A fuzzy multiobjective algorithm for multiproduct batch plant: Application to protein production

This paper addresses the problem of the optimal design of batch plants with imprecise demands and proposes an alternative treatment of the imprecision by using fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm (MOGA) developed in previousworks, taking into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage

Optimisation-based Framework for Resin Selection Strategies in Biopharmaceutical Purification Process Development

This work addresses rapid resin selection for integrated chromatographic separations when conducted as part of a high-throughput screening (HTS) exercise during the early stages of purification process development. An optimisation-based decision support framework is proposed to process the data generated from microscale experiments in order to identify the best resins to maximise key performance metrics for a biopharmaceutical manufacturing process, such as yield and purity. A multiobjective mixed integer nonlinear programming (MINLP) model is developed and solved using the ε-constraint method. Dinkelbach's algorithm is used to solve the resulting mixed integer linear fractional programming (MILFP) model. The proposed framework is successfully applied to an industrial case study of a process to purify recombinant Fc Fusion protein from low molecular weight and high molecular weight product related impurities, involving two chromatographic steps with 8 and 3 candidate resins for each step, respectively. The computational results show the advantage of the proposed framework in terms of computational efficiency and flexibility. This article is protected by copyright. All rights reserved

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation

Toward Performance Improvement of a Baculovirus–Insect Cell System under Uncertain Environment: A Robust Multiobjective Dynamic Optimization Approach for Semibatch Suspension Culture

The baculovirus expression vector system (BEVS) is one of the well-known versatile platforms for the recombinant protein/vaccine production. Mathematical modeling and optimization of a baculovirus-insect cell system can have significant industrial relevance as this reduces the number of expensive experiments and time involved in the experiment-based optimization. However, modeling and control of such a nonlinear system remains challenging due to the presence of uncertainties in the model. In this context, we propose a novel computational framework combining the principles of systems biology and dynamic optimization under uncertainty for optimizing a semibatch baculovirus-insect cell system. Toward this, first, a mathematical model replicating the dynamic experimental data on cell and virus growth was identified. Next, the proposed model was used for deterministic multiobjective dynamic optimization of the control variables, substrate, and multiplicity of infection (MOI) to achieve the conflicting objectives of productivity maximization and substrate minimization, simultaneously. Finally, based on the sensitivity analysis, six of the most influential parameters depicting model uncertainties have been considered for the robust multiobjective optimal control of the system. A comprehensive comparison displays up to 114% and 76% increases in the cell densities for the deterministic and stochastic semibatch processes, respectively, compared to the batch process. Semibatch operation also favors a minimum 40% reduction in MOI required to achieve the same level of infected cell density compared to the batch operation. This study provides a generic methodology for exhibiting a proof of concept that a semibatch suspension culture considering uncertainty in model parameters can give better productivity compared to a batch suspension culture for a BEVS

Model discrimination in time-course kinetics : the glyoxalase pathway in S. cerevisiae

