High-Throughput Screening of Acyl-CoA Thioesterase I Mutants Using a Fluid Array Platform

Screening target microorganisms from a mutated recombinant library plays a crucial role in advancing synthetic biology and metabolic engineering. However, conventional screening tools have several limitations regarding throughput, cost, and labor. Here, we used the fluid array platform to conduct high-throughput screening (HTS) that identified Escherichia coli ???TesA thioesterase mutants producing elevated yields of free fatty acids (FFAs) from a large (106) mutant library. A growth-based screening method using a TetA-RFP fusion sensing mechanism and a reporter-based screening method using high-level FFA producing mutants were employed to identify these mutants via HTS. The platform was able to cover >95% of the mutation library, and it screened target cells from many arrays of the fluid array platform so that a post-analysis could be conducted by gas chromatography. The ???TesA mutation of each isolated mutant showing improved FFA production in E. coli was characterized, and its enhanced FFA production capability was confirmed

Controlled open-cell two-dimensional liquid foam generation for micro- and nanoscale patterning of materials

Liquid foam consists of liquid film networks. The films can be thinned to the nanoscale via evaporation and have potential in bottom-up material structuring applications. However, their use has been limited due to their dynamic fluidity, complex topological changes, and physical characteristics of the closed system. Here, we present a simple and versatile microfluidic approach for controlling two-dimensional liquid foam, designing not only evaporative microholes for directed drainage to generate desired film networks without topological changes for the first time, but also microposts to pin the generated films at set positions. Patterning materials in liquid is achievable using the thin films as nanoscale molds, which has additional potential through repeatable patterning on a substrate and combination with a lithographic technique. By enabling direct-writable multi-integrated patterning of various heterogeneous materials in two-dimensional or three-dimensional networked nanostructures, this technique provides novel means of nanofabrication superior to both lithographic and bottom-up state-of-the-art techniques

Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms

Understanding the genetic basis of human variation is an important goal of biomedical research. In this study, we used structural equation models (SEMs) to construct genetic networks to model how specific single-nucleotide polymorphisms (SNPs) from two genes known to cause acute myeloid leukemia (AML) by somatic mutation, runt-related transcription factor 1 (RUNX1) and ets variant gene 6 (ETV6), affect expression levels of other genes and how RUNX1 and ETV6 are related to each other. The SEM approach allows us to compare several candidate models from which an explanatory genetic network can be constructed

Robust imputation method for missing values in microarray data

<p>Abstract</p> <p>Background</p> <p>When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis cannot be applied when the data have missing values. Numerous imputation algorithms have been proposed to estimate the missing values. In this study, we develop a robust least squares estimation with principal components (RLSP) method by extending the local least square imputation (LLSimpute) method. The basic idea of our method is to employ quantile regression to estimate the missing values, using the estimated principal components of a selected set of similar genes.</p> <p>Results</p> <p>Using the normalized root mean squares error, the performance of the proposed method was evaluated and compared with other previously proposed imputation methods. The proposed RLSP method clearly outperformed the weighted <it>k</it>-nearest neighbors imputation (kNNimpute) method and LLSimpute method, and showed competitive results with Bayesian principal component analysis (BPCA) method.</p> <p>Conclusion</p> <p>Adapting the principal components of the selected genes and employing the quantile regression model improved the robustness and accuracy of missing value imputation. Thus, the proposed RLSP method is, according to our empirical studies, more robust and accurate than the widely used kNNimpute and LLSimpute methods.</p

Boolean networks using the chi-square test for inferring large-scale gene regulatory networks

BACKGROUND: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that its computation time is very high or often impractical to construct large-scale gene networks. We propose a variable selection method that are not only reduces BN computation times significantly but also obtains optimal network constructions by using chi-square statistics for testing the independence in contingency tables. RESULTS: Both the computation time and accuracy of the network structures estimated by the proposed method are compared with those of the original BN methods on simulated and real yeast cell cycle microarray gene expression data sets. Our results reveal that the proposed chi-square testing (CST)-based BN method significantly improves the computation time, while its ability to identify all the true network mechanisms was effectively the same as that of full-search BN methods. The proposed BN algorithm is approximately 70.8 and 7.6 times faster than the original BN algorithm when the error sizes of the Best-Fit Extension problem are 0 and 1, respectively. Further, the false positive error rate of the proposed CST-based BN algorithm tends to be less than that of the original BN. CONCLUSION: The CST-based BN method dramatically improves the computation time of the original BN algorithm. Therefore, it can efficiently infer large-scale gene regulatory network mechanisms

A 72 × 60 Angle-Sensitive SPAD Imaging Array for Lens-less FLIM

We present a 72 × 60, angle-sensitive single photon avalanche diode (A-SPAD) array for lens-less 3D fluorescence lifetime imaging. An A-SPAD pixel consists of (1) a SPAD to provide precise photon arrival time where a time-resolved operation is utilized to avoid stimulus-induced saturation, and (2) integrated diffraction gratings on top of the SPAD to extract incident angles of the incoming light. The combination enables mapping of fluorescent sources with different lifetimes in 3D space down to micrometer scale. Futhermore, the chip presented herein integrates pixel-level counters to reduce output data-rate and to enable a precise timing control. The array is implemented in standard 180 nm complementary metal-oxide-semiconductor (CMOS) technology and characterized without any post-processing

Response projected clustering for direct association with physiological and clinical response data

<p>Abstract</p> <p>Background</p> <p>Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients' residual tumor sizes after chemotherapy or glucose levels at various stages of diabetic patients. Current clustering analysis cannot directly incorporate such quantitative metadata into the clustering heatmap of gene expression. It will be quite useful if these clinical response data can be effectively summarized in the high-dimensional clustering display so that important groups of genes can be intuitively discovered with different degrees of relevance to target disease phenotypes.</p> <p>Results</p> <p>We introduced a novel clustering analysis approach, <it>response projected clustering </it>(RPC), which uses a high-dimensional geometrical projection of response data to the gene expression space. The projected response vector, which becomes the origin in the projected space, is then clustered together with the projected gene vectors based on their different degrees of association with the response vector. A bootstrap-counting based RPC analysis is also performed to evaluate statistical tightness of identified gene clusters. Our RPC analysis was applied to the <it>in vitro </it>growth-inhibition and microarray profiling data on the NCI-60 cancer cell lines and the microarray gene expression study of macrophage differentiation in atherogenesis. These RPC applications enabled us to identify many known and novel gene factors and their potential pathway associations which are highly relevant to the drug's chemosensitivity activities and atherogenesis.</p> <p>Conclusion</p> <p>We have shown that RPC can effectively discover gene networks with different degrees of association with clinical metadata. Performed on each gene's response projected vector based on its degree of association with the response data, RPC effectively summarizes individual genes' association with metadata as well as their own expression patterns. Thus, RPC greatly enhances the utility of clustering analysis on investigating high-dimensional microarray gene expression data with quantitative metadata.</p