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Workshop 1 Titles and Abstracts
Presentation materials: PPT Streaming Video: Real Media Streaming Video: Real Media
Presentation materials: PPT Streaming Video: Real Media The green alga Chlamydomonas reinhardtii is a good biological model to study fundamental biological questions ranging from motility, photosynthesis to metabolism. As a photosynthetic cell, Chlamydomonas thrives in minimal media, carbon dioxide and light, but it can also grow heterotrophically on acetate. Unlike yeast or E.coli, any metabolite detected in the intra- or extracellular space is therefore a product of its enzymatic machinery, since only inorganic nutrients are needed. In addition, cell division of Chlamydomonas can be synchronized by light periods, so at any given time >95% of all cells in a culture are identical. Chlamydomonas batch cultures enable pursuing multiple experiments with limited efforts in small time frames, yet high analytical accuracy and precision. Work done in collaboration with Do Yup Lee, Jan Budzcies, and Frank Kose.
Presentation materials: PDF Streaming Video: Real Media Human biology functions through (largely unknown and) bewildering sets of interacting molecules. Even the parts list is incomplete - while we know that roughly 23,000 human proteins are expressed, each protein can be changed by alternative splicing, variable post-translational adducts, and protein processing. Perhaps the number of different human proteins is a couple hundred thousand, but no one really knows. The situation for RNA is worse - it is likely that in human cells every possible RNA is expressed at a low level, adding another 60,000,000 non-overlapping RNA 100-mers to the mix. If one loves the idea of a pre-biotic RNA World, one would be foolish to discount the possible functions available today in this deep human RNA sequence space. Bacteria have smaller genomes and parts lists, and have had more "genetics" - between faster generation times and higher mutation rates, bacteria are engineered today through a more complete Darwinian process than has been possible for mammals.Thinking this way raises a question for metabolic engineering - should one design or evolve novel biochemical systems? Clunky mammals and less-clunky bacteria suggest that evolution strategies should be tried. The deepest combinatorial selection paradigm (SELEX) has provided an astonishing set of reagents (aptamers), and at the same time provided lessons for how to manage evolution/selective strategies. Even though I'd like to lecture about the human plasma proteome (the main interest at SomaLogic), I will focus my talk on the possible value of in vitro and in vivo selections aimed at the discovery of novel biochemical systems.
a) Metabolic fluxes of a plant secondary metabolite pathway. In this case study dynamic labelling experiments were used to elucidate pathway fluxes in native potatoe and after addition of an elucidator. b) Regulation of central metabolic fluxes in Bacillus subtilis. Fluxes in various mutants of B. subtilis using different substrate combinations were determined after model based experimental planning.
Presentation materials: PPT Streaming Video: Real Media Malaria infects 300-500 million people and causes 1-2 million deaths each year, primarily children in Africa and Asia. More than half of the deaths occur among the poorest 20% of the world's population. One of the principal obstacles to addressing this global health threat is a lack of effective, affordable drugs. The chloroquine-based drugs that were used widely in the past have lost effectiveness because the Plasmodium parasite, which causes malaria, has become resistant to them. The faster-acting, more effective artemisinin-based drugs - as currently produced from plant sources - are too expensive for large-scale use in the countries where they are needed most.We have metabolically engineered E. coli to produce high levels of mono-, sesqui-, and diterpenes, most notably the sesquiterpene precursor to artemisinin, amorphadiene. The result of these studies is an E. coli host capable of producing 1,000,000-fold higher levels of amorphadiene than the strains and expression systems that had been available previously. The engineered strain contains a heterologous mevalonate-based terpene biosynthetic pathway and an amorphadiene cyclase gene resynthesized with the E. coli codon usage. Recently, we cloned the final steps in the artemisinin biosynthetic pathway and engineered yeast to produce artemisinic acid at high levels. The development of this technology will eventually reduce the cost of artemisinin-based combination therapies significantly below their current price. Streaming Video: Real Media The cellular environment is abuzz with noise. Generated by random molecular events, cellular noise not only results in random fluctuations within individual cells but it is also a source of phenotypic variability among clonal cellular populations. In some instances fluctuations are suppressed downstream through an intricate dynamical network that acts to filter the noise. Yet in other instances, noise induced fluctuations are exploited to the