Over the past two decades, biologists have accumulated a wide array of knowledge regarding interactions between different types of biological entities. This accumulation of knowledge gave rise to the field of biological network analysis which focuses on using network representations of biological systems to gain insights into various aspects of biology, including, but not limited to, interpreting the results of high-throughput experiments (e.g., RNA-seq or proteomics). There exists a wide variety of different types of biological networks, such as gene regulatory networks (GRNs), networks of protein-protein interactions (PPIs), or networks of metabolic and signaling pathways. 

One of the relatively straightforward, but technically complicated, tasks related to biological network analysis is the prediction of causal relationships between entities in these networks. In metabolic reactions, for example, prediction of the causal impact of changes in the concentration of a compound on the downstream chemical reactions is complicated by the complex kinetics of these metabolic reactions. 

The main goal of the current project is to try to build a predictive model of the causal effects in biological networks based on network topology and node/edge attributes (e.g., degrees of nodes, presence of feedback/feedforward loops, etc.). To achieve the above goal, we will need to solve three key tasks: 

1. Extensively review scientific literature on the topic of causal inference in biological networks;

2. Create a comprehensive dataset of causal relations among biological entities (e.g., genes, proteins, metabolites) using the available data or mathematical models;

3. Conduct an extensive analysis of the paths connecting entities with or without known cause-effect relationships;

4. Develop a model/scoring scheme that could predict the existence of a causal relationship between the two entities based on their connections in biological networks.

As part of the project, we may try to train machine learning models (including those based on deep learning) as an alternative way of causal inference.

We are looking for students or graduates in biological sciences with experience in bioinformatics or computational biology. Motivated students with math or computer science backgrounds are also welcome to apply. An ideal candidate has:

• Knowledge of molecular biology and genetics;

• Experience in data analysis in R or Python;

• Basic understanding of graph theory;

Experience with command line utilities (in Linux/macOS), as well as knowledge and experience in the field of machine learning, would be an advantage for the applicant.