New DDLS Fellow: Muhammad Arif
The SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) continues to recruit outstanding early career scientists, both nationally and internationally, as DDLS fellows. Learn more about our latest fellow, Muhammad Arif (University of Gothenburg), in our Q&A-style article. Muhammad will be joining the DDLS Precision medicine and diagnostics research area.
Muhammad has always been fascinated by networks and the potential of computers in studying big data. He began his career as a computer network engineer at a company in Singapore after earning his bachelor’s degree in Indonesia. He then moved to Barcelona and later to Stockholm to complete a dual-degree master’s program in information technology. During his PhD at KTH Royal Institute of Technology and SciLifeLab (2017-2021), Muhammad shifted his focus to developing and applying network and computational approaches to analyze large-scale biological (omics) data. He continued this work during his postdoctoral research at the National Institutes of Health in the USA, where he focused on deciphering the complexity of lung fibrosis and aging. The transition from computer science and engineering to systems and network biology has been a significant and rewarding step in Muhammad’s career.
How do you think your expertise can contribute to the program?
As I mentioned earlier, I am very passionate about networks. Through network integration and analysis, we can study the relationships between different biological analytes, like genes, metabolites, proteins, and microbiomes. Additionally, networks can reveal cross-talk between different tissues. However, networks are often perceived as extremely complex. With my expertise in network analysis, I aim to untangle these so-called ‘hairballs’ to identify useful sub-networks and extract information that is more accessible and easier to interpret. These findings could lead to the discovery of new biomarkers or therapeutic targets.
Shortly describe your research in an easy to understand way.
Many systemic diseases progress over time and need different biomarkers at each stage to detect them early. Multiple factors often cause these diseases and require a combination of drugs or multi-targeted treatments to be effective. Additionally, because patients can differ greatly from one another, treating these diseases becomes even more complex. Current data-driven approaches often overlook how diseases change over time and their complexity. As a result, many studies lack direct relevance to patients. Incorporating these factors helps us discover time-specific biomarkers and drug combinations that can target different aspects of a disease.
A great example of this, that affects so many people in the world, is cardiovascular and metabolic diseases (CMD), such as obesity, insulin resistance, high cholesterol, and high blood pressure. CMD progresses over time and affects multiple organs, such as the liver, fat tissue, muscles, heart, and even the gut microbiome. If left untreated, it can lead to severe diseases like type 2 diabetes, heart disease, and kidney failure. Because of their complexity and varying responses between individuals, CMD is hard to detect early and treat effectively. The goal of my research is to develop a data-driven model that can track how different biological data change over time as CMD progresses. This model will help doctors make better decisions by identifying personalized and effective treatment options that can be translated into real-world use.
How do you think the program and interactions with the other DDLS-Fellows will benefit you?
This program and its generous funding will allow me to set up my own lab and kick off my career as an independent researcher. On top of that, DDLS provides huge resources and collaboration opportunities with experts that have similar interests.
Name one thing that people generally do not know about you.
I am an avid gamer. I love all types of games, from RPGs to adventure and football games. I play a lot of board games too. Recently, I’ve been really into Geoguessr, a web-based geography game, and have been playing it a lot!
Where do you see yourself in five years regarding the DDLS aspect?
In five years, I want to have a well-established and productive lab. I want us to develop strong in-silico approaches to study multi-omics and multi-organ interactions of progressive diseases. I hope that these tools can identify a few candidate biomarkers/targets that we can propose as diagnostics and/or treatment options in the clinics for cardiovascular and metabolic diseases. I also want to be involved in developing and mentoring the next generation of data-driven researchers.
In one word, describe how you feel about becoming a DDLS-Fellow.
Excited!!
