Great Deluge Algorithm for Protein Structure Prediction
|
|
- Tyler Robinson
- 6 years ago
- Views:
Transcription
1 Great Deluge Algorithm for Protein Structure Prediction Edmund Burke 1, Yuri Bykov 2, Jonathan Hirst 3 1,2 School of Computer Science & IT, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK {ekb,yxb}@cs.nott.ac.uk 3 School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK jonathan.hirst@nottingham.ac.uk 1. Introduction Proteins are the basis of organic life on the Earth. These macromolecules appear as polymer chains comprising 70 to more than 500 residues (amino acids) of 20 different monomer types. In nature such chains fold into certain 3-dimensional structures (see example in Fig.1). The distinct property of proteins (which distinguishes them from other polymers) is that all molecules with the same order of residues fold into the same structure (so-called native state conformation). There is a general hypothesis that in the native state all interatomic forces are balanced and a molecule has the minimum possible internal energy. Fig. 1: The typical structure of a real protein The synthesis of new proteins is an important direction of modern biochemistry. Understanding how proteins fold is useful for creating new drugs, including ones against fatal diseases. However, the real engineering of new proteins and the recognition of their native state is a very slow and expensive process. The expenses of new proteins design can be substantially reduced by employing of different ways of prediction of the 3-dimensional structures of proteins. Every two
2 years the most effective prediction methods are evaluated at the CASP (Critical Assessment of Structure Prediction) research competition. However, up to the present time the best results have come from semi-empirical prediction techniques, which are based partly on human experience supplemented by computational tools. Most completely automated systems (such as ab initio methods) generally achieved weaker results. Our analysis of several such systems (e.g. [1],[5]) suggested that there is scope for the application of improved optimisation techniques. Our project lies at the interface of biochemistry and computer science (operations research) and aims to apply modern and powerful metaheuristics in order to improve ab initio protein structure prediction. 2. Great Deluge Algorithm The Great Deluge algorithm was introduced by Dueck in [4], but unfortunately was not widely useful in succeeding years. This local search metaheuristic is different to its predecessors (e.g. Hill-Climbing or Simulated Annealing) in the acceptance of a candidate solution from a neighbourhood. The Great Deluge algorithm accepts all solutions, for which absolute values of the cost function are less than or equal to the current boundary value, called level. The local search starts with the initial value of level equal to an initial cost function and during the search its value is monotonically reduced. A decrement of the reduction (defined by the user) appears as a single algorithmic parameter. In [2] the authors extended the Great Deluge algorithm by accepting all downhill moves (hybridising it with Hill-Climbing). This variant was successfully applied to university exam timetabling problems and its performance was thoroughly investigated. Further multiobjective modifications of this algorithm, which drive the search through specified trajectories in the criteria space were introduced and evaluated in [3] and [6]. These investigations reveal several advantages of this technique, in particular, that it allows in advance the definition of the characteristics of a search process (such as a processing time and a region of an expected final solution) more precisely than other approaches. The proper utilisation of these properties significantly increases the performance of a local search. When
3 experimentally compared with other existing techniques, the Great Deluge algorithm produced higher quality results on most benchmark problem instances. 3. The application of the Great Deluge Algorithm to a Protein Folding Problem In the terms of optimisation, the protein structure prediction can be viewed as solving the so-called Protein Folding Problem. It considers a chain of residues, which folds into a 3-dimensional conformation. Given the energy of interactions between each pair of residues as a function of the distance between them, the goal is to find the conformation, which imposes the minimum sum energy for the whole chain. It is supposed that the resulting conformation should conform to the native state of a real protein. Due to the high complexity of this problem, several simplifications were proposed in the literature: excluding most atoms from amino-acids, leaving only one; reduction of the number of residues types according to their properties; use of several types of lattices to restrict the number of possible conformations. In the course of our research, the performance of the Great Deluge algorithm applied to the Protein Folding Problem. Different levels of simplification were investigated. In most cases the algorithm was able to produce near-optimum solutions, however these solutions did not reflect real protein structures. One of the major weaknesses of the simplified models is that their inter-residue interactions are not well-defined. 4. Results of All-Atom Off-Lattice Model The most promising performance was achieved by rejecting the proposed simplifications and using an all-atom off-lattice model. Here all (or most) atoms participate in the search procedure and their positions are not restricted by any lattice. However, it is the most complex and a highly time-consuming variant. Therefore, our initial experiments were done with fragments of real proteins, in particular with those, which in the real world would fold into a helix (see Fig. 1). The model was developed in Delphi 7 and run on a PC Intel Celeron 2.2GHz. During development of the
