For decades, biology has advanced by breaking systems apart. We isolated genes, purified proteins, and ran assays one variable at a time. This reductionist approach built modern molecular biology, but it is increasingly strained by the complexity of living systems.

A new paradigm is emerging: the virtual cell.

Rather than studying components in isolation, the virtual cell aims to computationally model cellular behavior as an integrated, dynamic system—capturing gene regulation, signaling pathways, metabolism, spatial organization, and environmental context in a single, evolving framework. While still incomplete, the trajectory is clear: biology is moving from static measurements toward predictive, executable models of life.

This shift will profoundly reshape how experiments are designed, how labs operate, and how discoveries move from hypothesis to impact.

From Data Collection to Biological Simulation

The virtual cell is not a single model or tool. It is an ecosystem built on:

• Multi-modal biological data (transcriptomics, proteomics, perturbation screens, external knowledge base)

• Connecting all cells from samples to represent patient phenotypes

• Mechanistic and statistical models that encode causal relationships

• Machine learning systems capable of integrating noisy, high-dimensional data

• Explainability and reasoning algorithms capable of deriving actionable insights from model predictions

• Scalable infrastructure that can iterate rapidly across hypotheses

As these components mature, virtual cells will increasingly serve as in silico testbeds, allowing researchers to simulate perturbations, predict outcomes, and prioritize experiments before stepping into the lab.

In this model, computation is no longer downstream of experimentation. It becomes the first pass.

What Happens to the Physical Lab?

The rise of the virtual cell enables us to embed computational intelligence directly into wet lab experiments, fundamentally transforming how they are designed and executed. Virtual cells will not replace wet labs, but wet labs powered by virtual cells will replace those that are not.

1. Fewer, better experiments
Instead of running broad, exploratory screens, labs will focus on high-value experiments that validate or refine computational predictions. Wet-lab work becomes more targeted, hypothesis-driven, and information-dense.

2. Faster iteration cycles
Virtual experiments can be run in hours or days, dramatically compressing the design–test–learn loop. Physical experiments become checkpoints rather than bottlenecks.

3. New skill boundaries
The line between computational and experimental biology will continue to blur. Successful labs will be those that tightly integrate modeling, data engineering, and bench science into a single workflow.

4. Capital efficiency
By reducing failed experiments and dead-end programs, virtual cells shift costs upstream, toward data quality, infrastructure, and modeling, while lowering downstream experimental risk.

The Hidden Challenge: Making Virtual Cells Useful

Despite the promise, building usable virtual cells is hard.

Biological data is fragmented across platforms, formats, and experiments. Models are often siloed, brittle, or difficult to extend. Many computational insights fail to translate into actionable experimental decisions because the infrastructure connecting data, models, and lab workflows is missing.

The future of the virtual cell will not be determined solely by better algorithms, it will be determined by integration:

• Connecting heterogeneous datasets into coherent representations

• Enabling models to evolve as new data arrives

• Making predictions interpretable and experimentally testable

• Embedding computation directly into R&D decision-making

This is less about flashy AI and more about building durable, biology-first systems that researchers can trust.

A New Division of Labor in Biology

As virtual cells mature, we should expect a rebalancing:

• Computational systems explore vast hypothesis spaces, simulate perturbations, and surface high-confidence predictions.

• Physical labs generate the critical measurements needed to ground truth models and push biology into regimes where data is still sparse or uncertain.

In other words, the lab becomes the source of signal, not scale.

Organizations that succeed in this future will not be those with the largest wet labs or the biggest datasets alone—but those that can seamlessly translate between data, models, and experiments.

Looking Ahead

The virtual cell represents a shift in how we do biology: from observing life to simulating it, from describing systems to predicting them.

This transition will take time. Models will be incomplete. Assumptions will fail. But each iteration brings us closer to a world where biological insight is not discovered by chance, but designed.

The labs of the future will still pipette, culture, and image, but they will do so guided by computation that understands biology not as a collection of parts, but as a living system.

And that may be the most transformative experiment of all.