Part.1 Why the old research paradigm is failing
Over the past two decades, we have observed the world through microscopes and interpreted life through PCR and immunoassays.
But as disease is no longer confined to a single organ and instead engages the entire metabolic and immune network, these "point-based" research approaches are beginning to fall short.
Have you noticed—
▊ We have more and more data, yet less and less of it can be integrated into systematic conclusions?
▋ Single-disease research goes ever deeper, yet translating the results into practice grows ever harder?
▋ Omics analysis runs faster than ever, yet the research cycle remains as long as before?
This is a "common predicament" across the entire biomedical industry—we are still using the perspective of a microscope to understand a big-data world.
Part.2 The "quantum entanglement" of life:
The real challenge we face
The essence of life can be abstracted as the dynamic interplay of three core classes of molecules:
DNA, which carries the blueprint; proteins, which execute function; and metabolites, which reflect state.
Yet the relationship among these three has never been a "one-way chain"; it is a complex network akin to "quantum entanglement." A single gene mutation may be amplified through protein function and set off a storm across the metabolic network; conversely, a metabolic change may also regulate gene expression in reverse.
Traditional "single-omics research" resembles the blind man who touches only the elephant's trunk and insists the elephant is like a snake. It tries to infer the laws of the entire living system by studying one class of molecules in isolation. When confronted with complex disease, this "serial" way of thinking has already reached the ceiling of what it can know.
Some researchers have estimated that:
resolving all the interactions among tens of thousands of genes, millions of proteins and millions of metabolites with conventional computational methods would mean "analyzing a single person could take fifty years."
Part.3 The price of "blind men and the elephant"
The limitations of this research paradigm come at an enormous cost:
● Low efficiency:
Development timelines are stretched indefinitely, and vast resources are consumed by trial and error.
● One-sided findings:
Only strongly associated single-dimensional markers can be found, while system-level signals are drowned out.
● Poor reproducibility:
Results from small samples cannot be replicated in broader populations.
What we miss may be the golden signal for early cancer screening,
the early warning sign of a neurodegenerative disease,
or, quite possibly, a perfect drug target.
Part.4 The question that breaks the deadlock: time for a new "perspective"
Now that we have touched the limits of complexity at the molecular level, should we keep patching things up inside the old paradigm? Or should we build a new model capable of observing the full panorama of DNA–protein–metabolite interactions and understanding their "semantic logic"?
The language of life is systemic, and so must our research methods be.
This is precisely the questionElixir™, our large model, seeks to answer:
giving research the ability to understand the "semantics" of life.
In the next article, we will explore in depth:
As medical big data moves beyond the "era of measurement" and into the "era of understanding," what kind of technological paradigm can carry this research revolution? Stay tuned.
