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Screw feeders, as the initial operation in continuous manufacturing of drug product processes, greatly influence the mass flow rate of pharmaceutical powders downstream. Existing flowsheet models can quickly simulate the average powder mass flow rate while custom Discrete Element Method models require prohibitively long times to simulate a minute of realistic, high-variance particle flow. We propose a hybrid deterministic-stochastic feeder flowsheet model that leverages time series analysis and an Autoregressive Moving Average (ARMA) model to quantify and simulate the observed non-random variation in feeder powder flow. To allow for improved process and controller design, our approach is quick-to-solve, high-variance, and has a low experimental overhead. By examining the deterministic model errors of three different volumetrically fed excipients, we demonstrate that the errors are leptokurtic, heavy-tailed, and display a linear dependence on their prior two seconds of state. These errors are all reasonably modeled by an ARMA(2,1) model and are parametrically distinct from each other. Furthermore, we show that refilling the feeder online significantly alters the error distribution, autocorrelation structure, and ARMA parameters. These findings lay the groundwork necessary to model and predict the realistic feeder dynamics of a much broader range of powders and operating conditions., (Copyright © 2022 Elsevier B.V. All rights reserved.)

A multitude of metrics exist to assign scores to synthetic routes within computer-aided synthesis planning (CASP) tools. A quantitative scoring method is necessary to identify the most promising synthetic approaches to a molecule. However, current CASP tools are limited in their capacity to evaluate reaction selectivity and are unable to fully account for the effect of side products on the purification sequences associated with chemical syntheses. We develop a novel quantitative metric called ExtractionScore for evaluating synthetic routes based on the predicted identities of side products as well as the separability of major and side products by liquid-liquid extraction based on chemical property prediction. By comparing industrially practiced routes to a collection of 200 pharmaceutically relevant compounds with routes suggested by state-of-the-art CASP software, we show that ExtractionScore may improve retrosynthetic recommendations by incorporating information about the formation of side products.

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

Protein structure alignment is the problem of determining an assignment between the amino-acid residues of two given proteins in a way that maximizes a measure of similarity between the two superimposed protein structures. By identifying geometric similarities, structure alignment algorithms provide critical insights into protein functional similarities. Existing structure alignment tools adopt a two-stage approach to structure alignment by decoupling and iterating between the assignment evaluation and structure superposition problems. We introduce a novel approach, SAS-Pro, which addresses the assignment evaluation and structure superposition simultaneously by formulating the alignment problem as a single bilevel optimization problem. The new formulation does not require the sequentiality constraints, thus generalizing the scope of the alignment methodology to include non-sequential protein alignments. We employ derivative-free optimization methodologies for searching for the global optimum of the highly nonlinear and non-differentiable RMSD function encountered in the proposed model. Alignments obtained with SAS-Pro have better RMSD values and larger lengths than those obtained from other alignment tools. For non-sequential alignment problems, SAS-Pro leads to alignments with high degree of similarity with known reference alignments. The source code of SAS-Pro is available for download at http://eudoxus.cheme.cmu.edu/saspro/SAS-Pro.html.

De novo sequence assembly is a ubiquitous combinatorial problem in all DNA sequencing technologies. In the presence of errors in the experimental data, the assembly problem is computationally challenging, and its solution may not lead to a unique reconstruct. The enumeration of all alternative solutions is important in drawing a reliable conclusion on the target sequence, and is often overlooked in the heuristic approaches that are currently available. In this paper, we develop an integer programming formulation and global optimization solution strategy to solve the sequence assembly problem with errors in the data. We also propose an efficient technique to identify all alternative reconstructs. When applied to examples of sequencing-by-hybridization, our approach dramatically increases the length of DNA sequences that can be handled with global optimality certificate to over 10,000, which is more than 10 times longer than previously reported. For some problem instances, alternative solutions exhibited a wide range of different ability in reproducing the target DNA sequence. Therefore, it is important to utilize the methodology proposed in this paper in order to obtain all alternative solutions to reliably infer the true reconstruct. These alternative solutions can be used to refine the obtained results and guide the design of further experiments to correctly reconstruct the target DNA sequence., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Motivation: The Basic Local Alignment Search Tool (BLAST) is one of the most widely used bioinformatics tools. The widespread impact of BLAST is reflected in over 53,000 citations that this software has received in the past two decades, and the use of the word 'blast' as a verb referring to biological sequence comparison. Any improvement in the execution speed of BLAST would be of great importance in the practice of bioinformatics, and facilitate coping with ever increasing sizes of biomolecular databases., Results: Using a general-purpose graphics processing unit (GPU), we have developed GPU-BLAST, an accelerated version of the popular NCBI-BLAST. The implementation is based on the source code of NCBI-BLAST, thus maintaining the same input and output interface while producing identical results. In comparison to the sequential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 and 4., Availability: The source code of GPU-BLAST is freely available at http://archimedes.cheme.cmu.edu/biosoftware.html.