4 software, significant attention was paid to the optimisation of the algorithm with respect to processing time. Our experiments were carried in order to define a dependence of the final results on the number of moves from the beginning to the convergence. The Great Deluge algorithm allows setting up this number (approximately) as an input parameter and thus, to define in advance the total search time. In Fig. 2 we present results of four different runs of the algorithm with the same 12-residue chain. All algorithmic parameters were the same except the predefined processing time (number of moves). Fig.2. Conformations of the same chain produced in different processing time In this figure the conformation a) was produced in moves (processing time was around 15 minutes) and has nothing similar to the native protein conformation (helix). In the result b), produced in moves (processing time 30 min) some helical tendencies are already visible. The result c), produced in moves( 1 hour of calculations) looks even more close to a helix. Finally, when launching the algorithm for moves (it took 2 hours) it produced the regular helix (result d), the same as in a real protein. 5. Conclusions and Future Work The obtained results demonstrate that the Great Deluge algorithm could be effective for protein structure prediction. The sample fragment should be gradually enlarged with the goal to envelop complete proteins. We recognize that computational expense
5 could rise disproportionately with the length of fragments. Therefore, the significant work should be done in order further increase processing speed. 6. Acknowledgment The research described in this paper was supported by BBSRC grant (42/BIO14458). References [1] R.Bonneau, J.Tsai, I.Ruczinski, D.Chivian, C.Rohl, C.E.M.Strauss, D.Baker. Rosetta in CASP4: Progress in Ab Initio Protein Structure Prediction, PROTEINS: Structure, Function, and Genetic Suppl, 5, 2001, [2] E. K. Burke, Y. Bykov, J. P. Newall, S. Petrovic. A Time-Predefined Local Search Approach to Exam Timetabling Problems. Accepted for publication in IIE transactions on Operations Engineering, [3] Y. Bykov. Time-Predefined and Trajectory-Based Search: Single and Multiobjective Approaches to Exam Timetabling. PhD Thesis. The University of Nottingham, Nottingham, UK, [4] G. Dueck. "New Optimization Heuristics. The Great Deluge Algorithm and the Record-to-Record Travel". Journal of Computational Physics 104, 1993, [5] D.T.Jones. Predicting Novel Protein Folds by Using FRAGFOLD, PROTEINS: Structure, Function, and Genetic Suppl, 5, 2001, [6] S. Petrovic, Y. Bykov. "A Multiobjective Optimisation Technique for Exam Timetabling Based on Trajectories". E. Burke, P. De Causmaecker (eds.), The Practice and Theory of Automated Timetabling IV: Selected Papers (PATAT 2002). Lecture Notes in Computer Science, Springer-Verlag, Berlin, Heidelberg, New York, 2003,
The Nottingham eprints service makes this work by researchers of the University of Nottingham available open access under the following conditions.
Burke, Edmund and Eckersley, Adam and McCollum, Barry and Sanja, Petrovic and Qu, Rong (2003) Using Simulated Annealing to Study Behaviour of Various Exam Timetabling Data Sets. In: The Fifth Metaheuristics
More informationStructural Bioinformatics (C3210) Conformational Analysis Protein Folding Protein Structure Prediction
Structural Bioinformatics (C3210) Conformational Analysis Protein Folding Protein Structure Prediction Conformational Analysis 2 Conformational Analysis Properties of molecules depend on their three-dimensional
More informationProtein design. CS/CME/Biophys/BMI 279 Oct. 20 and 22, 2015 Ron Dror
Protein design CS/CME/Biophys/BMI 279 Oct. 20 and 22, 2015 Ron Dror 1 Optional reading on course website From cs279.stanford.edu These reading materials are optional. They are intended to (1) help answer
More informationProtein design. CS/CME/BioE/Biophys/BMI 279 Oct. 24, 2017 Ron Dror
Protein design CS/CME/BioE/Biophys/BMI 279 Oct. 24, 2017 Ron Dror 1 Outline Why design proteins? Overall approach: Simplifying the protein design problem Protein design methodology Designing the backbone
More informationProtein design. CS/CME/BioE/Biophys/BMI 279 Oct. 24, 2017 Ron Dror
Protein design CS/CME/BioE/Biophys/BMI 279 Oct. 24, 2017 Ron Dror 1 Outline Why design proteins? Overall approach: Simplifying the protein design problem < this step is really key! Protein design methodology
More informationThe Nottingham eprints service makes this work by researchers of the University of Nottingham available open access under the following conditions.
