Simplified ontologies allowing comparison of developmental mammalian gene expression

Model organisms represent an important resource for understanding the fundamental aspects of mammalian biology. Mapping of biological phenomena between model organisms is complex and if it is to be meaningful, a simplified representation can be a powerful means for comparison. The Developmental eVOC ontologies presented here are simplified orthogonal ontologies describing the temporal and spatial distribution of developmental human and mouse anatomy. We demonstrate the ontologies by identifying genes showing a bias for developmental brain expression in human and mouse.     

A Unified Anatomy Ontology of the Vertebrate Skeletal System

The skeleton is of fundamental importance in research in comparative vertebrate morphology, paleontology, biomechanics, developmental biology, and systematics. Motivated by research questions that require computational access to and comparative reasoning across the diverse skeletal phenotypes of vertebrates, we developed a module of anatomical concepts for the skeletal system, the Vertebrate Skeletal Anatomy Ontology (VSAO), to accommodate and unify the existing skeletal terminologies for the species-specific (mouse, the frog Xenopus, zebrafish) and multispecies (teleost, amphibian) vertebrate anatomy ontologies. Previous differences between these terminologies prevented even simple queries across databases pertaining to vertebrate morphology. This module of upper-level and specific skeletal terms currently includes 223 defined terms and 179 synonyms that integrate skeletal cells, tissues, biological processes, organs (skeletal elements such as bones and cartilages), and subdivisions of the skeletal system. The VSAO is designed to integrate with other ontologies, including the Common Anatomy Reference Ontology (CARO), Gene Ontology (GO), Uberon, and Cell Ontology (CL), and it is freely available to the community to be updated with additional terms required for research. Its structure accommodates anatomical variation among vertebrate species in development, structure, and composition. Annotation of diverse vertebrate phenotypes with this ontology will enable novel inquiries across the full spectrum of phenotypic diversity.     

Expression profiles of 109 apoptosis pathway-related genes in 82 mouse tissues and experimental conditions

Apoptosis is an important process in development and tissue homeostasis. To understand the similarities and differences in the apoptosis machinery in different normal, developmental, and diseased tissues, the expression profiles of 109 apoptosis-pathway-related genes in 82 mouse tissues and experimental conditions were examined using Incyte Mouse GEMI cDNA arrays. It has been found that the compositions of the apoptotic machinery vary among different tissues, developmental stages, and disease states, with subsets of apoptotic genes co-ordinately expressed in the 82 tissues and experimental conditions. Additional genes whose expression profiles resemble selected genes from the 109 apoptotic gene list were also identified. This study provides valuable information on possible molecular mechanisms of differential apoptotic responses to developmental signals, environmental stimuli, and therapeutic treatments in tissue-specific manner.     

Medaka fish stem cells and their applications

Stem cells are present in developing embryos and adult tissues of multicellular organisms. Owing to their unique features, stem cells provide excellent opportunities for experimental analyses of basic developmental processes such as pluripotency control and cell fate decision and for regenerative medicine by stem cell-based therapy. Stem cell cultures have been best studied in 3 vertebrate organisms. These are the mouse, human and a small laboratory fish called medaka. Specifically, medaka has given rise to...     

Brainbow: New Resources and Emerging Biological Applications for Multicolor Genetic Labeling and Analysis

Brainbow is a genetic cell-labeling technique where hundreds of different hues can be generated by stochastic and combinatorial expression of a few spectrally distinct fluorescent proteins. Unique color profiles can be used as cellular identification tags for multiple applications such as tracing axons through the nervous system, following individual cells during development, or analyzing cell lineage. In recent years, Brainbow and other combinatorial expression strategies have expanded from the mouse nervous system to other model organisms and a wide variety of tissues. Particularly exciting is the application of Brainbow in lineage tracing, where this technique has been instrumental in parsing out complex cellular relationships during organogenesis. Here we review recent findings, new technical improvements, and exciting potential genetic and genomic applications for harnessing this colorful technique in anatomical, developmental, and genetic studies.     

Silence is golden: combining RNAi and live cell imaging to study cell cycle regulatory genes during Caenorhabditis elegans development

Much of the pioneering work on the genetics of cell cycle regulation was accomplished using budding and fission yeast. The relative simplicity of these single-celled organisms allowed investigators to readily identify and assign roles to individual genes. While the molecular mechanisms worked out in yeast are more or less identical to those operating in higher organisms, additional layers of control must exist in multicellular organisms to coordinate the timing of developmental events occurring in different cells and tissues. Here we discuss experimental approaches for studying cell cycle processes in the nematode Caenorhabditis elegans.     

