OptFlux3: an improved platform for in silico design of cellular factories

The rational design of cellular factories for industrial biotechnology aims to create optimized organisms for the production of bulk chemicals, pharmaceuticals, food ingredients and enzymes, among others. Metabolic engineering (ME) plays a key role in this process, supported by the latest advances in genetic engineering in combination with computational tools to define targets for strain improvement. OptFlux is an open-source reference computational platform for the optimization of cellular factories by the application of in silico ME methods, designed for non-computational experts by providing a user-friendly interface. It allows to load genome-scale models from several sources to be used in the prediction of cellular behavior and identification of metabolic targets for genetic engineering. Its latest version, OptFlux3, allows to perform the simulation of wild type and mutant strains (allowing the simulation of gene/ reaction deletion and over/under expression). Regarding strain optimization, the new architecture opts for a multi-objective framework, allowing users to easily add different goals as optimization targets in a flexible way. Specialized multi-objective algorithms, co-exist with traditional single objectives algorithms to be applied for each case. Also, OptFlux3 includes a new visualization framework for metabolic models and phenotype simulations and a new plug-in management interface that allows to install and remove plug-ins in execution time. Currently available plug-ins include the calculation and visualization of elementary modes, topological analysis and the ability to add reactions/ pathways to existing models. OptFlux is made freely available for all major operating systems, together with suitable documentation in www.optflux.org

Water Distribution System Computer-Aided Design by Agent Swarm Optimization

Optimal design of water distribution systems (WDS), including the sizing of components, quality control, reliability, renewal and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly-dimensional, multimodal, non-linear problems, especially given inaccurate, noisy, discrete and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent based systems. It is aimed at supporting decisionmaking processes by solving multi-objective optimization problems. ASO offers robustness through a framework where various population-based algorithms co-exist. The ASO framework is described and used to solve the optimal design of WDS. 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Advances in Data-Driven Modeling and Global Optimization of Constrained Grey-Box Computational Systems

The effort to mimic a chemical plant’s operations or to design and operate a completely new technology in silico is a highly studied research field under process systems engineering. As the rising computation power allows us to simulate and model systems in greater detail through careful consideration of the underlying phenomena, the increasing use of complex simulation software and generation of multi-scale models that spans over multiple length and time scales calls for computationally efficient solution strategies that can handle problems with different complexities and characteristics. This work presents theoretical and algorithmic advancements for a range of challenging classes of mathematical programming problems through introducing new data-driven hybrid modeling and optimization strategies. First, theoretical and algorithmic advances for bi-level programming, multi-objective optimization, problems containing stiff differential algebraic equations, and nonlinear programming problems are presented. Each advancement is accompanied with an application from the grand challenges faced in the engineering domain including, food-energy-water nexus considerations, energy systems design with economic and environmental considerations, thermal cracking of natural gas liquids, and oil production optimization. Second, key modeling challenges in environmental and biomedical systems are addressed through employing advanced data analysis techniques. Chemical contaminants created during environmental emergencies, such as hurricanes, pose environmental and health related risks for exposure. The goal of this work is to alleviate challenges associated with understanding contaminant characteristics, their redistribution, and their biological potential through the use of data analytics

METABOLIC MODELING AND OMICS-INTEGRATIVE ANALYSIS OF SINGLE AND MULTI-ORGANISM SYSTEMS: DISCOVERY AND REDESIGN

Computations and modeling have emerged as indispensable tools that drive the process of understanding, discovery, and redesign of biological systems. With the accelerating pace of genome sequencing and annotation information generation, the development of computational pipelines for the rapid reconstruction of high-quality genome-scale metabolic networks has received significant attention. These models provide a rich tapestry for computational tools to quantitatively assess the metabolic phenotypes for various systems-level studies and to develop engineering interventions at the DNA, RNA, or enzymatic level by careful tuning in the biophysical modeling frameworks. in silico genome-scale metabolic modeling algorithms based on the concept of optimization, along with the incorporation of multi-level omics information, provides a diverse array of toolboxes for new discovery in the metabolism of living organisms (which includes single-cell microbes, plants, animals, and microbial ecosystems) and allows for the reprogramming of metabolism for desired output(s). Throughout my doctoral research, I used genome-scale metabolic models and omics-integrative analysis tools to study how microbes, plants, animal, and microbial ecosystems respond or adapt to diverse environmental cues, and how to leverage the knowledge gleaned from that to answer important biological questions. Each chapter in this dissertation will provide a detailed description of the methodology, results, and conclusions from one specific research project. The research works presented in this dissertation represent important foundational advance in Systems Biology and are crucial for sustainable development in food, pharmaceuticals and bioproduction of the future. Advisor: Rajib Sah

