Browsing by Author "Adames, Neil R."

Now showing 1 - 6 of 6
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    Adaptive Imaging Cytometry to Estimate Parameters of Gene Networks Models in Systems and Synthetic Biology
    Ball, David A.; Lux, Matthew W.; Adames, Neil R.; Peccoud, Jean (PLOS, 2014-09-11)
    The use of microfluidics in live cell imaging allows the acquisition of dense time-series from individual cells that can be perturbed through computer-controlled changes of growth medium. Systems and synthetic biologists frequently perform gene expression studies that require changes in growth conditions to characterize the stability of switches, the transfer function of a genetic device, or the oscillations of gene networks. It is rarely possible to know a priori at what times the various changes should be made, and the success of the experiment is unknown until all of the image processing is completed well after the completion of the experiment. This results in wasted time and resources, due to the need to repeat the experiment to fine-tune the imaging parameters. To overcome this limitation, we have developed an adaptive imaging platform called GenoSIGHT that processes images as they are recorded, and uses the resulting data to make real-time adjustments to experimental conditions. We have validated this closed-loop control of the experiment using galactose-inducible expression of the yellow fluorescent protein Venus in Saccharomyces cerevisiae. We show that adaptive imaging improves the reproducibility of gene expression data resulting in more accurate estimates of gene network parameters while increasing productivity ten-fold.
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    Experimental testing of a new integrated model of the budding yeast Start transition
    Adames, Neil R.; Schuck, P. Logan; Chen, Katherine C.; Murali, T. M.; Tyson, John J.; Peccoud, Jean (American Society for Cell Biology, 2015-11-05)
    The cell cycle is composed of bistable molecular switches that govern the transitions between gap phases (G1 and G2) and the phases in which DNA is replicated (S) and partitioned between daughter cells (M). Many molecular details of the budding yeast G1–S transition (Start) have been elucidated in recent years, especially with regard to its switch-like behavior due to positive feedback mechanisms. These results led us to reevaluate and expand a previous mathematical model of the yeast cell cycle. The new model incorporates Whi3 inhibition of Cln3 activity, Whi5 inhibition of SBF and MBF transcription factors, and feedback inhibition of Whi5 by G1–S cyclins. We tested the accuracy of the model by simulating various mutants not described in the literature. We then constructed these novel mutant strains and compared their observed phenotypes to the model’s simulations. The experimental results reported here led to further changes of the model, which will be fully described in a later article. Our study demonstrates the advantages of combining model design, simulation, and testing in a coordinated effort to better understand a complex biological network.
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    Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants
    Gallegos, Jenna E.; Adames, Neil R.; Rogers, Mark F.; Kraikivski, Pavel; Ibele, Aubrey; Nurzynski-Loth, Kevin; Kudlow, Eric; Murali, T. M.; Tyson, John J.; Peccoud, Jean (2020-05-06)
    Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.
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    GenoLIB: a database of biological parts derived from a library of common plasmid features
    Adames, Neil R.; Wilson, Mandy L.; Fang, Gang; Lux, Matthew W.; Glick, Benjamin S.; Peccoud, Jean (2015-05-26)
    Synthetic biologists rely on databases of biological parts to design genetic devices and systems. The sequences and descriptions of genetic parts are often derived from features of previously described plasmids using ad hoc, error-prone and time-consuming curation processes because existing databases of plasmids and features are loosely organized. These databases often lack consistency in the way they identify and describe sequences. Furthermore, legacy bioinformatics file formats like GenBank do not provide enough information about the purpose of features. We have analyzed the annotations of a library of similar to 2000 widely used plasmids to build a non-redundant database of plasmid features. We looked at the variability of plasmid features, their usage statistics and their distributions by feature type. We segmented the plasmid features by expression hosts. We derived a library of biological parts from the database of plasmid features. The library was formatted using the Synthetic Biology Open Language, an emerging standard developed to better organize libraries of genetic parts to facilitate synthetic biology workflows. As proof, the library was converted into GenoCAD grammar files to allow users to import and customize the library based on the needs of their research projects.
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    Measurement and modeling of transcriptional noise in the cell cycle regulatory network
    Ball, David A.; Adames, Neil R.; Reischmann, Nadine; Barik, Debashis; Franck, Christopher T.; Tyson, John J.; Peccoud, Jean (Landes Bioscience, 2013-10-01)
    Fifty years of genetic and molecular experiments have revealed a wealth of molecular interactions involved in the control of cell division. In light of the complexity of this control system, mathematical modeling has proved useful in analyzing biochemical hypotheses that can be tested experimentally. Stochastic modeling has been especially useful in understanding the intrinsic variability of cell cycle events, but stochastic modeling has been hampered by a lack of reliable data on the absolute numbers of mRNA molecules per cell for cell cycle control genes. To fill this void, we used fluorescence in situ hybridization (FISH) to collect single molecule mRNA data for 16 cell cycle regulators in budding yeast, Saccharomyces cerevisiae. From statistical distributions of single-cell mRNA counts, we are able to extract the periodicity, timing, and magnitude of transcript abundance during the cell cycle. We used these parameters to improve a stochastic model of the cell cycle to better reflect the variability of molecular and phenotypic data on cell cycle progression in budding yeast.
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    A stochastic model for error correction of kinetochore-microtubule attachments in budding yeast
    Banerjee, Anand; Adames, Neil R.; Peccoud, Jean; Tyson, John J. (2020-08-06)
    To divide replicated chromosomes equally between daughter cells, kinetochores must attach to microtubules emanating from opposite poles of the mitotic spindle (biorientation). An error correction mechanism facilitates this process by destabilizing erroneous kinetochore-microtubule attachments. Here we present a stochastic model of kinetochore-microtubule attachments, via an essential protein Ndc80 in budding yeast,Saccharomyces cerevisiae. Using the model, we calculate the stochastic dynamics of a pair of sister kinetochores as they transition among different attachment states. First of all, we determine the kinase-to-phosphatase balance point that maximizes the probability of biorientation, while starting from an erroneous attachment state. We find that the balance point is sensitive to the rates of microtubule-Ndc80 dissociation and derive an approximate analytical formula that defines the balance point. Secondly, we determine the probability of transition from low-tension amphitelic to monotelic attachment and find that, despite this probability being approximately 33%, biorientation can be achieved with high probability. Thirdly, we calculate the contribution of the geometrical orientation of sister kinetochores to the probability of biorientation and show that, in the absence of geometrical orientation, the biorientation error rate is much larger than that observed in experiments. Finally, we study the coupling of the error correction mechanism to the spindle assembly checkpoint by calculating the average binding of checkpoint-related proteins to the kinetochore during the error correction process.