Computational models of plant development and form

The use of computational techniques increasingly permeates developmental biology, from the acquisition, processing and analysis of experimental data to the construction of models of organisms. Specifically, models help to untangle the non-intuitive relations between local morphogenetic processes and global patterns and forms. We survey the modeling techniques and selected models that are designed to elucidate plant development in mechanistic terms, with an emphasis on: the history of mathematical and computational approaches to developmental plant biology; the key objectives and methodological aspects of model construction; the diverse mathematical and computational methods related to plant modeling; and the essence of two classes of models, which approach plant morphogenesis from the geometric and molecular perspectives. In the geometric domain, we review models of cell division patterns, phyllotaxis, the form and vascular patterns of leaves, and branching patterns. In the molecular-level domain, we focus on the currently most extensively developed theme: the role of auxin in plant morphogenesis. The review is addressed to both biologists and computational modelers.     

Phenotype Ontologies and Cross-Species Analysis for Translational Research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.     

Phylogenetic analysis of developmental and postnatal mouse cell lineages

SUMMARY Fate maps depict how cells relate together through past lineage relationships, and are useful tools for studying developmental and somatic processes. However, with existing technologies, it has not been possible to generate detailed fate maps of complex organisms such as the mouse. We and others have therefore proposed a novel approach, "phylogenetic fate mapping," where patterns of somatic mutation carried by the individual cells of an animal are used to retrospectively deduce lineage relationships through phylogenetic inference. Here, we have cataloged genomic polymorphisms at 324 mutation-prone polyguanine tracts for nearly 300 cells isolated from a single mouse, and have explored the cells' lineage relationships both phylogenetically and through a network-based approach. We present a model of mouse embryogenesis, where an early period of substantial cell mixing is followed by more coherent growth of clones later. We find that cells from certain tissues have greater numbers of close relatives in other specific tissues than expected from chance, suggesting that those populations arise from a similar pool of ancestral lineages. Finally, we have investigated the dynamics of cell turnover (the frequency of cell loss and replacement) in postnatal tissues. This work offers a longitudinal study of developmental lineages, from conception to adulthood, and provides insight into basic questions of mouse embryology as well as the somatic processes that occur after birth.     

The developmental roles of the extracellular matrix: beyond structure to regulation

Cells in multicellular organisms are surrounded by a complex three-dimensional macromolecular extracellular matrix (ECM). This matrix, traditionally thought to serve a structural function providing support and strength to cells within tissues, is increasingly being recognized as having pleiotropic effects in development and growth. Elucidation of the role that the ECM plays in developmental processes has been significantly advanced by studying the phenotypic and developmental consequences of specific genetic alterations of ECM components in the mouse. These studies have revealed the enormous contribution of the ECM to the regulation of key processes in morphogenesis and organogenesis, such as cell adhesion, proliferation, specification, migration, survival, and differentiation. The ECM interacts with signaling molecules and morphogens thereby modulating their activities. This review considers these advances in our understanding of the function of ECM proteins during development, extending beyond their structural capacity, to embrace their new roles in intercellula signaling.     

Anatomical ontologies: names and places in biology

Ontology has long been the preserve of philosophers and logicians. Recently, ideas from this field have been picked up by computer scientists as a basis for encoding knowledge and with the hope of achieving interoperability and intelligent system behavior. In bioinformatics, ontologies might allow hitherto impossible query and data-mining activities. We review the use of anatomy ontologies to represent space in biological organisms, specifically mouse and human.     

Genetic network inference: from co-expression clustering to reverse engineering

MOTIVATION: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.     

Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function.

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology. Several studies have been done using the Boolean network as a model of a genetic network. This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mnD+1) time naive algorithm and a simple O (mnD) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum in degree. This paper presents an improved O(momega-2nD + mnD+omega-3) time Monte-Carlo type randomized algorithm, where omega is the exponent of matrix multiplication (currently, omega < 2.376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is nontrivial and the technique can be applied to several related problems.     

Single-embryo metabolomics and systematic prediction of developmental stage in zebrafish

Metabolites, the end products of gene expression in living organisms, are tightly correlated with an organism's development and growth. Thus, metabolic profiling is a potentially important tool for understanding the events that have occurred in cells, tissues, and individual organisms. Here, we present a method for predicting the developmental stage of zebrafish embryos using novel metabolomic non-target fingerprints of "single-embryos". With this method, we observed the rate of development at different temperatures. Our results suggest that this method allows us to analyse the condition, or distinguish the genotype, of single-embryos before expression of their ultimate phenotype.     

On the elementary units of multicellularity and their classification in the shape of a periodic table

An approach to calculation of a space of logical possibilities in the development of multicellular organisms is proposed. The approach is based on formalized analysis of cell specialization/integration, which produces multicellularity units called hystions. Certain parameters and criteria for determination of quantitative developmental characteristics are introduced. They allow the space described to be systematized in the shape of a periodic table and divided into plastic, adaptive, and rigid zones. These zones are described quantitatively.