novel bin Chapter 206: Laboratory Seven (4)
Chapter 206: Laboratory Seven (4)
Chapter 206: Laboratory Seven (4)
Once upon a time, biology was a discipline within natural history. Natural historians began with crude anatomy and taxonomy, looking for similarities and differences between plants and animals. Then, it went through glittering scientists like Robert Hooke, Darwin, and Mendel, ultimately becoming modern biology that is based on cytology and genetics.
However, twenty-first-century biology was about to undergo another major transformation.
“There’s been a movement towards the BIT industry for a while. However, it hasn’t been easy to achieve because biology is so complex that it’s hard to quantify and make it into a diagram,” Young-Joon said.
BIT: it was a fusion of biology (B) and information technology (IT), a discipline called bioinformatics. The genomic data of a single human was about seven hundred thirty megabytes, calculated when each base was represented as about two bytes. If the three billion bases of genomic data was written down in a notepad, it would amount to seven hundred thirty megabytes.
However, experimental data from a genome analysis showed that each letter in the DNA sequence was checked at least a thousand times. This redundancy allowed scientists to be confident in the genome data, even if there was an error ten times out of a thousand. In other words, one would have to process seven hundred thirty gigabytes of data to decode the entire genome of a patient like Mimi, the genetically modified baby.
“From that point on, it’s no longer biology, but it enters the field of computation and data science,” Young-Joon said. “That’s where biology and IT intersect. There aren’t many experts who can run through seven hundred thirty gigabytes of big data and find disease-related DNA. But what if you multiply that data by one hundred million? What if we’re tracking disease sequences based on decoded genome data from one hundred million people, like the project we’re doing?”
“That would be very difficult,” said Doctor Taylor.
“Right now, the genome data decoding team is doing a great job, but they were only able to do so because they are backed by the country’s top experts in computer science,” Young-Joon said. “There are a few programmers who came to A-GenBio from SG Electronics. Collaborative development with them resulted in the GWAS analysis, which had a lot of influence on our ten billion dollar lawsuit and the declaration of the moratorium.”
Young-Joon went on.
“Our in silico experiments with them allowed us to skip preclinical studies for Mimi’s treatment, which had only been used on a few mice, and use it clinically to cure her in time.”
Technically, Rosaline was the one who came up with the clinical plan, but Young-Joon also contacted Kim Young-Hoon, one of the board directors of A-GenBio, and conducted in silico experiments as well. He only did this so that he had an explanation, but the results were surprising. The data from the in silico experiments were more similar to Rosaline’s simulations than he had expected. From then on, Young-Joon planned to start the project; it seemed like it had potential.
Then, Bae Sun-Mi asked, “So are we going to collaborate with SG Electronics again, not with GWAS or in silico, but with artificial intelligence that predicts ecological changes...?”
“That’s right,” Young-Joon said. “And we will maintain our collaborative research relationship if possible.”
* * *
Young-Joon was chatting with a guest in his office, who he was happy to see.
“I apologize for asking to meet so suddenly before the board meeting,” Young-Joon said.
“No worries, sir. Of course I should come if the CEO wants to see me,” replied the middle-aged man, who was sitting on the sofa.
It was Kim Young-Hoon, a director of A-Gen and an executive who had worked at SG Electronics for over twenty years.
“By the way, Mr. RKim, how did you end up at A-Gen? Weren’t you very successful at SG Electronics?”
“Was that how I was evaluated? I’m glad,” Kim Young-Hoon replied, chuckling. “Actually, it’s a little different. I competed for the vice president position there, but I lost. I was actually going to retire after my executive term ended, but the president of SG Electronics convinced me to come to A-Gen.”
“I see.”
“SG Electronics had invested a lot of money in A-Gen when it was still a small company, and investors always want to put their own people on the board of directors, not only to have a grasp on management, but also to make sure the money is well-spent. When I came in, A-Gen didn’t have the power to decline,” Kim Young-Hoon said. “SG Electronics and A-Gen were in very different industries, so it was a bold move at first, joining as an interlocking director. Although, things changed at SG Electronics over the years and I’ve basically become part of A-Gen now.”
“Kek!”
Kim Young-Hoon coughed.
“We used the artificial intelligence developed by Doctor Tanya Manker’s company during the red mold disaster when we made cultured meat,” Young-Joon said. “We’re going to bring her to A-GenBio.”
“Isn’t she the CEO of her own start-up?”
“I can’t get her to join A-GenBio, but I can get her on board as an advisor,” Young-Joon said. “More importantly, there’s no one in charge of this project that the three companies are working on together.”
“You’re asking me to take on that role?”
“Yes. You’ve been a senior executive at SG Electronics, and you’ve been a director at A-Gen for a long time. During that time, you studied biology and earned a degree as well. You’re probably the only one who knows well about both fields,” Young-Joon said. “I’m only asking the most qualified person to do it.”
Kim Young-Hoon smiled sheepishly.
“I’m not that great of a person, but how could I refuse when you’re asking me, Doctor Ryu? I’ll push for the project.”
“Thank you.”
* * *
Tanya Manker was stunned when Young-Joon contacted her.
—You’re making what program? Doctor Ryu, you’re a biologist, not a programmer, right?
“That’s right. And that’s why I’m asking you. We need this item for several projects we’re going to be working on in the future.”
—Oh my... Doctor Ryu, this project isn’t going to work. We have a great AI prediction technology, and we can model climate change. But an ecosystem adds one more giant variable, right?
“Ecosystems?”
—Let’s say the weather is getting warmer and more humid. You can draw out a crude sketch of the success or failure of a particular crop from that, but it’s not the same as, say, how that ecosystem is disturbed when you wipe out a mosquito. You have to figure out all the organisms in the food chain that are related to that mosquito and input them as variables...
“I have it figured out.”
—What?
“I have it figured out. We just looked at Guangdong Province because we didn’t know what was going to happen, but we have that big data.
—...
“We know the population and the ecological pyramid of every species in Guangdong, from a single ant to a bird.”