Biomedical subjects

Alon Kaufman

Publications and source records attributed to Alon Kaufman.

5 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

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.

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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↗

PubMed2004

Fair localization of function via multi-lesion analysis.

Acknowledging that causal localization of function in a processing network requires a multi-lesion analysis, this paper presents a rigorous and efficient method for defining and calculating the functional contributions of network elements as well as their interactions. The method's applicability to biological networks is demonstrated in the investigation of spatial attention in cats via lesion and reversible deactivation experiments.

Acoustic Stimulation↗

PubMed2004

Multi-knockout genetic network analysis: the Rad6 example.

A novel and rigorous Multi-perturbation Shapley Value Analysis (MSA) method has been recently presented [12]. The method addresses the challenge of defining and calculating the functional causal contributions of elements of a biological system. This paper presents the first study applying MSA to the analysis of gene knockout data. The MSA identifies the importance of genes in the Rad6 DNA repair pathway of the yeast S. cerevisiae, quantifying their contributions and characterizing their functional interactions. Incorporating additional biological knowledge, a new functional description of the Rad6 pathway is provided, predicting the existence of additional DNA polymerase and RFC-like complexes. The MSA is the first method for rigorously analyzing multi-knockout experiments, which are likely to soon become a standard and necessary tool for analyzing complex biological systems.

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