Tese de doutoramento, Bioquímica (Bioquímica Teórica), Universidade de Lisboa, Faculdade de Ciências, 2010.The present work addresses the problem of model discrimination in enzyme kinetics. Frequently, more than one kinetic model is considered during the characterization of an enzymatic reaction or a metabolic pathway. The statistical selection of a model may be difficult if the candidate models fit the experimental data with very similar fitting scores. Since each model corresponds to a different possible mechanism of the studied process, model selection also reflects the choice of a particular mechanism. In addition, predictions given by models with equal fitting scores may be different. The glyoxalase system is a metabolic pathway that has been studied using two alternative kinetic models. These models could not be discriminated despite extensive kinetic experiments and an alternative branched mechanism combining the two models has been proposed. This pathway is therefore ideal for model discrimination research in Biochemistry. The glyoxalase pathway comprises the enzymes glyoxalase I and glyoxalase II. Glyoxalase I catalyzes the isomerization of the hemithioacetal that forms from the condensation of methylglyoxal (a by-­product of glycolysis) and glutathione to S-­D-­lactoylglutathione. Glyoxalase II catalyzes the hydrolysis of S-­D-­glutathione to D-­ lactate and glutathione. The methylglyoxal-­glutathione hemithioacetal forms spontaneously without the presence of enzymes. Therefore the glyoxalase I reaction can be described either as irreversible single-­substrate or as irreversible two-­substrate, considering that the hemithioacetal forms before binding the enzyme or that it forms in the active centre of the enzyme after sequential binding of glutathione and methylglyoxal, respectively. The glyoxalase system is the most important catabolic pathway for methylglyoxal. Methylglyoxal is a toxic agent due to its ability to react with proteins and nucleic acid amine groups that leads to formation of advanced glycation end-­products. Therefore the glyoxalase pathway was suggested to be a potential dug target for its cellular defensive role against methylglyoxal. An introduction to the subjects developed through this dissertation is given in chapter 1, covering the state of art of research on the glyoxalase pathway and methylglyoxal metabolism and on relevant mathematical and computational methods for model analysis and discrimination. In chapter 2 the glyoxalase system is investigated by analyzing the algebraic solutions of the rate equations describing the pathway at steady state. The two mentioned glyoxalase I kinetic models were used in this approach. It is observed that for the existence of a steady state a minimum amount of glutathione must be available;; in addition, glyoxalase I and II activities must exceed thresholds higher than the flux of the pathway. It is shown that methylglyoxal steady-­state concentration is not sensitive to variations of glyoxalase II activity but varies significantly with total glutathione concentration and methylglyoxal formation rate. Sensitivity to glyoxalase I activity depends on the kinetic model describing the enzyme: highly sensitivity if the two-­ substrate model is used but not so for the one-­substrate model. The pathway seems to operate very far from the conditions of disruption of the physiological steady state to assure a very low methylglyoxal concentration and a fast regeneration and high concentration of free glutathione. Time-­course kinetic studies with purified yeast enzyme and yeast permeabilized cells are described in chapter 3. Akaike’s information criterion and residual analysis are used to discuss the selection of the most appropriate kinetic model for glyoxalase I. Parameter least-­square estimates for this study are obtained with a combination of the stochastic Differential Evolution with the deterministic downhill-­simplex optimization algorithms. Although the two-­substrate model performs slightly better for the purified enzyme data, the Akaike score differences for both data sets and the residual analysis for the permeabilized cell data are not conclusive. A method developed to design optimized experimental conditions for model discrimination is explained in chapter 4. The method employs a multiobjective optimization algorithm (the Generalized Differential Evolution, generation 3) to search for the experimental conditions that maximize the divergence between the reaction time courses predicted by the models. The Kullback-­Leibler distance is the measure of divergence employed. The combination of the chosen algorithm and divergence criterion is successful in finding solutions that result in very different predictions from the two models for glyoxalase I in the presence of glyoxalase II, proving to be useful for planning model discrimination experiments. The importance of keeping a high free glutathione concentration seems to establish the properties of the glyoxalase pathway identified in chapter 2. Glutathione is also a key antioxidant and its oxidized form is reduced through the glutathione reductase system at the expense of NADPH. Indeed, the pyridine nucleotides NADPH and NADH have crucial metabolic roles. NADH, formed mainly in catabolic reactions, is the substrate of the respiratory chain and therefore it ultimately supplies the synthesis of ATP. NADPH is the main reducing agent in biosynthetic pathways. In addition, the pyridine nucleotides are among the metabolites that participate in a larger number of reactions in the cell. Therefore it is important to understand the effects of concentration changes of these metabolites. In chapter 5 perturbations to pyridine nucleotide concentrations are studied in living yeast cells cultured in bioreactors. The results for five recombinant S. cerevisiae strains overexpressing a cytosolic NADH oxidase, a mitochondrial NADH oxidase, a cytosolic NADH kinase, a mitochondrial NADH kinase and a cytosolic soluble pyridine nucleotide transhydrogenase are discussed. Extracellular and intracellular metabolite measurements and a stoichiometric model are used to assess the consequences of such perturbations, unveiling how metabolism in intact cells adapts to different redox conditions. Strains with enhanced NADH oxidation in the cytosol show a lower glycerol production. On the other hand enhanced NADH consumption in the mitochondrion lowers ethanol production and enhances ATP synthesis efficiency. The results presented here show that different kinetic models may fit experimental data equally well, making the selection of one model extremely. An original contribution is established to aid planning experiments for model discrimination. In addition, a broad characterization of the effects of perturbations to pyridine nucleotide metabolism is given, which is valuable to understand the complex response of yeast’s metabolic network, with direct biotechnological application.Fundação para a Ciência e Tecnologia – Ministério da Ciência, Tecnologia e Ensino Superior, Portugal (SFRH/BD/21947/2005

