A few weeks ago, I published a short write-up explaining the data-driven paradigm in biomedical research and how data has become an integral part of the hypothesis generation process. Now let's focus on the data itself.

Both hypothesis- and data-driven research generate data, but the scale is different:

• Hypothesis-driven research generates low-throughput, targeted data (1-10 measured variables) using approaches like RT-PCR, ELISA, biochemical assays, etc.
• Data-driven research deals with high-throughput data (1000 to millions of variables measured simultaneously).

For simplicity, I will limit this post to high-throughput molecular biology data, often referred to as omics data. Omics data can be derived from different molecular components of biological systems, but I will highlight four key building blocks: DNA, RNA, proteins, and metabolites.

Understanding Molecular Systems Through a Restaurant Metaphor

To help with understanding, let's imagine our molecular system as a restaurant.

DNA --> Genomics

Genomics is the study of the whole genome, and its data originates from DNA, including DNA sequences, variations, and mutations.

DNA is the blueprint of our body, just like a recipe book in a restaurant. It has been passed down through generations—from your great-great-grandparents to your biological parents and finally to you.

Although you always follow the recipe book, you sometimes need to adjust ingredients based on what’s available in the kitchen. For example, if a recipe says to add one teaspoon of salt, but today's salt is stronger, you might reduce it to half. Similarly, DNA maintains the blueprint, but it adapts slightly to environmental and lifestyle factors. These adaptations are called mutations, which can be: Beneficial (e.g., higher resistance to diseases), Neutral (e.g., changes in eye color), Harmful (e.g., increased risk of diseases or chromosomal abnormalities)

RNA --> Transcriptomics

Transcriptomics refers to the study of the whole transcriptome. The data originates from RNA, which is transcribed from DNA and reflects gene expression dynamics under different conditions.

When a customer places an order in a restaurant, the waiter writes it down and gives an order slip to the kitchen staff. The lead chef then opens a specific page from the recipe book to prepare the requested dish. Since different customers have different food preferences, the dish might need adjustments, such as substituting ingredients for allergies. Similarly, depending on what your body needs, DNA provides instructions to RNA to create specific proteins at a given moment. For example:

• After a heart attack, many RNA (or gene) expressions in the heart change to compensate and repair the damage.
• RNA expression levels also change based on environmental exposure, diet, or stress levels.

Read more about my post on gene expression in heart attack here.

Note that DNA is usually very stable and rarely mutates, while RNA is much more flexible and dynamic, changing in response to the body's immediate needs.

Proteins --> Proteomics

Proteomics is the study of all proteins in the body. Proteins are the translation results of RNA. In a restaurant, there is one lead chef and many specialized subordinate chefs, such as a pantry chef, grill chef, or sushi chef. Once the lead chef receives an order slip, they assign the task to the appropriate chef.

• If a customer orders grilled steak, the grill chef takes over.
• If sushi is ordered, the sushi chef steps in.

Similarly, your body requires different proteins in different conditions:

• After a meal, digestive proteins help break down food.
• After an injury, wound-healing proteins become active.
• During stress, heat shock proteins protect cells.

What Happens When Proteins Go Wrong?

Just like a chef can make mistakes—such as using the wrong ingredients—proteins can malfunction by misfolding, causing them to lose their function.

Fortunately, the body has quality control mechanisms to fix or remove misfolded proteins. However, if too many misfolded proteins accumulate, they can lead to diseases such as Alzheimer’s or Parkinson’s.

Metabolites --> Metabolomics

Metabolomics is the study of all small molecules (metabolites) in the body. Metabolites are the result of biological (or more precisely, biochemical) processes that reflect the body's current physiological state. In a restaurant, metabolites are like the dishes served to customers. The quality of the dishes depends largely on what ingredients are available in the kitchen. Similarly, our metabolites depend on our body's state—such as diet, exercise, disease, and environmental factors. For example, if a customer orders a vegan or lactose-free dish, the recipe will be adjusted accordingly. Likewise, our body produces different metabolites when we are sick.

However, sometimes food doesn’t turn out well, perhaps it is undercooked or has too much salt. Similarly, when metabolites are not produced correctly, they can lead to diseases such as diabetes or metabolic syndrome.

Summary: Small but mighty

Our body contains millions of molecules working together to keep us functioning. When we measure many of these molecules at the same time, we generate omics data, which is crucial in data-driven biology. Each molecule has its own specific role, much like the chefs and ingredients in a restaurant. I’ve also highlighted how things can go wrong, leading to various diseases. In my research, I aim to uncover what goes wrong in disease states by analyzing omics data.

