Cellular vulnerabilities of glioblastoma

Glioblastoma (GB) is the most fatal and frequent malignant brain tumor, and it is driven by multiple oncogenic pathways. Despite intensive screening of genomic, transcriptomic, metabolic, and post-translational landscape of GB, targeted therapies have provided no improvements for the survival of GB patients. This incurability of GB is due to its infiltrative growth, intratumoral heterogeneity and intrinsic resistance towards treatment modalities which are driven by its subpopulations, such as glioblastoma stem cells (GSCs). Therefore, it is crucial to try to understand the mechanisms of GBs cellular resistance and potential vulnerabilities of GSCs. In this thesis we demonstrate alternative targets for GB therapy. Protein phosphatase 2A (PP2A) is inhibited in GB by non-genetic mechanisms, therefore, its therapeutic reactivation is possible. We described that small molecule reactivators of PP2A (SMAPs) efficiently cross the blood-brain barrier (BBB) and exhibit robust cytotoxicity towards heterogenous GB cell lines. Furthermore, we present specific kinases which inhibition induce synthetic lethality under PP2A reactivation. Collectively, these studies present SMAPs as a novel therapy for GB and propose an alternatives for multikinase inhibitors. In GB, nanoparticles have been researched for their potential to circumvent insufficient drug properties. However, opposed to traditional utilization of nanoparticles, we discovered an alternative use of them in GB. We demonstrated that mesoporous silica nanoparticles (MSNs) functionalized with polyethylenimine (PEI) induce cell death specifically in GSCs. The PEI-MSNs accumulated in the lysosomes of GSCs and caused lysosomal membrane permeabilization potentially through proton sponge effect. Furthermore, we determined that PEI-MSNs efficiently cross the BBB in mice. In summary, this thesis presents a novel therapy concepts for GB.Glioblastooman solutason haavoittuvuudet Glioblastooma (GB) on yleisin ja pahanlaatuisin aivosyöpä, jossa useat onkogeeniset signalointipolut ovat yliaktiivisia. Huolimatta genomiikan, transkriptomiikan, metabolomiikan ja translaation jälkeisten muutosten intensiivisestä seulonnasta GB:ssa, kohdennetut hoidot eivät ole tuottaneen lisäelinaikaa GB-potilaille. GB:n hoidon vaikeus johtuu sen infiltratiivisesta kasvusta, kasvaimen sisäisestä heterogeenisyydestä ja synnynnäisestä resistenssistä hoitoja vastaan. Syynä näihin on usein glioblastooman kantasolut. Tästä syystä, on erittäin tärkeää pyrkiä ymmärtämään GB:n solutason resistanssimekanismeja ja glioblastooman kantasolujen potentiaalisia heikkouksia. Tässä väitöskirjassa esitämme uusia kohteita GB:n hoitoon. GB:ssa proteiinifosfataasi 2A (PP2A) on estetty muilla tavoin kuin geneettisillä mekanismeilla. Tästä syystä sen terapeuttinen uudelleenaktivointi on mahdollista. Osoitimme tutkimuksissamme, että pienimolekyyliset PP2A aktivaattorit (SMAP) läpäisevät veri-aivoesteen ja ovat sytotoksisia GB:n heterogeenisiä solulinjoja kohtaan. Tämän lisäksi selvitimme, minkä kinaasien hiljentäminen altistaa GBsoluja entisestään PP2A:n aktivaatiolle. Yhteenvetona tutkimus esittää SMAP lääkkeet uutena terapiamuotona GB:n hoitoon ja ehdottaa vaihtoehtoja multikinaasiestäjille. Nanopartikkelitutkimus GB:aan liittyen on pääasiassa pyrkinyt parantamaan lääkkeiden ominaisuuksia. Me löysimme kuitenkin vaihtoehtoisen tavan käyttää nanopartikkeleita GB:ssa. Osoitimme, että mesohuokoiset piioksidi-nanopartikkelit, jotka on pinnoitettu polyetyyliemiinillä, aiheuttavat solukuoleman glioblastooman kantasoluissa. Kyseiset nanopartikkelit kerääntyivät glioblastooman kantasolujen lysosomeihin ja aiheuttivat sen membraanin tuhoutumisen ”proton sponge” efektin avulla. Kokonaisuudessaan väitöskirja esittää uusia heikkouksia glioblastooman kantasoluissa

