Will genomics revolutionise pharmaceutical R&D?

The successful identification of drug targets requires an understanding of the high-level functional interactions between the key components of cells, organs and systems, and how these interactions change in disease states. This information does not reside in the genome, or in the individual proteins that genes code for, it is to be found at a higher level. Genomics will succeed in revolutionising pharmaceutical research and development only if these interactions are also understood by determining the logic of healthy and diseased states. The rapid growth in biological databases, models of cells, tissues and organs, and in computing power has made it possible to explore functionality all the way from the level of genes to whole organs and systems. Combined with genomic and proteomic data, in silico simulation technology is set to transform all stages of drug discovery and development. The major obstacle to achieving this will be obtaining the relevant experimental data at levels higher than genomics and proteomics.     

Molecular evolutionary analysis of seminal receptacle sperm storage organ genes of Drosophila melanogaster

Sperm storage organs are common and broadly distributed among animal taxa. However, little is known about how these organs function at the molecular level. Additionally, there is a paucity of knowledge about the evolution of genes expressed in these organs. This investigation is an evolutionary expressed sequence tag (EST) study of genes expressed in the seminal receptacle, one of the sperm storage organs in Drosophila. The incidence of positive selection is higher for the seminal receptacle genes than Drosophila reproductive genes as a whole, but lower than genes associated with the spermatheca, a second type of Drosophila sperm storage organ. By identifying overrepresented classes of proteins and classes for which sperm storage function is suggested by the nature of the proteins, candidate genes were discovered. These candidates belong to protein classes such as muscle contraction, odorant binding and odorant receptor, protease inhibitor and immunity.     

Differential growth in plants--a phenomenon that occurs at all levels of organization

Differential growth is a feature of cells, the organs which they construct and the whole plant itself. The control of differential growth at each of these three levels of organization resides in the level lower than that in which it is expressed. Thus, differential growth of cells is regulated by the patterns of intracellular microtubules and cellulose microfibrils of the walls, that of organs by the pattern of growth of their cells, and that of the organism by the relative rates of organ growth. The latter is, in turn, determined an all-pervading system of correlative interactions. Plant hormones by may play a role in each of these regulatory systems.     

Computational neurogenetic modelling: a pathway to new discoveries in genetic neuroscience

The paper presents a methodology for using computational neurogenetic modelling (CNGM) to bring new original insights into how genes influence the dynamics of brain neural networks. CNGM is a novel computational approach to brain neural network modelling that integrates dynamic gene networks with artificial neural network model (ANN). Interaction of genes in neurons affects the dynamics of the whole ANN model through neuronal parameters, which are no longer constant but change as a function of gene expression. Through optimization of interactions within the internal gene regulatory network (GRN), initial gene/protein expression values and ANN parameters, particular target states of the neural network behaviour can be achieved, and statistics about gene interactions can be extracted. In such a way, we have obtained an abstract GRN that contains predictions about particular gene interactions in neurons for subunit genes of AMPA, GABAA and NMDA neuro-receptors. The extent of sequence conservation for 20 subunit proteins of all these receptors was analysed using standard bioinformatics multiple alignment procedures. We have observed abundance of conserved residues but the most interesting observation has been the consistent conservation of phenylalanine (F at position 269) and leucine (L at position 353) in all 20 proteins with no mutations. We hypothesise that these regions can be the basis for mutual interactions. Existing knowledge on evolutionary linkage of their protein families and analysis at molecular level indicate that the expression of these individual subunits should be coordinated, which provides the biological justification for our optimized GRN.     

Anticipatory Functions, Digital-Analog Forms and Biosemiotics: Integrating the Tools to Model Information and Normativity in Autonomous Biological Agents

We argue that living systems process information such that functionality emerges in them on a continuous basis. We then provide a framework that can explain and model the normativity of biological functionality. In addition we offer an explanation of the anticipatory nature of functionality within our overall approach. We adopt a Peircean approach to Biosemiotics, and a dynamical approach to Digital-Analog relations and to the interplay between different levels of functionality in autonomous systems, taking an integrative approach. We then apply the underlying biosemiotic logic to a particular biological system, giving a model of the B-Cell Receptor signaling system, in order to demonstrate how biosemiotic concepts can be used to build an account of biological information and functionality. Next we show how this framework can be used to explain and model more complex aspects of biological normativity, for example, how cross-talk between different signaling pathways can be avoided. Overall, we describe an integrated theoretical framework for the emergence of normative functions and, consequently, for the way information is transduced across several interconnected organizational levels in an autonomous system, and we demonstrate how this can be applied in real biological phenomena. Our aim is to open the way towards realistic tools for the modeling of information and normativity in autonomous biological agents.     

Exploring mixed microbial community functioning: recent advances in metaproteomics

System approaches to elucidate ecosystem functioning constitute an emerging area of research within microbial ecology. Such approaches aim at investigating all levels of biological information (DNA, RNA, proteins and metabolites) to capture the functional interactions occurring in a given ecosystem and track down characteristics that could not be accessed by the study of isolated components. In this context, the study of the proteins collectively expressed by all the microorganisms present within an ecosystem (metaproteomics) is not only crucial but can also provide insights into microbial functionality. Overall, the success of metaproteomics is closely linked to metagenomics, and with the exponential increase in the availability of metagenome sequences, this field of research is starting to experience generation of an overwhelming amount of data, which requires systematic analysis. Metaproteomics has been employed in very diverse environments, and this review discusses the recent advances achieved in the context of human biology, soil, marine and freshwater environments as well as natural and bioengineered systems.     

