Covid-19 or SARS-CoV-2 commonly known as Corona virus, is a single stranded RNA virus. It is infectious disease and can primarily spread from person to person. An infected person can show sever symptoms, mild symptoms and at times no symptom at all. Thus, a person infected with virus can be be completely unaware of the fact that he/she is infected and is transmitting it to others in the close vicinity. This is one of the main reasons of the disease being a pandemic.
Is Sweden under lockdown?
No, Sweden is not under lockdown. As mentioned in my previous post, Sweden is aiming to achieve heard immunity. Thus, unlike most of the governments in the world, Swedish government has not ordered a complete lockdown. Rather, the FHM (Swedish Public Health Agency) has given a set of recommendations for the general public to follow. All the international flights are stopped, people are recommended to avoid travelling unless absolutely necessary. Further, gathering for more than 50 people are prohibited. Although, the public transport is running on the regular schedule, the number of people travelling has reduced significantly. Further, measures are taken to increase the safety of the employees of the public transport.
How does this affect students?
All the academic institutions and universities such as KTH, Stockholm University, Karolinska Institute campuses are closed down for the students attending classes and other activities. The lectures and final examinations are currently conducted via online platforms such as zoom. However, the PhD students and other students working on their thesis are granted special access to continue their work.
Since, SciLifeLab is the national platform for Life Science research, it is currently converted into a Corona Virus research facility by the generous donations of Kunt and Alice Wallenberg foundation. Being a proud member of this strong research community, we at SciLifeLab are currently focusing our research on various aspects of the corona virus. One of the collaborating lab has developed rapid detection kit. We have many journal club discussions discussing the recent research and how it can be implemented into developing vaccines and cure.
Hello everyone, it has been a while since I wrote the last blog. I hope you are well during these difficult times. Today I am going to tell you about the pandemic caused by the Corona virus and the measures taken by Sweden to tackle it.
Sweden’s approach to tackle the corona virus is to achieve heard immunity. The idea behind heard immunity is to have enough number of people already infected and develop immunity against the virus. This means that large number of people are already immune to the virus and can fight it causing reduction in the rate of spread until a vaccine or cure is out in the market. This is also the natures way of dealing with infectious diseases. This approach is favourable for a country like Sweden, where the population density is very low and social distancing is a way of life for the Swedish people.
Statistics in Sweden
WHO, FHM (Folkhälsomyndighetens – Swedish Public Health Agency) and similar organisations all around the globe are currently trying to cease the increase in number of cases referred to as flattening the curve. The FHM is constantly monitoring the number of confirmed cases and the deaths in all across Sweden. Detailed statistics can be found here. Since, the website is in Swedish here is a short guide: Sjukdomsfall – Confirmed cases; Intensivvårdade – Intensive Care Patients; Avlidna – Deaths.
What to remember when comparing global statistics?
Multiple different sources are currently reporting global statistics and reports on the current situations. Once such site is World0meter. However, it is crucial to understand the caveats when comparing reported global statistics. First and most importantly, the statistics do not reflect the actual number of cases, rather just gives an idea of the number of cases in a particular country or region. Since, the disease does not always manifest with symptoms, the individuals without symptoms are not reported, further, the individual with mild symptoms that did not approach the health care facility but got better by themselves are also not reported in the statistics. Looking at the number of patients in intensive care or deceased patients is relatively a better way to assess general situation. Secondly, it is not ideal to compare the statistics reported by different organisations, as the method of reporting can be very different between them. Instead comparing the number of deaths in one week as compared to the previous week in the same region/country gives more accurate information.
In the coming weeks I will be writing more about the changes we are currently observing in Stockholm to fight the situation at hand and how this has affected the student life. Stay safe!
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.
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.
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.
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).
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.
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.
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.
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.
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!
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.
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.
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.
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!