Computational Design Methods Research Articles

Beyond geometric complexity: a critical review of complexity theory and how it relates to architecture engineering and construction

ABSTRACTThe wide application of digital design, the advances of digital fabrication and robotic processes have facilitated the materialization of bespoke geometries. In turn it has raised the issue of how architects can reduce design complexity using computational techniques. This paper presents a survey on complexity theory inclusive of work from the disciplines which range from cybernetics to systems and information theory. We synthesize a taxonomy of different definitions of complexity and ways of managing design complexity by decomposing its different levels as they relate to the fields of architecture, engineering and construction. Our hypothesis is that by reviewing the literature on complexity theory which appears to be highly fragmented, we can aid designers build a better understanding of the underlying principles. Thus designers can develop a more system approach towards the use of digital design tools and make use of concepts coming from the field of complexity theory such as abstraction, adaptation and self-organization in order to come up with novel computational design methods. Such methods can enable designers to deal with design problems holistically and manage design complexity in the contemporary digital design context.

Computational pastoralism

What we call ‘computational pastoralism’ visualizes cities as naturalized systems of provisional flows and cycles. We find this conception of cities problematic because it neglects the sociopolitical dimensions and human needs of cities. While landscape urbanism and later ecological urbanism initially criticized pastoral aesthetics, computational pastoralism has been increasingly advanced in the online environments of landscape and ecological urbanism. We speculate that this is due to the historical development of landscape and ecological urbanism and the rise of specific computational design methods. These approaches include computational design’s capacity to produce natural-looking forms, the generation of simulations of natural systems through relational models, the trend towards hyperreal images and the sheer reproducibility of digital images. We also argue that computational pastoralism has been exacerbated by landscape and ecological urbanism’s expansion alongside the spread of social media. Finally, we suggest that these historical parallels with social media may be a reason why computational pastoralism has become so widespread, while at the same time increasingly detached from the social and political lives of those who live in cities.

Adaptive low computational cost optimisation method for performance-based seismic design of friction dampers

This study aims to improve the computational efficiency and convergence rate of an already existing optimisation method for friction-based dampers based on the concept of Uniform Distribution of Deformation (UDD) and demonstrate the reliability of the results compared to Heuristic optimisation methods such as GA. In the proposed approach, the computational cost is considerably reduced by using a convergence factor that is modified in proportion to the level of performance violation. To investigate the efficiency of the proposed method, 3, 5 and 10-storey RC frames with friction dampers are optimised using adaptive UDD method, Genetic Algorithm (GA) and a coupled UDD-GA approach. The results indicate that the adaptive UDD method can lead to optimum design solutions with significantly lower computational costs (up to 300 times lower number of non-linear dynamic analyses) compared to both GA and coupled UDD-GA methods. It is shown that frames optimised under a single spectrum-compatible earthquake can efficiently satisfy the predefined performance targets under a set of synthetic earthquakes representing the design spectrum. Therefore, the proposed method should provide a reliable approach for more efficient design of friction-based dampers.

Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions.

Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.

Eschatological scrutiny of unprofessional usage of molecular docking; how unreliability in computational methods arises from amateurish mistakes

Abstract The impact of computational methods in drug design and discovery is prevailing in both academic and industrial scales. Molecular docking is one of the favorite tools for assessment of the interactions between a ligand and its congener macromolecule. In silico approaches and especially molecular docking are gaining much attention in recent years due to their cost-effective nature. In this letter to editor, we want to briefly describe how an undisciplined and unorganized research with molecular docking can result in highly equivocal results. This discussion can be useful for other scientist to avoid these pitfalls. This paper addresses the article by V. Suganya and V. Anuradha entitled “In silico molecular docking of astaxanthin and sorafenib with different apoptotic proteins involved in hepatocellular carcinoma” published in 6th of March 2019 on Biocatalysis and Agricultural Biotechnology.