Global approaches to protein-protein interactions

The availability of complete, annotated genome sequences for a variety of eukaryotic organisms has paved the way for a paradigm shift in biomedical research from the 'one gene-one hypothesis' approach to more global, systematic strategies that analyse genes or proteins on a genome- and proteome-wide scale. One daunting task in the post-genome era is to determine how the complement of expressed cellular proteins - the proteome - is organised into functional, higher-order networks, by mapping all constitutive and dynamic protein-protein interactions. Traditionally, reductionist approaches have typically focused on a few, selected gene products and their interactions in a particular physiological context. In contrast, more holistic strategies aim at understanding complex biological systems, for example global protein-protein interaction networks on a cellular or organismal level. Several large-scale proteomics technologies have been developed to generate comprehensive, cellular protein-protein interaction maps.     

Expressed sequence tags of Chinese cabbage flower bud cDNA.

We randomly selected and partially sequenced cDNA clones from a library of Chinese cabbage (Brassica campestris L. ssp. pekinensis) flower bud cDNAs. Out of 1216 expressed sequence tags (ESTs), 904 cDNA clones were unique or nonredundant. Five hundred eighty-eight clones (48.4%) had sequence homology to functionally defined genes at the peptide level. Only 5 clones encoded known flower-specific proteins. Among the cDNAs with no similarity to known protein sequences (628), 184 clones had significant similarity to nucleotide sequences registered in the databases. Among these 184 clones, 142 exhibited similarities at the nucleotide level only with plant ESTs. Also, sequence similarities were evident between these 142 ESTs and their matching ESTs when compared using the deduced amino acid sequences. Therefore, it is possible that the anonymous ESTs encode plant-specific ubiquitous proteins. Our extensive EST analysis of genes expressed in floral organs not only contributes to the understanding of the dynamics of genome expression patterns in floral organs but also adds data to the repertoire of all genomic genes.     

Comparative microarray data analysis for the expression of genes in the pathway of glioma

Our present work focuses on the set of genes, which are involved in primary brain tumors - the glioma pathway. These gliomas are mostly malignant (cancerous) in nature and are difficult to be cured and that's why they attract the attention of all the workers. To understand the relative functionality of these genes, we analyzed the expression pattern of all genes, using gene expression data, at genomic level, and then to check their universality in all other cancers, we compared their expression levels and patterns in all other types of cancers by using gene expression graphs, and observed their expression levels in all these cancers, whether they are over or under expressed. We found that every gene has its own unique expression pattern and level and on that basis it can be classified. We also found that oncogenes and tumor suppressor genes that were involved in the glioma pathway were showing similar expression patterns in other cancers too but their expression level is low.     

Expression analysis of the family of 14-3-3 proteins in zebrafish development

14-3-3 proteins comprise a family of dimeric multi-functional proteins present in all eukaryotes, that are important in a whelm of ubiquitous biological processes. We have analyzed the genomic structure of all 14-3-3s from zebrafish comprising 11 genes and have analyzed their phylogeny. The gene family was cloned and its expression pattern in zebrafish embryogenesis was analyzed by whole mount in situ hybridization and microarray analysis with gene specific probes. We demonstrate that maternal mRNA of 14-3-3s is expressed evenly at the first cell division. At later stage all genes are expressed in a patterned way with, in most cases, intricate patterns in the developing brain. Our result shows distinct expression patterns of various genes. Microarray results show that differences in expression levels of highly similar 14-3-3 genes also occur in the adult stage.     

Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks

The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding of physiology, but also for practical applications of using biological systems as cell factories. We present some of the recent “-omics” approaches that have expanded our understanding of regulation at the gene, protein, and metabolite level, followed by analysis of the impact of this progress on the advancement of metabolic engineering. Although this review is by no means exhaustive, we attempt to convey our ideology that combining global information from various levels of metabolic hierarchy is absolutely essential in understanding and subsequently predicting the relationship between changes in gene expression and the resulting phenotype. The ultimate aim of this review is to provide metabolic engineers with an overview of recent advances in complementary aspects of regulation at the gene, protein, and metabolite level and those involved in fundamental research with potential hurdles in the path to implementing their discoveries in practical applications.     

Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding of physiology, but also for practical applications of using biological systems as cell factories. We present some of the recent "-omics" approaches that have expanded our understanding of regulation at the gene, protein, and metabolite level, followed by analysis of the impact of this progress on the advancement of metabolic engineering. Although this review is by no means exhaustive, we attempt to convey our ideology that combining global information from various levels of metabolic hierarchy is absolutely essential in understanding and subsequently predicting the relationship between changes in gene expression and the resulting phenotype. The ultimate aim of this review is to provide metabolic engineers with an overview of recent advances in complementary aspects of regulation at the gene, protein, and metabolite level and those involved in fundamental research with potential hurdles in the path to implementing their discoveries in practical applications.     

Insights from models of rhythmic motor systems

Computational models of rhythmic motor systems are valuable tools for the study of motor pattern generation and control. Recent modeling advances, together with experimental results, suggest that rhythmic behaviors, such as breathing or walking, are influenced by complex interactions among motor system components. Such interactions occur at all levels of organization, from the subcellular through to the cellular, synaptic, and network levels to the level of neuromuscular interactions and that of the whole organism. Simultaneously, safety mechanisms at all levels contribute to network stability and the generation of robust motor patterns.