That's not all the omics out there. There are many others, such as metagenomics, lipidomics, glycomics, and more.

Disclaimer: This analogy is not new. I came across the general idea many years ago, and it has stuck with me since, but I can't recall the exact source (apologies). I’m now explaining it in my own words and adding my own spin on the specific topics and diseases. If you are the original author of this idea, please let me know, and I will gladly give you credit.

(This write-up is a reflection from the two internal talks that I gave this week.)

We hear this term a lot lately. Over the past decade or two, we have witnessed a data explosion in biomedical research. The decreasing costs of omics data generation and computing power are major drivers of this trend. Naturally, with big data, biological research has become more appealing to researchers from other, more data-intensive fields, such as mathematics, physics, and computer science. This shift has really propelled the rise of the data-driven paradigm in biological research. But what does it really mean? What is the advantage? How does it differ from “the other” biology? Is it better? Let’s try to answer these questions.

Hypothesis-Driven Research: Old but Gold

Most biological research begins with a hypothesis. But what is a hypothesis? Simply put, it is an educated guess or a hunch. For example, a scientist may suspect that X causes Y. They then design experiments to test this hypothesis, measure relevant variables, and analyze the evidence. The results will determine whether the hypothesis is supported or not. This approach is known as hypothesis-driven research.

With a hypothesis, researchers can frame their studies and focus on a specific set of related variables and processes. This paradigm has been a cornerstone of scientific research, not only in biology, for thousands of years and has led to groundbreaking discoveries, such as radioactivity (Curie), relativity (Einstein), and penicillin (Fleming).

Data-Driven: The Future of Biomedical Research

In the past two decades, high-throughput data that measure hundreds or thousands of variables simultaneously have become cheaper and more accessible. This type of data is called omics data. As I mentioned earlier, this, along with increased computational power and interdisciplinary collaboration, has led to the rise of a new paradigm: data-driven research.

Instead of starting with a hypothesis, data-driven research begins with a question—such as "What causes Y?" To answer this, we analyze datasets containing thousands or even millions of variables. We then apply statistical analysis, network analysis, and machine learning techniques to identify differences between groups, uncover relationships between variables, and detect patterns in the data.

This analysis allows us to generate a systems-level model that provides a more comprehensive view of the system rather than focusing on isolated subsystems. This approach helps uncover insights that might otherwise be missed when focusing on a single subsystem in isolation.

Hypothesis-Driven and Data-Driven: Friend or Foe?

One is not better than the other. Rather than debating the pros and cons of each paradigm, I want to highlight how they are deeply intertwined. Data-driven research often generates new hypotheses, which is why it is sometimes called hypothesis-generating research. These hypotheses must then be validated through experiments to determine whether they should be accepted or rejected—bringing us back to hypothesis-driven research. In turn, the outcomes of hypothesis-driven research help refine systems-level models and provide context for data-driven research.

Hypothetical Example: Candy in Our Body

Imagine a researcher from a university studying the effects of excessive candy consumption on the body. They generate omics data from cells (in vitro) or animals (in vivo) exposed to higher sugar levels in culture media or diet. Using statistical or machine learning analysis on 20,000 measured variables from the omics data, they create a large-scale map of how the body responds to candy consumption. From this map, they identify a new receptor, X, that appears to be associated with higher sugar intake.

Based on this finding, they develop a hunch that blocking receptor X could reduce sugar consumption. To test this, they conduct experiments where receptor X is blocked in vitro or in vivo, followed by sugar exposure and consumption measurement. However, their results show that even after blocking the receptor, sugar consumption remains the same. This suggests that increased levels of receptor X are a consequence of higher sugar intake, not the cause. With this insight, they refine their original model and develop a new hypothesis for further testing.

This is an example of modern biomedical research, where both wet-lab (experimental) and dry-lab (computational) scientists collaborate to enhance research effectiveness. Without the data-driven approach, researchers may never have identified the association between receptor X and sugar consumption. And without hypothesis-driven research, they might have wrongly concluded that receptor X was the cause of higher sugar consumption.

Conclusion: The Best of Both Worlds

The rise of data-driven research has transformed biomedical science, allowing us to uncover hidden patterns and generate new hypotheses. However, data alone is not enough. Without hypothesis-driven research, we risk misinterpreting correlations as causation or missing critical biological mechanisms. True scientific progress happens when these two approaches work hand in hand.

On Friday, 11 June 2021, I successfully defended my PhD Thesis! It’s been my lifelong dream and finally, on that day, I became Dr. Muhammad Arif. :) The defense was really interesting and, I would say now, fun. I had an amazing faculty opponent (Prof. Dr. Thomas Sauter) and evaluation committee members (Dr. Claudia Kutter, Assoc. Prof. Marc Friedländer, and Dr. David Moyes) in my defense, which made the discussions to be really good and interesting!

