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BIF602 : Bioinformatics Computing II

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Course Info

Course Category

Bioinformatics

Course Level

Undergraduate

Credit Hours

3

Pre-requisites

N/A

Instructor

Dr. Muhammad Haroon Khan
Ph.D Bioinformatics
Department of Bioinformatics and Biosciences, Capital University of Science and Technology,Islamabad,Pakistan.

Course Contents

Introduction  to Natural Computing its Motivation,philosophy,branches. The simulation and emulation of nature in computers,Computing with natural materials,Natural computing approaches, Natural Phenomena, Models and metaphores-I&II, Natural Phenomena, From nature to computing and back again, Individuals, entities and agents, Parallelism and distributivity, Interactivity, Connectivity, Stigmergy, Adoptation, Evolution-I&II Feedback, Positive feedback, Negative  feedback, Self-organization and its Characterization and Alternatives, Complexity, Emergence, Reductionism, Bottom-up vs top-down, Determinsim, Chaos, Fractals, Introduction to physiological systems modeling, Simulation, and control-I ,Comprehensive definition of physiological systems modeling,simulation, and control-II,Input and the output of a system, Modeling, Evolution of Computer Power and Advancements in Physiological Systems Modeling ,Construction of projects phenomenon. Physiome-I,II and its databases, From molecules to humankind Strategies Toward Constructing Large Models-I,Strategies Toward Constructing Large Models-II,The Virtual Physiological Human (VPH) initiative with examples. The Cardiome Effort,Levels of modeling, types and classifications, Deterministic models,Stochastic models,Parametric models,Non-parametric models, Compartmental Modeling, Detailed Compartmental Models and types and examples. Linear modeling of physiological control system with examples, Nonlinear modeling of physiological control systems, The future of physiological systems modeling, simulation, and control,Professional societies and organizations,Bio-inspired computation,Life its Information Organizes and Breeds its Emergence, Explanation and Information,The Logical Mechanisms of Life,The Nature of Information and Information Processes in Nature,Formalizing Knowledge: Uncovering the Design Principles of Nature,Self-Organization and Emergent Complex Behavior,Life on the Edge of Chaos?,Complex Self-organization,Evolutionary Computing,Evolutionary Biology,On the theory of Evolution,The Darwins Idea,Basic Principles of Genetics,Principles of Genetics in detail,Evolution as an outcome of Variation and Selection,A classic example of evolution,The appeal of evolution  Pillars of  Evolutionary theory,The genotype  ,Cell Replication: Mitosis,Cell Replication: Meoisis,Genetic mutations ,Evolutionary computation ,Artificial Evolution-I&II, Standard Evolutionary Algorithms,Genetic encoding ,Binary Real-Valued and Tree based representation, Evolvability, Fitness Functions,Population,Selection Operators Selection Pressure, Genetic drift,Proportionate selection,Roulette wheel Rank based and Truncated rank based selection ,Tournament selection Elitism ,Genetic operators,Crossover ,Mutation ,Survivor selection, Initialization and termination, Evolutionary measures , Evolutionary and Genetic Algorithms (GAs) Robustness of traditional search and optimization methods, The goals of optimization, GA and traditional search methods ,Elements of GAs , GA operators, A simple GA -I&II at work: A simulation by hand-I, GA at work: A simulation by hand-II, Applications of GAs ,Genetic Programming (GP), Genetic Programming Challenges, Progress in Genetic Programming Representation in Tree-based GP, Initialising the Population, Selection, Recombination and Mutation, Genetic Programming steps with examples. 
Evolutionary Programming, Evolutionary Programming operators, Evolution Strategies-I&II, Swarm Intellegence, Ant colony optimization-I&II with Example, Particle Swarm Optimization-I&II, Bees Algorithm,Bacterial Foraging Optimization Algorithm, Introduction to Neural Networks, Biological Neural Network and Artificial Neural Networks ,Perceptron-I&II,Back-propagation-I&II, Hopfield Network-I&II,Learning Vector Quantization,Self-Organizing Map-I&II, Advantages and disadvantages of artificial neural networks and applications in Bioinformatics, An introduction to Artificial Life, Recent developments in the field of Artificial Life,Historical & theoretical roots of ALife,Software artificial life,Hardware artificial life, Wetware  artificial life,Artificial cells,Autonomous agents, Digital evolution, Stiquito: A Hexapod Insectoid Robot-I,Salamandra Robotica-I&II,Artificial Immune Systems, Biological immune system,Immune Network Theory,Negative Selection mechanism,Clonal Selection Principle,Intrusion Detection Systems,Initialization / Encoding,Similarity or Affinity Measure,Negative, Clonal or Neighbourhood Selection,Somatic Hypermutation,Comparison of artificial immune systems to genetic algorithms and neural networks,Danger Theory,Danger Theory in Artificial Immune Systems,Some promising areas for application for AIS,DNA Computing,Concepts of DNA computing and DNA computer,Why DNA Computing,Basics of DNA,Uniqueness of DNA computing,Motivation for DNA computing,General working aspects of DNA computing,Information storage and processing capabilities,Efficiency,Success of DNA computing,How it works,Part I: Generate all possible routes,Part II: Select itineraries that start and end with the correct cities,Part III: Select itineraries that contain the correct number of cities,Part IV: Select itineraries that have a complete set of cities,Reading out the answer,Caveats,Applications of DNA computing,Comparison of DNA and conventional electronic computers,Advantages of DNA comuting,Disadvantages of DNA computing,String Matching,Apprximate string matching,Dynamic Programming (DP),Sequence alignment,Pairwise sequence alignment,Global vs Local Alignment,Global Alignment Fundamentals,Initializtion and Matrix filling,Trace Back,Practicle Example,Local Alignment,Initializtion and Matrix filling,Trace Back,Practicle Example,Importance of Cost Functions,Multiple Sequence Alignment (MSA),Biological Motivation,Scoring a multiple sequence alignment,Dynamic Programming Algorithm for MSA,Progressive alignment approaches,Star Alignment,Complexity Analysis,Exercise,ClustalW,T-Coffee,Decision Trees,Nodes and Branches,Classification Trees,An Example,Applications to computational biology