Undergraduate Program in Computational Biology
BComp (Computational Biology) by School of Computing
Program Essential Module Descriptions
CS1101
Programming Methodology
Prerequisites: Soc – None; CFM – A-level Mathematics or its equivalent
The aim of this module is to introduce students to the discipline of computing and
to the problem solving process. The module stresses on good program design
and good programming styles, and structured program development using a
high-level programming language. Some basic concepts in procedural
abstraction, structured programming and top-down design with stepwise
refinement will be introduced. Topics to be covered include: algorithm design
process, program development/coding/debugging, programming concepts in a
high-level language including program structure, simple data types and
structured types, various control structures (sequencing, loops, conditionals,
etc.), and linear data structures, such as arrays and linked-lists. The utility of
recursion will also be highlighted using a variety of sorting algorithms. Laboratory
work is essential in this course.
CS1101S
Programming Methodology
Prerequisites: None
The aim and topics for this module are similar to those of CS1101. However,
students taking this S-option will be given advanced assignments. To support
such advanced study into selected topics, extra recitation classes will be
scheduled. Advanced students, as determined by the School, can subscribe this
S-option.
CS1102
Data Structures and Algorithms
Prerequisites: Pass CS1101 or CS1101C or CS1101S or IT1002
The aim of this module is to give a systematic introduction to data structures and
algorithms for constructing efficient computer programs. Emphasis is on data
abstraction issues (through ADTs) in the program development process, and on
efficient implementations of chosen data structures and algorithms. Commonly
used data structures covered include stacks, queues, trees (including binary
search tree, heap and AVL trees), hashing tables, and graphs; together with their
corresponding algorithms (tree and graph traversals, minimum spanning trees).
Simple algorithmic paradigms, such as generate-and-test (search) algorithms,
greedy algorithms and divide-and-conquer algorithms will be introduced.
Elementary analyses of algorithmic complexities will also be taught. Laboratory
work is essential in this course.
CS1104
Computer Organization
Prerequisites: CS1101 or CS1101S or A-level Computing.
This course aims to familiarize students with the fundamental building blocks of
computer systems. Students will learn the basic design of digital logic circuits.
They will be exposed to the register-level architecture of a computer system. The
basic building blocks of a computer system like CPU, memory, and I/O units are
discussed. Students will gain a better understanding on how a high-level
language program actually works inside a computer. Topics to be covered
include: Binary system; Boolean algebra and logic gates; simplification of
Boolean functions; adders, subtractors, and multiplexers; flip-flop and memory;
registers and counters; basic organisation and von Neumann model; memory
organisation; input/output; CPU structure and organisation; control unit; parallel
organisation - multiprocessing and vector computation; RISC architecture; and
comparison between RISC and CISC; overview of assembly language
programming; representation of numeric and character data, types of instruction
operators and operands; arithmetic and logic instructions; string and bit
operations; arrays and addressing modes; branching and loops; procedures;
input and output at the assembly level.
CS1231
Discrete Structures
Prerequistes: A-level Mathematics
This module aims to introduce mathematical tools that are required in the study
of computer science. Topics include: (1) Logic and proof techniques:
propositions, conditionals, quantifications. Some formations of logic rules may be
taught via the use of logic systems such as Natural deduction, sequent calculus,
etc. (2) Relations and Functions: Equivalence relations and partitions. Partially
and totally ordered sets. Well-Ordering Principle. Equality of functions. Boolean
function. Identity function. Inverse functions. Bijection. (3) Mathematical
formulation of data models (linear model, trees, graphs). (4) Counting and
Combinatoric: Pigeonhole Principle. Inclusion-Exclusion Principle. Number of
relations on a set, number of injections from one finite set to another,
Diagonalisation proof: An infinite countable set has an uncountable power set;
Algorithmic proof: An infinite set has a countably infinite subset. Subsets of
countable sets are countable.
CS1231S Discrete Structures (Accelerated)
Prerequisites: A-level Mathematics
Preclusion: MA1100
The module covers the same content as CS1231 Discrete Structures. It covers
the topics at a faster pace, and provides more in-depth treatment in these topics.
The module is aimed at students with stronger mathematics background.