Efficient scheduling of batch processes in continuous processing lines

This thesis focuses mainly on the development of efficient formulations for scheduling in industrial environments. Likewise, decisions over the processes more related to advanced process control or production planning are included in the scheduling; in this way, the schedule obtained will be more efficient than it would be if the additional restrictions were not considered. The formulations have to emphasize obtaining online implementations, as they are planned to be used in real plants. The most common scheduling problems handled in the industrial environments are: the assignment of tasks to units, the distribution of production among parallel units and the distribution of shared resources among concurrent processes. Most advances in this work are the result of a collaborative work.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable

Co-Design of Time-Invariant Dynamical Systems

Design of a physical system and its controller has significant ramifications on the overall system performance. The traditional approach of first optimizing the physical design and then the controller may lead to sub-optimal solutions. This is due to the interdependence between the physical design and control parameters through the dynamic equations. Recognition of this fact paved the way for investigation into the ``Co-Design" research theme wherein the overall system's physical design and control are simultaneously optimized. Co-design involves simultaneous optimization of the design and the control variables with respect to certain structural property as constraint. The structural property may be in the form of stability, observability or controllability leading to different types of co-design problems. Co-design optimization problems are non-convex optimization problems involving bilinear matrix inequality (BMI) constraints and are NP-hard in general. In this dissertation, four interrelated research tasks in the area of co-design are undertaken. In the first research task, a theoretical and computational framework is developed to co-design a class of linear time invariant (LTI) dynamical systems. A novel solution procedure based on an iterative combination of generalized Benders decomposition and gradient projection method is developed guaranteeing convergence to a solution in a finite number of iterations which is within a tolerance bound from the nearest local/global minimum. In the second research task, the sparse and structured static feedback design problem is modeled as a co-design problem. A formulation based on the alternating direction method of multipliers is used to solve the sparse feedback design problem which has given robustness as a constraint. In the third research task, the optimal actuator placement problem is formulated as a co-design problem. The actuator positions are modeled as $0/1-$binary design variables and result in a mixed integer nonlinear programming (MINLP) problem. In the fourth research task, a heuristic procedure to place sensors and design observer is developed for a class of Lipschitz nonlinear systems. The procedure is based on the relation between Lipschitz constant, sensor locations and observer gain. The vast and diverse application potential of co-design across all engineering branches is the primary motivation and relevance of the research work carried out in this dissertation

Efficient information collection in stochastic optimisation

This thesis focuses on a class of information collection problems in stochastic optimisation. Algorithms in this area often need to measure the performances of several potential solutions, and use the collected information in their search for high-performance solutions, but only have a limited budget for measuring. A simple approach that allocates simulation time equally over all potential solutions may waste time in collecting additional data for the alternatives that can be quickly identified as non-promising. Instead, algorithms should amend their measurement strategy to iteratively examine the statistical evidences collected thus far and focus computational efforts on the most promising alternatives. This thesis develops new efficient methods of collecting information to be used in stochastic optimisation problems. First, we investigate an efficient measurement strategy used for the solution selection procedure of two-stage linear stochastic programs. In the solution selection procedure, finite computational resources must be allocated among numerous potential solutions to estimate their performances and identify the best solution. We propose a two-stage sampling approach that exploits a Wasserstein-based screening rule and an optimal computing budget allocation technique to improve the efficiency of obtaining a high-quality solution. Numerical results show our method provides good trade-offs between computational effort and solution performance. Then, we address the information collection problems that are encountered in the search for robust solutions. Specifically, we use an evolutionary strategy to solve a class of simulation optimisation problems with computationally expensive blackbox functions. We implement an archive sample approximation method to ix reduce the required number of evaluations. The main challenge in the application of this method is determining the locations of additional samples drawn in each generation to enrich the information in the archive and minimise the approximation error. We propose novel sampling strategies by using the Wasserstein metric to estimate the possible benefit of a potential sample location on the approximation error. An empirical comparison with several previously proposed archive-based sample approximation methods demonstrates the superiority of our approaches. In the final part of this thesis, we propose an adaptive sampling strategy for the rollout algorithm to solve the clinical trial scheduling and resource allocation problem under uncertainty. The proposed sampling strategy method exploits the variance reduction technique of common random numbers and the empirical Bernstein inequality in a statistical racing procedure, which can balance the exploration and exploitation of the rollout algorithm. Moreover, we present an augmented approach that utilises a heuristic-based grouping rule to enhance the simulation efficiency by breaking down the overall action selection problem into a selection problem involving small groups. The numerical results show that the proposed method provides competitive results within a reasonable amount of computational time

A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation