Exploring the combinatorial space of complete pathways to chemicals

2018 ◽  
Vol 46 (3) ◽  
pp. 513-522 ◽  
Author(s):  
Lin Wang ◽  
Chiam Yu Ng ◽  
Satyakam Dash ◽  
Costas D. Maranas
Keyword(s):  
De Novo ◽  
Design Procedure ◽  
Search Space ◽  
Performance Criterion ◽  
Mixed Integer ◽  
Design Tools ◽  
Minimum Number ◽  
Comprehensive Search ◽  
Synthetic Routes ◽  
Combinatorial Space

Computational pathway design tools often face the challenges of balancing the stoichiometry of co-metabolites and cofactors, and dealing with reaction rule utilization in a single workflow. To this end, we provide an overview of two complementary stoichiometry-based pathway design tools optStoic and novoStoic developed in our group to tackle these challenges. optStoic is designed to determine the stoichiometry of overall conversion first which optimizes a performance criterion (e.g. high carbon/energy efficiency) and ensures a comprehensive search of co-metabolites and cofactors. The procedure then identifies the minimum number of intervening reactions to connect the source and sink metabolites. We also further the pathway design procedure by expanding the search space to include both known and hypothetical reactions, represented by reaction rules, in a new tool termed novoStoic. Reaction rules are derived based on a mixed-integer linear programming (MILP) compatible reaction operator, which allow us to explore natural promiscuous enzymes, engineer candidate enzymes that are not already promiscuous as well as design de novo enzymes. The identified biochemical reaction rules then guide novoStoic to design routes that expand the currently known biotransformation space using a single MILP modeling procedure. We demonstrate the use of the two computational tools in pathway elucidation by designing novel synthetic routes for isobutanol.

Designing a Compact Wireless Network Based Device-Free Passive Localisation System for Indoor Environments

2018 ◽  
pp. 1424-1439
Author(s):  
Philip Vance ◽  
Girijesh Prasad ◽  
Jim Harkin ◽  
Kevin Curran
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Design Procedure ◽  
The Elderly ◽  
Healthcare Services ◽  
Indoor Environments ◽  
Signal Processing Technique ◽  
Minimum Number ◽  
The Individual ◽  
Device Free

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and ambient assisted living for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical environments and technologies to determine an individual's movements are key to ensuring that home emergencies are detected through lack of movement can be responded to promptly. This paper proposes a device-free localisation (DFL) system which would enable the individual to proceed with normal daily activities without the concern of having to wear a traceable device. The principle behind this is that the human body absorbs/reflects the radio signal being transmitted from a transmitter to one or more receiving stations. The proposed system design procedure facilitates the use of a minimum number of wireless nodes with the help of a principle component analysis (PCA) based intelligent signal processing technique. Results demonstrate that human detection and tracking are possible to within 1m resolution with a minimal hardware infrastructure.

A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization

2013 ◽  
Vol 300-301 ◽  
pp. 645-648 ◽  
Author(s):  
Yung Chien Lin
Keyword(s):  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Memetic Algorithms ◽  
Batch Processes ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Genetic Operators ◽  
Search Methods

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

DE NOVO SEQUENCING WITH LIMITED NUMBER OF POST-TRANSLATIONAL MODIFICATIONS PER PEPTIDE

2013 ◽  
Vol 11 (04) ◽  
pp. 1350007 ◽  
Author(s):  
LIN HE ◽  
XI HAN ◽  
BIN MA
Keyword(s):  
De Novo ◽  
Search Space ◽  
De Novo Sequencing ◽  
Peptide Sequence ◽  
Post Translational Modification ◽  
Post Translational Modifications ◽  
Protein Databases ◽  
Sequencing Algorithm ◽  
Modified Peptides ◽  
Proteomics Research

De novo sequencing derives the peptide sequence from a tandem mass spectrum without the assistance of protein databases. This analysis has been indispensable for the identification of novel or modified peptides in a biological sample. Currently, the speed of de novo sequencing algorithms is not heavily affected by the number of post-translational modification (PTM) types in consideration. However, the accuracy of the algorithms can be degraded due to the increased search space. Most peptides in a proteomics research contain only a small number of PTMs per peptide, yet the types of PTMs can come from a large number of choices. Therefore, it is desirable to include a large number of PTM types in a de novo sequencing algorithm, yet to limit the number of PTM occurrences in each peptide to increase the accuracy. In this paper, we present an efficient de novo sequencing algorithm, DeNovoPTM, for such a purpose. The implemented software is downloadable from http://www.cs.uwaterloo.ca/~l22he/denovo_ptm .

scholarly journals Computational protein design with backbone plasticity

2016 ◽  
Vol 44 (5) ◽  
pp. 1523-1529 ◽  
Author(s):  
James T. MacDonald ◽  
Paul S. Freemont
Keyword(s):  
Protein Design ◽  
De Novo ◽  
Protein Structures ◽  
Search Space ◽  
Computational Protein Design ◽  
Artificial Enzymes ◽  
Backbone Flexibility ◽  
Artificial Proteins ◽  
Naturally Occurring ◽  
Backbone Structure

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.

scholarly journals Bias Free Multiobjective Active Learning for Materials Design and Discovery

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Giriprasad Melpatti Jothiappan ◽  
Shefang Wang ◽  
Berend Smit ◽  
Brian Yoo
Keyword(s):  
Active Learning ◽  
De Novo ◽  
Learning Algorithm ◽  
Random Search ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Pareto Optimal ◽  
Design Rules ◽  
Learning Techniques ◽  
Screening Approaches

<div>The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. </div><div>In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. <br></div><div>We apply our algorithm to de novo polymer design with a prohibitively large search space.</div><div>Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.</div><div>This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.</div>

scholarly journals Bias Free Multiobjective Active Learning for Materials Design and Discovery

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Giriprasad Melpatti Jothiappan ◽  
Shefang Wang ◽  
Berend Smit ◽  
Brian Yoo
Keyword(s):  
Active Learning ◽  
De Novo ◽  
Learning Algorithm ◽  
Random Search ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Pareto Optimal ◽  
Design Rules ◽  
Learning Techniques ◽  
Screening Approaches

<div>The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. </div><div>In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. <br></div><div>We apply our algorithm to de novo polymer design with a prohibitively large search space.</div><div>Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.</div><div>This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.</div>

scholarly journals Task Scheduling for Multiunit Parallel Test Using Mixed-Integer Linear Programming

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhao Yang ◽  
Han-Shan Xiao ◽  
Rui Guan ◽  
Yang Yang ◽  
Hong-Liang Ji
Keyword(s):  
Linear Programming ◽  
Task Scheduling ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Search Space ◽  
Mixed Integer ◽  
Utilization Rate ◽  
Parallel Test ◽  
Second Procedure ◽  
Space Method

Parallel test is an efficient approach for improving test efficiency in the aerospace field. To meet the challenges of implementing multiunit parallel test in practical projects, this paper presented a mixed-integer linear programming (MILP) model for solving the task scheduling problem. A novel sequence-based iterative (SBI) method is proposed to solve the model in reasonable time. The SBI method is composed of an implied sequence finding procedure (ISF) and a sequence-based iterative optimization (SBIO) procedure. The first procedure can reduce the search space by fixing free sequence variables according to the original test flowcharts, and the second procedure can solve the model iteratively in a reasonable amount of time. In addition, two indexes, namely, speed rate and average resource utilization rate, are introduced to evaluate the proposed methods comprehensively. Computational results indicate that the proposed method performs well in real-world test examples, especially for larger examples that cannot be solved by the full-space method. Furthermore, it is proved that the essence of the parallel test is trading space for time.

scholarly journals doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows

2018 ◽  
Author(s):  
Daniel Svensson ◽  
Rickard Sjögren ◽  
David Sundell ◽  
Andreas Sjödin ◽  
Johan Trygg
Keyword(s):  
Systematic Approach ◽  
De Novo ◽  
Optimal Parameter ◽  
Variant Calling ◽  
Search Space ◽  
Potential Interaction ◽  
Novel Approach ◽  
Practice Parameters ◽  
Wide Range ◽  
Response Surface Designs

AbstractBackgroundSelecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.ResultsWe presentdoepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling.doepipelinewas used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases,doepipelinefound parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python packagedoepipeline.ConclusionsOur proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation indoepipelinemakes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code ofdoepipelineis available athttps://github.com/clicumu/doepipelineand it can be installed through conda-forge.

A general framework for task-constrained motion planning with moving obstacles

Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 575-598 ◽  
Author(s):  
Massimo Cefalo ◽  
Giuseppe Oriolo
Keyword(s):  
Dynamic Models ◽  
Randomized Algorithm ◽  
Time History ◽  
Search Space ◽  
Performance Criterion ◽  
Control Input ◽  
Constrained Motion ◽  
Motion Generation ◽  
Moving Obstacles ◽  
Main Component

SUMMARYConsider the practically relevant situation in which a robotic system is assigned a task to be executed in an environment that contains moving obstacles. Generating collision-free motions that allow the robot to execute the task while complying with its control input limitations is a challenging problem, whose solution must be sought in the robot state space extended with time. We describe a general planning framework which can be tailored to robots described by either kinematic or dynamic models. The main component is a control-based scheme for producing configuration space subtrajectories along which the task constraint is continuously satisfied. The geometric motion and time history along each subtrajectory are generated separately in order to guarantee feasibility of the latter and at the same time make the scheme intrinsically more flexible. A randomized algorithm then explores the search space by repeatedly invoking the motion generation scheme and checking the produced subtrajectories for collisions. The proposed framework is shown to provide a probabilistically complete planner both in the kinematic and the dynamic case. Modified versions of the planners based on the exploration–exploitation approach are also devised to improve search efficiency or optimize a performance criterion along the solution. We present results in various scenarios involving non-holonomic mobile robots and fixed-based manipulators to show the performance of the planner.

A Controlled Stability Genetic Algorithm With the New BLF2G Guillotine Placement Heuristic for the Orthogonal Cutting-Stock Problem

2021 ◽  
pp. 1471-1491
Author(s):  
Slimane Abou-Msabah ◽  
Ahmed-Riadh Baba-Ali ◽  
Basma Sager
Keyword(s):  
Genetic Algorithm ◽  
Orthogonal Cutting ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Data Sets ◽  
Item Order ◽  
Minimum Number ◽  
Qualified Individual

The orthogonal cutting-stock problem tries to place a given set of items in a minimum number of identically sized bins. Combining the new BLF2G heuristic with an advanced genetic algorithm can help solve this problem with the guillotine constraint. According to the item order, the BLF2G heuristic creates a direct placement of items in bins to give a cutting format. The genetic algorithm exploits the search space to find the supposed optimal item order. Other methods try to guide the evolutionary process. A new enhancement guides the evolutionary process, enriching the population via qualified individuals, without disturbing the genetic phase. The evolution of the GA process is controlled, and when no improvements after some number of iterations are observed, a qualified individual is injected to the population to avoid premature convergence to a local optimum. A generated set of order-based individuals enriches the evolutionary process with qualified chromosomes. The proposed method is compared with other heuristics and metaheuristics found in the literature on existing data sets.

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