The graphics processing unit (GPU) is used to solve large linear systems derived from partial differential equations. The differential equations studied are strongly convection-dominated, of various sizes, and common to many fields, including computational fluid dynamics, heat transfer, and structural mechanics. The paper presents comparisons between GPU and CPU implementations of several well-known iterative methods, including Kaczmarz's, Cimmino's, component averaging, conjugate gradient normal residual (CGNR), symmetric successive overrelaxation-preconditioned conjugate gradient, and conjugate-gradient-accelerated component-averaged row projections (CARP-CG). Computations are preformed with dense as well as general banded systems. The results demonstrate that our GPU implementation outperforms CPU implementations of these algorithms, as well as previously studied parallel implementations on Linux clusters and shared memory systems. While the CGNR method had begun to fall out of favor for solving such problems, for the problems studied in this paper, the CGNR method implemented on the GPU performed better than the other methods, including a cluster implementation of the CARP-CG method.

This paper discusses recent optimization approaches to the protein side-chain prediction problem, protein structural alignment, and molecular structure determination from X-ray diffraction measurements. The machinery employed to solve these problems has included algorithms from linear programming, dynamic programming, combinatorial optimization, and mixed-integer nonlinear programming. Many of these problems are purely continuous in nature. Yet, to this date, they have been approached mostly via combinatorial optimization algorithms that are applied to discrete approximations. The main purpose of the paper is to offer an introduction and motivate further systems approaches to these problems.

Paclitaxel loaded biodegradable poly-(DL-lactic-co-glycolic) acid (PLGA) foams with microporous matrix were fabricated by a novel pressure quenching approach to provide a sustained paclitaxel release. The foams with micropores provided increased surface area to volume ratio and were also implantable for post-surgical chemotherapy applications. The two formulations 5% (w/w) paclitaxel loaded PLGA 85:15 foam (F1) and 10% (w/w) paclitaxel loaded PLGA 50:50 foam (F2), were evaluated in vitro and in vivo. Both the foams were found to provide a paclitaxel release beyond a month in vitro with a near zero-order kinetics and with minimum burst release. Furthermore, apoptosis of C6 glioma cells in vitro demonstrated the benefits of sustained paclitaxel release by the foams in comparison to acute Taxol exposure. Both the foams exhibited continuous paclitaxel release in an in vivo (subcutaneous) environment up to a month which correlated well with the in vitro release profiles. Bio-distribution results in the rat brain showed paclitaxel penetration at therapeutic levels up to 3mm into the tissue from the site of foam implantation. Hence these foams could be employed as potential implants for post-surgical chemotherapy against malignant glioma.

A new version of the direct-methods program SnB has been developed. This version incorporates the triplet sieve method for phasing centrosymmetric structures in a way that is transparent to users. The triplet sieve procedure may decrease significantly the time required to achieve a solution for such structures.

Aligning proteins based on their structural similarity is a fundamental problem in molecular biology with applications in many settings, including structure classification, database search, function prediction, and assessment of folding prediction methods. Structural alignment can be done via several methods, including contact map overlap (CMO) maximization that aligns proteins in a way that maximizes the number of common residue contacts. In this paper, we develop a reduction-based exact algorithm for the CMO problem. Our approach solves CMO directly rather than after transformation to other combinatorial optimization problems. We exploit the mathematical structure of the problem in order to develop a number of efficient lower bounding, upper bounding, and reduction schemes. Computational experiments demonstrate that our algorithm runs significantly faster than existing exact algorithms and solves some hard CMO instances that were not solved in the past. In addition, the algorithm produces protein clusters that are in excellent agreement with the SCOP classification. An implementation of our algorithm is accessible as an on-line server at http://eudoxus.scs.uiuc.edu/cmos/cmos.html.

