller. Inferring Models of Rearrangements, Recombinations and Horizontal Transfers by the Minimum Evolution Criterion. The 7th workshop on Algorithms in Bioinformatics (WABI), University of Pennsylvania , USA , September 7-9, 2007.
17. Guohua Jin, Luay Nakhleh, Sagi Snir and Tamir Tuller. A New Linear-time Heuristic Algorithm for Computing the Parsimony Score of Phylogenetic Networks: Theoretical Bounds and Empirical Performance. International Symposium on Bioinformatics Research and Applications (ISBRA),Georgia State University in Atlanta, USA, May 7-10, 2007.
16. Guohua Jin, Luay Nakhleh, Sagi Snir and Tamir Tuller. Efficient Parsimony-based Methods for Phylogenetic Network Reconstruction. ECCB 2006, Eilat, Israel, January 21-24, 2007.
15. Tamir Tuller, Benny Chor. Biological Networks: Comparison, Conservation, and Evolutionary Trees. RECOMB 2006, Venice, Italy, 30-44, April 2-5, 2006.
14. Tamir Tuller, Efrat Oron, Erez Makavy, Daniel A. Chamovitz, Benny Chor. Time - Window Analysis of Developmental Gene Expression Data with Multiple Genetic Backgrounds. (WABI) 2005, Mallorca, Spain, Ooctober 3-6, 2005.
13. Benny Chor, Tamir Tuller. Maximum Likelihood of Evolutionary Trees: Hardness and Approximation. ISMB 2005, Detroit Marriot Renaissance Center, Michigan, USA, June 25-29, 2005. Proceedings p. 97.
12. David Burstein, Igor Ulitsky, Tamir Tuller, Benny Chor. Information Theoretic Approaches to Whole Genome Phylogenomics. RECOMB 2005, Cambridge, MA, USA, May 14-18, 2005. Proceedings p. 283.
11. Benny Chor, Tamir Tuller. Maximum Likelihood of Evolutionary Trees is Hard. RECOMB 2005, Cambridge, MA, USA, May 14-18, 2005. Proceedings p. 296.
10. Benny Chor, Tamir Tuller. Adding hidden nodes to gene network. The 4th workshop on Algorithms in Bioinformatics (WABI), 2004, Bergen, Norway, September 17-21, 2004. Proceedings p. 123.
Book Chapters
9. H. Zommer and T. Tuller. The potential of computational genomics in the design of oncolytic viruses (2020). Book chapter in the Virus bioinformatics book.
8. Y. Zarai, M. Margaliot, T. Tuller. Modeling and Analyzing the Flow of Molecular Machines in Gene Expression (2018). Book chapter in Systems Biology .
7. E. Goz*, H. Zur*, T. Tuller. Hidden Silent Codes in Viral Genomes (2017). Book chapter in Evolutionary Biology book.
6. A. Diament and T. Tuller. Three dimensional genomic organization of genes' function in eukaryotes (2016). Book chapter in Evolutionary Biology book.
5. M. Margaliot, T. Tuller, E. D. Sontag. Checkable Conditions for Contraction After Small Transients in Time and Amplitude. to appear .
4. T. Tuller. The effect of codon usage on the success of horizontal gene transfer. Book chapter in Lateral Gene Transfer in Evolution. 2013.
3. T. Tuller. Co-evolution of gene families: algorithms and evolutionary systems biology. Book chapter Evolutionary Biology: Mechanisms and Trends. 2012.
2. T. Tuller and H. Zur. Computational modeling of gene translation and its potential applications in individualized medicine. Book chapter in "Patient-Specific Modeling in Tomorrow's Medicine".2011.
Others
1. A. Dana and T. Tuller. Comment regarding the paper 'Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes' Cell 2012.
Selected Posters
Tamir Tuller, Ohad Greenshpan, Eran Cohen, Benny Chor. Regulation and Genomic Arrangement of microRNA Genes. RECOMB 2006.
Tamir Tuller, Sagi Snir. The NET-HMM: a HMM Based Likelihood Model for Evolutionary Networks. RECOMB 2007.
Yifat Felder, Tamir Tuller, Nana Ginzbourg, Udi Rubinstein, Michael Gurevich, Anat Achiron. A new approach for inferring regulatory networks from literature and gene expression measurements. RECOMB 2007.
Tamir Tuller, Martin Kupiec, Eytan Ruppin. Co-evolutionary Networks of Genes and Cellular Processes Across Fungal Species. RECOMB-CG 2008.