CS2102
Introduction to Database Systems
Prerequisites: Pass CS1102
Preclusion: IT2002
The aim of this module is to provide students with the knowledge and
understanding of basic issues and techniques in manipulating large volumes of
data stored on secondary storage. The module covers manipulation of data
stored on secondary storage, physical organisation of data, access methods that
facilitate data retrieval, concepts of database management systems in
manipulating large volume of shared data, principles of DBMS, particularly the
relational database management systems and their use in application
development, SQL language, and new developments in non-conventional data
processing techniques.
CS2103
Software Engineering
Prerequisites: Pass CS1102
This module introduces the necessary conceptual and analytical tools for
systematic and rigorous development of software systems. It covers four main
areas of software development, namely object-oriented analysis, object-oriented
design, implementation and testing, with emphasis on the design and
implementation of software modules that work cooperatively to fulfill the
requirements of a system. Tools and techniques for software development, such
as Universal Modelling Language (UML), program specification, and testing
methods, will be taught. Major software engineering issues such as
modularisation criteria, program correctness, and software quality will also be
covered.
CS2105
Computer Networks I
Prerequisites: Pass CS1102 & CS1104
This module introduces the basic principles and concepts of data
communications and computer networks.
Essential data communication
knowledge required in the use and understanding of computer networks is first
covered without resorting to mathematical approach. The layered architecture is
introduced, and the services provided by each layer, the principles of the
protocols that are responsible for providing those services, etc. are discussed.
Emphasis is placed on general principles of protocol messaging, network multiple
access control, error control, flow and congestion controls, routing, and etc.
Important protocols used in local area networks and TCP/IP Internet are
explained.
CS2220 Introduction to Computational Biology
Prerequisites: CS1102. LSM1102 highly recommended.
The aim of this course is three folds. First, the course provides, from
programmers’ viewpoint, an overview of common computational techniques used
in the field of bioinformatics, including similarity operations, clustering and
classification techniques, and techniques in gene recognition. Second, the basic
theory behind these techniques will be covered. Last, but not least, the course
demonstrates the role of bioinformaticians as a bridge between the field of
computer science and biology, and prepares students for advanced computerscience topics relevant to bioinformatics.
CS3225
Post-Genome Informatics
Prerequisites: Pass CS2220
In the post-genome era, the genomic research is advanced to a new stage and
many new biology areas are evolved, including proteomic, population genetics.
This module aims to teach students the algorithms related to these areas.
To achieve the aim, the module will discuss in detail the related problems and
algorithms. For example, to learn proteomic, we will introduce the problems like
De Novo Peptide sequencing, protein-protein interaction, etc. Then, we will let
the students know the existing solutions and the algorithms behind those
solutions.
CS3230
Design and Analysis of Algorithms
Prerequisites: Pass CS1102
This module is to teach students different techniques of designing and analysing
algorithms. Students learn about the framework for algorithm analysis, for
example, lower bound arguments, average case analysis, and the theory of NPcompleteness. In addition, students are exposed to various algorithm design
paradigms. The module serves two purposes: To improve the students’ ability to
design algorithms in different areas, and to prepare students for the study of
more advanced algorithms. The module covers lower and upper bounds,
recurrences, basic algorithm paradigms (such as prune-and-search, dynamic
programming, branch-and-bound, graph traversal, and randomised approaches),
amortized analysis, NP-completeness, and some selected advanced topics.
CS4220
Computational Analysis of Biological Data
Prerequisite: LSM1102 and ST2132
With the advancement of high throughput technologies, biologists are being
overloaded with lots of information (e.g., gene expression data). To be able to
make sense out of these data, there is a need to have a systematic way to
analyze them. This course is introduced to provide students with knowledge of
techniques that can be used to analyze biological data to enable them to
discover new knowledge. Topics include: Clustering analysis, classification;
Microarray analysis; Support vector machines; Hidden Markov Models
(homology, gene finding).
LSM1101
Biochemistry of Biomolecules
Prerequisite: “A” level Biology or equivalent, or LSM1301
pH and buffers and an overview of the physical-chemical basis for the structures
of biological molecules: nucleic acids, proteins, carbohydrates and lipids; their
structure-function relationships. Enzyme kinetics and application of enzymes.
