Synthetic biology: advancing biological frontiers by building synthetic systems

Advances in synthetic biology are contributing to diverse research areas, from basic biology to biomanufacturing and disease therapy. We discuss the theoretical foundation, applications, and potential of this emerging field

Error Correction in DNA Computing: Misclassification and Strand Loss

We present a method of transforming an extract-based DNA computation that is error-prone into one that is relatively error-free. These improvements in error rates are achieved without the supposition of any improvements in the reliability of the underlying laboratory techniques. We assume that only two types of errors are possible: a DNA strand may be incorrectly processed or it may be lost entirely. We show to deal with each of these errors individually and then analyze the tradeoff when both must be optimized simultaneously

Contribution of Vegetation to the Microbial Composition of Nearby Outdoor Air.

UnlabelledGiven that epiphytic microbes are often found in large population sizes on plants, we tested the hypothesis that plants are quantitatively important local sources of airborne microorganisms. The abundance of microbial communities, determined by quantifying bacterial 16S RNA genes and the fungal internal transcribed spacer (ITS) region, in air collected directly above vegetation was 2- to 10-fold higher than that in air collected simultaneously in an adjacent nonvegetated area 50 m upwind. Nonmetric multidimensional scaling revealed that the composition of airborne bacteria in upwind air samples grouped separately from that of downwind air samples, while communities on plants and downwind air could not be distinguished. In contrast, fungal taxa in air samples were more similar to each other than to the fungal epiphytes. A source-tracking algorithm revealed that up to 50% of airborne bacteria in downwind air samples were presumably of local plant origin. The difference in the proportional abundances of a given operational taxonomic unit (OTU) between downwind and upwind air when regressed against the proportional representation of this OTU on the plant yielded a positive slope for both bacteria and fungi, indicating that those taxa that were most abundant on plants proportionally contributed more to downwind air. Epiphytic fungi were less of a determinant of the microbiological distinctiveness of downwind air and upwind air than epiphytic bacteria. Emigration of epiphytic bacteria and, to a lesser extent, fungi, from plants can thus influence the microbial composition of nearby air, a finding that has important implications for surrounding ecosystems, including the built environment into which outdoor air can penetrate.ImportanceThis paper addresses the poorly understood role of bacterial and fungal epiphytes, the inhabitants of the aboveground plant parts, in the composition of airborne microbes in outdoor air. It is widely held that epiphytes contribute to atmospheric microbial assemblages, but much of what we know is limited to qualitative assessments. Elucidating the sources of microbes in outdoor air can inform basic biological processes seen in airborne communities (e.g., dispersal and biogeographical patterns). Furthermore, given the considerable contribution of outdoor air to microbial communities found within indoor environments, the understanding of plants as sources of airborne microbes in outdoor air might contribute to our understanding of indoor air quality. With an experimental design developed to minimize the likelihood of other-than-local plant sources contributing to the composition of airborne microbes, we provide direct evidence that plants are quantitatively important local sources of airborne microorganisms, with implications for the surrounding ecosystems

Computational Analysis of Mass Spectrometric Data for Whole Organism Proteomic Studies

In the last decades great breakthroughs have been achieved in the study of the genomes, supplying us with the vast knowledge of the genes and a large number of sequenced organisms. With the availability of genome information, the new systematic studies have arisen. One of the most prominent areas is proteomics. Proteomics is a discipline devoted to the study of the organism’s expressed protein content. Proteomics studies are concerned with a wide range of problems. Some of the major proteomics focuses upon the studies of protein expression patterns, the detection of protein-protein interactions, protein quantitation, protein localization analysis, and characterization of post-translational modifications. The emergence of proteomics shows great promise to furthering our understanding of the cellular processes and mechanisms of life. One of the main techniques used for high-throughput proteomic studies is mass spectrometry. Capable of detecting masses of biological compounds in complex mixtures, it is currently one of the most powerful methods for protein characterization. New horizons are opening with the new developments of mass spectrometry instrumentation, which can now be applied to a variety of proteomic problems. One of the most popular applications of proteomics involves whole organism high-throughput experiments. However, as new instrumentation is being developed, followed by the design of new experiments, we find ourselves needing new computational algorithms to interpret the results of the experiments. As the thresholds of the current technology are being probed, the new algorithmic designs are beginning to emerge to meet the challenges of the mass spectrometry data evaluation and interpretation. This dissertation is devoted to computational analysis of mass spectrometric data, involving a combination of different topics and techniques to improve our understanding of biological processes using high-throughput whole organism proteomic studies. It consists of the development of new algorithms to improve the data interpretation of the current tools, introducing a new algorithmic approach for post-translational modification detection, and the characterization of a set of computational simulations for biological agent detection in a complex organism background. These studies are designed to further the capabilities of understanding the results of high-throughput mass spectrometric experiments and their impact in the field of proteomics

