1 - 26 of 26 results

BeReTa / Beneficial Regulator Targeting

An algorithm for prioritization of transcriptional regulator (TR) manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli.

cRegMCSs / constrained regulatory MCSs

Generalizes the minimal cut sets (MCS)-based framework to constrained regulatory MCSs. cRegMCSs has been integrated as a new functionality in the CellNetAnalyzer package, a MATLAB toolbox for biological network analysis. In comparison to constrained MCSs (cMCSs) involving only reaction deletions, it can lead to (i) a lower number of required modifications and (ii) an increased number of possible intervention strategies. The approach was applied to (i) to a small illustrative network and (ii) to a genome-scale metabolic model of E.coli.

OptStrain

A hierarchical computational framework aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. OptStrain Provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.

GCM / Global Catalogue of Microorganisms

Allows members to organize, make public, and explore their data resources. GCM is an online catalogue and a data management system that contains information from (i) culture collection staff, (ii) public data sources (ii) links to external databases and (iv) tools for bioinformatics analysis including a search engine to explore GCM data. The catalogue helps culture collections manage, disseminate and share the information related to their holdings and allows the scientific and industrial communities to access the comprehensive microbial resource information.

GDLS / Genetic Design through Local Search

A scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.

Redirector

A Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest.

LASER / Learning Assisted Strain EngineeRing

Obsolete
A repository for metabolic engineering strain designs and a formal standard for disseminating designs to metabolic engineers. LASER, provides an immense resource of metabolic engineering knowledge with over 300 papers and 417 designs, representing the known bibliome of papers containing genetically defined yeast and E. coli designs. A public interface for the database has been setup to facilitate this effort and provide a public platform for design storage and analysis.

CosMos

A strain design method with continuous modifications that provides strategies for deletions, downregulations, and upregulations of fluxes that will lead to the production of the desired products. The method is conceptually simple and easy to implement, and can provide additional strategies over current approaches. We found that the method was able to find strain design strategies that required fewer modifications and had larger predicted yields than strategies from previous methods in example and genome-scale networks.

MESSI / Metabolic Engineering target Selection and best Strain Identification tool

Predicts efficient chassis and regulatory components for yeast bio-based production. MESSI provides an integrative platform for users to analyse ready-to-use public high-throughput metabolomic data, which are transformed to metabolic pathway activities for identifying the most efficient S. cerevisiae strain for the production of a compound of interest. As input MESSI accepts metabolite KEGG IDs or pathway names. MESSI outputs a ranked list of S. cerevisiae strains based on aggregation algorithms. Furthermore, through a genome-wide association study of the metabolic pathway activities with the strains’ natural variation, MESSI prioritizes genes and small variants as potential regulatory points and promising metabolic engineering targets. Users can choose various parameters in the whole process such as (i) weight and expectation of each metabolic pathway activity in the final ranking of the strains, (ii) Weighted AddScore Fuse or Weighted Borda Fuse aggregation algorithm, (iii) type of variants to be included, (iv) variant sets in different biological levels.

OptORF

An effective method to systematically integrate transcriptional regulatory networks and metabolic networks. This allows for the formulation of linear optimization problems that search for metabolic and/or regulatory perturbations that couple biomass and biochemical production, thus proposing adaptive evolutionary strain designs. OptORF is a bi-level optimization problem which identifies the optimal metabolic and regulatory gene deletions as well as gene overexpressions that maximize biochemical production at the maximum cellular growth under transcriptional regulatory constraints. The inner problem of OptORF, which is a linear programming (LP) problem, maximizes growth under the given gene deletions and regulatory states that are determined by the constraints of the outer problem. OptORF is formulated as a single level mixed integer linear program (MILP) by replacing the inner maximization problem with its optimality conditions as constraints.

CiED / Cipher of Evolutionary Design

Identifies genetic perturbations, such as gene deletions and other network modifications, that result in optimal phenotypes for the production of end products, such as recombinant natural products. Coupled to an evolutionary search, CiED demonstrates the utility of a purely stoichiometric network to predict improved Escherichia coli genotypes that more effectively channel carbon flux toward malonyl coenzyme A (CoA) and other cofactors in an effort to generate recombinant strains with enhanced flavonoid production capacity.