Biomedical subjects

Eytan Ruppin

Publications and source records attributed to Eytan Ruppin.

At least 19 recordsLinked to original sources

PubMed2006

Gene expression of Caenorhabditis elegans neurons carries information on their synaptic connectivity.

The claim that genetic properties of neurons significantly influence their synaptic network structure is a common notion in neuroscience. The nematode Caenorhabditis elegans provides an exciting opportunity to approach this question in a large-scale quantitative manner. Its synaptic connectivity network has been identified, and, combined with cellular studies, we currently have characteristic connectivity and gene expression signatures for most of its neurons. By using two complementary analysis assays we show that the expression signature of a neuron carries significant information about its synaptic connectivity signature, and identify a list of putative genes predicting neural connectivity. The current study rigorously quantifies the relation between gene expression and synaptic connectivity signatures in the C. elegans nervous system and identifies subsets of neurons where this relation is highly marked. The results presented and the genes identified provide a promising starting point for further, more detailed computational and experimental investigations.

Animals↗

PubMed2006

Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes.

The rate of conservation of a gene in evolution is believed to be correlated with its biological importance. Recent studies have devised various conservation measures for genes and have shown that they are correlated with several biological characteristics of functional importance. Specifically, the state-of-the-art propensity for gene loss (PGL) measure was shown to be strongly correlated with gene essentiality and its number of protein-protein interactions (PPIs). The observed correlation between conservation and functional importance varies however between conservation measures, underscoring the need for accurate and general measures for the rate of gene conservation. Here we develop a novel maximum-likelihood approach to computing the rate in which a gene is lost in evolution, motivated by the same principles as those underlying PGL. However, in difference to PGL which considers only the most parsimonious ancestral states of the internal nodes of the phylogenetic tree relating the species, our approach weighs in a probabilistic manner all possible ancestral states, and includes the branch length information as part of the probabilistic model. In application to data of 16 eukaryotic genomes, our approach shows higher correlations with experimental data than PGL, including data on gene lethality, level of connectivity in a PPI network and coherence within functionally related genes.

Algorithms↗

PubMed2006

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm.

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody-antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody-antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein-protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs.

Algorithms↗

PubMed2006

A direct comparison of protein interaction confidence assignment schemes.

BACKGROUND: Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing large protein interaction networks for various species at an ever-growing pace. However, common technologies like yeast two-hybrid may experience high rates of false positive detection. To combat false positive discoveries, a number of different methods have been recently developed that associate confidence scores with protein interactions. Here, we perform a rigorous comparative analysis and performance assessment among these different methods. RESULTS: We measure the extent to which each set of confidence scores correlates with similarity of the interacting proteins in terms of function, expression, pattern of sequence conservation, and homology to interacting proteins in other species. We also employ a new metric, the Signal-to-Noise Ratio of protein complexes embedded in each network, to assess the power of the different methods. Seven confidence assignment schemes, including those of Bader et al., Deane et al., Deng et al., Sharan et al., and Qi et al., are compared in this work. CONCLUSION: Although the performance of each assignment scheme varies depending on the particular metric used for assessment, we observe that Deng et al. yields the best performance overall (in three out of four viable measures). Importantly, we also find that utilizing any of the probability assignment schemes is always more beneficial than assuming all observed interactions to be true or equally likely.

Caenorhabditis elegans Proteins↗

PubMed2006

Inferring functional pathways from multi-perturbation data.

BACKGROUND: Recently, a conceptually new approach for analyzing gene networks, the Functional Influence Network (FIN) was presented. The FIN approach uses the measured performance of a given cellular function under different multi-perturbations, to identify the main functional pathways and interactions underlying its processing. Here we present and study an iterative, extended version of FIN, the Functional Influence Network Extractor (FINE), which is specifically geared towards the accurate analysis of sparse cellular systems. We employ it to study a conceptually fundamental question of practical importance--how well should we know the system studied (such that we can predict its performance) so that we can understand its workings (i.e., chart its underlying functional network)? RESULTS AND CONCLUSIONS: The performance of FINE is studied in both simulated and biological sparse systems. It successfully obtains an accurate and compact description of the underlying functional network even with limited data, and outperforms FIN. We show that prior estimates of a system's functional complexity are instrumental in determining how much predictive knowledge is required to accurately chart its underlying functional network. AVAILABILITY: The FINE software is available for download at http://www.cns.tau.ac.il/resc.html.

Algorithms↗

PubMed2006

Conservation of expression and sequence of metabolic genes is reflected by activity across metabolic states.

