A theoretical model for template-free synthesis of long DNA sequence

This theoretical scheme is intended to formulate a potential method for high fidelity synthesis of Nucleic Acid molecules towards a few thousand bases using an enzyme system. Terminal Deoxyribonucleotidyl Transferase, which adds a nucleotide to the 3′OH end of a Nucleic Acid molecule, may be used in combination with a controlled method for nucleotide addition and degradation, to synthesize a predefined Nucleic Acid sequence. A pH control system is suggested to regulate the sequential activity switching of different enzymes in the synthetic scheme. Current practice of synthetic biology is cumbersome, expensive and often error prone owing to the dependence on the ligation of short oligonucleotides to fabricate functional genetic parts. The projected scheme is likely to render synthetic genomics appreciably convenient and economic by providing longer DNA molecules to start with

Optimal design of gene knockout experiments for gene regulatory network inference

Motivation: We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference. Results: We developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates. Conclusions: REDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality. Availability and implementation: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Ensemble inference of <i>S. cerevisiae</i> GRN.

<p>Let of any two digraphs and denote the number of edges in the set .</p

Ensemble Inference and Inferability of Gene Regulatory Networks

<div><p>The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of <i>E. coli</i> and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 <i>in silico</i> network inference challenge.</p></div

Effects of and on AUROC and AUPR of TRaCE in DREAM4 100-gene subchallenge: single gene KO data.

<p>The AUROC and AUPR values are the average standard devation over 5 gold standard networks. The values used in the comparison with existing methods are highlighted in bold.</p

Ensemble inference and inferability of random scale-free networks of order (a) and (b) genes.

<p>The mean network distances of the lower and upper bounds from are shown as a function of network size (i.e. number of edges). The error bars indicate the standard deviations.</p

Effects of , and the preprocessing threshold of on AUROC and AUPR of TRaCE in DREAM4 100-gene subchallenge: single- and double-gene KO data.

<p>The AUROC and AUPR values are the average standard devation over 5 gold standard GRNs. The values used in the comparison with existing methods are highlighted in bold.</p

Examples of the ensemble inference of random networks with 10 genes.

<p>In case I, the GRN has 8 edges and is inferable from the accessibility matrices and as few as three 's. In case II, the GRN has 13 edges and is not inferable.</p

Construction of accessibility matrix from expression data.

<p>The data come from KOs of genes in the set and an additional gene , . For each replicate, the expression data are arranged into a matrix where the rows correspond to the experiments and the columns correspond to the genes. (1) The sample mean and standard deviation of the expression of gene , denoted by and , respectively, are obtained using the expression data in the -th column of the data matrix. (2) For each replicate, a z-score matrix is computed according to Eq. (5). (3) Subsequently, the z-score matrices are averaged over the technical replicates to give . (4) The accessibility matrix is determined from based on a threshold criterion in Eq. (6).</p