Burke, Edmund and Drake, John H. and McCollum, Barry and Özcan, Ender (2015) Comments on: An overview of curriculum-based course timetabling. TOP, 23 (2). pp. 355-358. ISSN 1863-8279 Access from the University
More informationA Parameter-Free Hyperheuristic for Scheduling a Sales Summit
MIC 2001-4th Metaheuristics International Conference 127 A Parameter-Free Hyperheuristic for Scheduling a Sales Summit Peter Cowling Graham Kendall Eric Soubeiga Automated Scheduling, optimisation and
More informationStochastic Fractal Search Algorithm for 3D Protein Structure Prediction Chuan SUN 1, Zi-qi WEI 2, Chang-jun ZHOU 1,* and Bin WANG 1
206 International Conference on Artificial Intelligence and Computer Science (AICS 206 ISBN: 978--60595-4-0 Stochastic Fractal Search Algorithm for 3D Protein Structure Prediction Chuan SUN, Zi-qi WEI
More informationProtein Structure Prediction
Homology Modeling Protein Structure Prediction Ingo Ruczinski M T S K G G G Y F F Y D E L Y G V V V V L I V L S D E S Department of Biostatistics, Johns Hopkins University Fold Recognition b Initio Structure
More informationNear-Native Protein Folding
Near-Native Protein Folding Stefka Fidanova Institute for Parallel Processing at Bulgarian Academy of Science, Sofia, Bulgaria Abstract: The protein folding problem is a fundamental problem in computational
More informationSelecting Quality Initial Random Seed For Metaheuristic Approaches: A Case Of Timetabling Problem
Abu Bakar Md Sultan, Ramlan Mahmod, Md Nasir Sulaiman, and Mohd Rizam Abu Bakar Selecting Quality Initial Random Seed For Metaheuristic Approaches: A Case Of tabling Problem 1 Abu Bakar Md Sultan, 2 Ramlan
More informationTIMETABLING EXPERIMENTS USING GENETIC ALGORITHMS. Liviu Lalescu, Costin Badica
TIMETABLING EXPERIMENTS USING GENETIC ALGORITHMS Liviu Lalescu, Costin Badica University of Craiova, Faculty of Control, Computers and Electronics Software Engineering Department, str.tehnicii, 5, Craiova,
More informationA Variable Neighbourhood Search for the Workforce Scheduling and Routing Problem
A Variable Neighbourhood Search for the Workforce Scheduling and Routing Problem Rodrigo Lankaites Pinheiro, Dario Landa-Silva, and Jason Atkin School of Computer Science, ASAP Research Group The University
More informationA Multi Criteria Meta-heuristic Approach to Nurse Rostering
A Multi Criteria Meta-heuristic Approach to Nurse Rostering Edmund K. Burke University of Nottingham School of Computer Science & IT Nottingham NG8 1BB, UK ekb@cs.nott.ac.uk Patrick De Causmaecker KaHo
More informationA selection hyper-heuristic for scheduling deliveries of ready-mixed concrete
MIC 2011: The IX Metaheuristics International Conference S1-30 1 A selection hyper-heuristic for scheduling deliveries of ready-mixed concrete Mustafa Misir 1,2, Wim Vancroonenburg 1,2, Katja Verbeeck
More informationCFSSP: Chou and Fasman Secondary Structure Prediction server
Wide Spectrum, Vol. 1, No. 9, (2013) pp 15-19 CFSSP: Chou and Fasman Secondary Structure Prediction server T. Ashok Kumar Department of Bioinformatics, Noorul Islam College of Arts and Science, Kumaracoil
More informationBIOINFORMATICS Introduction
BIOINFORMATICS Introduction Mark Gerstein, Yale University bioinfo.mbb.yale.edu/mbb452a 1 (c) Mark Gerstein, 1999, Yale, bioinfo.mbb.yale.edu What is Bioinformatics? (Molecular) Bio -informatics One idea
More informationA Variable Neighbourhood Search for the Workforce Scheduling and Routing Problem
A Variable Neighbourhood Search for the Workforce Scheduling and Routing Problem Rodrigo Lankaites Pinheiro, Dario Landa-Silva and Jason Atkin Abstract The workforce scheduling and routing problem (WSRP)
More informationComputational methods in bioinformatics: Lecture 1
Computational methods in bioinformatics: Lecture 1 Graham J.L. Kemp 2 November 2015 What is biology? Ecosystem Rain forest, desert, fresh water lake, digestive tract of an animal Community All species
More informationLate Acceptance-Based Selection Hyper-heuristics for Cross-domain Heuristic Search
228 Late Acceptance-Based Selection Hyper-heuristics for Cross-domain Heuristic Search Warren G. Jackson, Ender Özcan and John H. Drake School of Computer Science University of Nottingham Jubilee Campus
More information4/10/2011. Rosetta software package. Rosetta.. Conformational sampling and scoring of models in Rosetta.
