Karthik Raman on The Computational Future of Biology and the Researcher-Entrepreneur
Episode #003
Introduction
Welcome to the latest episode of The Inductive Economy where we dive deep into the fascinating world of Computational Systems Biology with our esteemed guest, Dr. Karthik Raman. Dr. Raman, a renowned figure from IIT Madras, brings a wealth of knowledge and experience to the table, illuminating complex concepts and the latest advancements in the field.
In this episode, we unravel the intricacies of Computational Systems Biology, starting with a basic understanding of the term and exploring the nuanced differences between systems and networks. We journey through the origins of network science in biology, delve into the groundbreaking data work at Dr. Raman's lab, and discuss the critical aspects of accuracy and replicability in research.
Dr. Raman shares insights on the challenges of modelling biological systems, the impact of computational power on biology, and the revolutionary role of tools like AlphaFold. Aspiring researcher-entrepreneurs will find invaluable advice, and we also touch upon the inspiring role of a lab leader in shaping future scientists and entrepreneurs.
Join us as we explore the future of Computational Biology and the impactful work being done at Dr. Raman's lab, culminating in a rapid-fire session that's as enlightening as it is entertaining. Don't miss this journey into the heart of one of science's most exciting fields!
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Connect with Us
Connect with Karthik on LinkedIn: https://www.linkedin.com/in/ramankarthik/
Connect with Karthik on Twitter: https://twitter.com/karthikraman
Visit the qBiome Website: http://www.qbiome.com/
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Learn more about ContraMinds Labs: https://www.contraminds.com
Selected Links
Centre for Integrative Biology and Systems mEdicine, IIT Madras
Computational Systems Biology Lab
Yuri Lazebnik - Can a biologist fix a radio?—Or, what I learned while studying apoptosis
LLMs (Large Language Models - Intro by Andrej Karpathy)
Robert Bosch Center for Data Science and Artificial Intelligence, IIT Madras
Prof. Smita Srivastava (IIT Madras)
Where Good Ideas Come From
by Steven Johnson
This Will Make You Smarter
Edited by John Brockman
The Man Who Knew Infinity
by Robert Kanigel
Fermat’s Last Theorem
by Simon Singh
Deep Work: Rules for Focused Success in a Distracted World
by Cal Newport
Make Time: How to Focus on What Matters Everyday
by Jake Knapp & John Zeratsky
Show Notes
00:00 - 01:12 - Cold Open
01:13 - 01:26 - Theme
01:27 - 03:30 - Introduction
03:31 - 07:53 - Deciphering the term, “Computational Systems Biology”
07:54 - 10:52 - What’s the Difference between a System and a Network?
10:53 - 14:42 - The Origins of Network Science Thinking in Biology
14:43 - 17:23 - The Data Work performed at Karthik’s Lab at IIT Madras
17:24 - 19:41 - Methods of Investigation in Computational and Systems Biology
19:42 - 25:19 - Ensuring Accuracy and Replicability in Computational and Systems Biology Research
25:20 - 30:08 - Collaborations and Inter-Disciplinary Work in Computational and Systems Biology
30:09 - 33:12 - Biggest Challenges when Modelling Biological Systems
33:13 - 38:01 - Impact of Compute Power on Biology
38:02 - 41:46 - Running Biological Datasets on LLMs
41:47 - 43:30 - On AlphaFold
43:31 - 51:26 - Advice for aspiring Researcher-Entrepreneurs
51:27 - 55:51 - Being a Lab Leader and Inspiring the Next Generation of Scientists and Entrepreneurs
55:52 - 01:00:10 - On Karthik’s Lab working on In-Silico Metabolic Engineering
01:00:11 - 01:01:26 - Looking at the Future of Computational Biology
01:01:27 - 01:03:28 - Thinking about the Impact of the work done at Karthik’s Lab at IIT Madras
01:03:29 - 01:22:20 - Rapid Fire
01:22:21 - 01:22:55 - Closing
Interesting Ideas
“Computational Biology involves looking at various aspects of biology through a computational lens.”
“You cannot understand a complex system by just looking at a few parts.”
“An engineer would be able to mark out the key components of a system, whereas in biology, you have to reverse engineer - the system is already in front of you, now you have to figure out: what is the function the system is performing, how do I go about manipulating it, how do I go about understanding it- and understand it well enough so that I can manipulate it.”
“Metabolism seems to be a very important aspect of how microbes interact with each other and it has a lot of implications for us to understand what kinds of relationships exist between microbes in different environments.”
“Often times the interesting parts (of research and experimentation) comes when your experimental observations are very different from what you computationally predicted- so then you go and try to fix your model. This what we call a Systems Biology Cycle.”
“My first brush with Systems Biology came when I was an undergrad student studying chemical technology where I was exposed to a course on Process Control. Process Control is a classic Chemical Engineering course, every chemical engineer does Process Control, but it has very direct application to Biology, because Biological Systems are basically Control Systems.”
“You can come up with as many smart algorithms you want but if you can’t really implement them appropriately, scale them, and future-proof them, it’s not going to be useful 5 years down the line.”
“In Biology, the biggest challenge comes from the fact you don’t know the truth- what is the ground truth?”
“Today, you can actually take a sequence, run it through AlphaFold, get a far stronger candidate for your protein structure, and use that for prediction.”
“DeepTech is hard. Biology is even harder.”
“Metabolic Engineering is all about predictably figuring out what roads to shut down, what roads to widen in the cell, so that you have more of the output you want.”
“Try to do as many courses as possible. Try and expose yourself to as many areas as possible.”
“Go pick up a book and read it.”