Every living thing runs on a small set of molecular rules. Information stored in nucleic acids is copied, read, and used to build proteins, and proteins in turn do most of the chemical work of the cell. Francis Crick stated the principle behind that flow as the “central dogma of molecular biology” and restated it in Nature in 1970. Half a century later, the rules have not changed. What has changed is our ability to read them at scale, reason about them with AI, and act on them with robots.
This essay was prompted by an introductory biochemistry lecture, Biochemistry 101: The Molecular Logic of Life, and by a simple question it raised for me as a data engineer. If the logic of life is a pipeline, from molecule to function, why are the systems we use to study and treat it still built as islands? Laboratories, instruments, genomic databases, hospital records and AI models usually sit in separate systems, owned by separate teams, and speak separate formats. The opportunity of the next decade is to join them into one bio-computational stack: a single pipeline that turns biological matter into data, data into predictions, and predictions into individualized decisions, and then feeds the result back down to the bench.
Why now: three signals
Three published results show that each piece of the stack now works on its own.
- AI can now predict protein structure from sequence. The AlphaFold team reported in Nature (2021) “the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known,” with “accuracy competitive with experimental structures in a majority of cases” in the CASP14 assessment.
- Robots can now run experiments without a person at the bench. In a 2020 Nature study, a mobile robot “operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space.” Its search “identified photocatalyst mixtures that were six times more active than the initial formulations.” The authors describe the approach as “automating the researcher rather than the instruments.”
- Population-scale clinical and genomic data is being assembled. The U.S. All of Us Research Program, described in the New England Journal of Medicine (2019), “plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health.”
Prediction, automation and data each exist today. What does not yet exist at scale is the architecture that connects them.
The six layers of the stack
Figure 1. The bio-computational stack. Information is synthesized upward, from molecule to decision. Instructions flow downward, from decision to experiment.
Layer 1: the molecular layer. DNA, RNA, proteins and metabolites: the substrate. Nothing in the stack is real unless it is anchored here.
Layer 2: the physical interface, or wet lab. Liquid handlers, bioreactors, sequencers and mobile robots. This is the layer that turns a digital instruction (“prepare these 96 variants”) into a physical experiment. Its engineering problems are the same as in any factory: calibration, throughput, contamination, and knowing exactly what was done to which sample.
Layer 3: the analytical layer. Sequencing, mass spectrometry, chromatography, spectroscopy and imaging measure the physical and chemical characteristics of what the lab produced. This is where matter becomes measurement, and where most of the noise enters.
Layer 4: the data plane. Genomic profiles, instrument outputs, and genetic and clinical records meet here, under common identifiers, provenance and consent. This is the layer most often missing, and it is the one I would build first (more below).
Layer 5: the AI computational layer. Here data becomes prediction:
- models that predict structure or response;
- generative models that propose new candidates;
- over time, a patient digital twin, a continuously updated model of one person’s biology that can be tested in silico before anything reaches the patient.
Layer 6: the synthesis and decision layer. Predictions become actions: the next experiment to run, the candidate to make, and, in the clinic, the decision a physician signs.
The direction matters. Information is synthesized upward: molecule, then measurement, then data, then model, then decision. Instructions flow downward: decision, then design, then robot, then molecule. A stack that only goes up is a reporting system. A stack that also goes down is a discovery engine.
From static records to a dynamic predictive system
Most biomedical data today is a snapshot: a sequence taken once, a lab panel from last spring, a diagnosis coded at discharge. The bio-computational stack turns snapshots into a dynamic predictive system, one that updates as new measurements arrive and gets better at predicting each individual the more it learns.
At the top of the stack sits a closed loop that laboratory scientists already know as design–make–test–learn:
- AI proposes candidates.
- Robots make them.
- Instruments test them.
- Models learn from the results.
The loop then turns again. The robotic chemist above is an early, narrow example of that loop closing without a human at every step.
Figure 2. The closed loop at the top of the stack. In medicine, a human signs off at every irreversible step.
A solution design approach: seven principles
Building this is less about any single model than about the architecture around the models. Seven principles follow from treating it as one system.
- Build the data plane first. Common identifiers for samples, patients and experiments; shared vocabularies; and provenance recorded with every measurement: which instrument, which protocol, which version. Without this, every AI model is trained on a different, undocumented version of the truth.
- Give every instrument a standard interface. Robots and analyzers should expose their capabilities and results through documented, machine-readable interfaces, so the stack is not rebuilt each time a lab changes vendors.
- Close the loop by design, with human sign-off at irreversible steps. Automate the reversible steps: simulation, sample preparation, analysis. Require a named person to approve anything that cannot be undone, above all anything that reaches a patient.
- Carry uncertainty all the way up. A prediction without a confidence estimate should not drive an experiment, and never a treatment. Each layer should pass its uncertainty to the next.
- Make privacy and consent part of the architecture. Consent should travel with the data it governs. Where data cannot move, move the computation to the data, so that models can be trained where the records live.
- Validate along a ladder. A candidate climbs from in silico to in vitro to clinical evidence, and the stack should record which rung every claim has reached.
- Keep a complete audit trail. Regulators, clinicians and patients will need to know why the system recommended what it did. Record every model version, input and decision.
Where it applies
The same stack could serve several frontiers. Each one needs the same data plane, the same closed loop and the same safeguards:
- Rare and complex diseases, where individualized profiles matter because no large population shares the condition.
- Oncology, where tumors differ from patient to patient.
- Antimicrobial resistance, where fast design–make–test cycles could keep pace with evolving pathogens.
- Biomanufacturing, where the same loop tunes how a therapy is produced, not only what it is.
The long-term promise is bio-synthetic engineering of individualized cures: therapies designed, tested and produced for one person, at a cost and speed that make them practical.
What stands in the way
Honesty about the limits is part of good design:
- Biology is noisier than software. Measurements drift and batches differ, and a model can only be as good as the provenance of its data.
- Predictive accuracy does not equal clinical benefit. Every individualized therapy must still earn its place through clinical evidence and regulatory review, and that is as it should be.
- Equity has to be designed in. If the data plane represents only some populations, the predictions will serve only some patients. That is one reason large, diverse programs like All of Us matter.
The takeaway
The pieces are already on the table: models that read molecules, robots that run experiments, and population-scale data. What connects them is an architecture: a data plane that makes every measurement trustworthy, an AI layer that turns data into calibrated predictions, and a decision layer that closes the loop back to the bench with a human hand on every irreversible step. The engineers who can build that stack end to end, and not just its parts, will shape how quickly individualized medicine moves from promise to practice.
References
- F. Crick, “Central Dogma of Molecular Biology,” Nature, vol. 227, pp. 561–563, 1970. doi:10.1038/227561a0
- J. Jumper et al., “Highly accurate protein structure prediction with AlphaFold,” Nature, vol. 596, pp. 583–589, 2021. doi:10.1038/s41586–021–03819–2
- B. Burger et al., “A mobile robotic chemist,” Nature, vol. 583, pp. 237–241, 2020. doi:10.1038/s41586–020–2442–2
- The All of Us Research Program Investigators, “The ‘All of Us’ Research Program,” New England Journal of Medicine, vol. 381, pp. 668–676, 2019. doi:10.1056/NEJMsr1809937
- Biochemistry 101: The Molecular Logic of Life & Essential Biological Chemistry, Sleepy Science Stuff (YouTube). Cited as inspiration only.
The views expressed are the author’s own.
I used an AI writing assistant to help draft and edit this article. I reviewed and revised the text and checked every source.
Originally published on Medium.
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