A Comprehensive Optimization Framework for Designing Sustainable Renewable Energy Production Systems

As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. The objective of this research is to develop and implement a novel decision-making framework for the optimal design of renewable energy systems. The proposed optimization framework is based on a distributed, systematic approach which is composed of different layers including systems-based strategic optimization, detailed mechanistic modeling and operational level optimization. In the strategic optimization the model is represented by equations which describe physical flows of materials across the system nodes and financial flows that result from the system design and material movements. Market uncertainty is also incorporated into the model through stochastic programming. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). The second stage consists of three main steps including simulation of the process in the simulation software, identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty. To exemplify the efficacy of the proposed framework a hypothetical lignocellulosic biorefinery based on sugar conversion platform that converts biomass to value-added biofuels and biobased chemicals is utilized as a case study. Furthermore, alternative technology options and possible process integrations in each section of the plant are analysed by exploiting the advantages of process simulation and the novel hybrid optimization framework. In conjunction with the simulation and optimization studies, the proposed framework develops quantitative metrics to associate economic values with technical barriers. The outcome of this work is a new distributed decision support framework which is intended to help economic development agencies, as well as policy makers in the renewable energy enterprises

Production planning of biopharmaceutical manufacture.

Multiproduct manufacturing facilities running on a campaign basis are increasingly becoming the norm for biopharmaceuticals, owing to high risks of clinical failure, regulatory pressures and the increasing number of therapeutics in clinical evaluation. The need for such flexible plants and cost-effective manufacture pose significant challenges for planning and scheduling, which are compounded by long production lead times, intermediate product stability issues and the high cost - low volume nature of biopharmaceutical manufacture. Scheduling and planning decisions are often made in the presence of variable product titres, campaign durations, contamination rates and product demands. Hence this thesis applies mathematical programming techniques to the planning of biopharmaceutical manufacture in order to identify more optimal production plans under different manufacturing scenarios. A deterministic mixed integer linear programming (MILP) medium term planning model which explicitly accounts for upstream and downstream processing is presented. A multiscenario MILP model for the medium term planning of biopharmaceutical manufacture under uncertainty is presented and solved using an iterative solution procedure. An alternative stochastic formulation for the medium term planning of biomanufacture under uncertainty based on the principles of chance constrained programming is also presented. To help manage the risks of long term capacity planning in the biopharmaceutical industry, a goal programming extension is presented which accounts for multiple objectives including cost, risk and customer service level satisfaction. The model is applied to long term capacity analysis of a mix of contractors and owned biopharmaceutical manufacturing facilities. In the final sections of this thesis an example of a commercial application of this work is presented, followed by a discussion on related validation issues in the biopharmaceutical industry. The work in this thesis highlighted the benefits of applying mathematical programming techniques for production planning of biopharmaceutical manufacturing facilities, so as to enhance the biopharmaceutical industry's strategic and operational decision-making towards achieving more cost-effective manufacture