Rosetta.. Ph.D. Thomas M. Frimurer Novo Nordisk Foundation Center for Potein Reseach Center for Basic Metabilic Research Breif introduction to Rosetta Rosetta docking example Rosetta software package Breif
More informationGenetic Algorithm for Predicting Protein Folding in the 2D HP Model
Genetic Algorithm for Predicting Protein Folding in the 2D HP Model A Parameter Tuning Case Study Eyal Halm Leiden Institute of Advanced Computer Science, University of Leiden Niels Bohrweg 1 2333 CA Leiden,
More informationProtein Structure Prediction. christian studer , EPFL
Protein Structure Prediction christian studer 17.11.2004, EPFL Content Definition of the problem Possible approaches DSSP / PSI-BLAST Generalization Results Definition of the problem Massive amounts of
More informationCMSE 520 BIOMOLECULAR STRUCTURE, FUNCTION AND DYNAMICS
CMSE 520 BIOMOLECULAR STRUCTURE, FUNCTION AND DYNAMICS (Computational Structural Biology) OUTLINE Review: Molecular biology Proteins: structure, conformation and function(5 lectures) Generalized coordinates,
More informationAnt Systems of Optimization: Introduction and Review of Applications
International Journal of Electronics Engineering Research. ISSN 0975-6450 Volume 9, Number 2 (2017) pp. 275-279 Research India Publications http://www.ripublication.com Ant Systems of Optimization: Introduction
More informationAPPLYING FEATURE-BASED RESAMPLING TO PROTEIN STRUCTURE PREDICTION
APPLYING FEATURE-BASED RESAMPLING TO PROTEIN STRUCTURE PREDICTION Trent Higgs 1, Bela Stantic 1, Md Tamjidul Hoque 2 and Abdul Sattar 13 1 Institute for Integrated and Intelligent Systems (IIIS), Grith
More informationBiochemistry Prof. S. DasGupta Department of Chemistry Indian Institute of Technology Kharagpur. Lecture - 5 Protein Structure - III
Biochemistry Prof. S. DasGupta Department of Chemistry Indian Institute of Technology Kharagpur Lecture - 5 Protein Structure - III This is lecture number three on protein structure. (Refer Slide Time:
More informationA Viral Systems Algorithm for the Traveling Salesman Problem
Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 6, 2012 A Viral Systems Algorithm for the Traveling Salesman Problem Dedy Suryadi,
More informationMolecular Structures
Molecular Structures 1 Molecular structures 2 Why is it important? Answers to scientific questions such as: What does the structure of protein X look like? Can we predict the binding of molecule X to Y?
More informationParallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model
Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model D. Chu, M. Till, A. Zomaya School of Information Technologies Madsen Building, F09 The University of Sydney
More informationNanobiotechnology. Place: IOP 1 st Meeting Room Time: 9:30-12:00. Reference: Review Papers. Grade: 50% midterm, 50% final.
Nanobiotechnology Place: IOP 1 st Meeting Room Time: 9:30-12:00 Reference: Review Papers Grade: 50% midterm, 50% final Midterm: 5/15 History Atom Earth, Air, Water Fire SEM: 20-40 nm Silver 66.2% Gold
More informationA nucleotide consists of: an inorganic phosphate group (attached to carbon 5 of the sugar) a 5C sugar (pentose) a Nitrogenous (N containing) base
Nucleic Acids! Nucleic acids are found in all living cells and viruses and the two main types are DNA and RNA. They are macromolecules made of chains of nucleotides bonded together. They carry genetic
More informationMolecular Structures
Molecular Structures 1 Molecular structures 2 Why is it important? Answers to scientific questions such as: What does the structure of protein X look like? Can we predict the binding of molecule X to Y?