Heme biosynthesis and metabolism.
LSM1102
Molecular Genetics
Prerequisite: “A” level Biology or equivalent, or LSM1301
Principles of genetics, covering heredity, phenotype and the molecular basis of
heredity in prokaryotes, eukaryotes and man. Quantitative and population
genetics with emphasis on gene pools, genetic variation and speciation.
LSM2101
Metabolism and Regulation
Prerequisite: A pass in LSM1101
Overview of the biosynthesis and catabolism of carbohydrates, proteins, nucleic
acids and lipids in the context of human health and disease. Emphasis on the
integration and regulation of metabolic pathways in different tissues and organs.
Principles of bioenergetics and mitochondrial energy metabolism, free radicals,
mitochondrial DNA damage in ageing and neurodegenerative diseases.
LSM2102
Molecular Biology
Prerequisite: Read LSM1101 and LSM1102 and pass one of them
This module deals with the structure, organization and function of genes and
genomes in both prokaryotes and eukaryotes. DNA topology, hierarchy of
packaging of DNA in chromosomes and relationship to gene activity will be
discussed. The functional roles of DNA regulatory elements (basal & upstream
promoters, enhancers, silencers, locus control region, insulators & gene
boundaries) and transcription factors (general- and sequence-specific factors, Zn
fingers) involved in gene expression will be examined extensively. The molecular
events of transcription; post-transcriptional modifications and RNA processing
(eg: 5’capping, polyadenylation, RNA splicing mechanisms); temporal and spatial
gene expression, control and regulation, signals of gene expression will be dealt
with in detail. The cause and/or effect of dysfunction of gene expression and
diseases will be discussed.
LSM2104
Essential Bioinformatics and Biocomputing
Prerequisite: A pass or advanced placement in LSM1101
Introduction to biological databases and bioinformatics software. Sequence
comparison algorithms and tools. Biomolecular 3D structure and modeling.
Students will be introduced to bioinformatics theory, tools, and techniques
LSM2201 Experimental Biochemistry
Prerequisite: LSM1101
Emphasis on principles and understanding of established methods of protein
isolation, purification and characterization. Techniques include gel-filtration, ionexchange, hydrophobic interaction, affinity chromatography and gel
electrophoresis. Integration of factual knowledge to laboratory practice.
Opportunities to develop strategies in the design of experiments. Analysis and
presentation of data
LSM3231
Protein Structure and Function
Prerequisite: LSM2101
The main objective is to provide a strong foundation in the study of protein
structure and function. Structures and structural complexity of proteins and
methods used to determine their primary, secondary and tertiary structures.
Biological functions of proteins in terms of their regulatory, structural, protective
and transport roles. The catalytic action of enzymes, their mechanism of action
and regulation. Various approaches used in studying the structure-function
relationship of proteins.
LSM4241
Functional Genomics
Prerequisite: LSM2104 and LSM3231
Assignment of functions to novel genes following from the genome-sequencing
projects of human and other organisms. The principles underlying enabling
technologies: DNA microchip arrays, proteomics, protein chips, yeast two-hybrid
system, transgenics, aspects of bioinformatics and its applications. To
understand the impact of functional genomics on the study of diseases such as
cancer, drug target discovery, pharmacogenetics and healthcare .
MA1102R
Calculus
Prerequisites: ‘A’ level Mathematics or its equivalent
The objective of this module is to provide a rigorous but not necessarily in-depth
coverage of calculus. It is a gentle introduction to the central concept of limit and
its consequences. Students will understand the concepts underlying some of the
common applications of calculus. With the aid of a computer algebra system,
students will better learn the definitions through visualization, experimentation
and exploration, know the terms involved in theorems and application and thus
know how, where and when to apply calculus and explore different ways to solve
problems. Critical thinking is encouraged by requiring precision in argument in
both solution or proof. Students will learn to appreciate mathematics and its inner
workings well. This module aims also to lay the foundations of analysis for
students intending to major in mathematics as well as to broaden and enrich
students’ perspective in mathematics. To this end a variety of supplementary
materials and activities are provided to help cultivate an appreciation of calculus
and enhance the learning process.
