Systems biology represents an experimental approach to biology that attempts to study biological systems in a holistic rather than an atomistic manner. Ideally this involves gathering dynamic and global data sets as well as phenotypic data from different levels of the biological information hierarchy, integrating them and modeling them graphically and/or mathematically to generate mechanistic explanations for the emergent systems properties. This requires that the biological frontiers drive the development of new measurement and visualization technologies and the pioneering of new computational and mathematical tools—all of which requires a cross-disciplinary environment composed of biologists, chemists, computer scientists, engineers, mathematicians, physicists, and physicians speaking common discipline languages. The Institute for Systems Biology has aspired to pioneer and seamlessly integrate each of these concepts.

The high-throughput analytical techniques used in genome, proteome and metabolome studies produce large sets of data that must be studied using appropriate tools. The construction of networks linking different genetic elements and/or functions makes it possible to obtain an integrated view of the cell molecular biology and will eventually help us to predict complex phenotypes from molecular data. Genetic networks can be constructed using different types of data such as genes involved in the control of complex phenotypic traits, genes controlling global gene expression, genetic elements involved in the same metabolic process, gene products interacting physically between them. The connections linking these genetic elements in the network reflect the genetic, physical and/or functional interaction among them. All these networks share common properties and reflect the different layers of the cell's complexity. In this review, we will study how different types of networks can be constructed, how the different networks complement each other and how this information can be used to obtain an integrated picture of the cell.

Challenges in modern biology demand shifting focus from components—genes and proteins—to their interacting whole. Integrating information from multiple genomic datasets is seen as a means to this end, capable of providing robust and accurate ways to unravel these functional associations. Integrative strategies, both novel and adapted from other well-studied organisms, are being employed in the model plant Arabidopsis thaliana to interpret genome-wide expression, metabolic profiling and protein interaction studies. Exciting inroads are being made in mining and interpretation of developmental, physiological and environmental-response ‘programs’ using sequence and functional information. The fundamental transcriptional regulatory logic is emerging in Arabidopsis, presently revealed as isolated conditional, spatial or temporal regulatory ‘modules’. This immediately calls for efforts towards assembling these building blocks together into a unifying model, thus creating standards for future work to compare with. As a young field, Arabidopsis systems biology is ripe with such an opportunity, now scarcely realizable in other model organisms.

Today's proteomic analyses are generating increasing numbers of biomarkers, making it essential to possess highly specific probes able to recognize those targets. Antibodies are considered to be the first choice as molecular recognition units due to their target specificity and affinity, which make them excellent probes in proteomics. In the post-genomic era and with high-throughput techniques available, the goal is to discriminate between all individual proteins from the proteome including their splice variants and post-translationally modified derivatives. Aided by advances in generation, selection and engineering of antibody-based recognition units, antibody fragments provide tools for detection of high- as well as low-abundant analytes even in complex, non-fractionated proteomes in conjunction with usage of small amounts of samples and reagents. In addition, large consortia aim at generating vast numbers of antibody-based recognition units suitable for future diagnostics and therapeutics.

Many ‘omics’ studies, gene expression microarray experiments in particular, aim at charting the molecular mechanisms of physiology, disease and drug response. This short review discusses the bias present in many such studies whereas the focus is set on the well understood and established molecular scenarios. The under-reporting rate of ‘hypothetical’ or uncharacterized genes and proteins, differentially regulated in disease context, is assessed here. Reasons for this bias are discussed. Particular examples from the genomics studies on respiratory diseases are presented. This review aims at increasing awareness of the unexplored genomics data and proposes remedies in order to refocus genomics studies on the less-charted territories of the genome, transcriptome and proteome. It is suggested that routine use of function prediction methods in conjunction with omics analyses may allow better interpretation of the data, and facilitate discovery of true novelty.

The ever increasing number of protein structures determined by structural genomic projects has spurred much interest in the development of methods for structure-based function prediction. Existing methods can be roughly classified in two groups: some use a comparative approach looking for the presence of structural motifs possibly associated with a known biochemical function. Other methods try to identify functional patches on the surface of a protein using only its physicochemical characteristics. This review will cover both kinds of approaches to structure-based function prediction as well as their use in real-world cases. The main issues and limitations in using protein structure to predict function will also be discussed. These are mainly: the assessment of the statistical significance of structural similarities and the extent to which these methods depend on the accuracy and availability of structural data.

The development of recombinant adeno-associated virus (rAAV) gene therapy applications is hampered by the inability to produce rAAV in sufficient quantities to support pre-clinical and clinical trials. Contrasting with adherent cell cultures, suspension cultures provide a straightforward means for expansion, however, transiently expressing the necessary, but cytotoxic virus proteins remains the challenge for rAAV production. Both the expansion and expression issues are resolved by using the baculovirus expression vector (bev) and insect cell culture system. This review addresses strategies for the production of rAAV exploiting baculovirus technology at different scales using different configurations of bioreactors as well as processing and product characterization issues. The yields obtained with these optimized processes exceed ~1 x 10¹⁴ vector particles per liter of cell culture suitable for pre-clinical and clinical trials and possible commercialization.

Proteomic research has proved valuable for understanding the molecular mechanisms of biological processes, as well as in the search for biomarkers for a variety of diseases which lack a molecular diagnostic. While several new approaches are being developed, two-dimensional (2-DE) gel electrophoresis is still one of the most commonly used techniques, despite its many limitations. However, for biomarker research, 2-DE gel electrophoresis alone does not fulfill the necessary pre-requisites. If such a technique is utilized exclusively, a great part of a given proteome remains unseen. Therefore, very precise and sensitive techniques are needed. Here, we present a brief review of known methodologies that try to overcome the limitations of conventional proteome analysis as well as their respective advantages and limitations.

High throughput genomic and proteomic methods are often used for comparisons between expression of genes and proteins, respectively in normal cells and malignant counterparts for the identification of potential tumor markers for diagnosis and prognosis. Some experiments use normal and malignant cells cultured in vitro as a source of the mRNA or proteins for analysis. The conditions used for cell culture can exert major effects on the expression of genes and proteins. The interpretation of results of some such studies can be erroneous if normal cells and cancer cells are cultured in serum-free medium (SFM) and serum-supplemented media, respectively as recommended for their optimal growth. The reason for potential complications in the data interpretation is that serum contains different factors that affect gene expression. Likewise, SFM is usually supplemented with specific growth factors as well as bovine pituitary extract. Experimental examples demonstrating the issue include the stimulatory effects of serum on the expression of retinoic acid-inducible genes (e.g. GPRC5A) leading to the potentially erroneous conclusion that such genes are overexpressed in cancer cells. Potential remedy for this problem is to grow the normal and malignant cells in the same medium (serum-free or serum-containing) before analysis.