Course Title

Mathematics, Physics and Basic Programming for the Structural Biologist

Course Code


Offered Study Year 2, Semester 2
Course Coordinators Mu Yuguang (Assoc Prof) 6316 2885
Lescar, Julien (Assoc Prof) 6908 2208
Pre-requisites None
AU 3
Contact hours Lectures: 24, Tutorials: 12, Laboratories: 9
Approved for delivery from
Last revised 17 Mar 2020, 14:38

Course Aims

This course aims to introduce concepts of mathematics and physics required in the day to day practice of structural biology. After learning signal analysis and image formation theory, you will learn mathematical modeling and computer programming to solve problems in structural biology.

Intended Learning Outcomes

Upon successfully completing this course, you should be able to:

  1. Make use of mathematical and physics tools to conceptualize experiments encountered in structural biology.
  2. Explain the theory of wave propagation, scattering, interference and diffraction and their use in structural biology
  3. Analyse and discuss current research topics in biomedical structural biology
  4. Write some simple Python scripts to solve problems in structural biology

Course Content

Complex numbers and vector calculus

Fourier coefficients and calculating Fourier transforms

Wave propagation including Electromagnetic waves

Analysis of interference experiments like Young two slits experiments

Simple diffraction experiments

Huygens, Malus and Fermat principles

Image formation

Basics of lasers and some of their biomedical applications

Python coding principles


Component Course ILOs tested SBS Graduate Attributes tested Weighting Team / Individual Assessment Rubrics
Continuous Assessment
Assignment 1, 2, 3, 4 1. b
2. g
3. e
4. a
5. c
6. c
7. b
100 both See Appendix for rubric
Total 100%

These are the relevant SBS Graduate Attributes.

1. Recognize the relationship and complexity between structure and function of all forms of life, resulting from an academically rigorous in-depth understanding of biological concepts

b. Explain the relationship between structure and function of all forms of life at the molecular level

2. Critically evaluate and analyze biological information by applying the knowledge, scientific methods and technical skills associated with the discipline

g. Evaluate the results of their own experiments and decide on the next step

3. Develop and communicate biological ideas and concepts relevant in everyday life for the benefit of society

e. Discuss current critical questions in the field of biology

4. Acquire transferable and entrepreneurial skills for career development

a. Demonstrate innovative approaches to solving problems in biological science, leading to new approaches or techniques

5. Develop communication, creative and critical thinking skills for life-long learning

c. Demonstrate critical thinking skills such as analysis, discrimination, logical reasoning, prediction and transforming knowledge

6. Develop codes of social responsibility and scientific ethics, particularly in relation to biological advancement and applications

c. Respect regulations involving plagiarism and copyright

7. Demonstrate information literacy and technological fluency

b. Work effectively with common technologies in biology

Formative Feedback

The lectures will use the ResponseWare system to provide feedback in class on concepts and details for each lecture and hence, you will receive regular feedback on your understanding of the details and concepts being taught. (This helps you to achieve intended learning outcomes 1-4).

In the tutorials, you will receive feedback in the following ways:

  1. By direct feedback for each answer given during the course of the tutorial.
  2. The class will discuss key answers at the end of each tutorial. (a. and b. helps you to achieve intended learning outcomes 1 & 2).
  3. You will discuss with the tutors selected research papers (This helps you to achieve intended learning outcomes 3).

In the labs, you will receive feedback on proper programming practices. (This helps you to achieve intended learning outcomes 4).

Learning and Teaching Approach

(24 hours)

The concepts will be introduced during the lectures and illustrated by examples. Further examples will be worked out together during the tutorials. Notes and the key concepts of each lecture will be uploaded to NTULearn at least 3 days before the lecture.

(12 hours)

1. We will post questions one week before each tutorial so you can work on answering them.
2. During the tutorial, you are given the opportunity to discuss some of the questions with the tutors.
3. Detailed solution steps will be discussed with you. The questions will cover the learning outcomes 1 & 2.
A paper will also be discussed to help you to achieve Intended Learning Outcome 3.

(9 hours)

1. We will introduce Python, a high level programing language.
2. Group discussion will be encouraged to further understand the process of programming.
The Laboratories will help you to achieve Intended Learning Outcome 4.