cell's advantage. Intriguing mechanisms that rely on noise include stochastic switches, coherence resonance in oscillators, and stochastic focusing. While mathematical models of genetic networks often represents gene expression and regulation as deterministic processes with continuous variables, the stochastic nature of cellular noise necessitates an approach that models these variables as discrete and stochastic. In this framework, probability densities of the system states evolve according to a (usually infinite dimensional) Chemical Master Equation (CME). Until recently, sample trajectories have been computed almost exclusively with Kinetic Monte Carlo methods, such as Gillespie's Stochastic Simulation Algorithm. In this talk we present a new direct approach for computing the relevant statistics, which involves the projection of the solution of the CME onto finite subsets. We illustrate the algorithm underlying our Finite State Projection approach and introduce a variety of systems theory based modifications and enhancements that enable large reductions and increased efficiency with little to no loss in accuracy. Model reduction techniques based on linear perturbation theory allow for the systematic projection of multiple time scale dynamics onto a slowly varying manifold of much smaller dimension. The proposed projection approach is illustrated on few important models of genetic regulatory networks.
Streaming Video: Real Media Bacteria are tiny chemical factories that can sense their environment and react to stimuli according to a pre-programmed response. These responses are controlled by genetic networks. By constructing synthetic versions of these genetic networks, we can program bacteria to behave in completely novel and useful ways. In this talk, we present our work on designing bacterial edge detectors: bacteria that use cell-cell communication and parallel computation to sense where borders between lighted and dark regions exist and report the edges. We use a mathematical model combining statistical mechanics with partial differential equations to determine which alterations to the synthetic gene network will improve the bacterial edge detection. Work done in collaboration with J. Tabor and C.A. Voigt. Metabolic networks have a bow-tie architecture: a wide diversity of nutrients is disassembled into a few molecular currencies, which are then reassembled into a large variety of other molecules. At the "knot" of this bowtie lie cycles whereby a set of reactions transfer a molecular group (moiety) from various donor metabolites to a common carrier (metabolic currency) from which another set of reactions transfers the moiety to various accepting metabolites. These circuits couple moiety supply to demand. Their role, and the performance criteria they should fulfill, are akin to those of a power-supply unit in an electronic circuit.
Presentation materials: PDF Streaming Video: Real Media Small noncoding RNAs are genes for which RNA, rather than protein is the functional end product. In bacteria, many small RNA genes (sRNAs) appear to act as post-transcriptional regulators by basepairing with target messenger RNAs. In this talk, we will look at computational and experimental approaches to characterize these sRNA genes in bacteria. First, we will describe high-throughput approaches for identifying sRNA genes in a bacterial genome. In particular, we will consider a probabilistic model that combines heterogeneous data sources (including primary sequence data, comparative genomics information, and microarray expression data) for the purpose of predicting sRNA genes throughout a genome. We will then investigate methods, both computational and experimental, for characterizing regulatory targets of sRNA action. Finally, we will explore how these high-throughput approaches are used to elucidate the roles of specific sRNA genes and the pathways in which the genes are involved.
Presentation materials: PPT Streaming Video: Real Media Stoichiometric approaches have been tremendously successful as mathematical models in metabolic engineering. Their linearity permits an unparalleled repertoire of mathematical and computational tools, and the combination of stoichiometric models with experimental data has yielded valuable insights into flux distributions under different conditions. However, as we strive to understand the details of control and regulation in vivo at a deeper level, refined models are needed, and these must be nonlinear. While simulations with nonlinear metabolic models are no longer a significant computational hurdle, the estimation of suitable parameter values continues to be a major challenge. In this presentation I will review current approaches to metabolic parameter estimation, especially for time series data, and demonstrate why it is important to obtain fast solutions on standard computers. As an example for many aspects of my presentation, I will use the regulation of glucose utilization in Lactococcus lactis, for which we have high-precision in vivo data describing the dynamics of intracellular metabolite pools.
Streaming Video: Real Media |
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