The evolution of developmental mechanisms

Over the past two to three decades, developmental biology has demonstrated that all multicellular organisms in the animal kingdom share many of the same molecular building blocks and many of the same regulatory genetic pathways. Yet we still do not understand how the various organisms use these molecules and pathways to assume all the forms we know today. Evolutionary developmental biology tackles this problem by comparing the development of one organism to another and comparing the genes involved and gene functions to understand what makes one organism different from another. In this review, we revisit a set of seven concepts defined by Lewis Wolpert (fate maps, asymmetric division, induction, competence, positional information, determination, and lateral inhibition) that describe the characters of many developmental systems and supplement them with three additional concepts (developmental genomics, genetic redundancy, and genetic networks). We will discuss examples of comparative developmental studies where these concepts have guided observations on the advent of a developmental novelty. Finally, we identify a set of evolutionary frameworks, such as developmental constraints, cooption, duplication, parallel and convergent evolution, and homoplasy, to adequately describe the evolutionary properties of developmental systems.     

Ontologies for data and knowledge sharing in biology: plant ROS signaling as a case study

Modern technologies have rapidly transformed biology into a data-intensive discipline. In addition to the enormous amounts of existing experimental data in the literature, every new study can produce a large amount of new data, resulting in novel ideas and more publications. In order to understand a biological process as completely as possible, scientists should be able to combine and analyze all such information. Not only molecular biology and bioinformatics, but all the other domains of biology including plant biology, require tools and technologies that enable experts to capture knowledge within distributed and heterogeneous sources of information. Ontologies have proven to be one of the most-useful means of constructing and formalizing expert knowledge. The key feature of an ontology is that it represents a computer-interpretable model of a particular subject area. This article outlines the importance of ontologies for systems biology, data integration and information analyses, as illustrated through the example of reactive oxygen species (ROS) signaling networks in plants.     

Phenotype ontologies: the bridge between genomics and evolution

Understanding the developmental and genetic underpinnings of particular evolutionary changes has been hindered by inadequate databases of evolutionary anatomy and by the lack of a computational approach to identify underlying candidate genes and regulators. By contrast, model organism studies have been enhanced by ontologies shared among genomic databases. Here, we suggest that evolutionary and genomics databases can be developed to exchange and use information through shared phenotype and anatomy ontologies. This would facilitate computing on evolutionary questions pertaining to the genetic basis of evolutionary change, the genetic and developmental bases of correlated characters and independent evolution, biomedical parallels to evolutionary change, and the ecological and paleontological correlates of particular types of change in genes, gene networks and developmental pathways.     

Proteome informatics II: bioinformatics for comparative proteomics

The present review attempts to cover the most recent initiatives directed towards representing, storing, displaying and processing protein-related data suited to undertake "comparative proteomics" studies. Data interpretation is brought into focus. Efforts invested into analysing and interpreting experimental data increasingly express the need for adding meaning. This trend is perceptible in work dedicated to determining ontologies, modelling interaction networks, etc. In parallel, technical advances in computer science are spurred by the development of the Web and the growing need to channel and understand massive volumes of data. Biology benefits from these advances as an application of choice for many generic solutions. Some examples of bioinformatics solutions are discussed and directions for on-going and future work conclude the review.     

Modeling and simulation of genetic regulatory systems: a literature review.

In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.     

How well do we understand the clusters found in microarray data?

We wished to quantify the state-of-the-art of our understanding of clusters in microarray data. To do this we systematically compared the clusters produced on sets of microarray data using a representative set of clustering algorithms (hierarchical, k-means, and a modified version of QT_CLUST) with the annotation schemes MIPS, GeneOntology and GenProtEC. We assumed that if a cluster reflected known biology its members would share related ontological annotations. This assumption is the basis of "guilt-by-association" and is commonly used to assign the putative function of proteins. To statistically measure the relationship between cluster and annotation we developed a new predictive discriminatory measure. We found that the clusters found in microarray data do not in general agree with functional annotation classes. Although many statistically significant relationships can be found, the majority of clusters are not related to known biology (as described in annotation ontologies). This implies that use of guilt-by-association is not supported by annotation ontologies. Depending on the estimate of the amount of noise in the data, our results suggest that bioinformatics has only codified a small proportion of the biological knowledge required to understand microarray data.     