More informationComputational Methods for Protein Structure Prediction
Computational Methods for Protein Structure Prediction Ying Xu 2017/12/6 1 Outline introduction to protein structures the problem of protein structure prediction why it is possible to predict protein structures
More informationEnergy Minimization of Protein Tertiary Structures by Local Search Algorithm and Parallel Simulated Annealing using Genetic Crossover
Energy Minimization of Protein Tertiary Structures by Local Search Algorithm and Parallel Simulated Annealing using Genetic Crossover Shinya Ogura Graduate School of Engineering, Doshisha University oguchan@mikilab.doshisha.ac.jp
More informationLecture 2: Central Dogma of Molecular Biology & Intro to Programming
Lecture 2: Central Dogma of Molecular Biology & Intro to Programming Central Dogma of Molecular Biology Proteins: workhorse molecules of biological systems Proteins are synthesized from the genetic blueprints
More informationRNA Structure Prediction and Comparison. RNA Biology Background
RN Structure Prediction and omparison Session 1 RN Biology Background Robert iegerich Faculty of Technology robert@techfak.ni-bielefeld.de October 13, 2013 Robert iegerich Overview of lecture topics The
More informationFrom single to double track: effects of alternative extension measures
Computers in Railways XIII 313 From single to double track: effects of alternative extension measures O. Lindfeldt Vectura Consulting AB, Stockholm, Sweden Abstract Extension of single-track lines into
More informationIterative train scheduling in networks with tree topologies: a case study for the Hunter Valley Coal Chain
22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 to 8 December 2017 mssanz.org.au/modsim2017 Iterative train scheduling in networks with tree topologies: a case study
More informationCENTRE FOR BIOINFORMATICS M. D. UNIVERSITY, ROHTAK
Program Specific Outcomes: The students upon completion of Ph.D. coursework in Bioinformatics will be able to: PSO1 PSO2 PSO3 PSO4 PSO5 Produce a well-developed research proposal. Select an appropriate
More informationA Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem
A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem John H. Drake, Matthew Hyde, Khaled Ibrahim and Ender Özcan School of Computer Science University of Nottingham Jubilee Campus
More informationUniversité Libre de Bruxelles
Université Libre de Bruxelles Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle An Experimental Study of Estimation-based Metaheuristics for the Probabilistic
More informationGenetic Algorithms in Matrix Representation and Its Application in Synthetic Data
Genetic Algorithms in Matrix Representation and Its Application in Synthetic Data Yingrui Chen *, Mark Elliot ** and Joe Sakshaug *** * ** University of Manchester, yingrui.chen@manchester.ac.uk University
More informationRNA Structure Prediction and Comparison. RNA Biology Background
RN Structure Prediction and omparison Session 1 RN Biology Background édric Saule Faculty of Technology cedric.saule@ni-bielefeld.de pril 13, 2015 édric Saule Overview of lecture topics The lecture plan
More informationPROCESS ACCOMPANYING SIMULATION A GENERAL APPROACH FOR THE CONTINUOUS OPTIMIZATION OF MANUFACTURING SCHEDULES IN ELECTRONICS PRODUCTION
Proceedings of the 2002 Winter Simulation Conference E. Yücesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, eds. PROCESS ACCOMPANYING SIMULATION A GENERAL APPROACH FOR THE CONTINUOUS OPTIMIZATION OF
More informationGeneral-purpose SPWA with the Class-type Skill by Genetic Algorithm
General-purpose SPWA with the Class-type Skill by Genetic Algorithm Daiki Takano Graduate School of Engineering, Maebashi Institute of Technology Email: futsal_ido_me_jp@yahoo.co.jp Kenichi Ida Graduate
More informationOptimal Tank Design And Operation Strategy To Enhance Water Quality In Distribution Systems
City University of New York (CUNY) CUNY Academic Works International Conference on Hydroinformatics 8-1-2014 Optimal Tank Design And Operation Strategy To Enhance Water Quality In Distribution Systems
More informationHow Do You Clone a Gene?
S-20 Edvo-Kit #S-20 How Do You Clone a Gene? Experiment Objective: The objective of this experiment is to gain an understanding of the structure of DNA, a genetically engineered clone, and how genes are
More informationBIOL1020 Study Guide Sample
BIOL1020 Study Guide Sample This study guide covers generally all of the content from weeks 1 to 13 primarily based on the textbook with moderate input from lecture slides. These study notes aim to balance
More informationWorkshop on Particle Swarm Optimization and Evolutionary Computation (20-21 February 2018)
Table of Contents Tutorial: An introduction to nature-inspired metaheuristic algorithms 2 A multiobjective memetic algorithm based on particle swarm optimization 3 A novel discrete particle swarm optimization
More informationHow to Analyze Polymers Using X-ray Diffraction
How to Analyze Polymers Using X-ray Diffraction Polymers An Introduction This tutorial will cover the following topics How to recognize different types of polymers Crystalline, semi-crystalline and amorphous
More informationUniversiti Malaysia Pahang examination timetabling problem: scheduling invigilators
Journal of the Operational Research Society (2014) 65, 214 226 2014 Operational Research Society Ltd. All rights reserved. 0160-5682/14 www.palgrave-journals.com/jors/ Universiti Malaysia Pahang examination
More informationTowards a reference model for timetabling and rostering
Towards a reference model for timetabling and rostering Patrick De Causmaecker Katholieke Universiteit Leuven Department Of Computerscience DDID group Campus Kortrijk E. Sabbelaan 53, 8500 Kortrijk, Belgium
More informationAn Evolutionary Optimization for Multiple Sequence Alignment
195 An Evolutionary Optimization for Multiple Sequence Alignment 1 K. Lohitha Lakshmi, 2 P. Rajesh 1 M tech Scholar Department of Computer Science, VVIT Nambur, Guntur,A.P. 2 Assistant Prof Department
More informationPoster Project Extended Report: Protein Folding and Computational Techniques Blake Boling. Abstract. Introduction
Poster Project Extended Report: Protein Folding and Computational Techniques Blake Boling Abstract One of the goals of biocomputing is to understand how proteins fold so that we may be able to predict
More informationISE480 Sequencing and Scheduling
ISE480 Sequencing and Scheduling INTRODUCTION ISE480 Sequencing and Scheduling 2012 2013 Spring term What is Scheduling About? Planning (deciding what to do) and scheduling (setting an order and time for
More informationWhat is Evolutionary Computation? Genetic Algorithms. Components of Evolutionary Computing. The Argument. When changes occur...