Exploring Low-Occlusion Qwerty Soft Keyboard Using Spatial Landmarks

The Qwerty soft keyboard is widely used on mobile devices. However, keyboards often consume a large portion of the touchscreen space, occluding the application view on the smartphone and requiring a separate input interface on the smartwatch. Such space consumption can affect the user experience of accessing information and the overall performance of text input. In order to free up the screen real estate, this article explores the concept of Sparse Keyboard and proposes two new ways of presenting the Qwerty soft keyboard. The idea is to use users’ spatial memory and the reference effect of spatial landmarks on the graphical interface. Our final design K3-SGK displays only three keys while L5-EYOCN displays only five line segments instead of the entire Qwerty layout. To achieve this, we employ a user-centered computational design method: first study the reference effect of a single landmark key (line segment) from empirical data, then make assumptions to generalize the effect to multiple landmarks, and finally optimize the best designs. To make the text entry function more complete, we also design and implement gestural interactions for editing operations and non-alphabetical characters’ input. User evaluation shows that participants can quickly learn how to type with K3-SGK and L5-EYOCN . After five 15-phrase typing sessions, participants achieve 88.1%--92.8% of the full Qwerty keyboard in terms of words per minute on the smartphone and 98.4%--99.1% on the smartwatch. The differences on character and word error rate between our keyboard designs and the full Qwerty keyboard are not significant. The results of out-of-vocabulary words input are also promising. In addition, participants can quickly recall the typing skills and maintain the input performance even after a few days. User feedbacks in real application contexts show that with the low occlusion keyboard, users can acquire more information and perform less scrolling on the smartphone and achieve a higher input efficiency on the smartwatch with a more fluent input experience.

Computational Methods in Design and Manufacturing Processes

Computational Methods in Design and Manufacturing Processes

Rational Design of Peptide Ligands Based on Knob–Socket Protein Packing Model Using CD13 as a Prototype Receptor

Structure-based computational peptide design methods have gained significant interest in recent years owing to the availability of structural insights into protein–protein interactions obtained from the crystal structures. The majority of these approaches design new peptide ligands by connecting the crucial amino acid residues from the protein interface and are generally not based on any predicted receptor–ligand interaction. In this work, a peptide design method based on the Knob–Socket model was used to identify the specific ligand residues packed into the receptor interface. This method enables peptide ligands to be designed rationally by predicting amino acid residues that will fit best at the binding site of the receptor protein. In this, specific peptide ligands were designed for the model receptor CD13, overexpression of which has been observed in several cancer types. From the initial library of designed peptides, three potential candidates were selected based on simulated energies in the CD13 bin...

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.

Economic Analysis of Batch and Continuous Biopharmaceutical Antibody Production: A Review.

There is a growing interest in continuous biopharmaceutical processing due to the advantages of small footprint, increased productivity, consistent product quality, high process flexibility and robustness, facility cost-effectiveness, and reduced capital and operating cost. To support the decision making of biopharmaceutical manufacturing, comparisons between conventional batch and continuous processing are provided. Various process unit operations in different operating modes are summarized. Software implementation, as well as computational methods used, are analyzed pointing to the advantages and disadvantages that have been highlighted in the literature. Economic analysis methods and their applications in different parts of the processes are also discussed with examples from publications in the last decade. The results of the comparison between batch and continuous process operation alternatives are discussed. Possible improvements in process design and analysis are recommended. The methods used here do not reflect Lilly's cost structures or economic evaluation methods. This paper provides a review of the work that has been published in the literature on computational process design and economic analysis methods on continuous biopharmaceutical antibody production and its comparison with a conventional batch process.

Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses

Influenza is a yearly threat to global public health. Rapid changes in influenza surface proteins resulting from antigenic drift and shift events make it difficult to readily identify antibodies with broadly neutralizing activity against different influenza subtypes with high frequency, specifically antibodies targeting the receptor binding domain (RBD) on influenza HA protein. We developed an optimized computational design method that is able to optimize an antibody for recognition of large panels of antigens. To demonstrate the utility of this multistate design method, we used it to redesign an antiinfluenza antibody against a large panel of more than 500 seasonal HA antigens of the H1 subtype. As a proof of concept, we tested this method on a variety of known antiinfluenza antibodies and identified those that could be improved computationally. We generated redesigned variants of antibody C05 to the HA RBD and experimentally characterized variants that exhibited improved breadth and affinity against our panel. C05 mutants exhibited improved affinity for three of the subtypes used in design by stabilizing the CDRH3 loop and creating favorable electrostatic interactions with the antigen. These mutants possess increased breadth and affinity of binding while maintaining high-affinity binding to existing targets, surpassing a major limitation up to this point.