This PhD journey has been really exciting, fun, and enjoyable. I’ve heard this cliché many times, but it’s true, “finding good PhD advisors is more important than finding a good PhD place or project”. I was very lucky to get all 3: amazing supervisors, group, and projects!

I have an amazing principal supervisor, Adil Mardinoglu, that supports me constantly, both as a supervisor and as a friend. Not only that, I was lucky enough to have Prof. Dr. Mathias Uhlén as my co-supervisor, one of the most prominent figures in this field, with all his experience and amazing vision. There are too many people that I would like to mention and thank that I cannot mention one by one. I wrote some of them on the “Acknowledgements” section of my thesis (below), but for sure, this still cannot cover everyone.

If you are interested to read my thesis, you can get it free from this link. And here’s my presentation.

This chapter is one of the best and exciting chapters of my life. Now, I’m looking forward to starting the new challenge in October 2021, which I have been very fortunate to be able to secure a few months ago.

Here’s to a better future for everyone!

It was a fun event to participate in! It’s hard to present in Indonesian language plus that it has to be presented in “popular science” style, which has to be understandable by non-expert.

I will definitely join for more presentations like this and will try to write more popular science style in this blog. So stay tuned!

PS: The full research is now published at eLife and can be accessed here

Image Source: Cardio Metabolic Institute

Before I start explaining the research, I’d like to give a brief context. Our body is like a car. Both need chemical energy (from fuel or food) to be able to perform their activities properly. Not only that, the car and human body are both complex systems, where all parts are connected. Because of this interconnectivity, when a car breaks down, the problem is usually not only impacting one part, but also other parts. Similarly, human diseases are often not only affecting one organ, but it has a systemic effect on the body.

Even though heart attack has been studied a lot, most of the research focused only on the heart. In our study, we expanded our research by including other organs to understand the systemic changes caused by heart attack to our body, specifically to our metabolism. We collected data from the heart and 3 other important tissues (muscle, fat, and liver) from a mouse model that mimics a heart attack. After that, we built a computational model and biological networks to simulate the interconnectivity of multiple organs in the human body. By doing that, we were able to get a complete view of the disease that led us to some important findings. We found interruptions in energy production, metabolism, and immune systems, and also changes in protein productions. Moreover, we identified four genes that showed important responses to the heart attack. These genes can be explored further as candidates for early detection systems or treatment for heart attack.

In summary, this study was able to reveal the systemic effect of a heart attack on the human body using the combination of experimental and computational biology. We hope that this study can help researchers to explore the mechanism of cardiovascular diseases, specifically heart attacks, and accelerate the discovery of new treatments for them.

PS: The full research is now published at eLife and can be accessed here

The decade started with the struggle to “leave” ITB, not going to lie, it was not easy ? Moved to a completely different environment and country, Singapore, to work in one of the best company in the world. Good times, some depressing period, but mostly fun. Meanwhile, I also took the best decision in my life, getting married to @fatimah_zahra_fira.

Year 2014 was the year of change. I decided to leave a stable well-paying job to pursue further study. I moved to Barcelona, Spain, one of the most beautiful place in the world, that I still miss until today. I will never say no to any chance to move back there. For my 2nd year of master degree (2015), I had to move to a city that I never thought of living in, Stockholm. I successfully completed my master degree in 2016 and then took a position as research engineer at SciLifeLab. In 2017, I started my PhD at KTH, something that i’ve been always dreaming about.

Best year of my life? 2018 indeed. Just 3 weeks after our 6th wedding anniversary, we had the most beautiful gift, a son. Oh boy, it was the most amazing thing that ever happened to me! And it still is!

2019 was the year where I grew up as a person and scientist/researcher. It was year of learning. I can say I grew a lot during this year. I closed the year by completing one of my biggest PhD project.

All in all, it was an amazing decade. I started to build my small family, best thing in my life. My wife has been non-stop supporter to all decisions that I made, I wouldn’t be able to do those without her support. My son, man, is definitely the best thing ever. This decade had been a long journey as well. Started the decade as a struggling student in Bandung, followed by moving to Singapore to start my short professional life. Few years after, the journey continued, I moved to Spain and then Sweden, where I spent most of my 2010s in. Along the way, I met some amazing people and friends as well. Nothing, literally nothing, to regret about. Thanks to everyone!

Here’s to awesome 2020s. Biggest target for now? Getting a PhD!