What is Evolutionary Computation? Genetic Algorithms Russell & Norvig, Cha. 4.3 An abstraction from the theory of biological evolution that is used to create optimization procedures or methodologies, usually
More informationGenetic Algorithms For Protein Threading
From: ISMB-98 Proceedings. Copyright 1998, AAAI (www.aaai.org). All rights reserved. Genetic Algorithms For Protein Threading Jacqueline Yadgari #, Amihood Amir #, Ron Unger* # Department of Mathematics
More information1997 Nobel Prize in Physiology or Medicine Dr. Stanley Prusiner
1997 Nobel Prize in Physiology or Medicine Dr. Stanley Prusiner for the discovery of prions* - a new biological principal of infection I. PRIONS - Definition Prions are simple proteins that are much smaller
More informationApplication of Activity-Based Costing in a Manufacturing Company: A Comparison with Traditional Costing
Application of Activity-Based Costing in a Manufacturing Company: A Comparison with Traditional Costing Gonca Tuncel, Derya Eren Akyol, Gunhan Mirac Bayhan, and Utku Koker Department of Industrial Engineering,
More informationSequence Analysis '17 -- lecture Secondary structure 3. Sequence similarity and homology 2. Secondary structure prediction
Sequence Analysis '17 -- lecture 16 1. Secondary structure 3. Sequence similarity and homology 2. Secondary structure prediction Alpha helix Right-handed helix. H-bond is from the oxygen at i to the nitrogen
More informationCompetitive Imperialistic Approach for Protein Folding
Competitive Imperialistic Approach for Protein Folding E. Khaji a, S.M.Mortazavi b a Department of Physics, Gteborg University, 41296 Gothenburg, Sweden. b School of Business, University of Colorado, CO
More informationEMM4131 Popülasyon Temelli Algoritmalar (Population-based Algorithms) Introduction to Meta-heuristics and Evolutionary Algorithms
2017-2018 Güz Yarıyılı Balikesir Universitesi, Endustri Muhendisligi Bolumu EMM4131 Popülasyon Temelli Algoritmalar (Population-based Algorithms) 2 Introduction to Meta-heuristics and Evolutionary Algorithms
More informationInformation Driven Biomedicine. Prof. Santosh K. Mishra Executive Director, BII CIAPR IV Shanghai, May
Information Driven Biomedicine Prof. Santosh K. Mishra Executive Director, BII CIAPR IV Shanghai, May 21 2004 What/How RNA Complexity of Data Information The Genetic Code DNA RNA Proteins Pathways Complexity
More informationOpening of Tokyo Academic Park
Opening of Tokyo Academic Park The grand ceremony for the opening of Tokyo Academic Park, was held on the 9 th of July, 2001 which has been under construction in Tokyo Waterfront, the Aomi area (Koto-ku,
More informationBioinformatics & Protein Structural Analysis. Bioinformatics & Protein Structural Analysis. Learning Objective. Proteomics
The molecular structures of proteins are complex and can be defined at various levels. These structures can also be predicted from their amino-acid sequences. Protein structure prediction is one of the
More informationA Simulation-based Multi-level Redundancy Allocation for a Multi-level System
International Journal of Performability Engineering Vol., No. 4, July 205, pp. 357-367. RAMS Consultants Printed in India A Simulation-based Multi-level Redundancy Allocation for a Multi-level System YOUNG
More informationEffects of protein binding on topological states of DNA minicircle
ISSN 1 746-7233, England, UK World Journal of Modelling and Simulation Vol. 4 (2008) No. 4, pp. 277-286 Effects of protein binding on topological states of DNA minicircle Yanhui Liu, Lin Hu, Wenbo Wang
More informationThis place covers: Methods or systems for genetic or protein-related data processing in computational molecular biology.