ST2131
Probability
Prerequisite: MA1102 or GM1304 or GM1307 or SA1102
Counting methods, sample space and events, axioms of probability, conditional
probability, independence, random variables, discrete and continuous
distributions, joint and marginal distributions, conditional distribution,
independence of random variables, expectation, conditional extectation, moment
generating function, central limit theorem, the weak law of large numbers.
ST2132
Mathematical Statistics
Prerequisite: ST2131
Random sample and statistics, method of moments, maximum likelihood estimate,
Fisher information, sufficiency and completeness, consistency and unbiasedness,
sampling distributions,  2 -, t- and F-distributions, confidence intervals, exact and
asymptotic pivotal method, concepts of hypothesis testing, likelihood ratio test,
Neyman-Pearson lemma.
CM1121
Basic Organic Chemistry (Essential) (4 MC)
Prerequisite : ‘AO’ level pass in chemistry or equivalent.
This module covers the characteristic properties, methods of preparation, and
reactions of alkanes/cycloalkanes, alkenes, alkynes, benzene and other aromatic
compounds, alkyl halides; alcohols; ethers; epoxides, phenols, aldehydes and
ketones; carboxylic acids and their derivatives; amines.
PC1221 Fundamentals of Physics I
Prerequisite: O level pass in Physics or equivalent
(Antirequisite: A or AO level pass in Physics or equivalent, or PC1131, or
PC1132)
Vectors, linear motion, velocity, acceleration, equations of kinematics, linear
momentum, conservation of energy and linear momentum, forces, laws of motion
and applications, work, energy, power, conservation laws, gravitation, circular
motion, temperature, zeroth law, first law of thermodynamics, heat, heat
capacity, ideal gas laws, thermal expansion of solids and liquids, work and heat
transfer in thermodynamic processes.
MA2214 Combinatorial Analysis (4 MC)
Prerequisite: MA1100 or MA1101 or CS1231
Basic tools of combinatorics, binomial and multinomial coefficients, principle of
inclusion and exclusion, ordinary and exponential generating functions,
recurrence relations.
Program Elective Module Descriptions
CS3232 Systems Modeling and Simulation
Prerequisites: Pass ST1232 or ST2131 or ST2334
This module provides students with the methodology and techniques that are
required for the planning and design of computer simulation models. At the end
of the module, the students should be able to carry out a study of a system using
simulation. Topics include: overview of ways to study a system; simulation
modelling; concepts, components and organisation of discrete-event simulation;
random number and random variate generators; input data collection; modelling
and analysis; model verification and validation; output analysis; design of
simulation experiments; examples of systems models from science and
business; and overview of parallel discrete-event simulation. A simulation
language will be covered in detailed, with a large simulation exercise to be
carried out.
CS3241 Computer Graphics
Prerequisites: CS1102
This module teaches some graphics hardware devices, reviews the mathematics
related to the understanding, and discusses the fundamental areas of computer
graphics. After completing the course, students are expected to understand the
basic computer graphics terminology and concepts, and to be able to design and
implement simple 2D and 3D interactive computer graphics related programs. As
an enrichment part of the course, students are introduced the state-of-the-art
development in computer graphics by viewing interesting video clips and
experimenting with demo program made available in the course web.
CS3243
Foundations of Artificial Intelligence
Prerequisites: Pass CS1102 and (CS1231 or CS1231S)
The module introduces students to the basic concepts in search and knowledge
representation as well as to a number of sub areas of artificial intelligence. It will
provide them with a good foundation and overview of the discipline. The
emphasis of the module is on covering the essential concepts in AI. The module
covers Turing test, blind search, iterative deepening, production systems,
heuristic search, A* algorithm, minimax and alpha-beta procedures, predicate
and first-order logic, resolution refutation, non-monotonic reasoning, assumptionbased truth maintenance systems, inheritance hierarchies, the frame problem,
certainly factors, Bayes' rule, frames and semantic nets, planning, learning,
natural language, vision, and expert systems and LISP.
