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BIF602 : Bioinformatics Computing II

Course Overview

Course Synopsis

This course has been designed to give an interdisciplinary perspective on computational problems in molecular biology. This course aims to addresses several algorithmic challenges in Bioinformatics. Students will learn various ways in which they can apply computing techniques to the problems of molecular life sciences. This area has been very hot in recent years and there is an extensive arsenal of computational methods available. The course will familiarize the students with the recent techniques of computing. By the end of the course, the students will have a handsome knowledge of natural computing, neural networks and pattern recognition etc. for solving bioinformatics problems and a grasp of the underlying principles that is adequate for them to evaluate, use and develop novel techniques as needed.

Course Learning Outcomes

Computationl Problems in Biology

  • Introduction to Computational Biology
  • Natural Computing and its Empowerment and How nature behaves and works.
  • Natural Computing processing and its attributes
  • Networking and Neural Networking
  • System Modeling
  • Databases and Virtual Physiological human Initiatives


Course Calendar

1 Introduction to Natural Computing
2 Introduction to Natural Computing Part-2
3 Introduction to Natural Computing Part-3 (A Small sample of ideas)
4 Introduction to Natural Computing Part-4 (The philosophy of natural computing)
5 Introduction to Natural Computing Part-5 (The Three branches: a brief overview)
6 Introduction to Natural Computing Part-6 (Computing inspired by nature)
7 Introduction to Natural Computing Part-7 (The simulation and emulation of nature in computers)
8 Computing with natural materials
9 When to use natural computing approaches

10 Natural Phenomena, models and metaphores-I
11 Natural Phenomena, models and metaphores-II
12 From nature to computing and back again
13 Individuals, entities and agents
14 Parallelism and distributivity
15 Interactivity
16 Connectivity
17 Stigmergy
18 Adaptation

19 Evolution-1
20 Evolution-II
21 Feedback
22 Positive Feedback
23 Negative feedback
24 Self-organization
25 Characteristics of Self-organization
26 Alternatives to Self-organization
27 Complexity
28 Emergence

29 Reductionism
30 Bottom-up vs top-down
31 Determinism
32 Chaos
33 Fractals
34 Introduction to physiological systems modeling
35 Comprehensive definition of physiological systems modeling, simulation, and control-I
36 Comprehensive definition of physiological systems modeling, simulation, and control-II
37 Input and the output of a system

38 Historical Background of Modeling
39 Historical Background of Modeling-II
40 Modeling from Literature
41 Evolution of Computer Power and Advancements in Physiological Systems Modeling
42 The Physiome Project
43 The Physiome Project in detail
44 Constructing The Physiome-I
45 Constructing The Physiome-II
46 PREREQUISITES FOR THE PHYSIOME PROJECT

47 WHY THE PHYSIOME-I
48 WHY THE PHYSIOME-II
49 Databases for the Physiome
50 From molecules to humankind
51 Strategies Toward Constructing Large Models-I
52 Strategies Toward Constructing Large Models-II
53 The Virtual Physiological Human (VPH) initiative
54 The Virtual Physiological Human (VPH)
55 An example, a cardiome effort
56 The cardiome effort

57 Levels of modeling
58 Modeling from cellular to organ and systems modeling
59 Classification of models
60 Deterministic models
61 Stochastic models
62 Parametric models
63 Non-Parametric models
64 Applied Example-1
65 Applied Example-2
66 Compartmental Modeling
Mid Term

67 Detailed Compartmental Modeling
68 Modified Compartmental Models
69 Expansion to Multi-Compartmental Models
70 Applied example
71 Linear modeling of physiological control systems
72 Applied Example-I
73 Applied Example No. 2
74 Nonlinear modeling of physiological control systems
75 The future of physiological systems modeling, simulation, and control
76 Professional societies and organizations

77 Bio-inspired computation
78 What is Life?
79 Information Organizes and Breeds Life
80 Emergence and Explanation
81 Life and Information
82 The Logical Mechanisms of Life
83 The Nature of Information and Information Processes in Nature
84 Formalizing Knowledge: Uncovering the Design Principles of Nature
85 Self-Organization and Emergent Complex Behavior
86 Life on the Edge of Chaos?
87 Complex Self-organization
88 Evolutionary Computing
89 Evolutionary Biology

90 On the theory of Evolution
91 On the theory of Evolution-II
92 Basic Principles of Genetics
93 Principles of Genetics in detail
94 Pillars of Evolutionary Theory
95 The genotype
96 Cell Replication: Mitosis
97 Cell Replication: Meoisis
98 Genetic mutations
99 A brief history of Evolutionary computation (EC)

100 Artificial Evolution-I
101 Artificial Evolution-II
102 Standard Evolutionary Algorithms
103 Genetic encoding
104 Binary representation
105 Real-Valued representation
106 Tree Based representation
107 Evolvability
108 Fitness Functions
109 Population
110 Selection Operators

111 Selection Pressure
112 Genetic drift
113 Proportionate selection
114 Roulette wheel selection
115 Rank based selection
116 Truncated Rank based selection
117 Tournament selection
118 Elitism

119 Genetic operators
120 Crossover
121 Mutation
122 Survivor selection
123 Initialization and termination
124 Evolutionary measures
125 Evolutionary Algorithms
126 Genetic Algorithms
127 Robustness of traditional search and optimization methods

128 The goals of optimization
129 Genetic Algorithm and traditional search methods
130 Elements of Genetic Algorithms
131 Genetic Algorithm operators
132 A simple Genetic Algorithm-I
133 A simple Genetic Algorithm-II
134 Genetic Algorithm at work: A simulation by hand-I
135 Genetic Algorithm at work: A simulation by hand-II
136 Applications of Genetic Algorithms
137 Genetic Programming (GP)
138 Genetic Programming Challenges
139 Progress in Genetic Programming

140 Representation in Tree-based Genetic Programming
141 Initialising the Population for Genetic Programming
142 Selection in Genetic Programming
143 Recombination and Mutation in Genetic Programming
144 Getting Ready to Run Genetic Programming
145 Steps 1: Terminal Set
146 Step 2: Function Set
147 Step 3: Fitness Function
148 Steps 4 and 5: Parameters and Termination

149 Evolutionary Programming
150 Evolutionary Programming operators
151 Evolution Strategies-I
152 Evolution Strategies-II
153 Swarm Intellegence
154 Ant colony optimization-I
155 Ant colony optimization-II
Final Term