Development of Chemical Process Design and Control for Sustainability

This contribution describes a novel process systems engineering framework that couples advanced control with sustainability evaluation for the optimization of process operations to minimize environmental impacts associated with products, materials and energy. The implemented control strategy combines a biologically-inspired method with optimal control concepts for finding more sustainable operating trajectories. The sustainability assessment of process operating points is carried out by using the U.S. EPA’s Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) tool that provides scores for the selected indicators in the economic, material efficiency, environmental and energy areas. The indicator scores describe process performance on a sustainability measurement scale, effectively determining which operating point is more sustainable if there are more than several steady states for one specific product manufacturing. Through comparisons between a representative benchmark and the optimal steady states obtained through the implementation of the proposed controller, a systematic decision can be made in terms of whether the implementation of the controller is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is illustrated through a case study of a continuous fermentation process for fuel production, whose material and energy time variation models are characterized by multiple steady states and oscillatory conditions

Life cycle assessment (LCA) applied to the process industry: a review

Purpose : Life cycle assessment (LCA) methodology is a well-established analytical method to quantify environmental impacts, which has been mainly applied to products. However, recent literature would suggest that it has also the potential as an analysis and design tool for processes, and stresses that one of the biggest challenges of this decade in the field of process systems engineering (PSE) is the development of tools for environmental considerations. Method : This article attempts to give an overview of the integration of LCA methodology in the context of industrial ecology, and focuses on the use of this methodology for environmental considerations concerning process design and optimization. Results : The review identifies that LCA is often used as a multi-objective optimization of processes: practitioners use LCA to obtain the inventory and inject the results into the optimization model. It also shows that most of the LCA studies undertaken on process analysis consider the unit processes as black boxes and build the inventory analysis on fixed operating conditions. Conclusions : The article highlights the interest to better assimilate PSE tools with LCA methodology, in order to produce a more detailed analysis. This will allow optimizing the influence of process operating conditions on environmental impacts and including detailed environmental results into process industry

Multi-Agent Orbit Design For Perception Enhancement Purpose

This paper develops a robust optimization based method to design orbits on which the sensory perception of the desired physical quantities are maximized. It also demonstrates how to incorporate various constraints imposed by many spacecraft missions such as collision avoidance, co-orbital configuration, altitude and frozen orbit constraints along with Sun-Synchronous orbit. The paper specifically investigates designing orbits for constrained visual sensor planning applications as the case study. For this purpose, the key elements to form an image in such vision systems are considered and effective factors are taken into account to define a metric for perception quality. The simulation results confirm the effectiveness of the proposed method for several scenarios on low and medium Earth orbits as well as a challenging Space-Based Space Surveillance program application.Comment: 12 pages, 18 figure

Framework for operability assessment of production facilities: an application to a primary unit of a crude oil refinery

This work focuses on the development of a methodology for the optimization, control and operability of both existing and new production facilities through an integrated environment of different technologies like process simulation, optimization and control systems. Such an integrated environment not only creates opportunities for op¬erational decision making but also serves as training tool for the novice engineers. It enables them to apply engineering expertise to solve challenges unique to the process industries in a safe and virtual environment and also assist them to get familiarize with the existing control systems and to understand the fundamentals of the plant operation. The model-based methodology proposed in this work, starts with the implementation of first principle models for the process units on consideration. The process model is the core of the methodology. The state of art simulation technologies have been used to model the plant for both steady state and dynamic state conditions. The models are validated against the plant operating data to evaluate the reliability of the models. Then it is followed by rigorously posing a multi-optimization problem. In addition to the basic economic variables such as raw materials and operating costs, the so-called “triple-bottom-line” variables related with sustainable and environmental costs are incorporated into the objective function. The methodologies of Life Cycle Assessment (LCA) and Environmental Damage Assessment (EDA) are applied within the optimization problem. Subsequently the controllability of the plant for the optimum state of conditions is evaluated using the dynamic state simulations. Advanced supervisory control strategies like the Model Predictive Control (MPC) are also implemented above the basic regulatory control. Finally, the methodology is extended further to develop training simulator by integrating the simulation case study to the existing Distributed Control System (DCS). To demonstrate the effectiveness of the proposed methodology, an industrial case study of the primary unit of the crude oil refinery and a laboratory scale packed distillation unit is thoroughly investigated. The presented methodology is a promising approach for the operability study and optimization of production facilities and can be extended further for an intelligent and fully-supportable decision making