MOFA: Multi-Objective Flux Analysis for the COBRA Toolbox

Multi-objective Optimization (MO) is an important tool for quantitative examination of the trade-offs faced by biological organisms. Using genome-scale constraint-based models of metabolism (GSMs),Multi-Objective Flux Analysis (MOFA) allows MO analyses of trade-offs among key biological tasks. The leading software package for conducting a plethora of different types of constraint-based analyses using GSMs is the COBRA Toolbox for MATLAB. We have developed a new add-on tool for this toolbox using Normalized Normal Constraint (NNC) that performs MOFA for a number of objectives only limited by computation power (n≤10). This development will facilitate MOFA analyses by COBRA's large user base and allow greater multi-faceted examination of metabolic trade-offs in complicated biological systems. Availability and Implementation: The MOFA software is freely available for download from https://bbs.llnl.gov under the GPL v2 license. The program runs on MATLAB with the COBRA software on Windows, Linux, and MacOS. It includes a detailed manual explaining the input and output of a simulation, a listing of the code's functions, and an example MOFA run using a well-curated GSM model of E. coli.

Development of a computational efficient tool for robust structural optimization

Purpose – Optimization under a deterministic approach generally leads to a final design in which the performance may degrade significantly and/or constraints can be violated because of perturbations arising from uncertainties. The purpose of this paper is to obtain a better strategy that would obtain an optimum design which is less sensitive to changes in uncertain parameters. The process of finding these optima is referred to as robust design optimization (RDO), in which improvement of the performance and reduction of its variability are sought, while maintaining the feasibility of the solution. This overall process is very time consuming, requiring a robust tool to conduct this optimum search efficiently. Design/methodology/approach – In this paper, the authors propose an integrated tool to efficiently obtain RDO solutions. The tool encompasses suitable multiobjective optimization (MO) techniques (encompassing: Normal-Boundary Intersection, Normalized Normal-Constraint, weighted sum method and min-max methods), a surrogate model using reduced order method for cheap function evaluations and adequate procedure for uncertainties quantification (Probabilistic Collocation Method). Findings – To illustrate the application of the proposed tool, 2D structural problems are considered. The integrated tool prove to be very effective reducing the computational time by up to five orders of magnitude, when compared to the solutions obtained via classical standard approaches. Originality/value – The proposed combination of methodologies described in the paper, leads to a very powerful tool for structural optimum designs, considering uncertainty parameters, that can be extended to deal with other class of applications.