Many projects at ISB, from cancers to rare genetic diseases, entail the development of computational and mathematical approaches for modeling biological systems and analyzing large-scale measurement data sets.
Genome sequences contain information with immense possibilities for research and personalized medical care, but their size, complexity and diversity make comparing sequences error-prone and slow. ISB researchers have created a method for summarizing a personal genome as a “fingerprint.”
In this study, we looked for evidence of such convergence through comparative analysis of 12 genome-scale yeast models.
Prostate cancer is the second most common cancer in men worldwide. The clinical behavior of prostate cancer is variable with some men exhibiting indolent prostate cancer which can be monitored over time while other men develop aggressive prostate cancer which can lead to metastasis and death.
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The ISB Cancer Genomics Cloud:
Leveraging Google Cloud Platform for TCGA Analysis
The ISB Cancer Genomics Cloud (ISB-CGC) is one of three pilot projects funded by the National Cancer Institute with the goal of democratizing access to the TCGA data by substantially lowering the barriers to accessing and computing over this rich dataset. The ISB-CGC is a cloud-based platform that will serve as a large-scale data repository for TCGA data, while also providing the computational infrastructure and interactive exploratory tools necessary to carry out cancer genomics research at unprecedented scales. The ISB-CGC will also facilitate collaborative research by allowing scientists to share data, analyses, and insights in a cloud environment.
Nearly a decade ago, ISB’s Baliga Lab published a landmark paper describing cMonkey, an innovative method to accurately map gene networks within any organism from microbes to humans.
Two new papers describe the benchmark results of cMonkey and also the release of cMonkey2, which performs with higher accuracy.
Using this approach, genetic and molecular data generated from any organism, be it a bacterium or a birch tree, can be explored and analyzed from a network perspective.
The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single-cell responses to increasingly complex networks of in vivo cellular interaction,
Mycobacterium tuberculosis (MTB) infects more than 1.5 billion people worldwide partly due to its ability to sense and adapt to the broad range of hostile environments that exist within hosts.
To study how MTB controls its responses at a molecular level, ISB researchers and their collaborators at Seattle Biomed perturbed almost all MTB transcription factor regulators and identified the affected genes.
This comprehensive map of molecular switches in MTB provides a basis for understanding control mechanisms and can inform future efforts to rewire MTB responses for more favorable disease outcomes.
‘Big data’ cancer research has revealed a new spectrum of genetic mutations across tumors that need understanding.
Existing methods for analyzing DNA defects in cancer are blind to how those mutations actually behave.
ISB scientists developed a new approach using physics- and structure-based modeling to systematically assess the spectrum of mutations that arise in several gene regulatory proteins in cancer.
Papillary thyroid cancer represents 80 percent of all thyroid cancer cases.
Integrative analysis resulted in the detection of significant molecular alterations not previously reported in the disease.
ISB researchers identified microRNAs which may lead to more precise therapy.
Computer simulation is a promising way to model multicellular biological systems to help understand complexity underlying health and disease.
Biocellion is a high-performance computing (HPC) framework that enables the simulation of billions of cells across multiple scales.
Biocellion facilitates researchers without HPC expertise to easily build and simulate large models.
Gastric cancer has a high mortality rate, but current classification systems haven’t been effective in helping to identify subtypes relevant for treatment of the disease.
TCGA researchers have integrated molecular data from 295 stomach tumors and have discovered four subtypes of gastric cancer.
Stratification of patients into these four subtypes paves the way for the development of new personalized therapies.
Understanding systems from a multiscale perspective gives us a more detailed and holistic view of how features or functions from each scale connect and interact in a given system.
The challenge is integrating the different types of information that come from each scale in an efficient way that yields the most insight.
ISB developed a new tool to make it easier for researchers to to integrate, analyze and visualize human genome data at multiple resolutions.
Price Lab researchers worked with collaborators at the University of Illinois to create an easy-to-use software toolkit for comparing microbial genomes.
Tools can be used to find orthologs, correct missing/inaccurate gene annotations, analyze gene gain and loss patterns, and build draft metabolic networks from reference networks.
Software offers predictive and analysis capabilities in a flexible way, making it easy to build customized analysis pipelines.
ISB researchers used the systems approach to develop a new way to integrate data from different classes of networks to better understand how cells function.
The method is a software program called GEMINI and it’s the first of its kind to integrate data from metabolic networks to refine transcriptional regulatory networks.
GEMINI has higher success rate than existing technologies.
Looking for biomarkers in different types of tissues requires comparing massive amounts of gene expression data.
In order to compare ‘digital transcriptome’ data, they have to be normalized or adjusted to a common standard of measurement.
ISB researchers developed new algorithmic methods that outperform existing methods for normalizing gene expression data from different samples.
There exist copious amounts of public research data that can reveal new biological information if they are integrated and analyzed.
One of ISB’s specialties is the ability to apply systems approaches to develop methods to integrate and analyze data.
In the latest publication, ISB demonstrates an open-access computational strategy that can help any researcher capitalize on large data sets.
The Cancer Genome Atlas research network has launched the Pan-Cancer project to analyze multiple tumor types together to find common events across different tumors. The availability of large cohorts and multiple different types of data at the DNA, RNA, and protein levels has made the Pan-Cancer project possible.
Updates to maintain a state-of-the art reconstruction of the yeast metabolic network are essential to reflect our understanding of yeast metabolism and functional organization, to eliminate any inaccuracies identified in earlier iterations, to improve predictive accuracy and to continue to expand into novel subsystems to extend the comprehensiveness of the model. Here, we present version 6 of the consensus yeast metabolic network (Yeast 6) as an update to the community effort to computationally reconstruct the genome-scale metabolic network of Saccharomyces cerevisiae S288c.
All living things are made of cells that contain DNA, which help determine their physical characteristics. In addition to this encoded genetic information, organisms are also defined by the way they decode information from interactions with their environments. Signals from the environment are interpreted by cells through a series of steps that turn proteins on or off, making up what is often referred to as the “signaling network.”
Scientists at Institute for Systems Biology (ISB), University of Luxembourg, and Tampere University of Technology have created a method that identifies the genetic toggle switches that determine a cell’s developmental fate. This research, published on April 21 in the journal Nature Methods, may lead to new discoveries in disease treatments and tissue-regeneration technologies.
“In this elegant work, the authors propose a new way to identify genetic factors that influence cell fate based on the analysis of gene regulatory networks,” said Paul Brazhnik, PhD, of the National Institutes of Health’s National Institute of General Medical Sciences, which partly funded the work.
ISB’s Nathan Price and Vangelis Simeonidis, a visiting scholar from Luxembourg Centre for Systems Biomedicine (LCSB is a major strategic partner with ISB), contributed to this paper – “A community-driven global reconstruction of human metabolism” – that was published today in Nature Biotechnology. An excerpt describing the collaborative project from a press release is below. An interactive map is forthcoming.