CS3244
Machine Learning and Neural Networks
Prerequisites: CS3243
This module introduces basic concepts and algorithms in machine learning and
neural networks. The main reason for studying computational learning is to make
better use of powerful computers to learn knowledge (or regularities) from the
raw data. The ultimate objective is to build self-learning systems to relieve human
from some of already-too-many programming tasks. At the end of the course,
students are expected to be familiar with the theories and paradigms of
computational learning, and capable of implementing basic learning systems.
CS4221
Database Design
Prerequisites: CS2102/CS2102S
The aim of this module is to provide students with the knowledge necessary to
design relational databases and object oriented databases. The module covers
normalisation theory: functional, multi-valued and join dependency, normal forms,
relational database schema design using decomposition method and
synthesizing method, eg., Bernstein's Algorithm and LTK's Algorithm; entityrelationship approach: normal form entity-relationship Diagram, convert entityrelationship diagrams to normal form entity-relationship diagrams, translate
entity-relationship diagrams to relational, network, and hierarchical database
schemas; schema integration: view integration and database integration, schema
conflict resolution; nested relations: normal form nested relations, nested
relations design using decomposition method and entity-relationship approach;
object-oriented databases: object-oriented DBMS concepts, inadequacies in
object-oriented data models, inheritance conflict resolution, translate relational
database schemas and entity-relationship diagrams to object-oriented database
schemas.
CS4234
Combinatorial and Graph Algorithms
Prerequisites: CS3230
This module presents advanced material on the design and analysis of
combinatorial algorithms with emphasis on efficient algorithms and data
structures. This module is meant for students who intend to (i) do research in
computer science in general, and algorithm design in particular, or (ii) do
advanced application/software development in other areas of computer science.
The module covers a wide range of standard combinatorial and graph algorithms,
including advanced data structures commonly used in these algorithms. Some of
the topics that may be covered include, advanced data structures, graph
algorithms, matching and network flow, theory of NP-Completeness,
combinatorial algorithms, geometric algorithms, mathematical programming,
probabilistic algorithms and Meta-Heuristic search methods.
CS4235 Computational Geometry
Prerequisites: CS3230 and MA1101R
Computational geometry is the study of algorithms for solving geometric
problems. This course introduces the main topics and techniques in this field.
They will be presented in connection with applications in CAD, databases,
geographic information systems, graphics and robotics. Students will learn the
main algorithmic techniques for solving geometric problems and the related
discrete geometric structures. At the end of this module, students will be able to
design and analyse geometric algorithms and data structures, and to apply these
techniques to solve problems arising in applications.
CS4242 Uncertainty Modeling in Artificial Intelligence
Prerequisites: Pass (CS1231 or CS1231S) and (ST1232 or ST2131 or ST2334)
and CS3243
This module introduces AI models for representation and reasoning of knowledge
in uncertain (real world) situations. Reasoning based on probability theory and
fuzzy theories to cope with typical real-world situations are studied. The module
is divided into two parts. The first part covers development of Bayesian network
for knowledge representation. This representation facilitates modelling
uncertainty reasoning in real-world situation as well as development of machine
learning for knowledge acquisition in complex knowledge domains. The second
part covers use of fuzzy sets and fuzzy logic as knowledge representation for
modelling human approximate reasoning in real-world situations.
CS4240 Virtual Reality and 3D Interaction
Prerequisites: CS3241
The objective of this module is to expose students to advanced 3D interactive
techniques by focusing on a set of selective subjects: fundamentals of virtual
reality, immersing devices (HMD, Gloves, magnetic trackers, stereo glasses and
other forces) and multi-user systems, techniques for real time or fix frame rate
simulation such as scene/object culling, representations of level of details (LOD),
and various image based rendering methods based on the dimensionality of the
Plenoptic function and related sampling and re-sampling issues.
CS4244 Knowledge-Based Systems
Prerequisites: CS3243
This is a module that contains both the theory and practice of building
knowledge-based systems. The aim of this module is to prepare students so that
they can design and build knowledge-based systems to solve real-world
problems. The module starts with motivations, background and history of
knowledge-based system development. The main content has five parts: rulebased programming language, uncertainty management, knowledge-based
systems design, development and life cycle, efficiency in rule-based language
and knowledge-based systems design examples.
