Tag Archives: science for life laboratory

Course Review: High-throughput data analysis

The course, BB2491 High-throughput data analysis, is the core of MTLS education at KTH. How does BB2491 differ from traditional biology lectures that we have so far? In this blog I will talk a bit about why, and how we should learn and master high-throughput science!

First, why study high-throughput? It is involved in a myriad of industrial applications, as well as the frontiers of research, to name but a few:

  • Next Generation Sequencing (NGS) is the best illustration of generating and analysing high-throughput data
  • High-throuput compound screening dominates lead discovery for manufacturing of small molecule drug
  • Traditional gene-targeting methods are sufficient for analysis of mendelian diseases, but diseases that involves more complex interplay, requires collecting and analysing “bigger data”

In BB2491, the teaching follows a logical transition from theory, practice to a hand-on project.

THEORY

In biology, high throughput analysis can be split into three parts, namely Genomics, Transcriptomics, and Proteomics. Correspondingly, we have three professors responsible for each part:

        

Lukas Käll                                Lars Arvestad                               Olof Emanuelsson

In contrast to traditional biology lectures, we have no designated text book; alternatively, we have 33 research papers or scientific reviews as mandatory reading materials! It sounds a bit daunting in the beginning, but under the careful guidance of teachers, as well as  fundamental building blocks in previous course (Genomics, Proteomics, Bioinformatics), we are able to dive into the ocean of knowledge!

For example, while Olof introduced the basic concept of RPKM in abundance estimation at the start of the transcriptomics part, it is concluded by four excellent students at the edge of RNA sequencing techniques.

COMPUTER LAB

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Summer in Research: interview with 7 students working at SciLifeLab

Vacation is not the only option for summer; a number of students from my program Molecular Techniques in Life Science make an alternative decision: a two-month, full-time research internship at Science For Life Laboratory in Stockholm. Among them, a large portion belongs to the annual SciLifeLab Summer Fellow Program (description at end end of this blog), while the rest devote themselves into individual research project.

Now, after spending one month pipetting, culturing tissue or even sitting in front of a computer doing simulation, can they eventually integrate their knowledge from classroom to bench-top, and convert their passion in science to fruitful results in lab? Let’s have a chat with them about their job, their happiness and frustration, pain and gain during their unique summer in research!

 Dimitri Wirjowerdojo, Indonesia

 LinkedIn | dawir@kth.se  

Group: Håkan Jönsson (Nanobiotechnology and Proteomics at KTH)

Project: Droplet microfluidics processing in single-cell gene expression analysis

Xueqing Wang, China

LinkedIn | wangicesunny@163.com

Group: Erik Lindahl (Biophysics at KTH and Stockholm University)

Project: Engineering anesthetic sensitivity via an allosteric transmembrane cavity in pentameric ligand-gated ion channels

Javier Arroyo Gomez, Spain

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Course Review: Biophysical Chemistry

Biology? Physics? Chemistry? Despite of its intimidating name, the course biophysical chemistry is one of the most inspiring one I have ever had, and is regarded as the most popular course by two branches of our students. Eager to know what it is?

All you need is Boltzmann

The core concept of the whole course is the Boltzmann distribution, which tells us how likely a state is according to its energy.

  The simplest form of Boltzmann Equation

Hmm….how can we link this awkward mathematical expression to biology? Think of this example: inside a protein, atoms are connected with bonds. Some of these bonds are very strong; the others are weaker. Besides bonds, they can also interact with the surrounding molecules, such as water or its ligand/receptor. Imagine that we know the magnitude of all these bonds and interactions, and the temperature is also given. The question is, can we predict what will happen next? Will the protein unfold spontaneously? If yes, how long will the process take? If no, will the protein binds to other molecules, and how long can this binding sustain?

The answer is, yes AND no. Boltzmann distribution says, it is all PROBABILITY that matters.

Expanding the same concept from a single protein to the entire world of biochemistry,  biophysical chemistry digs into the universal physical laws under the complex reactions and ever-changing states, acting as a key to unmask them, and make prediction about the future. It is not difficult to imagine how understanding of biophysical chemistry is essential to pharmaceutical applications, such as study of protein-protein docking and ligand screening in early drug development.

Structure of the course

The course was divided into two parts: three hours lecture in the morning and four hours of computer lab, which is a extension of the content in lecture.  In order to pass the course, you need to get a “Pass” grade in all the 8 computer lab assignments (Only Pass/Fail for these assignments). The course is graded as A (highest) to F (fail), which is 100% dependent of the four-hour final exam.

After spending the first lecture reviewing the properties of common biological molecules and interactions, we were introduced the Boltzmann distribution at the first lecture. In the computer lab on the same day, we quickly had a feeling of the effect of sampling on this distribution by trying the simulation by ourselves on computer:

Taking more “steps”, closer the results of simulation to prediction

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Our Future is no longer “Crystallized”

After the course Introduction to Bioinformatics, we now switch our focus from protein structural prediction to understanding protein physics and functions. Therefore, a visit to the national facility of Cryo-EM at the Science For Life Laboratory is a part of our new course Biophysical Chemistry. We also got the opportunity to talk to the scientists and students who work there!

As a brief introduction to Cryo-EM, we must start from X-ray crystallography, which is a classical method to determine the structure of biomolecules. The double helix structure of DNA, our genomic material, revealed by Rosalind Franklin is the most renown example:

However, this technology encounters obstacles in determining the structure of membrane proteins, as they are by nature hard to be crystallized but are at the same time critical for both fundamental science and translational medicine. As a consequence, Cryo-EM appears to be a key: by flash-freezing the protein can be frozen in its native-like conformation and through shining a beam of electrons, image of the protein structure can be reconstructed by collecting the electrons bouncing out.

From first electron microscope image of liver catalase in 1970s to high-resolution                       gamma-secreatase (a crucial protein in Alzheimer Disease) structure by Cryo-EM in 2015

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Sci-Life-Lab unlocked!

 

You may not be familiar with adenosine(A), thymine(T), guanine(G), cytosine(C), but it is impossible that you never heard of DNA sequencing! As we all know, DNA sequencing is an amazing technique that “deciphers” the secret codes which hide in your body. It differentiates you from any other individual in the world; it can probably tell your eye color, where you come from, and even prognose diseases! It is difficult to explain DNA sequencing in this blog, but I can tell you where the most DNA sequencings in Stockholm are performed: Science For Life Laboratory, or”Scilifelab” we usually call it!

Thanks to the course Frontier in Translational Medicine, a guided tour inside the Scilifelab was arranged  for my class. We were fascinated not only because it would allow us to learn how the institute operates; but also we could get in tough with the bioinformaticians working there —- which would certainly be beneficial for our future career path.

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