Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

Background Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space. Methods Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process. Results We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis. Conclusions Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences

Systematic Methods for Reaction Solvent Design and Integrated Solvent and Process Design

Otto-von-Guericke-Universität Magdeburg, Fakultät für Verfahrens- und Systemtechnik, Dissertation, 2016by: M. Sc. Teng ZhouLiteraturverzeichnis: Seite 100-10

APPLICATION OF PROCESS SYSTEMS ENGINEERING TOOLS AND METHODS TO FERMENTATION-BASED BIOREFINERIES

Biofuels produced from lignocellulosic biomass via the fermentation platform are sustainable energy alternatives to fossil fuels. Process Systems Engineering (PSE) uses computer-based tools and methods to design, simulate and optimize processes. Application of PSE tools to the design of economic biorefinery processes requires the development of simulation approaches that can be integrated with existing, mature PSE tools used to optimize traditional refineries, such as Aspen Plus. Current unit operation models lack the ability to describe unsteady state fermentation processes, link unsteady state fermentation with in situ separations, and optimize these processes for competing factors (e.g., yield and productivity). This work applies a novel architecture of commercial PSE tools, Aspen Plus and MATLAB, to develop techniques to simulate time-dependent fermentation without and with in situ separations for process design, analyses and optimization of the operating conditions. Traditional batch fermentation simulations with in situ separations decouple these interdependent steps in a separate “steady state” reactor followed by an equilibrium separation of the final fermentation broth. A typical mechanistic system of ordinary differential equations (ODEs) describing a batch fermentation does not fit the standard built-in power law reaction kinetics model in Aspen Plus. To circumvent this challenge, a novel platform that links the batch reactor to a FORTRAN user kinetics subroutine (incorporates the ODEs) combined with component substitution (to simulate non-databank components) is utilized to simulate an unsteady state batch and in situ gas stripping process. The resulting model system predicts the product profile to be sensitive to the gas flow rate unlike previous “steady state” simulations. This demonstrates the importance of linking a time-dependent fermentation model to the fermentation environment for the design and analyses of fermentation processes. A novel platform linking the genetic algorithm multi-objective and single-objective optimizations in MATLAB to the unsteady state batch fermentation simulation in Aspen Plus through a component object module communication platform is utilized to optimize the operating conditions of a typical batch fermentation process. Two major contributions are: prior concentration of sugars from a typical lignocellulosic hydrolysate may be needed and with a higher initial sugar concentration, the fermentation process must be integrated with an in situ separation process to optimize the performance of fermentation processes. With this framework, fermentation experimentalists can use the full suite of PSE tools and methods to integrate biorefineries and refineries and as a decision-support tool to guide the design, analyses and optimization of fermentation-based biorefineries

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

Efficient power generation from low to medium grade heat is an important challenge to be addressed to ensure a sustainable energy future. Organic Rankine Cycles (ORCs) constitute an important enabling technology and their research and development has emerged as a very active research field over the past decade. Particular focus areas include working fluid selection and cycle design to achieve efficient heat to power conversions for diverse hot fluid streams associated with geothermal, solar or waste heat sources. Recently, a number of approaches have been developed that address the systematic selection of efficient working fluids as well as the design, integration and control of ORCs. This paper presents a review of emerging approaches with a particular emphasis on computer-aided design methods

Systematic approaches for design of ionic liquids and their mixtures for enhanced carbon capture purpose

Post-combustion capture using amine-based solvents has been considered as the most viable technology for carbon capture, to mitigate industrial carbon dioxide (CO2) emissions; but the solvents show a number of shortcomings. Recently, ionic liquids (ILs) are suggested as possible alternative to amine-based solvents, for they can be molecularly engineered to match various target thermophysical properties. This work focused on the development of systematic approaches to design IL-based solvents for carbon capture purpose. The first focus of this work is to develop an insight-based based visual approach to determine potential IL solvents as substitute to conventional carbon capture solvents. This approach allows visualisation of high-dimensional problem to be visualised in two or three dimensions, and assist designers without mathematical programming background in IL design. Following that, a mathematical optimisation approach to design optimal IL solvent for CO2 capture purpose was developed as second focus of this thesis. This has been done by formulating the IL solvent design problem as mixed integer non-linear programming (MINLP) optimisation problem. The abovementioned approaches were developed to design task-specific ILs with high CO2 absorption capacity as substitute to common carbon capture solvents. However, studies show that such ILs are relatively more expensive and have higher viscosities. To reduce the cost and viscosity of solvent, task-specific IL can be mixed with conventional IL, ensuring CO2 solubility remains high, while viscosity and cost are acceptable. Hence, the previously developed visual approach was extended to design pure ILs and IL mixtures, specifically to capture CO2. In order to ensure the designed IL is performing at its optimum (highest CO2 solubility in this case), the operating conditions of the carbon capture process shall be considered because they will affect the thermophysical properties and CO2 solubility of ILs. Therefore, the forth focus of this work will be incorporation of operating temperature and pressure into design of IL solvents. Similarly, the design problem was formulated as MINLP problem and solved using mathematical optimisation approach, where operating temperature and pressure were defined as variables through disjunctive programming. Replacing solvent for carbon capture system to IL-based solvent or installing carbon capture system will affect the overall process, as this will affect the utilities consumption of carbon capture system. Therefore, process design has been integrated with IL design in this thesis, to study how the solvent substitution affects the entire process, and followed by retrofitting of the entire process including carbon capture system accordingly. The design problem was formulated and solved as MINLP problem. Finally, this thesis concludes with possible extensions and future works in this area of research work

