What to do when waiting for admission results at KTH? Call up week!

Hello everyone! It is the waiting time before the notification of selection results are out. Let me remind you they are announced in the mid-March i.e. in the week 10. However, this waiting time can be quite stressful and full of anxiety, so it is crucial that you take de-stress and enjoy your time before the results are out. As soon as the results are out there will be so many things to decide and prepare for before you start counting the days to leave for Stockholm.

Almost 1 & 1/2 years ago during this time I was working on my bachelors thesis so was very busy but to de-stress on weekends I used to go exploring the city and go to various trekking and hiking places around the city with my friends and lab members. The time between the announcement of results and till the time I reached Stockholm was the most busy for me and I am glad I enjoyed during the this time while I could do nothing but just wait.

Image result for waiting meme
Waiting for notification of selection results… Source : https://imgflip.com/

Although, I was very nervous on the day of results, after the results selection day I was also very confused because now I had to make the actual decision if I really wanted to go the MTLS programme or to accept offers from other university.

Image result for confused between two choices meme
Last minute confusions and considerations?                   Source : https://www.cnet.com/

Sometimes it can be difficult to compare some programmes, especially unique programmes like MTLS, this is when the call up week helped me. During the call up week each selected student is contacted by a senior from your particular programme (MTLS programme senior in my case) calls you to talk about questions regarding the course content as well as general questions on the life stye in Sweden, etc. Since, MTLS is a comparatively new and unique programme I was unsure if the programme focused more on the wet-lab aspects or the dry-lab aspects and if my limited knowledge in programming would be a problem. However, these doubts were solved when I talked to my senior. I was able to specifically ask my doubts and address my concerns that could possibly not be explained on any programme websites.

Image result for mr bean call animation
Source : https://www.youtube.com/

So I strongly recommend all the prospective students to actively take part in the call up week and freely ask all and any questions. You might be surprised at the number of ways to solve problems and answer questions that you might not be able to imagine sitting miles away (Something like Mr Bean above :p).


KTH course : Systems Biology part-2

A quick recap to the course : Systems biology course has 3 main modules, and I talked about the 1st module of Statistics in the previous blog. Today I will be telling more about the 2nd and the 3rd modules.

Module 2: Metabolic modelling

As the name suggests it is the modelling of complex metabolic and other types of regulatory networks. In the course we focus on the metabolic networks and touch the topic of integration of various forms of omics data into the metabolic network to predict various types of models.

Summary of metabolic modelling module

This kind of studies are useful in drug development or performing gene knockout studies. In such cases it is experimentally not feasible to test hundreds of drugs in multiple conditions due to time constraints as well as financial concerns, thus, as a replacement these computationally generated metabolic networks are used to predict the top 10 drugs/genes that can be knocked-out to get the desired phenotype. These top candidates can then be easily tested experimentally and one with the most optimal results can be selected.

Metabolic networks can be used to study interactions between various organs

We learned about the theoretical basics of metabolic modelling and various methods used to improve prediction, such as Flux Balance Analysis (FBA), parsimonious FBA and Flux Variability Analysis (FVA) of genome scale metabolic models. Later, we implemented these methods in the practical lab exercises where we were given a metabolic model of liver cancer cell line, and we predicted the top 10 candidates for gene knockout studies.

Module 3: Models of Gene Regulation

The third module focused on the integration of various omics data sets. Before jumping into the integration of variety of omics data, we were first informed about the need for various omics data and the caveats, biases and problems of each type of omics datasets during the theory classes. Further, we learned about various proteomics methods and the advantages and disadvantages of using specific methods in relation to the sample size and the quality of the data expected for the study.

Need for various omics data (Map tells the general geography and available paths, GPS tells about your current position)

Finally, after understanding the basics we dived into the lab exercises where we were given transcriptomics and proteomics data of drug treated versus non-treated samples over a time series experiment. We had to integrate these two omics data using PECA tool and answer the questions regarding changes in the gene expression with relation to the protein expression at various time points and associate the general changes caused by the drugs to the general mechanistic pathways that were affected by drug treatment.

Information that could be missed by single omics data is picked up in multi-omics data

While analysing these real datasets we encountered different problems and caveats for each type of datasets. For eg. proteomics datasets are generally sparse and tend to have large number of missing values, thus it becomes important to deal with these missing values before proceeding to analysis else the data seems to be more or less useless.

Learning and exploring real datasets while tackling the problems faced by researchers made the course very exciting!