MA3259
Mathematical Methods in Genomics
Prerequisite: MA2216or ST2131 or ST2334 or LS1104. Knowledge of genetics
is required.
Primers in molecular biology. Sequence assembly, sequence comparison,
multiple alignment, finding signals in DNA, gene prediction, genome
rearrangements, phylogenetic reconstruction, computational proteomics.
ST3236
Stochastic Processes 1
Prerequisite: (MA1101 or GM1302) or (ST2131 or MA2216)
This module introduces the concept of modelling dependence and focuses on
discrete-time Markov chains. Topics include discrete-time Markov chains,
examples of discrete-time Markov chains, classification of states, irreducibility,
periodicity, first passage times, recurrence and transience, convergence
theorems and stationary distributions.
ST4238
Stochastic Processes 2
Prerequisites: MA3238 or ST3236
This module builds on ST3236 and introduces an array of stochastic models with
real-world applications. Topics include Poisson process. Continuous-time Markov
chains, examples of continuous-time Markov chain, birth and death processes,
queing theory.
ST3131 Regression Analysis (Essential, 4 MC)
Prerequisites: ST2131 or MA2216 or ST2334
This module focuses on data analysis using multiple regression models. Topics
include simple linear regression, multiple regression, model building and regression
diagnostics.
LSM2103
Cell Biology
Prerequisite: Read LSM1101 and LSM1102 and pass one of them
This course provides a comprehensive understanding of cellular structures,
functions and interaction in unicellular and multi-cellular systems. Emphasis is on
cellular functions with the following topics: Brief overview of the structure and
functions of organelles, the molecular basis for cell and tissue organization (cell
surface; extracellular matrix; cell wall; cell adhesion; cell junctions), cytoskeleton
and cell movements, current concepts of intercellular and intracellular signaling,
protein synthesis, post-translational modifications & and intracellular protein
trafficking –
(including endocytosis; exocytosis; import of proteins into
organelles; nuclear transport), molecular basis of cell proliferation, senescence
and apoptosis.
LSM2202
Experimental Molecular and Cell Biology
Prerequisite:Read LSM1101 and LSM1102 and pass one of them. Must be
read with LSM2102 or LSM2103.
Introduction to the theory and application of techniques relevant to molecular and
cell biology. Mammalian cell culture, growth curve / cycles of bacteria,
bacteriophage and eukaryotes. Emphasis on general recombinant DNA
techniques, isolation of mRNA, synthesis of cDNA, making cDNA libraries,
screening of cDNA and genomic libraries; polymerase chain reaction (PCR), and
RNA Northern blots for analysis of gene expression.
LSM3222
Human Genetic and Infectious Diseases
Prerequisite: LSM2101 and (LSM 2102 or LSM 2103)
Overview of the molecular and cellular basis of human genetic and infectious
diseases. Different paradigms of human genetic diseases and genetic aspects of
some common complex diseases: familial hypercholesterolemia, coronary heart
disease, Alzheimer’s diseases. Roles of oncogenes and tumour suppressor
genes in cell cycle control and apoptosis, predisposition to cancer and other
genetic diseases. Infectious diseases worldwide and specifically within the
context of Singapore. Emphasis on principles of human genetics in
understanding human health and disease and to appreciate the principles of
medical microbiology in the diagnosis and treatment of human infectious
diseases.
LSM3223 IMMUNOLOGY
Prerequisite: LSM2103
This course provides the central concepts of immunology and the foundation for
understanding how immunity functions. The subjects of innate immunity and
haematopoiesis introduce the origin and role of different cell types in immunity.
The mechanics of how the body protects itself from disease are explored in
relation to T and B cell biology, monoclonal antibodies, cytokines, major
histocompatibility complex and antigen presentation. Other topics include
hypersensitivity, immunodeficiencies, tolerance, autoimmunity, resistance and
immunization to infectious diseases.
LSM3243
Molecular Biophysics
Prerequisite: LSM1101 and LSM3231
Introduction to the conformation of biological macromolecules. Biophysical
characterization of proteins, nucleic acids and biological membranes. To
understand their biological function and action at the molecular level in terms of
their structure, dynamics and interactions. The protein folding, protein-protein,
protein-nucleic acid and protein-lipid interactions. Biophysical techniques used in
studying biological structure and function.