In this paper, a new integer minimal principle model for centrosymmetric structures is presented; one which fully accounts for reciprocal-space phase shifts present in non-symmorphic space groups. Additionally, characterization of false minima of the model is done in terms of even and odd triplets. Based on this characterization, a triplet sieve method is proposed. First, Gaussian elimination using only a subset of reliable triplets is employed for phasing. Triplet subsets are generated using a progressively smaller set of the strongest reflections. Several phase solution sets are generated by enumerating the degrees of freedom present. To facilitate computational evaluation of the quality of these phase solutions, these phase sets are passed into the crystallographic software SnB, which expands the reflection set in two cycles. The final solution is identified via statistics of two crystallographic figures of merit. Computational results are presented for a variety of structures.

Drug-releasing implants delivering chemotherapeutic and radio-sensitizing agents are beginning to play a major role in the post-surgical eradication of residual glioma in the brain. Benefits from early arresting of tumor growth and tumor recovery dynamics stress the impact of drug release profiles of the implants on the efficacy of the treatment. This paper examines responses of BALB/c nude mice, bearing C6 glioma tumors subcutaneously, to treatments by PLGA microspheres, microparticles and discs-delivering Paclitaxel and Etanidazole. The experimental results are used to correlate the efficacy of treatment to in vitro release profiles from the various formulations. Our study demonstrates that radio-sensitizing effects during irradiation could be achieved by double burst profiles from Etanidazole-loaded discs, when compared to controls 17 days after implantation despite the short half-life of Etanidazole (1.4h) in vivo. These results also showed inhibited tumor growth on tumor volumes of 59%, 65% and 70% over the blank placebo groups after 21 days of tumor growth for spray-dried microspheres, electrohydrodynamic atomization microparticles and spray-dried discs, respectively.

Motivation: The protein side-chain conformation problem is a central problem in proteomics with wide applications in protein structure prediction and design. Computational complexity results show that the problem is hard to solve. Yet, instances from realistic applications are large and demand fast and reliable algorithms., Results: We propose a new global optimization algorithm, which for the first time integrates residue reduction and rotamer reduction techniques previously developed for the protein side-chain conformation problem. We show that the proposed approach simplifies dramatically the topology of the underlining residue graph. Computations show that our algorithm solves problems using only 1-10% of the time required by the mixed-integer linear programming approach available in the literature. In addition, on a set of hard side-chain conformation problems, our algorithm runs 2-78 times faster than SCWRL 3.0, which is widely used for solving these problems., Availability: The implementation is available as an online server at http://eudoxus.scs.uiuc.edu/r3.html

The minimal principle for structure determination from single-crystal X-ray diffraction measurements has recently been formulated as an integer linear optimization model for the case of centrosymmetric structures. Solution of this model via established combinatorial branch-and-bound algorithms provides the true global minimum of the minimal principle while operating exclusively in reciprocal space. However, integer programming techniques may require an exponential number of iterations to exhaust the search space. In this paper, a new approach is developed to solve the integer minimal principle to global optimality without requiring the solution of an optimization problem. Instead, properties of the solution of the optimization problem, as observed in a large number of computational experiments, are exploited in order to reduce the optimization formulation to a system of linear equations in the number field of two elements (F(2)). Two specialized Gaussian elimination algorithms are then developed to solve this system of equations in polynomial time in the number of atoms. Computational results on a collection of 38 structures demonstrate that the proposed approach provides very fast and accurate solutions to the phase problem for centrosymmetric structures. This approach also provided much better crystallographic R values than SHELXS for all 38 structures tested.

The problem addressed in this paper is the determination of three-dimensional structures of centrosymmetric crystals from X-ray diffraction measurements. The 'minimal principle' that a certain quantity is minimized only by the crystal structure is employed to solve the phase problem. The mathematical formulation of the minimal principle is a nonconvex nonlinear optimization problem. To date, local optimization techniques and advanced computer architectures have been used to solve this problem, which may have a very large number of local optima. In this paper, the minimal principle model is reformulated for the case of centrosymmetric structures into an integer programming problem in terms of the missing phases. This formulation is solvable by well established combinatorial optimization techniques that are guaranteed to provide the global optimum in a finite number of steps without explicit enumeration of all possible combinations of phases. Computational experience with the proposed method on a number of structures of moderate complexity is provided and demonstrates that the approach yields a fast and reliable method that resolves the crystallographic phase problem for the case of centrosymmetric structures.