G16B BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY Methods or systems for genetic
More informationAdvances in powderdosing
Advances in powderdosing technology A new technology offers a step-change in the way that early clinical trial supplies can be formulated resulting in real savings in development times and costs. Simon
More informationSeptember 11, Abstract. of an optimization problem and rewards instances that have uniform landscapes,
On the Evolution of Easy Instances Christos H. Papadimitriou christos@cs.berkeley.edu Martha Sideri sideri@aueb.gr September 11, 1998 Abstract We present experimental evidence, based on the traveling salesman
More informationCollege of information technology Department of software
University of Babylon Undergraduate: third class College of information technology Department of software Subj.: Application of AI lecture notes/2011-2012 ***************************************************************************
More informationA Genetic Algorithm for Order Picking in Automated Storage and Retrieval Systems with Multiple Stock Locations
IEMS Vol. 4, No. 2, pp. 36-44, December 25. A Genetic Algorithm for Order Picing in Automated Storage and Retrieval Systems with Multiple Stoc Locations Yaghoub Khojasteh Ghamari Graduate School of Systems
More informationCHEM 4420 Exam I Spring 2013 Page 1 of 6
CHEM 4420 Exam I Spring 2013 Page 1 of 6 Name Use complete sentences when requested. There are 100 possible points on this exam. The multiple choice questions are worth 2 points each. All other questions
More informationSMART MEANS FOR THE ESTIMATION AND SELECTION OF EFFICIENT SUBTRACTIVE MACHINING STRATEGIES
Jr. of Industrial Pollution Control 33(1)(2017) pp 981-987 www.icontrolpollution.com Review SMART MEANS FOR THE ESTIMATION AND SELECTION OF EFFICIENT SUBTRACTIVE MACHINING STRATEGIES VLADIMIR VASILYEVICH
More informationArtificial Immune Algorithms for University Timetabling
Artificial Immune Algorithms for University Timetabling Muhammad Rozi Malim 1, Ahamad Tajudin Khader 2, and Adli Mustafa 3 1 Faculty of Info. Technology & Quantitative Sciences, UiTM, 40450 Shah Alam,
More informationProceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.
Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. SIMULATION-BASED CONTROL FOR GREEN TRANSPORTATION WITH HIGH DELIVERY SERVICE
More informationLecture 1. Bioinformatics 2. About me... The class (2009) Course Outcomes. What do I think you know?
Lecture 1 Bioinformatics 2 Introduction Course Overview & Assessment Introduction to Bioinformatics Research Careers and PhD options Core topics in Bioinformatics the central dogma of molecular biology
More informationFrancis Crick, Still grinding away getting a Ph.D. in Cambridge, England, working on the structure of proteins using X-ray crystallography.
Lecture 2 (FW) January 26, 2009 DNA is a double helix. Replication. Mitosis Reading assignment: DNA structure and replication, pp. 81-104. Cell structure review, pp. 19-26. Mitosis, pp. 31-36. Lecture
More informationBioinformatics 2. Lecture 1
Bioinformatics 2 Introduction Lecture 1 Course Overview & Assessment Introduction to Bioinformatics Research Careers and PhD options Core topics in Bioinformatics the central dogma of molecular biology
More informationJust the Facts: A Basic Introduction to the Science Underlying NCBI Resources
National Center for Biotechnology Information About NCBI NCBI at a Glance A Science Primer Human Genome Resources Model Organisms Guide Outreach and Education Databases and Tools News About NCBI Site Map
More informationThe genetic information. The genetic code and the central dogma. The genetic code. Genetic information and genetic code
The genetic information The genetic code and the central dogma The digital information is coded in triplets of nucleobases called codons Each codon uses 3 of the 4 nucleobases and can express 4 3 =64 possible
More informationDistributions of Beta Sheets in Proteins with Application to Structure Prediction
Distributions of Beta Sheets in Proteins with Application to Structure Prediction Ingo Ruczinski Department of Biostatistics Johns Hopkins University Email: ingo@jhu.edu http://biostat.jhsph.edu/ iruczins
More informationCS273: Algorithms for Structure Handout # 5 and Motion in Biology Stanford University Tuesday, 13 April 2004
CS273: Algorithms for Structure Handout # 5 and Motion in Biology Stanford University Tuesday, 13 April 2004 Lecture #5: 13 April 2004 Topics: Sequence motif identification Scribe: Samantha Chui 1 Introduction
More informationGenetic algorithms as a new tool to study protein stability
WJJ. van den Tweel, A. Harder and R.M. Buitelaar (Eds.), Stability and Stabilization of Enzymes Proceedings of an International Symposium held in Maastricht, The Netherlands, 22-25 November 1992 1993 Elsevier
More informationGenetic Algorithm for Supply Planning Optimization under Uncertain Demand
Genetic Algorithm for Supply Planning Optimization under Uncertain Demand Tezuka Masaru and Hiji Masahiro Hitachi Tohoku Software, Ltd. 2-16-10, Honcho, Aoba ward, Sendai City, 980-0014, Japan {tezuka,hiji}@hitachi-to.co.jp
More informationCENTER FOR BIOTECHNOLOGY
CENTER FOR BIOTECHNOLOGY Keith A. McGee, Ph.D., Program Director Math and Science Building, 3 rd Floor 1000 ASU Drive #870 Phone: 601-877-6198 FAX: 601-877-2328 Degree Offered Required Admission Test M.
