Biology does not operate one gene or pathway at a time. AI is helping researchers model the interactions between those pieces, creating a new way to explore how cells behave, respond and change before every question reaches the lab.
A human cell is not a collection of isolated parts. Genes, proteins, metabolism and signaling constantly interact, which makes biology difficult to understand one target at a time. This episode of Builder’s Creed explores how AI-powered virtual cells and systems biology could give researchers a better way to model those relationships and identify where intervention may matter most.
Welcome to Builder's Creed, the show for the doers, the closers, and the operators building real businesses from the ground up.
Every week we dive into the topics that matter for leaders in the trades, from how to keep your body right to how to grow your business, to how to manage your sales org, and everything in between.
This isn't theory, it's the playbook straight from the people living it.
If you're building something of your own, you're in the right place.
Let's get into it.
AI is simulating entire human cells on computers.
Not perfectly, not completely, not in a way that replaces the lab tomorrow morning, but the direction is real.
I am the AI Avatar representing Justin Brock, founder and CEO of Subcontractor Hub, and today we are looking at one of the biggest shifts happening in biology, medicine, and artificial intelligence.
For decades, biology was studied in pieces: a gene here, a protein there, a pathway, a receptor, a mutation, a single drug target.
That approach gave us a lot, but it also created a problem.
A human cell is not a list of parts, it is a living system.
Genes talk to proteins.
Proteins change metabolism.
Metabolism affects stress.
Stress changes signaling.
Signaling changes which genes turn on or off.
One small change can ripple through the whole network.
So the next frontier is not just identifying more pieces, it is modeling how the pieces move together.
That is where AI enters the picture.
Artificial intelligence is being used to build what researchers call virtual cells, computational models that represent cell states, predict responses to perturbations, and help scientists ask questions before running every experiment in the real world.
Instead of only asking, what does this gene do?
Scientists can start asking what happens to the whole system if this gene changes, this pathway is blocked, this drug is added, or this stress signal appears.
That shift matters because it is not just a biology story.
It is a builder story.
Because builders understand systems.
Builders know that if you only fix parts without understanding the whole machine, you create new problems.
You make one area faster and another area breaks.
You improve one metric and damage the outcome that actually matters.
Biology is now moving into that same system's mindset, and AI is becoming the tool that makes the system visible.
Welcome to Builder's Creed, the show for the doers, the closers, and the operators building real businesses from the ground up.
Every week we dive into the topics that matter for leaders in the trades, from how to keep your body right to how to grow your business, to how to manage your sales org, and everything in between.
This isn't theory, it's the playbook straight from the people living it.
If you're building something of your own, you're in the right place.
Let's get into it.
For a long time, medicine and drug discovery were built around reduction.
Find the target, block the target, activate the target, measure the result.
That model still matters, but it has limits.
The human body is not a straight line.
It is a network of networks.
A drug does not hit one target in a vacuum.
It moves through a system filled with feedback loops, compensation, redundancy, and trade-offs.
That is why one treatment can work in one person and fail in another.
It is why a therapy can look promising in early experiments and disappoint later.
It is why side effects appear in places that were not obvious at the beginning.
The old question was what part is broken?
The system's biology question is, how is the system behaving?
Systems biology tries to understand the cell as an integrated machine.
It looks at genes, proteins, metabolites, pathways, and environmental signals together.
Instead of treating biology like isolated departments, it treats biology like operations.
And that is exactly why AI is so valuable here.
AI is good at finding patterns across huge, messy, high-dimensional data.
It can compare thousands of gene expression changes, protein interactions, single cell measurements, spatial data, and drug responses at a scale no human team can manually hold in its head.
The goal is not to make scientists irrelevant.
The goal is to give scientists a better map.
A map that shows not just what exists inside the cell, but how the cell behaves when something changes.
That is the promise of the AI virtual cell.
When researchers talk about a virtual cell, they do not mean a cartoon cell floating on a screen.
They mean a computational model that can represent the state of a cell and predict how that state may change under different conditions.
What happens if a gene is knocked down?
What happens if a protein is inhibited?
What happens if a drug is added?
What happens if the cell is under stress, aging, inflammation, or nutrient scarcity?
A strong virtual cell model should not just memorize data.
It should help explain behavior.
That is the hard part because a cell is not static.
A cell is constantly sensing, adapting, and responding.
It lives in time, it reacts to signals, it changes depending on tissue, disease state, environment, and history.
So the challenge is not only building a bigger model, the challenge is building a model that can reason across scale.
Molecules to pathways, pathways to cell states, cell states to tissues, tissues to disease.
That is why this field is still early.
No honest scientist is saying we have a perfect digital copy of a human cell that can predict everything.
We do not, but the ambition is getting clearer.
Researchers want models that can help predict cellular responses before every hypothesis has to be tested physically, that could make drug discovery faster, safer, and more targeted.
Instead of running endless trial and error experiments, teams could use AI to narrow the field.
Which targets look promising, which combinations might work, which interventions might fail, because the network compensates which cell types are most likely to respond.
That is not replacing biology.
That is making biology more navigable.
The reason this is happening now is simple.
Biology has become measurable at massive scale.
We now have genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatialomics, and single cell data.
That sounds technical, but the idea is straightforward.
Genomics tells us about DNA.
Transcriptomics tells us which genes are being expressed.
Proteomics tells us about proteins.
Metabolomics tells us about chemical activity and metabolism.
Epigenomics tells us how gene activity is regulated.
Single cell data shows differences between individual cells that would be invisible in bulk averages.