The EM Algorithm and the Rise of Computational Biology

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Mitochondrial DNA Analysis by Denaturing High-Performance Liquid Chromatography for the Characterization and Separation of Mixtures in Forensic Samples

A mixture of different mtDNA molecules in a single sample is a significant obstacle to the successful use of standard methods of mtDNA analysis (i.e., dideoxy dye-terminator sequencing). Forensic analysts often encounter either naturally occurring mixtures (e.g., heteroplasmy) or situational mixtures typically arising from a combination of body fluids from separate individuals. The ability to accurately resolve and interpret these types of samples in a timely and cost efficient manner would substantially increase the power of mtDNA analysis and potentially provide valuable investigative information by allowing its use in cases where the current approach is limited or fails. Therefore, this research was aimed at developing a strategy for the use of Denaturing High-Performance Liquid Chromatography (DHPLC) as a developmentally-validated forensic application for resolving mixtures of mtDNA. To facilitate the adoption of this technology by the forensic community, a significant effort has been made to ensure that this technology meets the Scientific Working Group on DNA Analysis Methods (SWGDAM) developmental validation criteria and interfaces smoothly with previously validated methods of forensic mtDNA analysis. To do this, the method developed using DHPLC employs mtDNA amplicons, PCR conditions and DNA sequencing protocols validated for use in forensic laboratories. These factors are essential in implementing DHPLC analysis in a forensic casework environment and for the admissibility of DHPLC and Linkage Phase Analysis in court

A DNA-based pattern classifier with in vitro learning and associative recall for genomic characterization and biosensing without explicit sequence knowledge

BACKGROUND: Genetic material extracted from in situ microbial communities has high promise as an indicator of biological system status. However, the challenge is to access genomic information from all organisms at the population or community scale to monitor the biosystem’s state. Hence, there is a need for a better diagnostic tool that provides a holistic view of a biosystem’s genomic status. Here, we introduce an in vitro methodology for genomic pattern classification of biological samples that taps large amounts of genetic information from all genes present and uses that information to detect changes in genomic patterns and classify them. RESULTS: We developed a biosensing protocol, termed Biological Memory, that has in vitro computational capabilities to “learn” and “store” genomic sequence information directly from genomic samples without knowledge of their explicit sequences, and that discovers differences in vitro between previously unknown inputs and learned memory molecules. The Memory protocol was designed and optimized based upon (1) common in vitro recombinant DNA operations using 20-base random probes, including polymerization, nuclease digestion, and magnetic bead separation, to capture a snapshot of the genomic state of a biological sample as a DNA memory and (2) the thermal stability of DNA duplexes between new input and the memory to detect similarities and differences. For efficient read out, a microarray was used as an output method. When the microarray-based Memory protocol was implemented to test its capability and sensitivity using genomic DNA from two model bacterial strains, i.e., Escherichia coli K12 and Bacillus subtilis, results indicate that the Memory protocol can “learn” input DNA, “recall” similar DNA, differentiate between dissimilar DNA, and detect relatively small concentration differences in samples. CONCLUSIONS: This study demonstrated not only the in vitro information processing capabilities of DNA, but also its promise as a genomic pattern classifier that could access information from all organisms in a biological system without explicit genomic information. The Memory protocol has high potential for many applications, including in situ biomonitoring of ecosystems, screening for diseases, biosensing of pathological features in water and food supplies, and non-biological information processing of memory devices, among many. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1754-1611-8-25) contains supplementary material, which is available to authorized users

Probabilistic base calling of Solexa sequencing data

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots

Design and implementation of computational systems based on programmed mutagenesis

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 35-37).by Julia Khodor.M.S