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Dec 1, 2013 (Vol. 33, No. 21)

The Rise of Systems Biology

  • Click Image To Enlarge +
    The homodimer of the ligand binding domain of the nuclear hormone receptor, LXR, is used to predict an interaction between another hormone receptor, PPARγ, and the homeodomain of PAX7. One subunit of the LXR homodimer is shown in red and the other in blue. A structure of the ligand binding domain of PPARγ, shown in green, is superimposed on the blue subunit, and a homology model of the homeodomain of PAX7 is shown in yellow, superimposed on the other. [Cliff Zhang, Donald Petrey, and Barry Honig—Howard Hughes Medical Institute/Columbia University]

    “Science is more than a body of knowledge, it’s a way of thinking,” remarked Carl Sagan, and probably his words were never more powerfully relevant than for portraying one of the newest biomedical fields, systems biology.

    A recent symposium inaugurating the department of systems biology at Columbia University Medical Center comes at a very auspicious time, one in which biomedical sciences, chemistry, physics, engineering, bioinformatics, and computer sciences are converging to shape a vibrant new discipline.

    As Lee Goldman, M.D., M.P.H., executive vice president for health and biomedical sciences at the Columbia University College of Physicians and Surgeons pointed out during the opening remarks, this new field “represents so much about our future.”

    Within a relatively short time, we have realized the possibility of sequencing and mapping the genome of virtually any organism. Concomitantly, as increasingly sophisticated technologies allow whole-genome sequences to be completed within hours to days, navigating the vast datasets has become the foremost challenge, opening a gap in our ability to understand and interpret their significance.

    “While elucidating the genome has given us a tremendous amount of information, it has revealed very little about how those parts work together,” says Andrea Califano, Ph.D., professor and chair of the department of systems biology at Columbia. At the recent systems biology inaugural symposium, several investigators affiliated with the department unveiled research programs and interdisciplinary approaches that promise to integrate a plethora of knowledge on cellular and molecular processes into cross-disciplinary frameworks.

  • Protein-Protein Interactions

    “We wanted to put a structural face on protein-protein interactions that occur in cells over time,” said Barry Honig, Ph.D., professor of biochemistry and molecular biophysics. In parallel with emerging knowledge about the cellular ensemble of protein-protein interactions, the possibility of using structural information to describe protein-protein interfaces on a large scale has attracted increasing attention. “But there are challenges associated with this, and the main challenge is the existence of many more genes than structures,” Dr. Honig added.

    Thousands of human proteins have at least one known domain structure, yet only approximately 500 protein-protein complex structures are available in the protein data bank. At the same time, approximately 80,000 interactions are described in publicly available databases, and many more are anticipated to occur in cells. “What we want to do is mine structural information about those interactions,” explained Dr. Honig.

    One of the key approaches used by Dr. Honig and colleagues, homology modeling, takes advantage of the possibility of finding, given a query sequence of unknown structure, another sequence that is related through alignment and whose structure is known. Models of proteins or protein fragments can be analyzed by three-dimensional superimposition, in an approach that Dr. Honig coined as structural BLAST.

    Building on this strategy, Dr. Honig and colleagues developed and validated an algorithm known as PrePPI (predicting protein-protein interactions), and used it to generate a database of over 300,000 predictions for protein interaction pairs. PrePPI is based on Bayesian statistics and relies on the input of structural and nonstructural information.

    For a pair of query proteins thought to interact, structures for subunits from each protein are obtained from databases or are generated by homology modeling. After finding structural neighbors for each subunit, a template is identified for a pair of structural neighboring chains, and a model is built by superimposing individual subunits on their corresponding structural neighbors.

    A single structure-derived score, combined from five different structure-based scores, generated to evaluate the model, reflects the likelihood that the interaction represents a true complex. Nonstructural information, such as functional and evolutionary similarities, is subsequently incorporated into the model. “This could not have been possible without applying systems biology thinking to structural biology,” said Dr. Honig.

  • Perturbations and Pathways

    “I was fascinated by processes that have evolved to create such amazingly precise spatiotemporal gene expression patterns, and I was even more astounded to realize that information underlying these patterns is encoded in the sequence,” explained Saeed Tavazoie, Ph.D., professor of biochemistry and molecular biophysics. A major effort in Dr. Tavazoie’s lab has focused on the concept of predicting gene expression dynamics from information encoded in the sequence.

    Decades ago, Dr. Tavazoie’s approach would have been difficult to implement. However, the advent of microarray technology, which allows the expression of thousands of genes to be surveyed simultaneously under a broad range of conditions, has been instrumental toward making this approach become reality.

    Previously, Dr. Tavazoie and colleagues developed a computational pipeline to profile a number of gene expression perturbations occurring in a large number of different cellular states, and the genes were subsequently clustered into co-expression modules. “We then looked at the regulatory regions for the occurrence of de novo motifs that are enriched in these genes with regard to the background of the genome,” said Dr. Tavazoie.

    These motifs were predicted to function as transcriptional regulator binding sites, and the strategy provided a powerful approach to perform reverse engineering in simple organisms. “However, going from yeast to humans has been more challenging, due to the scale and the complexity of the human genome,” reported Dr. Tavazoie.

    To address this challenge, Dr. Tavazoie’s lab developed new algorithms based on information theory that enable sensitive and specific detection of transcriptional and post-transcriptional regulatory elements within the human genome. In particular, TEISER (Tool for Eliciting Informative Structural Elements in RNA) is a new framework that enables the discovery of structural RNA elements by using context-free grammars and mutual information. “The application of TEISER to mammalian datasets is revealing a rich picture of mRNA stability regulation by these elements and the RNA-binding proteins that bind them,” said Dr. Tavazoie.

    In a recent genome-wide, systems-level analysis of 46 different cancers, Dr. Tavazoie and colleagues used cancer gene expression datasets to identify known pathways and processes that are perturbed. “We have identified some of the most commonly recurring elements in several cancers, and this allowed us to dig deeper into individual functional categories and pathways, such as those involved in regulating apoptosis or the mitotic cell cycle,” Dr. Tavazoie added.

    Quantitative gene expression measurements and pathway analyses on these data also allowed causality between perturbations and the pathways that are changed to be explored. This approach revealed that, as opposed to a “universal” signature for tumor pathways, perturbed pathways are very diverse, and cellular modifications underlying the malignant state are broadly heterogeneous.

    Additionally, the systematic analysis of cis-regulatory elements showed that only approximately 25% of the newly discovered motifs corresponded to known binding sites, illustrating the complexity of the malignant state and the limited amount of information that we currently have on describing cellular perturbations that characterize it.

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