Understanding the Properties of Generated Corpora

Models for text generation have become focal for many research tasks and especially for the generation of sentence corpora. However, understanding the properties of an automatically generated text corpus remains challenging. We propose a set of tools that examine the properties of generated text corpora. Applying these tools on various generated corpora allowed us to gain new insights into the properties of the generative models. As part of our characterization process, we found remarkable differences in the corpora generated by two leading generative technologies

Computational Design of Auxotrophy-Dependent Microbial Biosensors for Combinatorial Metabolic Engineering Experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools)

Efficient Modeling of MS/MS Data for Metabolic Flux Analysis

<div><p>Metabolic flux analysis (MFA) is a widely used method for quantifying intracellular metabolic fluxes. It works by feeding cells with isotopic labeled nutrients, measuring metabolite isotopic labeling, and computationally interpreting the measured labeling data to estimate flux. Tandem mass-spectrometry (MS/MS) has been shown to be useful for MFA, providing positional isotopic labeling data. Specifically, MS/MS enables the measurement of a metabolite tandem mass-isotopomer distribution, representing the abundance in which certain parent and product fragments of a metabolite have different number of labeled atoms. However, a major limitation in using MFA with MS/MS data is the lack of a computationally efficient method for simulating such isotopic labeling data. Here, we describe the tandemer approach for efficiently computing metabolite tandem mass-isotopomer distributions in a metabolic network, given an estimation of metabolic fluxes. This approach can be used by MFA to find optimal metabolic fluxes, whose induced metabolite labeling patterns match tandem mass-isotopomer distributions measured by MS/MS. The tandemer approach is applied to simulate MS/MS data in a small-scale metabolic network model of mammalian methionine metabolism and in a large-scale metabolic network model of <i>E</i>. <i>coli</i>. It is shown to significantly improve the running time by between two to three orders of magnitude compared to the state-of-the-art, cumomers approach. We expect the tandemer approach to promote broader usage of MS/MS technology in metabolic flux analysis. Implementation is freely available at <a href="http://www.cs.technion.ac.il/~tomersh/methods.html" target="_blank">www.cs.technion.ac.il/~tomersh/methods.html</a></p></div

An outline of the tandemers approach.

<p>First, given MS/MS measurements and a metabolic model, a minimal set of MFPs is identified constructing an MFP graph. Second, MFPs are clustered and sorted and third, isotopic balance equations are formulated for each MFP cluster. Given a candidate flux vector, tandemer distributions are calculated by solving the set of isotopic balance equations.</p

Efficient Modeling of MS/MS Data for Metabolic Flux Analysis - Fig 1

<p>(a) Metabolite A and its MFP </p><p></p><p></p><p></p><p><mi>A</mi></p><p></p><p></p><p><mn>2,3</mn></p><p></p><p></p><p></p><p></p><p><mn>2,3</mn><mo>,</mo><mn>4</mn></p><p></p><p></p><p></p><p></p><p></p> with parent fragment N = {2,3,4} and product fragment K = {2,3}. (b) The tandemer distribution matrix <p></p><p></p><p></p><p><mo>[</mo><mi>A</mi><mo>]</mo></p><p></p><p></p><p><mn>2,3</mn></p><p></p><p></p><p></p><p></p><p><mn>2,3</mn><mo>,</mo><mn>4</mn></p><p></p><p></p><p></p><p></p><p></p>. The abundance of infeasible tandemers in <p></p><p></p><p></p><p><mo>[</mo><mi>A</mi><mo>]</mo></p><p></p><p></p><p><mn>2,3</mn></p><p></p><p></p><p></p><p></p><p><mn>2,3</mn><mo>,</mo><mn>4</mn></p><p></p><p></p><p></p><p></p><p></p> is, by definition, zero.<p></p

Comparison of the performance of the cumomers and tandemers methods in calculating tandemer distributions on mammalian methionine and <i>E</i>. <i>coli</i> networks.

<p>Comparison of the performance of the cumomers and tandemers methods in calculating tandemer distributions on mammalian methionine and <i>E</i>. <i>coli</i> networks.</p

The distribution of isotopomers of metabolite A (shown in Fig 2) within tandemers of A, defined with respect to A2,32,3,4.

<p>Isotopomers are represented by sequences of zeroes and ones, denoting non-labeled and labeled atoms, respectively.</p><p>The distribution of isotopomers of metabolite A (shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130213#pone.0130213.g002" target="_blank">Fig 2</a>) within tandemers of A, defined with respect to </p><p></p><p></p><p></p><p><mi>A</mi></p><p></p><p></p><p><mn>2,3</mn></p><p></p><p></p><p></p><p></p><p><mn>2,3</mn><mo>,</mo><mn>4</mn></p><p></p><p></p><p></p><p></p><p></p>.<p></p

Methionine metabolism, including transmethylation cycle, polyamine biosynthesis and methionine salvage cycle.

<p>Metabolites abbreviations: SAM: S-Adenosylmethionine; SAH: S-Adenosylhomocysteine; HCyc: L-Homocysteine; MTA: Methylthioadenosine.</p