Variation in gene expression levels on a genomic scale has been detected among different strains, among closely related species, and within populations of genetically identical cells. What are the driving forces that lead to expression divergence in some genes and conserved expression in others? Here we employ flux balance analysis to address this question for metabolic genes. We consider the genome-scale metabolic model of Saccharomyces cerevisiae, and its entire space of optimal and near-optimal flux distributions. We show that this space reveals underlying evolutionary constraints on expression regulation, as well as on the conservation of the underlying gene sequences. Genes that have a high range of optimal flux levels tend to display divergent expression levels among different yeast strains and species. This suggests that gene regulation has diverged in those parts of the metabolic network that are less constrained. In addition, we show that genes that are active in a large fraction of the space of optimal solutions tend to have conserved sequences. This supports the possibility that there is less selective pressure to maintain genes that are relevant for only a small number of metabolic states.

Cell Proliferation↗

PubMed2006

Direct evolution of genetic robustness in microRNA.

Genetic robustness, the invariance of the phenotype in the face of genetic perturbations, can endow the organism with reduced susceptibility to mutations. A large body of work in recent years has focused on the origins, mechanisms, and consequences of robustness in a wide range of biological systems. Despite the apparent prevalence of mutational robustness in nature, however, its evolutionary origins are still unclear. Does robustness evolve directly by natural selection or is it merely a correlated byproduct of other phenotypic traits? By examining microRNA (miRNA) genes of several eukaryotic species, we show that the structure of miRNA precursor stem-loops exhibits a significantly high level of mutational robustness in comparison with random RNA sequences with similar stem-loop structures. Hence, this excess robustness of miRNA goes beyond the intrinsic robustness of the stem-loop hairpin structure. Furthermore, we show that it is not the byproduct of a base composition bias or of thermodynamic stability. These findings suggest that the excess robustness of miRNA stem-loops is the result of direct evolutionary pressure toward increased robustness. We further demonstrate that this adaptive robustness evolves to compensate for structures with low intrinsic robustness.

Animals↗

PubMed2006

QPath: a method for querying pathways in a protein-protein interaction network.

BACKGROUND: Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks. RESULTS: We present a comprehensive framework for protein network searches using pathway queries. Given a linear query pathway and a network of interest, our algorithm, QPath, efficiently searches the network for homologous pathways, allowing both insertions and deletions of proteins in the identified pathways. Matched pathways are automatically scored according to their variation from the query pathway in terms of the protein insertions and deletions they employ, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We applied QPath to systematically infer protein pathways in fly using an extensive collection of 271 putative pathways from yeast. QPath identified 69 conserved pathways whose members were both functionally enriched and coherently expressed. The resulting pathways tended to preserve the function of the original query pathways, allowing us to derive a first annotated map of conserved protein pathways in fly. CONCLUSION: Pathway homology searches using QPath provide a powerful approach for identifying biologically significant pathways and inferring their function. The growing amounts of protein interactions in public databases underscore the importance of our network querying framework for mining protein network data.

Algorithms↗

PubMed2006

Modeling brain energy metabolism and function: a multiparametric monitoring approach.

Mathematical modeling of brain function is an important tool needed for a better understanding of experimental results and clinical situations. In the present study, we are constructing and testing a mathematical model capable of simulating changes in brain energy metabolism that develop in real time under various pathophysiological conditions. The model incorporates the following parameters: cerebral blood flow, partial oxygen pressure, mitochondrial NADH redox state, and extracellular potassium. Accordingly, all the model variables are only time dependent (;point-model' approach). Numerical runs demonstrate the ability of the model to mimic pathological conditions, such as complete and partial ischemia, cortical spreading depression under normoxic and partial ischemic conditions. They also show that, when properly tuned, a model of this type permits the monitoring of only one or two crucial variables and the computation of the remaining variables in real time during clinical or experimental procedures.

Algorithms↗

PubMed2006

Multiple knockout analysis of genetic robustness in the yeast metabolic network.

Genetic robustness characterizes the constancy of the phenotype in face of heritable perturbations. Previous investigations have used comprehensive single and double gene knockouts to study gene essentiality and pairwise gene interactions in the yeast Saccharomyces cerevisiae. Here we conduct an in silico multiple knockout investigation of a flux balance analysis model of the yeast's metabolic network. Cataloging gene sets that provide mutual functional backup, we identify sets of up to eight interacting genes and characterize the 'k robustness' (the depth of backup interactions) of each gene. We find that 74% (360) of the metabolic genes participate in processes that are essential to growth in a standard laboratory environment, compared with only 13% previously found to be essential using single knockouts. The genes' k robustness is shown to be a solid indicator of their biological buffering capacity and is correlated with both the genes' environmental specificity and their evolutionary retention.

Computer Simulation↗

PubMed2006

Facial attractiveness: beauty and the machine.

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.

Algorithms↗

PubMed2006

Neural processing of counting in evolved spiking and McCulloch-Pitts agents.

This article investigates the evolution of autonomous agents that perform a memory-dependent counting task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiking integrate-and-fire networks. The results demonstrate the superiority of the spiky model in evolutionary success and network simplicity. The combination of spiking dynamics with incremental evolution leads to the successful evolution of agents counting over very long periods. Analysis of the evolved networks unravels the counting mechanism and demonstrates how the spiking dynamics are utilized. Using new measures of spikiness we find that even in agents with spiking dynamics, these are usually truly utilized only when they are really needed, that is, in the evolved subnetwork responsible for counting.

