Publications by authors named "Michael Fero"

Article Synopsis
  • - Synthetic biology is revolutionizing cell and gene therapies for various diseases by engineering cells to target disease signals while protecting healthy tissue from damage.
  • - The Keystone eSymposium held in May 2021 focused on the therapeutic applications of synthetic biology, highlighting its advancement into clinical trials and its potential impact on human health.
  • - Presentation topics included enhancing T cell therapies, gene therapies, viral therapies, and innovating probiotics and other cell-based therapy methods.
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Capturing, storing, and sharing biological DNA parts data are integral parts of synthetic biology research. Here, we detail updates to the ICE biological parts registry software platform that enable these processes, describe our implementation of the Web of Registries concept using ICE, and establish Bioparts, a search portal for biological parts available in the public domain. The Web of Registries enables standalone ICE installations to securely connect and form a distributed parts database.

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Microfluidic applications have expanded greatly over the past decade. For the most part, however, each microfluidics platform is developed with a specific task in mind, rather than as a general-purpose device with a wide-range of functionality. Here, we show how a microfluidic system, originally developed to investigate protein phase behavior, can be modified and repurposed for another application, namely DNA construction.

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Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms.

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Modern DNA assembly techniques are known for their potential to link multiple large DNA fragments together into even larger constructs in single pot reactions that are easier to automate and work more reliably than traditional cloning methods. The simplicity of the chemistry is in contrast to the increased work needed to design optimal reactions that maximize DNA fragment reuse, minimize cost, and organize thousands of potential chemical reactions. Here we examine available DNA assembly methods and describe through example, the construction of a complex but not atypical combinatorial and hierarchical library using protocols that are generated automatically with the assistance of modern synthetic biology software.

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We have determined the three-dimensional (3D) architecture of the Caulobacter crescentus genome by combining genome-wide chromatin interaction detection, live-cell imaging, and computational modeling. Using chromosome conformation capture carbon copy (5C), we derive ~13 kb resolution 3D models of the Caulobacter genome. The resulting models illustrate that the genome is ellipsoidal with periodically arranged arms.

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Caulobacter crescentus is a model organism for the integrated circuitry that runs a bacterial cell cycle. Full discovery of its essential genome, including non-coding, regulatory and coding elements, is a prerequisite for understanding the complete regulatory network of a bacterial cell. Using hyper-saturated transposon mutagenesis coupled with high-throughput sequencing, we determined the essential Caulobacter genome at 8 bp resolution, including 1012 essential genome features: 480 ORFs, 402 regulatory sequences and 130 non-coding elements, including 90 intergenic segments of unknown function.

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Cytokinesis in Gram-negative bacteria is mediated by a multiprotein machine (the divisome) that invaginates and remodels the inner membrane, peptidoglycan and outer membrane. Understanding the order of divisome assembly would inform models of the interactions among its components and their respective functions. We leveraged the ability to isolate synchronous populations of Caulobacter crescentus cells to investigate assembly of the divisome and place the arrival of each component into functional context.

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Advances in microscopy automation and image analysis have given biologists the tools to attempt large scale systems-level experiments on biological systems using microscope image readout. Fluorescence microscopy has become a standard tool for assaying gene function in RNAi knockdown screens and protein localization studies in eukaryotic systems. Similar high throughput studies can be attempted in prokaryotes, though the difficulties surrounding work at the diffraction limit pose challenges, and targeting essential genes in a high throughput way can be difficult.

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Bacterial cells are highly organized with many protein complexes and DNA loci dynamically positioned to distinct subcellular sites over the course of a cell cycle. Such dynamic protein localization is essential for polar organelle development, establishment of asymmetry, and chromosome replication during the Caulobacter crescentus cell cycle. We used a fluorescence microscopy screen optimized for high-throughput to find strains with anomalous temporal or spatial protein localization patterns in transposon-generated mutant libraries.

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The bacterium Caulobacter crescentus has morphologically and functionally distinct cell poles that undergo sequential changes during the cell cycle. We show that the PopZ oligomeric network forms polar ribosome exclusion zones that change function during cell cycle progression. The parS/ParB chromosomal centromere is tethered to PopZ at one pole prior to the initiation of DNA replication.

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Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

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Background: Much of the microarray data published at Stanford is based on mouse and human arrays produced under controlled and monitored conditions at the Brown and Botstein laboratories and at the Stanford Functional Genomics Facility (SFGF). Nevertheless, as large datasets based on the Stanford Human array began to accumulate, a small but significant number of discrepancies were detected that required a serious attempt to track down the original source of error. Due to a controlled process environment, sufficient data was available to accurately track the entire process leading to up to the final expression data.

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Background: Obtaining reliable and reproducible two-color microarray gene expression data is critically important for understanding the biological significance of perturbations made on a cellular system. Microarray design, RNA preparation and labeling, hybridization conditions and data acquisition and analysis are variables difficult to simultaneously control. A useful tool for monitoring and controlling intra- and inter-experimental variation is Universal Reference RNA (URR), developed with the goal of providing hybridization signal at each microarray probe location (spot).

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The most common group of cancers among American women involves malignancies of the breast. Breast cancer is a complex disease, involving several different types of tissues and specific cells with various functions, that is categorized into many distinct subtypes. Microarray analysis has recently revealed that different biological subtypes of breast cancer are accompanied by differences in their specific gene expression profile.

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Article Synopsis
  • * Six clear cell carcinomas showed distinct gene expression from 36 other ovarian cancers, potentially explaining their worse prognosis and resistance to treatment.
  • * We found 62 genes that effectively distinguish between breast and ovarian cancers, highlighting key genes like PAX8, mesothelin, and ephrin-B1 that are more highly expressed in ovarian cancers, with different coexpression patterns related to the estrogen receptor.
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Renal cell carcinoma comprises several histological types with different clinical behavior. Accurate pathological characterization is important in the clinical management of these tumors. We describe gene expression profiles in 41 renal tumors determined by using DNA microarrays containing 22,648 unique cDNAs representing 17,083 different UniGene Clusters, including 7230 characterized human genes.

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