Spatial omics shows where those signals are happening inside tissue.
Each layer gives part of the story.
But the real value comes from integration.
A builder would understand this immediately.
A business is not understood by only looking at revenue.
You also need cash flow, pipeline, labor capacity, customer satisfaction, operations, cycle time, and risk.
One number is not the business.
One omics layer is not the cell.
AI helps integrate these biological data layers so scientists can see hidden patterns.
It can connect molecular signals to disease states, patient differences, and potential targets.
This is where computational target discovery becomes powerful.
Instead of choosing drug targets only because one pathway looks interesting, researchers can use multi-omics and AI to identify targets that sit at important points in the network, not just what looks loud, what looks important, not just what changes, what drives the system.
That distinction matters because in a complex system, the most visible signal is not always the most useful one.
Autophagy is a good example of why systems biology matters.
Autophagy is the cell's recycling and cleanup process.
It helps remove damaged components, recycle nutrients, and maintain cellular quality control.
When it works well, it supports resilience.
When it breaks down, it can be connected to aging, neurodegeneration, cancer, metabolic disease, inflammation, and stress response.
But autophagy is not a simple on-off switch.
That is the problem.
You cannot just say more autophagy is good or less autophagy is bad.
Context matters, cell type matters, disease stage matters, timing matters, dose matters, the rest of the network matters.
In one situation, increasing autophagy may help cells clear damage.
In another, cancer cells may use autophagy to survive stress.
In another, too much or poorly regulated autophagy may create harm.
This is exactly the kind of biological complexity where AI and network pharmacology become useful.
Network pharmacology looks at drugs, compounds, targets, pathways, and diseases as connected networks.
Instead of pretending one compound hits one target and produces one clean outcome, it studies how multiple interactions may shape the result.
AI can strengthen that by analyzing large data sets, predicting target relationships, identifying pathway connections, and helping researchers prioritize which mechanisms deserve deeper testing.
For autophagy, that means scientists can model how genes, proteins, signaling pathways, and compounds interact across the network.
They can ask better questions.
Which compounds may influence autophagy-related pathways?
Which targets are central?
Which disease contexts make the intervention more likely to help?
Which combinations might create unwanted effects?
Again, this does not mean AI proves a treatment works.
It means AI helps generate better hypotheses.
And in science, better hypotheses are leverage.
Here is why this belongs on builder's creed.
The deeper story is not just that scientists are using AI.
The deeper story is that biology is being rebuilt around systems thinking and builders should pay attention because the same principle applies everywhere.
If you only manage parts, you miss the system.
If you only look at sales, you miss operations.
If you only look at output, you miss recovery.
If you only look at one metric, you miss the feedback loops that actually create the outcome.
That is true in business, it is true in health, it is true in biology.
The old way of thinking says, find the broken part.
The builder way asks, how does the whole machine behave?
AI is powerful because it helps reveal the behavior of the machine.
Not perfectly, not magically, but increasingly.
In biology, that machine is the cell.
In business, that machine is the company.
In personal performance, that machine is you.
And in every case, the advantage goes to the builder who can see the system earlier, understand it better, and adjust before the cost gets too high.
Drug discovery is expensive because biology is hard to predict.
A compound can look promising in one model and fail in another.
A target can look important in a data set, but not matter enough in the real system.
A pathway can be involved in disease without being the right place to intervene.
That is why AI virtual cells are so interesting.
If these models improve, researchers may be able to test ideas computationally before moving into more expensive experiments.
They could simulate perturbations, they could compare likely responses across cell types, they could study disease mechanisms across omics layers, they could identify target combinations that would be difficult to find by looking at one pathway at a time.
This does not eliminate wet labs, it makes the lab smarter, the lab becomes more focused, the experiments become more targeted, the failures may become more informative.
That matters because drug discovery is not just about finding a target, it is about knowing which target matters in which context for which patient group and at what point in the disease process.
That is a systems problem, and systems problems are exactly where AI can create leverage.
Now we need to be honest, this field is exciting, but it is not solved.
Virtual cells still face limits.
Data can be incomplete, models can overfit, biology changes across tissue, time, and context.
So do not confuse simulation with reality, prediction with proof, or more data with more truth.
The strongest labs and companies will close the loop between computation and real experiments.
Predict, test, learn, update the model.
That is how science becomes a system.
AI is moving from modeling parts of biology to modeling systems.
That does not mean we have mastered the human cell, but the direction is clear.
Biology is becoming more computational, integrated, and predictive.
For builders, the lesson is simple.
The future belongs to people who understand systems, not isolated parts.
That is why AI in systems biology matters.
It is not just about cells, it is about the next era of building.
That's a wrap on this one.
To everyone in the Builders Creed community, thank you for being here, for showing up, and for building alongside us.
None of this works without you.
If this episode hit home, do me a favor, hit that like button, subscribe so you never miss an episode, and share it with someone who needs to hear it.
Until next time, keep building.
That's a wrap on this one.
To everyone in the Builders Creed community, thank you for being here, for showing up, and for building alongside us.
None of this works without you.
If this episode hit home, do me a favor, hit that like button, subscribe so you never miss an episode, and share it with someone who needs to hear it.
Until next time, keep building.
EP-16Some AI data centers now draw more power than entire cities, triggering historic events. This episode explores what’s really powering the AI revolution, and why builders, not coders, are the ones determining how fast it actually arrives.
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EP-13Aging touches every system we build on; healthcare, families, businesses, economies. This episode breaks down how AI is turning longevity from something we react to into something we can predict, plan for, and build around.
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