Animals↗

PubMed2006

Axiomatic scalable neurocontroller analysis via the Shapley value.

One of the major challenges in the field of neurally driven evolved autonomous agents is deciphering the neural mechanisms underlying their behavior. Aiming at this goal, we have developed the multi-perturbation Shapley value analysis (MSA)--the first axiomatic and rigorous method for deducing causal function localization from multiple-perturbation data, substantially improving on earlier approaches. Based on fundamental concepts from game theory, the MSA provides a formal way of defining and quantifying the contributions of network elements, as well as the functional interactions between them. The previously presented versions of the MSA require full knowledge (or at least an approximation) of the network's performance under all possible multiple perturbations, limiting their applicability to systems with a small number of elements. This article focuses on presenting new scalable MSA variants, allowing for the analysis of large complex networks in an efficient manner, including large-scale neurocontrollers. The successful operation of the MSA along with the new variants is demonstrated in the analysis of several neurocontrollers solving a food foraging task, consisting of up to 100 neural elements.

Models, Statistical↗

PubMed2006

Neurocontroller analysis via evolutionary network minimization.

This study presents a new evolutionary network minimization (ENM) algorithm. Neurocontroller minimization is beneficial for finding small parsimonious networks that permit a better understanding of their workings. The ENM algorithm is specifically geared to an evolutionary agents setup, as it does not require any explicit supervised training error, and is very easily incorporated in current evolutionary algorithms. ENM is based on a standard genetic algorithm with an additional step during reproduction in which synaptic connections are irreversibly eliminated. It receives as input a successfully evolved neurocontroller and aims to output a pruned neurocontroller, while maintaining the original fitness level. The small neurocontrollers produced by ENM provide upper bounds on the neurocontroller size needed to perform a given task successfully, and can provide for more effcient hardware implementations.

Algorithms↗

PubMed2005

Quantitative analysis of genetic and neuronal multi-perturbation experiments.

Perturbation studies, in which functional performance is measured after deletion, mutation, or lesion of elements of a biological system, have been traditionally employed in many fields in biology. The vast majority of these studies have been qualitative and have employed single perturbations, often resulting in little phenotypic effect. Recently, newly emerging experimental techniques have allowed researchers to carry out concomitant multi-perturbations and to uncover the causal functional contributions of system elements. This study presents a rigorous and quantitative multi-perturbation analysis of gene knockout and neuronal ablation experiments. In both cases, a quantification of the elements' contributions, and new insights and predictions, are provided. Multi-perturbation analysis has a potentially wide range of applications and is gradually becoming an essential tool in biology.

Algorithms↗

PubMed2005

Unsupervised learning of natural languages.

We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The adios (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.

Algorithms↗

PubMed2005

Regulatory on/off minimization of metabolic flux changes after genetic perturbations.

Predicting the metabolic state of an organism after a gene knockout is a challenging task, because the regulatory system governs a series of transient metabolic changes that converge to a steady-state condition. Regulatory on/off minimization (ROOM) is a constraint-based algorithm for predicting the metabolic steady state after gene knockouts. It aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type. ROOM is shown to accurately predict steady-state metabolic fluxes that maintain flux linearity, in agreement with experimental flux measurements, and to correctly identify short alternative pathways used for rerouting metabolic flux in response to gene knockouts. ROOM's growth rate and flux predictions are compared with previously suggested algorithms, minimization of metabolic adjustment, and flux balance analysis (FBA). We find that minimization of metabolic adjustment provides accurate predictions for the initial transient growth rates observed during the early postperturbation state, whereas ROOM and FBA more successfully predict final higher steady-state growth rates. Although FBA explicitly maximizes the growth rate, ROOM does not, and only implicitly favors flux distributions having high growth rates. This indicates that, even though the cell has not evolved to cope with specific mutations, regulatory mechanisms aiming to minimize flux changes after genetic perturbations may indeed work to this effect. Further work is needed to identify metrics that characterize the complete trajectory from the initial to the final metabolic steady states after genetic perturbations.

Algorithms↗

PubMed2005

Motif extraction and protein classification.

We present a novel unsupervised method for extracting meaningful motifs from biological sequence data. This de novo motif extraction (MEX) algorithm is data driven, finding motifs that are not necessarily over-represented in the data. Applying MEX to the oxidoreductases class of enzymes, containing approximately 7000 enzyme sequences, a relatively small set of motifs is obtained. This set spans a motif-space that is used for functional classification of the enzymes by an SVM classifier. The classification based on MEX motifs surpasses that of two other SVM based methods: SVMProt, a method based on the analysis of physical-chemical properties of a protein generated from its sequence of amino acids, and SVM applied to a Smith-Waterman distances matrix. Our findings demonstrate that the MEX algorithm extracts relevant motifs, supporting a successful sequence-to-function classification.

Algorithms↗