Systematic approaches for design of ionic liquids and their mixtures for enhanced carbon capture purpose

Post-combustion capture using amine-based solvents has been considered as the most viable technology for carbon capture, to mitigate industrial carbon dioxide (CO2) emissions; but the solvents show a number of shortcomings. Recently, ionic liquids (ILs) are suggested as possible alternative to amine-based solvents, for they can be molecularly engineered to match various target thermophysical properties. This work focused on the development of systematic approaches to design IL-based solvents for carbon capture purpose. The first focus of this work is to develop an insight-based based visual approach to determine potential IL solvents as substitute to conventional carbon capture solvents. This approach allows visualisation of high-dimensional problem to be visualised in two or three dimensions, and assist designers without mathematical programming background in IL design. Following that, a mathematical optimisation approach to design optimal IL solvent for CO2 capture purpose was developed as second focus of this thesis. This has been done by formulating the IL solvent design problem as mixed integer non-linear programming (MINLP) optimisation problem. The abovementioned approaches were developed to design task-specific ILs with high CO2 absorption capacity as substitute to common carbon capture solvents. However, studies show that such ILs are relatively more expensive and have higher viscosities. To reduce the cost and viscosity of solvent, task-specific IL can be mixed with conventional IL, ensuring CO2 solubility remains high, while viscosity and cost are acceptable. Hence, the previously developed visual approach was extended to design pure ILs and IL mixtures, specifically to capture CO2. In order to ensure the designed IL is performing at its optimum (highest CO2 solubility in this case), the operating conditions of the carbon capture process shall be considered because they will affect the thermophysical properties and CO2 solubility of ILs. Therefore, the forth focus of this work will be incorporation of operating temperature and pressure into design of IL solvents. Similarly, the design problem was formulated as MINLP problem and solved using mathematical optimisation approach, where operating temperature and pressure were defined as variables through disjunctive programming. Replacing solvent for carbon capture system to IL-based solvent or installing carbon capture system will affect the overall process, as this will affect the utilities consumption of carbon capture system. Therefore, process design has been integrated with IL design in this thesis, to study how the solvent substitution affects the entire process, and followed by retrofitting of the entire process including carbon capture system accordingly. The design problem was formulated and solved as MINLP problem. Finally, this thesis concludes with possible extensions and future works in this area of research work

Novel integrated design techniques for biorefineries

Utilisation of biomass is identified as one of the promising solutions to reduce society’s dependence on fossil fuels and mitigate climate change caused by the exploitation of fossil fuels. By using the concept of biorefinery, biomass can be converted into value-added products such as biofuels, biochemical products and biomaterials in a greener and sustainable way. To enhance the efficiency of biorefinery, the concept of integrated biorefinery which focuses on the integration of various biomass conversion technologies is utilised. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the substantial amount of potential products and conversion technologies, determining of chemical products and processing routes in an integrated biorefinery have become more challenging. Hence, there is a need for a methodology capable of evaluating the integrated process in order to identify the optimal products as well as the optimal conversion pathways that produce the identified products. This thesis presents a novel approach which integrates process with product design techniques for integrated biorefineries. In the proposed approach, integration between synthesis of integrated biorefinery and computer-aided molecular design (CAMD) techniques is presented. By using CAMD techniques, optimal chemical product in terms of target properties which fulfils the required product needs is designed. On the other hand, in order to identify the conversion pathways that produce the identified optimal chemical product in an integrated biorefinery, chemical reaction pathway map (CRPM) and superstructural mathematical optimisation approach have been utilised. Furthermore, this thesis also presents various chemical product design approaches. In order to solve chemical design problems where multiple product needs are required to be considered and optimised, a novel multi-objective optimisation approach for chemical product design has been presented. By using fuzzy optimisation approach, the developed multi-objective optimisation approach identifies optimal chemical product based on multiple product properties. In addition, fuzzy optimisation approach has been further extended to address chemical product design problems where the accuracy of property prediction model is taken into account. A robust chemical product design approach is developed to design optimal chemical products with consideration of accuracy of property prediction model. Furthermore, together with CAMD techniques and superstructural mathematical optimisation approach, the developed multi-objective optimisation approach has been utilised for the design of mixtures in an integrated biorefinery. For this purpose, a systematic optimisation approach has been developed to identify optimal mixture based on multiple desired product needs as well as the optimal conversion pathways that convert biomass into the optimal mixture. Finally, possible extensions and future opportunities for the realm of the research work have been highlighted in the later part of this thesis

Multi-scale modeling and optimization for industries with formulated products

[ES] La tesis titulada "Multi-scale Modeling and optimization for Industries with Formulated Products" se centra en el desarrollo de modelos matemáticos y técnicas de optimización para este tipo de productos. Por un lado la tesis se focaliza en modelado de secadores con diferentes metodologías. Primero, se desarrolla un modelo cinético de secado de una una única gota. Luego, se desarrolla un modelo basado en mecánica de fluidos computacional (CFD) para los secadores y el cuál se ha validado a escala industrial. Finalmente, se desarrollan modelos basados en "data-driven" y modelos subrogados para reducir el coste computacional del modelo en CFD sin perder su nivel de detalle. Por otro lado, la tesis tiene una segunda parte donde se focaliza en el desarrollo de modelos de optimización matemática para el tratamiento de residuos y la revalorización del biogás