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!