LSM4231 Structural Biology (Elective, 3MC)
Prerequisite: LSM3241/CS2220 and LSM3243
Introduction to structural biology of proteins and nucleic acids. Emphasis on the
theory, applications and limitations of major techniques used in determining
three-dimensional structures: nuclear magnetic resonance spectroscopy, X-ray
diffraction, cryo-electron microscopy and molecular modeling methods.
CZ2105 Numerical Methods I
Modular Credits: 4
Workload: 1.5-1.5-1-3-4
Pre-requisite(s): (CZ1102 or CS1101C), {MA1102R and MA1101R} or (MA1505
or MA1505C)
Introduction to methods used to numerically approximate solutions of a variety of
mathematical and physical problems. Includes: generation and propagation of
round-off errors and convergence criteria, solutions of linear systems, function
approximation and interpolation, special function evaluation, fitting/modeling of
data, numerical differentiation & integration, and minimization & maximization of
functions.
CZ3102 Scientific Modeling
Modular Credits: 4
Workload: 1.5-1.5-1-3-4
Pre-requisite(s): CZ3105
Methods for developing models that represent physical, biological, and social
systems, including use of dimensional analysis, empirical data, approximation
levels of models, and model validation. Emphasis will be on eventual
development of computational models and on the use of prototyping software
environments. Examples include least-square fitting, dynamical processes
described using rate equations (ordinary differential equations), and modeling of
stochastic (Markov) processes.
CZ3105 Numerical Methods II
Modular Credits: 4
Workload: 1.5-1.5-1-3-4
Pre-requisite(s): CZ2105 and any of {MA2221, MA1104, MA1506, MA1506C}
Provides skills for the development of efficient computational methods for several
topics including: systems of linear and nonlinear equations; matrix methods and
eigenvalues from physical problems such as vibrating springs; ordinary
differential equations- initial and boundary value problems; and Fourier
series/transform methods.
CZ3253 Computer Aided Drug Design
Modular Credits: 4
Workload: 2-1-2-2-3
Pre-requisit(s): CZ2251/LSM2104
3D structure and function of biomolecules. The targets of drugs and design
principles. Genetic algorithms and quantitative structure-activity relationships.
Chemical compound. Databases and search tools. Interactive graphics in drug
design. Molecular surfaces and Algorithm of automated docking of drugs into
receptor sites. Introduction to docking Software package DOCK.
CZ4102 High Performance Computing
Modular Credits: 4
Workload: 2-1-2-3-3
Pre-requisite(s): CZ3105
Introduction to high-performance computing, including comparison of parallel
architecture (vector/distributed) and software environments (threads vs. MPI),
performance analysis of programs, algorithms in scientific computations (matrix
multiplication, linear systems), use of parallel software libraries.
CZ4105 Computational Differential Equations
Modular Credits: 4
Workload: 2-1-2-3-3
Pre-requisite(s): CZ3105
Matrix techniques used in numerical solutions of differential equations; advanced
techniques for ordinary differential equations. Physical and mathematical
principles leading to partial differential equations, such as locality, conservation
laws, and variational principles, and introduction to numerical methods for them
(finite difference and spectral methods).
CZ4225 Methods in Computational Biology
Modular Credits: 3
Workload: 2-1-2-2-2
Pre-requisite: CZ3252
Proteins: sequence => structure => function. Protein structural organizations and
families. Basic modeling and simulation techniques. Protein structural modeling:
homology modeling, threading, ab initio methods. Cell as a complex machine:
Genetic and protein circuits (pathways). Development of a mathematical model
of pathways. Computer simulation of pathways.
CZ4226 Advanced Bioinformatics
Modular Credits: 3
Workload: 2-1-2-2-2
Pre-requisite(s): CZ3252
Overview of bioinformatics and its role in biology. Protein sequence analysis
algorithms. Sequence comparison and scoring functions: PAM, BLOSUM, Motifs.
Prediction of Protein function and Protein-Protein Binding. Protein Structural
prediction methods.