Experimental checking the effectiveness of methods of teaching computer modeling and design to future teachers of technologies

Experimental checking the effectiveness of methods of teaching computer modeling and design to future teachers of technologies

Using molecular dynamics simulations to evaluate active designs of cephradine hydrolase by molecular mechanics/Poisson-Boltzmann surface area and molecular mechanics/generalized Born surface area methods.

The poor predictive accuracy of current computational enzyme design methods has led to low success rates of producing highly active variants that target non-natural substrates. In this report, a quantitative assessment approach based on molecular dynamics (MD) simulations was developed to eliminate false-positive enzyme designs at the computational stage. Taking cephradine hydrolase as an example, the apparent Michaelis binding constant (Km) and catalytic efficiency (kcat/Km) of designed variants were correlated with binding free energies and activation energy barriers, respectively, as calculated by molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) methods with explicit water considered based on general MD simulation protocols. The correlation results showed that both the MM/GBSA and MM/PBSA methods with a protein dielectric constant (εp = 4) could rank the variants well based on the predicted binding free energies between enzyme and the substrate. Furthermore, the activation energy barriers calculated by the MM/PBSA method with an εp = 24 correlated well with kcat/Km. Thus, false-positive variants obtained by the enzyme design program PRODA were eliminated prior to experimentation. Therefore, MD simulation-based quantitative assessment of designed variants greatly enhanced the predictive accuracy of computational enzyme design tools and should facilitate the construction of artificial enzymes with high catalytic activities toward non-natural substrates.

Computational Modeling and Constructal Design Method Applied to the Geometric Evaluation of Stiffened Thin Steel Plates Considering Symmetry Boundary Condition

Computational Modeling and Constructal Design Method Applied to the Geometric Evaluation of Stiffened Thin Steel Plates Considering Symmetry Boundary Condition

Design and Implementation of a Heterogeneous Safety Critical Computer

In recent years, the rapid development of high-speed railway in our country, followed by a rail safety problem has become the most important issue, as the core of the train operation control equipment, the development of the safety critical computer should be considered, firstly. At present, the mainstream safety critical computers all adopt the method of isomorphism, which can eliminate the multiple non-common fault of the same error structure, but can do nothing for some common fault. In this paper, a heterogeneous safety critical computer design and implementation method is proposed, which adopts the method of heterogeneous hardware and software, and analyzes its reliability and security by using fault tree model.

Therapeutic Inhibition of Myc in Cancer. Structural Bases and Computer-Aided Drug Discovery Approaches.

Myc (avian myelocytomatosis viral oncogene homolog) represents one of the most sought after drug targets in cancer. Myc transcription factor is an essential regulator of cell growth, but in most cancers it is overexpressed and associated with treatment-resistance and lethal outcomes. Over 40 years of research and drug development efforts did not yield a clinically useful Myc inhibitor. Drugging the “undruggable” is problematic, as Myc inactivation may negatively impact its physiological functions. Moreover, Myc is a disordered protein that lacks effective binding pockets on its surface. It is well established that the Myc function is dependent on dimerization with its obligate partner, Max (Myc associated factor X), which together form a functional DNA-binding domain to activate genomic targets. Herein, we provide an overview of the knowledge accumulated to date on Myc regulation and function, its critical role in cancer, and summarize various strategies that are employed to tackle Myc-driven malignant transformation. We focus on important structure-function relationships of Myc with its interactome, elaborating structural determinants of Myc-Max dimer formation and DNA recognition exploited for therapeutic inhibition. Chronological development of small-molecule Myc-Max prototype inhibitors and corresponding binding sites are comprehensively reviewed and particular emphasis is placed on modern computational drug design methods. On the outlook, technological advancements may soon provide the so long-awaited Myc-Max clinical candidate.

Computationally Designed Bispecific MD2/CD14 Binding Peptides Show TLR4 Agonist Activity.

