Alex replayed Mr. Hong's words over and over:

“Certain glycoproteins exposed on the cellular surface are unique to cancerous cells”

Her AP Bio teacher mentioned this during a casual update on recent biological breakthroughs. But it really stuck with Alex. Because just a week earlier, she'd stumbled upon a paper discussing a novel method for designing T-cell receptors based on any cellular surface structure.

Despite extensive research online, she found no evidence of anyone trying to engineer a T-cell receptor for cancer-specific glycoproteins.

For weeks, Alex had used the bio-cloud to explore this idea. 

The first step, deciphering the structure of the glycoprotein’s protein backbone, was challenging. The researchers had provided the amino acid sequence, but not the protein structure file (PDB), which meant she had to create the “folded” structure herself using AI.

Figuring out the structure of such a complex glycoprotein, with thousands of amino acids, was tough, even for the AI. It generated a prediction, but its confidence was only 95%. Since accuracy was paramount, Alex went the extra mile: she conducted crystallography experiments to verify the AI's predictions. It turned out to be simpler than she expected; an API call that cost about $10, and a day later she had the PDB waiting for her in her virtual bench.

To her delight, the AI's predicted PDB and her crystallography PDB were nearly identical.

After that, using the AI model that created a T-cell receptor that would bind to any surface structure, in this case the glycoprotein, was well documented and straightforward to use. With a simple Python script, $20, and 48 hours on the bio-cloud, Alex had 1,000 T-cell receptors ready to test.

Now, she was ready for the ultimate test: the in-vivo validation.

——

On this particular Wednesday night, Alex trudges home, her energy drained after a demanding soccer practice and an excursion to a friend’s house to immerse themselves in a virtual reality depiction of the Battle of Gettysburg for history homework.

But as soon as Alex gets home, thoughts of her ongoing experiments create a re-energizing rush of excitement. She heads straight to her room and puts on her computer.

The mayhem of five monitors greets her, but with a swift hand motion, she clears all but one essential screen: her bio-cloud bench.

Though she tries to lower her expectations, anxiety settles in. It’s a familiar sensation she equates to wrestling with an elegant math problem, where growing complexity signals a probable error. Today, during a rather uneventful class, she had spotted a potential flaw, a lack of a transmembrane domain in the T-cell Receptor (TCR). Could it be that her script had overlooked adding this vital component?

Her suspicions find confirmation as glaring red exception logs inform her of a critical test failure. Although the experiment technically concluded without hitches, the cancerous tumor remained untouched by the T-cells.

Alex mentally kicks herself a bit, especially since Mr. Hong had always emphasized intermittent quality control checks. A silver lining, however, is the economical nature of her experiments, which utilize cost-effective 3D printed lung organoids. Alex gets a shudder down her spine as she reflects on historical practices where actual animals and humans were the test subjects.

Instead of giving in to the discouragement, a sense of accomplishment washes over Alex, proud that the majority of her experimental protocol executed without a glitch.

Determined to rectify her oversight, Alex dives into the protocol script. 

Almost on cue, her digital assistant, Zyon, springs into action, offering an analytical breakdown of the most probable reasons for the experiment's failure. Zyon’s top guess is a mismatch between the antigen recognition domain and the glycoprotein. However, its second theory, concerning the TCR's failure to adhere to the T-cell lipid bilayer, resonates with Alex.

Line 58 of her protocol script jumps out at her. It was commented out, which meant that the protocol never added the transmembrane domain. That had to be why the experiment failed.

Leveraging her recent learnings from her python5 class, she starts to make the necessary adjustments. Querying Zyon from time to time to help her reason about biology.

“Hey Zyon, please compare the structure of these hundred transmembrane domains and return the one with the highest predicted affinity”

“Optimize this protein to make the printing process the cheapest”

After a meticulous half-hour, she's confident in her revisions.

She tells Zyon to automatically debug and retry 3 times if it fails again, but without spending more than $100.

She runs a few commands in her terminal and gets a successful confirmation. Her experiments are once again off to the races!

A sudden voice punctures her focus. “It's bedtime!” Her mother's reminder jolts her back to reality, and a quick glance at the clock confirms it's way past midnight. Reluctantly, she takes off her computer, changes into her pajamas, and nestles into bed, but her thoughts remain firmly tethered to the bio-cloud.

Her knowledge about the bio-cloud's intricate workings is limited. Mr. Hong had once showcased an old pipette from his early postdoc days, a relic from an era when manual experimentation was the norm. The thought of past scientists laboriously conducting experiments by hand, at exorbitant costs and painfully slow speeds, boggles her mind.

She knows that these fleeting encounters with historical tools, coupled with her self-guided readings on various lab instruments, have only scratched the surface. Much of her current experiments' intricacies remain an enigma for her, fueling her aspiration to study to become a bioengineer. The bio-cloud, while a revolutionary tool, is just the tip of the iceberg. To pioneer groundbreaking discoveries, she acknowledges the need to understand the underlying theory and mechanisms of biochemistry.

As she drifts into sleep, a tantalizing thought flies across her mind: What if her experiment actually worked? Zyon had hinted that she could publish a working protocol on the Biorxiv, a platform used by renowned professors to read and publish results! In a dream scenario, a biopharmaceutical company might take notice, replicate her work, and drive it toward clinical trials, potentially transforming it into a life-saving therapy.

Alex was too young and naive to understand the life-changing money such a situation would imply. All she cared about was those young kids battling cancer that she could help. 

The possibility of her experiment, or a similar one in the future, culminating in a life-altering therapy sends her imagination soaring into other experiments that she could try. 

Perhaps tomorrow she will find out…