KTH course : Systems Biology part-1

The second half of KTH semester is reserved for the elective courses for the MTLS students. As mentioned earlier, get to choose 2 out of 3 courses. I selected the two electives of Systems Biology and the Project course. I will be telling more about the project course later.

Source : International Journal of Epidemiology (doi: 10.1093/ije/dyw304)

The systems biology course has 3 main modules and labs associated to each module. Just like the previous courses the content of the course is repeated multiple times in different formats so that it can be understood in a complete manner. In systems biology course we did the same.

Module 1: Statistics

The first module contains 3 sub-modules each has about 2-3 chapters. Thus 8 chapters in total. The first module is taken by Lukas Käll who is also the course leader.

The pattern for study was similar to the one from the Bioinformatics course in Stockholm University semester. Before each class we had to read the given material and then submit at least one doubt (question) related to the topic and write 3 most important/interesting concepts from the readings. These two were graded later and considered as bonus points for the course examination conducted at the end.

Sub-module 1: Statistics

Statistics sub-module contains the Hypothesis testing, Multiple hypothesis testing and Linear Models. These topics are some of the most important aspects to study as a researcher. Because irrespective of the field the content of this module is applicable in every field and every study. The basic concepts of p-value, q-value, False Discovery Rate (FDR), etc are covered in this course.

Most of the students from our class did not have strong background in statistics. So this was literally us :p
Source : businesscartoonshop.net

Initially looking at the topics I thought that that I have learned about these in my Bachelors, however, I was deeply surprised about the various misinterpretations and shallow understanding of these basic concepts is prevalent among many researchers. Here’s a funny example cartoon of what I mean by misinterpretation

Sub-module 2 : Machine Learning

Recently the field of machine learning is booming and almost everyone is trying to apply machine learning methods to various fields. Biologists are not very far behind in trying out these methods to achieve realistic predictions by applying machine learning methods on high throughput datasets like NGS.

An important lesson we learned
Source : https://www.slideshare.net/rickwendell

The module mainly contains main topics of Supervised learning, and forms of unsupervised learning such as Clustering and Principal Component Analysis (PCA)

Sub-module 3: Network Biology

Biology in itself is full of complex inter connected networks. Thus, biologists need tools that can understand and explain the complex networks. This is where the network biology comes in handy. The modules of Pathway analysis and Network analysis are covered in the course. These methods are generally applied to get big-picture information post the analysis. For example, if we know the set of genes differentially expressed then we can use pathway analysis to understand the pathways that are enriched in a system. Thus, understanding the biology at the systems level.

Stay tuned for the information on the other modules we covered in the course i.e. Metabolic modelling and Gene regulation by integration of Omics data!

Internship opportunities while studying at KTH

Looking for an international internship? KTH offers them too!

KTH has various opportunities for doing internships not just in Europe but also in US and Canada. One such organisation at KTH is called the Chust. It has tie ups with various universities in Western countries. These internships are available for students of School of Chemistry, Biotechnology and Health at KTH.

Each year the Chust calls for applications from students of CBH. The students are to give a list of Universities and the professors from university that they are interested to do an internship. The Chust approaches the universities and based on the response from the universities they give open up positions for internships to which the students can apply. The positions are minimally funded by Chust and variably from the host universities as well.

For more information refer to their website here


Course Reviews (Batch : 2018-2020)

KI courses

1.  First course at Karolinska Institute: Genetics

2.  Second Course: Applied Communication

3.  Third course: Applied Programming for Life Sciences

4.  a) Fourth course: Fromtiers in Translational Medicine (Part-1)

     b) Fourth course: Frontiers in Translational Medicine (Part-2)

SU course

1.  a) SU course: Bioinformatics (Part-1)

     b) SU course: Bioinformatics (Part-2)

2.  SU course: Structure and dynamics of biological membrane

3.  a) SU Course: Methods in Molecular Life Sciences Part-1

     b) SU Course: Methods in Molecular Life Sciences Part – 2

     c) SU Course: Methods in Molecular Life Sciences Part – 3

4.  SU Course: Applied Programming for Life Sciences II

5.  SU Course : Comparative Genomics

KTH courses

Mandatory courses

1.  a) KTH course: Applied Gene Technology and large-scale data analysis part-1

     b) KTH course: Applied Gene Technology and large-scale data analysis part-2

2.  KTH course: Applied Programming for Life Sciences III

3.  KTH course: Clinical applications of biotechnology

Elective courses

1.  a) KTH course : Systems Biology part-1

     b) KTH course : Systems Biology part-2