Current Funding

NSF EF 0850100 GenoCAD: A Computer Assisted Design Environment for Synthetic Biology (J. Peccoud, PI)

The Virginia Bioinformatics Institute at Virginia Tech is awarded a grant to develop GenoCAD, a web-based computer assisted design environment for synthetic biology. This infrastructure helps molecular biologists design custom DNA molecules by combining basic genetic parts used in protein expression, protein engineering, metabolic engineering, and other applications. The project has four aims. Computer languages will be developed to represent the structure of synthetic DNA molecules used in E.coli, yeast, mice, and Arabidopsis thaliana cells. The research team will develop a compiler capable of translating the DNA sequences into mathematical models of the gene network encoded in the DNA sequence to predict the phenotype encoded in the designed DNA sequences. Collaborative features will allow teams to work together on complex projects. A collaborative workflow environment will allow workgroups to share parts, designs, share fabrication resources, and define custom design strategies suitable for their projects. The award promotes the use of computer assisted design for genetic constructs by advancing discovery through teaching, training, and learning. The project contributes to broadening the participation of underrepresented groups through a joint summer program with a neighboring historically black college. Finally, the project will ensure a broad dissemination of the results by reaching out to the user community through an external advisory board, an annual user conference, and outreach to industry.

This project engages a broad community of users whose experience provides valuable feed-back. GenoCAD is made available to 1,000 undergraduate students enrolled in iGEM, a synthetic biology competition. An external advisory board composed of leaders of the academic and industrial community ensures that GenoCAD developments meet the needs of a large community of potential users. Finally, specific plans combining research grants and technology transfer will ensure the project long-term sustainability.

Additional information about GenoCAD can be found at www.genocad.org.

NSF DBI-0963988 Development of CytoIQ, an Adaptive Cytometer to Measure the Noisy of Dynamics of Gne Expression in Individual Live Cells  (J. Peccoud, PI)

The Virginia Bioinformatics Institute at Virginia Tech is awarded a grant to develop Cyto.IQ, an adaptive imaging system specifically designed to characterize the noisy dynamics of gene expression and other molecular interactions in individual live cells. Cyto.IQ analyzes microscopic images on the fly to produce statistical plots and other quantitative indicators capturing important parameters of the cell physiology. The instrument has the capability to optimize the frequency of image acquisition and the total number of images taken using machine learning algorithms. It finds an optimal tradeoff between the cost of an experiment and the information it generates using a priori knowledge of the expected gene expression dynamics and previously acquired data. The control software is able to determine what cells to observe and when to observe them to ensure a fast convergence of statistical estimators while minimizing adverse effects of light exposure, and the overall duration of the experiment.

Cyto.IQ is specifically designed to meet the needs of systems biologists, bioengineers, or biophysicists who are developing quantitative models of gene networks. Due to the noise affecting gene expression mechanisms, this rapidly growing community of users needs an instrument to observe the state of many individual cells over time. Current methods used to extract this type of data out of time-series of images collected using standard imaging platforms are inherently inefficient. They represent a major obstacle to the refinement of our understanding of the dynamics of cellular processes. Cyto.IQ increases the productivity of scientists working in this field by reducing the time it takes to perform an experiment and the number of experiments needed to collect suitable data sets.

The adaptive control software is open source and available from www.cytoiq.org.

NIH 5R01GM078989 STOCHASTIC MODELS OF CELL CYCLE REGULATION IN EUKARYOTES (John Tyson, PI)

