Antibiotic Molecular Design Using Artificial Bee Colony Algorithm

Research is acutely needed to develop novel therapies to treat resistant infections. This project aims to design a drug molecule via a computer aided molecular design approach to provide lead candidates for the treatment of bacterial infections caused by Staphylococcus aureus. In a recently published WHO report, a list of bacteria which pose the greatest threat to human health was given. The purpose of this report was to identify the most important resistant bacteria at global level for which immediate treatment is required. Staphylococcus aureus, which is on this list, is a pathogen causing infections such as pneumonia and bone disorders. A methodology which determines the structures of candidate antibiotic molecules is described. The Artificial Bee Colony algorithm has been used for the first time for molecular design in this work. It is necessary to predict physical and/or biological properties of compounds in order to design them. The prediction of properties is performed using Quantitative Structure Property Relationships (QSPRs). QSPRs are equations, which are developed using reported data for properties of interest by the method of regression analysis. This work applies connectivity indices and 3D MoRSE descriptors to develop QSPRs. The properties used in this work are minimum inhibitory concentration and Log P values. 3D MoRSE descriptors have been used for the first time for molecular design in this work. The QSPRs are combined with structural feasibility and connectivity constraints to formulate an optimization problem, which is a mixed integer nonlinear program (MINLP). Because of the large number of potential chemical structures and the uncertainty in the structure-property correlations, stochastic algorithms are preferred to solve the resulting MINLP. One stochastic algorithm which has shown promise to solve these problems is the Artificial Bee Colony algorithm, which relies on principles of swarm intelligence to find near-optimal solutions efficiently. The Artificial Bee Colony algorithm described in this work is used to derive solutions which serve as lead compounds for a narrowed search for novel antibiotics. Results show that the ABC algorithm is very effective in finding near optimal solutions to the MINLP, which is a combinatorial optimization problem. Molecular structures were obtained by optimizing objective function for individual property values and simultaneously for both the properties

Molecular design of interfacial modifications to alter adsorption/desorption equilibria at fluid-adjoining interfaces

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2013."February 2013." Cataloged from PDF version of thesis.Includes bibliographical references (p. 151-168).The thermodynamics and mass transfer kinetics of adsorption and desporption at interfaces play vital roles in chemical analysis, separation processes, and many natural phenomena. In this work, computer simulations were used to design interfacial modifications to alter the physical processes of adsorption and desorption, using two different approaches to molecular design. In the first application, the finite-temperature string method was used to elucidate the mechanism of water's evaporation at its liquid/vapor interface, with the goal of designing a soluble additive that could impede evaporation there. These simulations used the SPC/E water model, and identified a minimum free energy path for this process in terms of 10 descriptive order parameters. The measured free energy change was 7.4 kcal/mol at 298 K, in reasonable agreement with the experimental value of 6.3 kcal/mol, and the mean first-passage time was 1375 ns for a single molecule, corresponding to an evaporation coefficient of 0.25. In the observed minimum free energy process, the water molecule diffuses to the surface, and tends to rotate so that its dipole and one 0-H bond are oriented outward as it crosses the Gibbs dividing surface. As the molecule moves further outwards through the interfacial region, a local solvation shell tends to protrudes from the interface. The water molecule loses donor and acceptor hydrogen bonds, and then, with its dipole nearly normal to the interface, stops donating its remaining donor hydrogen bond. After the final, accepted hydrogen bond is broken, the water molecule is free. An analysis of reactive trajectories showed that the relative orientation of nearby water molecules, and the number of accepted hydrogen bonds, were important variables in a kinetic description of the process. In the second application, we developed an in silico screening process to design organic ligands which, when chemically bound to a solid surface, would constitute an effective adsorption for a pharmaceutically relevant mixture of reaction products. This procedure employs automated molecular dynamics simulations to evaluate potential ligands, by measuring the difference in adsorption energy of two solutes which differed by one functional group. Then, a genetic algorithm was used to iteratively improve a population of ligands through selection and reproduction steps. This procedure identified chemical designs of the surface-bound ligands that were outside the set considered using chemical intuition. The ligand designs achieved selectivity by exploiting phenyl-phenyl stacking which was sterically hindered in the case of one solution component. The ligand designs had selectivity energies of 0.8 to 1.6 kcal/mol in single-ligand, solvent-free simulations, if entropic contributions to the relative selectivity are neglected. This molecular evolution technique presents a useful method for the directed exploration of chemical space or for molecular design.by Nicholas J. Musolino.Sc.D

A Meshless Modelling Framework for Simulation and Control of Nonlinear Synthetic Biological Systems

Synthetic biology is a relatively new discipline that incorporates biology and engineering principles. It builds upon the advances in molecular, cell and systems biology and aims to transform these principles to the same effect that synthesis transformed chemistry. What distinguishes synthetic biology from traditional molecular or cellular biology is the focus on design and construction of components (e.g. parts of a cell) that can be modelled, characterised and altered to meet specific performance criteria. Integration of these parts into larger systems is a core principle of synthetic biology. However, unlike some areas of engineering, biology is highly non-linear and less predictable. In this thesis the work that has been conducted to combat some of the complexities associated with dynamic modelling and control of biological systems will be presented. Whilst traditional techniques, such as Orthogonal Collocation on Finite Elements (OCFE) are common place for dynamic modelling they have significant complexity when sampling points are increased and offer discrete solutions or solutions with limited differentiability. To circumvent these issues a meshless modelling framework that incorporates an Artificial Neural Network (ANN) to solve Ordinary Differential Equations (ODEs) and model dynamic processes is utilised. Neural networks can be considered as mesh-free numerical methods as they are likened to approximation schemes where the input data for a design of a network consists of a set of unstructured discrete data points. The use of the ANN provides a solution that is differentiable and is of a closed analytic form, which can be further utilised in subsequent calculations. Whilst there have been advances in modelling biological systems, there has been limited work in controlling their outputs. The benefits of control allow the biological system to alter its state and either upscale production of its primary output, or alter its behaviour within an integrated system. In this thesis a novel meshless Nonlinear Model Predictive Control (NLMPC) framework is presented to address issues related to nonlinearities and complexity. The presented framework is tested on a number of case studies. A significant case study within this work concerns simulation and control of a gene metabolator. The metabolator is a synthetic gene circuit that consists of two metabolite pools which oscillate under the influence of glycolytic flux (a combination of sugars, fatty acids and glycerol). In this work it is demonstrated how glycolytic flux can be used as a control variable for the metabolator. The meshless NLMPC framework allows for both Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) control. The dynamic behaviour of the metabolator allows for both top-down control (using glycolytic flux) and bottom-up control (using acetate). The benefit of using MIMO (by using glycolytic flux and acetate as the control variables) for the metabolator is that it allows the system to reach steady state due to the interactions between the two metabolite pools. Biological systems can also encounter various uncertainties, especially when performing experimental validation. These can have profound effect on the system and can alter the dynamics or overall behaviour. In this work the meshless NLMPC framework addresses uncertainty through the use of Zone Model Predictive Control (Zone MPC), where the control profile is set as a range, rather than a fixed set point. The performance of Zone MPC under the presence of various magnitudes of random disturbances is analysed. The framework is also applied to biological systems architecture, for instance the development of biological circuits from well-characterised and known parts. The framework has shown promise in determining feasible circuits and can be extended in future to incorporate a full list of biological parts. This can give rise to new circuits that could potentially be used in various applications. The meshless NLMPC framework proposed in this work can be extended and applied to other biological systems and heralds a novel method for simulation and control

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

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