Reading and References

For Maths/Physics for the Biophysicist:

  • Grant R. Fowles, Introduction to Modern Optics, 2nd Edition, Dover, 1989. ISBN-13: 978-0486659572
  • Eugene Hecht, Optics, Fifth edition, Pearson, ISBN-10:0-133-97722_6
  • Igor N. Serdyuk, Nathan R. Zaccai, Giuseppe Zaccai, Methods in Molecular Biophysics 1st edition, Cambridge University Press, 2007 ISBN: 0-521-81524-X
  • John D. Jackson, Classical Electrodynamics, 3rd Edition, John Wiley & Sons, 1998. ISBN: 978-0-471-30932-1
  • M. Born & E. Wolf, Principles of Optics, 7th Edition Cambridge University Press, 1999 ISBN: 0 521 64 2221
  • Frank J (2006) Three-dimensional electron microscopy of macromolecular assembly. Oxford University Press
  • Murphy DB (2001) Fundamentals of light microscopy and electric imaging. Wiley-Liss ISBN 0-471-25391-X
  • Vretblad A (2003) Fourier Analysis and Its Applications. Springer-Verlag ISBN 0-387-00836-5

For Scientific Programming:

  • Wentworth, P., Elkner, J., Downey, A. B., & Meyers, C. (2012). Learning with Python 3 (RLE). Retrieved from

Course Policies and Student Responsibilities

To be successful in this course, it is expected that you put in a significant amount of effort into preparation (via the readings), and coding practice, in addition to attending the classes/tutorials/labs.

Academic Integrity

Good academic work depends on honesty and ethical behaviour. The quality of your work as a student relies on adhering to the principles of academic integrity and to the NTU Honour Code, a set of values shared by the whole university community. Truth, Trust and Justice are at the core of NTU’s shared values.

As a student, it is important that you recognize your responsibilities in understanding and applying the principles of academic integrity in all the work you do at NTU. Not knowing what is involved in maintaining academic integrity does not excuse academic dishonesty. You need to actively equip yourself with strategies to avoid all forms of academic dishonesty, including plagiarism, academic fraud, collusion and cheating. If you are uncertain of the definitions of any of these terms, you should go to the Academic Integrity website for more information. Consult your instructor(s) if you need any clarification about the requirements of academic integrity in the course.

Course Instructors

Instructor Office Location Phone Email
Lescar, Julien (Assoc Prof) LKC 06-10 6908 2208
Mu Yuguang (Assoc Prof) 04s-46a 6316 2885

Planned Weekly Schedule

Week Topic Course ILO Readings/ Activities

Complex numbers
Vector calculus

1, 3



Fourier series
Fourier Transform

1, 2



Waves polarization
Electromagnetic waves polarization

1, 2, 3




1, 2, 3




1, 2, 3



Basic optics and image formation

1, 2, 3



Lasers and biomedical applications

1, 2, 3



The way of the program
Variables, expressions, statements


Computer lab


Hello, little turtles!


Computer lab


Fruitful Functions


Computer lab




Computer lab


Event handling


Computer lab



1, 2, 3, 4

Computer lab

Appendix 1: Assessment Rubrics

Rubric for Continuous Assessment: Assignment (100%)

There are four graded assignments that form the Continuous Assessment, each assignement is worth 25 marks.

Graded assignments are based on

1-Graded Report on Scientific programming in Python

Graded report on the computer practical session displaying the code you have written and and an example of its application.

Marks Quality indicators
16 to 20 Well-articulated coding supported by appropriate comments explaining the logic behind the code
10 to 15 The code works efficiently but explanations are not detailed enough.
Less than 10 Confusing code with little or no explanations and comments

2, 3 and 4: Exercises and assays (covering e.g. vectors, complex numbers, Fourier series, waves, polarization, interference, image formation, lasers ... )

Points will be awarded as follows for each assignment:

Marks Quality indicators
16 to 20 Sound and/or cogent answers supported with appropriate scientific

reasoning and explanations related to biomedical structural biology.
10 to 15 Generally acceptable answers but not adequately supported with

appropriate scientific reasoning and explanations related to biomedical

structural biology.
Less than 10 Majority of the answers are unsound or not cogent, with little or unacceptable explanations.

Total: The total mark for each part will be scaled to 25, giving a total of 100 for the four assignments.

Appendix 2: Intended Affective Outcomes

As a result of this course, it is expected you will develop the following "big picture" attributes:

Have enhanced critical thinking ability

Awareness of the importance of mathematics, physics and programming skills in the practice of biology

Willingness to diligently debug scripts