In my new research project, I investigate Non-alcoholic fatty liver disease (NAFLD). This term describes a variety of conditions that are associated with fatty livers. While the early stages of this disease are not harmful, it can lead to cirrhosis (Cirrhosis is the scaring of liver tissue that prevents a liver to function properly). Ultimately, if a liver stops working it can be fatal unless treated, for example, with a liver transplant. NAFLD is the most common liver disease in developed countries and is expected to become the leading cause of liver transplant by 2020 [1].

The disease progresses in four stages: Continue reading →

Our course was titled Netzwerke und Komplexe Systeme (Networks and Complex Systems) and rather than going too much in depth in one particular area we covered a broad selection of topics, as we wanted to give students an overview and also an idea of how different disciplines approach complex phenomena. We discussed pure Mathematics topics as the colouring of graphs, algorithmic discussions as the travelling salesman problem, social network analysis, computational neuroscience, dynamical systems, and fractals.

A network of the former monastery in Rossleben, where the summer school was held. The students created the network themselves. To parallelise the task they split up into four groups, each covering one level of the building. They then used this network to simulate the spread of a contagious disease, starting at the biological lab (A35, in red).

A couple of thoughts on what went well and which parts might need improvement for further of such events:

• We did a questionnaire before and asked the pupils some questions like “Do you know what a vector is?” and also concerning their motivation to join the course. This was very helpful in getting a rough idea about their knowledge level.
• We gave them some material to read before the course. In retrospective, it probably would be better to give them something to read, as well as, some problems to solve, such that the learning outcome is clearer and more effective.
• The students gave presentations on topics we choose for them based on their answers to the questionnaire. The presentations were good but a lot of students overrun the allocated time because they were very enthusiastic about the topics.
• The students were also enthusiastic about the programming exercises, for which we used Python and the NetworkX library. One challenge was the heterogeneity in programming experience, this made the splitting up into two groups, beginner and advanced, necessary.
• In contrast to courses covering similar topics at university-level, the students did not have the necessary mathematical background for the more complicated aspects of network science. Accordingly, it is better to choose less of these and allocate time to introduce the mathematical methods, for example, eigenvectors or differential equations, beforehand.
• The students very much liked hand on exercises, for example, the creation of random networks of different connection probabilities with the help of dice or the creation of a network of the floor plan of the building in which the summer school was held, as shown in the Figure.

It was great fun to introduce the students to the topic of network science and I strongly can recommend others to organise similar outreach events! You can find some of our teaching materials, including the worksheets and programming exercises in the original German and a translated English version, online. A paper describing our endeavours is under review.

Four Erdős–Rényi random graphs as generated by the participants by rolling dice. A twenty-sided dice was used for the probabilities p = 1/20 and p = 10 and a six-sided dice for p = 1/6 and p=1/3. This fun exercise allows the discussion of degree distributions, the size of the largest connected component, and similar topics for ER random graphs.

Cells are the basic functional and structural units of living organisms. They are the location of many different biological processes, which can be probed by various biological techniques. Until recently such data sets have been analysed separately. The aim is to better understand the underlying biological processes and how they influence each other. Therefore techniques that integrate the data from different sources might be applicable [1].

In the image below you see the four main entities that are active throughout the cell: Genome, RNA, proteins, and metabolites. All of them are in constant interaction, for example, some proteins are transcription factors and influence the transcription of DNA into RNA. Metabolites that are present in the cell also influence the activity of proteins as ligands but at the same time are altered through enzymatic activity. This ambiguity of interactions makes it clear that probing the system at a single level gives only limited insight into the structure and function of the cellular processes.

The different levels of biological information (genome, proteome, …) work mutually and influence each other through processes as transcription regulation through transcription factors. All levels are influenced by external factors, as drug treatment or nutrient availability. Multiomics is the measurement of multiple of those populations and their integrated analysis.

In the last years, different ways to integrate such data have been developed. Broadly speaking there are three levels of data integration: conceptual integration, statistical integration, and model-based integration [2]. Conceptual integration means that the data sets are analysed separately and the conclusions are compared and integrated. This method can easily use already existing analysis pipelines but the way in which conclusions are compared and integrated is non-trivial. Statistical Integration combines data sets and analyses them jointly, reaching conclusions that match all data and potentially finding signals that are not observable with the conceptual approach. Model-based integration indicates the joint analysis of the data in a combination of training of a model, which itself might incorporate prior beliefs of a system.

[1] Gehlenborg, Nils, Seán I. O’donoghue, Nitin S. Baliga, Alexander Goesmann, Matthew A. Hibbs, Hiroaki Kitano, Oliver Kohlbacher et al. “Visualization of omics data for systems biology.” Nature methods 7 (2010): S56-S68.

[2] Cavill, Rachel, Danyel Jennen, Jos Kleinjans, and Jacob Jan Briedé. “Transcriptomic and metabolomic data integration.” Briefings in bioinformatics 17, no. 5 (2016): 891-901.

In our last group meeting, I talked about a recent paper which presents a vast amount of genetic interaction data as well as some spatial analysis of the created data. Constanzo et al. used temperature-sensitive mutant alleles to measure the interaction of ~6000 genes in the yeast Saccharomyces cerevisiae [1]. A typical way to analyse such data would be the use of community detection to find groups of genes with similar interaction pattern, see for example [2] for a review. Instead, the authors of this paper created a two-dimensional embedding of the network with a spring-layout, which places nodes close to each other if they show similar interaction pattern.

The network layout is then compared with Gene Ontology by applying a spatial analysis of functional enrichment (SAFE) [3].  Clusters enriched are associated for example with cell polarity, protein degradation, and ribosomal RNA. By filtering the network for different similarities they find a hierarchical organisation of genetic function with small dense modules of pathways or complexes at the bottom and sparse clusters representing different cell compartments at the top.

In this extensive paper, they then go further into detail to quantify gene pleiotropy, predict gene function, and how the interaction structure differs between essential and non-essential genes. They also provide that data online under http://thecellmap.org/costanzo2016/ .

(Left) A network of genetic interaction was embedded into a two-dimensional space using a spring-layout (Right) This embedding was compared with Gene Ontology terms to find regions of spatial enrichment.
Image from [1]

References:

[1] Costanzo, Michael, et al. “A global genetic interaction network maps a wiring diagram of cellular function.” Science 353.6306 (2016): aaf1420.

[2] Fortunato, Santo. “Community detection in graphs.” Physics Reports 486.3 (2010): 75-174.

[3] Baryshnikova, Anastasia. “Systematic Functional Annotation and Visualization of Biological Networks.” Cell Systems (2016).