Protein function from the perspective of molecular interactions and genetic networks

Protein function is a complex notion, which is now receiving renewed attention from a bioinformatics and genomics perspective. After a general discussion of the principles of experimental methods employed to decipher gene/protein function, the contributions made by new, high-throughput methods in terms of function discovery are discussed. Recent work on functional ontologies and the necessity to describe function within the context of hierarchical levels of complexity are presented. The concepts of molecular interactions and genetic networks are then discussed, leading to a useful new framework with which to describe protein function using new tools such as 2D interaction maps. Finally, it is proposed that interaction data could be used to develop new methods for the functional classification of proteins. An example of functional comparisons on a real data set of yeast chromosomal proteins is presented.     

Ionizing radiation and genetic risks. XIII. Summary and synthesis of papers VI to XII and estimates of genetic risks in the year 2000

This paper recapitulates the advances in the field of genetic risk estimation that have occurred during the past decade and using them as a basis, presents revised estimates of genetic risks of exposure to radiation. The advances include: (i) an upward revision of the estimates of incidence for Mendelian diseases (2.4% now versus 1.25% in 1993); (ii) the introduction of a conceptual change for calculating doubling doses; (iii) the elaboration of methods to estimate the mutation component (i.e. the relative increase in disease frequency per unit relative increase in mutation rate) and the use of the estimates obtained through these methods for assessing the impact of induced mutations on the incidence of Mendelian and chronic multifactorial diseases; (iv) the introduction of an additional factor called the "potential recoverability correction factor" in the risk equation to bridge the gap between radiation-induced mutations that have been recovered in mice and the risk of radiation-inducible genetic disease in human live births and (v) the introduction of the concept that the adverse effects of radiation-induced genetic damage are likely to be manifest predominantly as multi-system developmental abnormalities in the progeny.For all classes of genetic disease (except congenital abnormalities), the estimates of risk have been obtained using a doubling dose of 1 Gy. For a population exposed to low LET, chronic/ low dose irradiation, the current estimates for the first generation progeny are the following (all estimates per million live born progeny per Gy of parental irradiation): autosomal dominant and X-linked diseases, approximately 750-1500 cases; autosomal recessive, nearly zero and chronic multifactorial diseases, approximately 250-1200 cases. For congenital abnormalities, the estimate is approximately 2000 cases and is based on mouse data on developmental abnormalities. The total risk per Gy is of the order of approximately 3000-4700 cases which represent approximately 0.4-0.6% of the baseline frequency of these diseases (738,000 per million) in the population.     

Dynamical modelling of pattern formation during embryonic development

The combination of genetic and molecular biology techniques has uncovered the intricacies of several gene networks controlling developmental processes. In the face of such complex regulatory networks, developmental geneticists cannot rely on reasoning alone; a thorough understanding of the spatio-temporal properties of these networks clearly requires the use of proper computational tools and methods.     

On gene ontology and function annotation

The effort of function annotation does not merely involve associating a gene with some structured vocabulary that describes action. Rather the details of the actions, the components of the actions, the larger context of the actions are important issues that are of direct relevance, because they help understand the biological system to which the gene/protein belongs. Currently Gene Ontology (GO) Consortium offers the most comprehensive sets of relationships to describe gene/protein activity. However, its choice to segregate gene ontology to subdomains of molecular function, biological process and cellular component is creating significant limitations in terms of future scope of use. If we are to understand biology in its total complexity, comprehensive ontologies in larger biological domains are essential. A vigorous discussion on this topic is necessary for the larger benefit of the biological community. I highlight this point because larger-bio-domain ontologies cannot be simply created by integrating subdomain ontologies. Relationships in larger bio-domain-ontologies are more complex due to larger size of the system and are therefore more labor intensive to create. The current limitations of GO will be a handicap in derivation of more complex relationships from the high throughput biology data.     

Ontologies for molecular biology and bioinformatics

About five years ago, ontology was almost unknown in bioinformatics, even more so in molecular biology. Nowadays, many bioinformatics articles mention it in connection with text mining, data integration or as a metaphysical cure for problems in standardisation of nomenclature and other applications. This article attempts to give an account of what concept ontologies in the domain of biology and bioinformatics are; what they are not; how they can be constructed; how they can be used; and some fallacies and pitfalls creators and users should be aware of.     

Using ontologies to describe mouse phenotypes

The mouse is an important model of human genetic disease. Describing phenotypes of mutant mice in a standard, structured manner that will facilitate data mining is a major challenge for bioinformatics. Here we describe a novel, compositional approach to this problem which combines core ontologies from a variety of sources. This produces a framework with greater flexibility, power and economy than previous approaches. We discuss some of the issues this approach raises.     

Semantic similarity analysis of protein data: assessment with biological features and issues

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.