Toll-like receptor 4 plays an important role in the regulation of the innate and adaptive immune response. The majority of TLR4 activators currently in clinical use are derivatives of its prototypic ligand LPS. The discovery of innovative TLR4 activators has the potential of providing new therapeutic immunomodulators and adjuvants. We used computational design methods to predict and optimize a total of 53 cyclic and linear peptides targeting myeloid differentiation 2 (MD2) and cluster of differentiation 14 (CD14), both coreceptors of human TLR4. Activity of the designed peptides was first assessed using NF-κB reporter cell lines expressing either TLR4/MD2 or TLR4/CD14 receptors, then binding to CD14 and MD2 confirmed and quantified using MicroScale Thermophoresis. Finally, we incubated select peptides in human whole blood and observed their ability to induce cytokine production, either alone or in synergy with LPS. Our data demonstrate the advantage of computational design for the discovery of new TLR4 peptide activators with little structural resemblance to known ligands and indicate an efficient strategy with which to identify TLR4 targeting peptides that could be used as easy-to-produce alternatives to LPS-derived molecules in a variety of settings.

Inverse Design of a Catalyst for Aqueous CO/CO2 Conversion Informed by the NiII-Iminothiolate Complex.

A computational inverse design method suitable to assist the development and optimization of molecular catalysts is introduced. Catalysts are obtained by continuous optimization of "alchemical" candidates in the vicinity of a reference catalyst with well-defined reaction intermediates and rate-limiting step. A NiII-iminoalkoxylate catalyst for aqueous CO/CO2 conversion is found with improved performance relative to a NiII-iminothiolate reference complex, previously reported as a biomimetic synthetic model of CO dehydroxygenase. Similar energies of other intermediates and transition states along the reaction mechanism show improved scaling relations relative to the reference catalyst. The linear combination of atomic potential tight-binding model Hamiltonian and the limited search of synthetically viable changes in the reference structure enable efficient minimization of the energy barrier for the rate-limiting step (i.e., formation of [LNiII(COOH)]-), bypassing the exponential scaling problem of high-throughput screening techniques. The reported findings demonstrate an inverse design method that could also be implemented with multiple descriptors, including reaction barriers and thermodynamic parameters for reversible reactivity.

On Hirth Ring Couplings: Design Principles Including the Effect of Friction

Rings with Hirth couplings are primarily used for the accurate positioning of axial-symmetric components in the machine tool industry and, generally, in mechanical components. It is also possible to use Hirth rings as connection tools. Specific industries with special milling and grinding machines are able to manufacture both tailor made and standard Hirth rings available on stock. Unfortunately, no international standard (for instance ISO, DIN or AGMA) is available for the production and the design of such components. In the best-case scenario, it is possible to find simplified design formulae in the catalogue of the suppliers. The aim of this work is to provide some accurate formulae and computational methods for design to provide better awareness on the limitations and the potential of this type of connection. The work consists of five parts: (i) a review of the base calculation derived mainly from the catalogues of manufacturers; (ii) an improved calculation based on a new analytical method including the friction phenomenon; (iii) an experimentation run for validating the method; (iv) a case study applied to a machine tool; and, (v) a closed form formulation to determine an upper threshold for friction, thus ensuring the Hirth coupling regular performance.

Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter.

Recently, different nanophotonic computational design methods based on optimization algorithms have been proposed which revolutionized the conventional design techniques of photonic integrated devices. The intelligently designed photonic devices have small footprints and high operating performance along with their fabrication feasibility. In this study, we introduce a new approach based on attractor selection algorithm to design photonic integrated devices. In order to demonstrate the potential of the proposed approach, we designed two structures: an optical coupler and an asymmetric light transmitter. The designed photonic devices operate at telecom wavelengths and have compact dimensions. The designed optical coupler has a footprint of only 4 × 2 μm2 and coupling efficiency of 87.5% at a design wavelength of 1550 nm with spatial beam width compression ratio of 10:1. Moreover, the designed optical coupler operates at a wide bandwidth of 6.45% where the transmission efficiency is above 80%. In addition, the designed asymmetric light transmitter with a size of 2 × 2 μm2 has the forward and backward transmission efficiencies of 88.1% and 8.6%, respectively. The bandwidth of 3.47% was calculated for the designed asymmetric light transmitter where the forward transmission efficiency is higher than 80% and the backward efficiency transmission is under 10%. In order to evaluate the operating performance of the designed photonic devices, coupling losses are analyzed. The presented results show that the attractor selection algorithm, which is based on artificial neural networks, can bring a conceptual breakthrough for the design of efficient integrated nanophotonic devices.