Aviv Regev

Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Much of a cells activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form regulator X regulates module Y under conditions W. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.

lincRNAs act in the circuitry controlling pluripotency and differentiation

Although thousands of large intergenic non-coding RNAs (lincRNAs) have been identified in mammals, few have been functionally characterized, leading to debate about their biological role. To address this, we performed loss-of-function studies on most lincRNAs expressed in mouse embryonic stem (ES) cells and characterized the effects on gene expression. Here we show that knockdown of lincRNAs has major consequences on gene expression patterns, comparable to knockdown of well-known ES cell regulators. Notably, lincRNAs primarily affect gene expression in trans. Knockdown of dozens of lincRNAs causes either exit from the pluripotent state or upregulation of lineage commitment programs. We integrate lincRNAs into the molecular circuitry of ES cells and show that lincRNA genes are regulated by key transcription factors and that lincRNA transcripts bind to multiple chromatin regulatory proteins to affect shared gene expression programs. Together, the results demonstrate that lincRNAs have key roles in the circuitry controlling ES cell state.

A module map showing conditional activity of expression modules in cancer

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor; for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.

Application of a stochastic name-passing calculus to representation and simulation of molecular processes

Abstract We describe a novel application of a stochastic name-passing calculus for the study of biomolecular systems. We specify the structure and dynamics of biochemical networks in a variant of the stochastic π -calculus, yielding a model which is mathematically well-defined and biologically faithful. We adapt the operational semantics of the calculus to account for both the time and probability of biochemical reactions, and present a computer implementation of the calculus for biochemical simulations.

BioAmbients: an abstraction for biological compartments

Biomolecular systems, composed of networks of proteins, underlie the major functions of living cells. Compartments are key to the organization of such systems. We have previously developed an abstraction for biomolecular systems using the π-calculus process algebra, which successfully handled their molecular and biochemical aspects, but provided only a limited solution for representing compartments. In this work, we extend this abstraction to handle compartments. We are motivated by the ambient calculus, a process algebra for the specification of process location and movement through computational domains. We present the BioAmbients calculus, which is suitable for representing various aspects of molecular localization and compartmentalization, including the movement of molecules between compartments, the dynamic rearrangement of cellular compartments, and the interaction between molecules in a compartmentalized setting. Guided by the calculus, we adapt the BioSpi simulation system, to provide an extended modular framework for molecular and cellular compartmentalization, and we use it to model and study a complex multi-cellular system.

Representation and simulation of biochemical processes using the pi-calculus process algebra.

Despite the rapidly accumulating body of knowledge about protein networks, there is currently no convenient way of sharing and manipulation of such information. We suggest that a formal computer language for describing the biomolecular processes underlying protein networks is essential for rapid advancement in this field. We propose to model biomolecular processes by using the pi-Calculus, a process algebra, originally developed for describing computer processes. Our model for biochemical processes is mathematically well-defined, while remaining biologically faithful and transparent. It is amenable to computer simulation, analysis and formal verification. We have developed a computer simulation system, the PiFCP, for execution and analysis of pi-calculus programs. The system allows us to trace, debug and monitor the behavior of biochemical networks under various manipulations. We present a pi-calculus model for the RTK-MAPK signal transduction pathway, formally represent detailed molecular and biochemical information, and study it by various PiFCP simulations.

From signatures to models: understanding cancer using microarrays

Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higher-level modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.

Inferring quantitative models of regulatory networks from expression data

MOTIVATIONnGenetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular systems.nnnRESULTSnWe present fine-grained dynamical models of gene transcription and develop methods for reconstructing them from gene expression data within the framework of a generative probabilistic model. Unlike previous works, we employ quantitative transcription rates, and simultaneously estimate both the kinetic parameters that govern these rates, and the activity levels of unobserved regulators that control them. We apply our approach to expression datasets from yeast and show that we can learn the unknown regulator activity profiles, as well as the binding affinity parameters. We also introduce a novel structure learning algorithm, and demonstrate its power to accurately reconstruct the regulatory network from those datasets.

Automatic genome-wide reconstruction of phylogenetic gene trees

UNLABELLEDnGene duplication and divergence is a major evolutionary force. Despite the growing number of fully sequenced genomes, methods for investigating these events on a genome-wide scale are still in their infancy. Here, we present SYNERGY, a novel and scalable algorithm that uses sequence similarity and a given species phylogeny to reconstruct the underlying evolutionary history of all genes in a large group of species. In doing so, SYNERGY resolves homology relations and accurately distinguishes orthologs from paralogs. We applied our approach to a set of nine fully sequenced fungal genomes spanning 150 million years, generating a genome-wide catalog of orthologous groups and corresponding gene trees. Our results are highly accurate when compared to a manually curated gold standard, and are robust to the quality of input according to a novel jackknife confidence scoring. The reconstructed gene trees provide a comprehensive view of gene evolution on a genomic scale. Our approach can be applied to any set of sequenced eukaryotic species with a known phylogeny, and opens the way to systematic studies of the evolution of individual genes, molecular systems and whole genomes.nnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

The π-calculus as an Abstraction for Biomolecular Systems

Biochemical processes, carried out by networks of proteins, underlies the major functions of living cells ([8, 60]). Although such systems are the focus of intensive experimental research, the mountains of knowledge about the function, activity, and interaction of molecular systems in cells remain fragmented. While computational methods are key to addressing this challenge ([8, 60]), they require the adoption of a meaningful mathematical abstraction [50s]. The research of biomolecular systems has yet to identify and adopt such a unifying abstraction.