More informationUsing Multi-Objective Evolutionary Algorithms in the Optimization of Polymer Injection Molding
Using Multi-Objective Evolutionary Algorithms in the Optimization of Polymer Injection Molding Célio Fernandes 1, António J. Pontes 1, Júlio C. Viana 1, and A. Gaspar-Cunha 1 Abstract. A Multi-objective
More informationIncludes 'study abroad' Description This lecture module will introduce the following topics: Assessment Assessment Type Exam 1 (100%) Convenor
Biomedical Sciences This edition of the University of Nottingham Catalogue of Modules went to press on 7th September 2011. It was derived from information held on the database. The Catalogue is also published
More informationGENETIC ALGORITHMS. Narra Priyanka. K.Naga Sowjanya. Vasavi College of Engineering. Ibrahimbahg,Hyderabad.
GENETIC ALGORITHMS Narra Priyanka K.Naga Sowjanya Vasavi College of Engineering. Ibrahimbahg,Hyderabad mynameissowji@yahoo.com priyankanarra@yahoo.com Abstract Genetic algorithms are a part of evolutionary
More informationTHE MCDA * METHODOLOGY APPLIED TO SOLVE COMPLEX TRANSPORTATION DECISION PROBLEMS
THE MCDA * METHODOLOGY APPLIED TO SOLVE COMPLEX TRANSPORTATION DECISION PROBLEMS Jace Za Faculty of Woring Machines and Transportation - Poznan University of Technology E-mail: jaceza@put.poznan.pl 1 INTRODUCTION
More informationIntroduction to Proteins
Introduction to Proteins Lecture 4 Module I: Molecular Structure & Metabolism Molecular Cell Biology Core Course (GSND5200) Matthew Neiditch - Room E450U ICPH matthew.neiditch@umdnj.edu What is a protein?
More informationProteomics 6/4/2009 WESTERN BLOT ANALYSIS
SDS-PAGE (PolyAcrylamide Gel Electrophoresis) Proteomics WESTERN BLOT ANALYSIS Presented by: Nuvee Prapasarakul Veterinary Microbiology Chulalongkorn University Proteomics has been said to be the next
More informationUnit 6: Biomolecules
Unit 6: Biomolecules Name: Period: Test 1 Table of Contents Title of Page Page Number Due Date Unit 6 Warm-Ups 3-4 Unit 6 KUDs 5-6 Biomolecules Cheat Sheet 7 Biomolecules Sorting Review 8-9 Unit 6 Vocabulary
More informationA Fast Genetic Algorithm with Novel Chromosome Structure for Solving University Scheduling Problems
2013, TextRoad Publication ISSN 2090-4304 Journal of Basic and Applied Scientific Research www.textroad.com A Fast Genetic Algorithm with Novel Chromosome Structure for Solving University Scheduling Problems
More informationThe University of Bradford Institutional Repository
The University of Bradford Institutional Repository http://bradscholars.brad.ac.uk This work is made available online in accordance with publisher policies. Please refer to the repository record for this
More informationCOURSES OFFERED FOR Ph.D. CURRICULUM
COURSES OFFERED FOR Ph.D. CURRICULUM July 2017 onwards Department of Biochemistry Faculty of Interdisciplinary and Applied Sciences University of Delhi South Campus Benito Juarez Road New Delhi-110021
More informationPROTEINS & NUCLEIC ACIDS
Chapter 3 Part 2 The Molecules of Cells PROTEINS & NUCLEIC ACIDS Lecture by Dr. Fernando Prince 3.11 Nucleic Acids are the blueprints of life Proteins are the machines of life We have already learned that
More informationWhat s New in Discovery Studio 2.5.5
What s New in Discovery Studio 2.5.5 Discovery Studio takes modeling and simulations to the next level. It brings together the power of validated science on a customizable platform for drug discovery research.
More information