The cycle of cell growth, DNA synthesis, mitosis and cell division is the fundamental process by which cells (and all living organisms) grow, develop and reproduce. Hence, it is of crucial importance to science and human health to understand the molecular mechanisms that control these processes in eukaryotic cells. The control system is so complex that mathematical and computational methods are needed to reliably track the interactions of all the relevant genes, mRNAs, proteins, and multiprotein complexes. Deterministic models (ordinary differential equations) are adequate for understanding the average behavior of groups of cells, but to understand the far-from-average behavior of individual cells requires stochastic models that accurately account for noise stemming from small numbers of participating molecules within a single cell and from vagaries of the division process (i.e., unequal partitioning of molecular components between daughter cells). Accurately modeling the variable responses among cells in a population may be critical to understanding abnormal and diseased cell proliferation. The goals of the proposed renewal are to 1) develop a realistic and accurate stochastic model of cell cycle control in budding yeast and to extend this model to the control of mammalian cell proliferation, 2) measure stochastic effects in single yeast cells in order to provide experimental constraints on and tests of the model, and 3) develop effective algorithms and software to support stochastic modeling and simulation, and to make these tools readily available to the scientific community. Our multi-disciplinary team at Virginia Tech has proven expertise in all aspects of the project and close collaborations with top researchers in the areas of stochastic simulation, sensitivity analysis, bifurcation theory, modeling software, and yeast genetics. Because all eukaryotic cells seem to employ the same fundamental molecular machinery that regulates the cell cycle of yeast, success in modeling growth and division of single yeast cells will translate into better understanding of the role of mammalian cell division in basic biological processes of significance to human health: e.g., embyronic development, tissue regeneration, wound healing, and carcinogenesis. PUBLIC HEALTH RELEVANCE: The cell division cycle is the fundamental process of biological growth and reproduction, and mistakes in this process underlie many serious health problems, especially cancer. An integrative understanding of the cellular basis of health and disease will require, among other things, a description of the cell cycle by computational models that account accurately for the reliability of DNA replication and inheritance despite the molecular fluctuations that inevitably occur in the small confines of a living cell. Hence, a validated stochastic model of the eukaryotic cell cycle is essential to progress in the field of molecular systems biology.

NIH 1R01GM095955 INTEGRATING TOP-DOWN AND BOTTOM-UP MODELS IN SYSTEMS BIOLOGY (T.M. Murali PI)

Two distinct approaches are being used to study complex cellular systems. The first approach automatically searches large datasets for correlations between genes and proteins and represents these as a graph with nodes and edges. The second approach painstakingly crafts detailed models that can be simulated by computer. These approaches have largely been developed separately until now. This project will meld these two approaches into a single framework, thereby allowing fast database searches to augment models that can be simulated. Specifically, the project will 1. Develop fast algorithms to search databases of molecular data to suggest extensions to models of cellular control systems 2. Develop new principles to test how well these extended models match experimental data and 3. Design experimental tests that can validate the predictions made by the first two steps. The project will validate this system by studying the mechanism of cell division, a system involved in the development of cancer. In the long term, the methods developed by this project can be used to study any complex cellular system, e.g., those implicated in infectious diseases. PUBLIC HEALTH RELEVANCE: Project Narrative This project will meld two distinct approaches for studying complex cellular systems, one top-down and the other bottom-up, into a single framework. The project will combine the power of fast database searches with hand-crafted models. The project will validate this system by studying the mechanism of cell division, a system involved in the development of cancer.

Past Funding

NSF DBI-1060776 Prototyping GenoTHREAT a biosecurity solution for synthetic genomics

The Virginia Bioinformatics Institute at Virginia Tech is awarded a grant to develop GenoTHREAT, a software application to screen DNA sequences ordered from gene synthesis companies for the possible presence of potentially harmful sequences. GenoTHREAT implements the screening algorithm recommended by the Department of Health and Human Services in a document entitled "Screening Frameworks Guidance for Synthetic Double-Stranded DNA Providers." It is important to characterize the relationship between the computational cost of the sequence screening algorithm, the rate of false positives or innocuous sequences that are incorrectly red flagged, and the rate of false negatives or sequences of concern not detected by the screening algorithm. The screen is applied to a database composed of a mixture of publicly available sets of synthetic DNA sequences and annotated test cases designed for this project. This large scale analysis will lead to the determination of optimal screen parameters that represent an acceptable compromise between the security concerns of the government and the operational constraints of gene synthesis companies.

The rapid progress in synthetic biology demonstrated by the recent publication of the first synthetic cell by Craig Venter and his team has raised biosecurity concerns among the public, its elected officials, and various administrations. This grant facilitates the adoption of DNA screening algorithm recommended by the federal government to detect, in the order books of gene synthesis companies, the presence of sequences of concern requiring further investigations. GenoTHREAT, the software developed with the grant, will be made broadly available (www.genothreat.org) to allow gene synthesis companies, users of synthetic DNA, or managers of bioinformatics resources for synthetic biology to implement the biosecurity screen recommended by the government. In order to provide another layer of biosecurity protection, GenoTHREAT will also be capable of screening DNA sequencing data. A sustainability plan that does not involve federal funding for the maintenance and future development of GenoTHREAT is being developed with industrial partners.