Run biology as a durable cloud job.
Execute versioned Hub labs from Python with the same open-source scientific runtime, plus managed compute, artifacts, provenance, credits, and webhooks.
from biosimulant import Client
client = Client() # BIOSIMULANT_API_KEY
result = client.run(
"demi/microbiology-hello-world-growth@1.0.0",
inputs={"initial_cells": 10, "available_food": 80},
timeout=300,
)
print(result.outputs)Durable by default
Every cloud run is persisted, queued, and observable. A client timeout never cancels the server job.
Reproducible inputs
Resolved package version, package digest, runtime, compute profile, and execution details stay attached to each result.
Polling or webhooks
Wait in sync or async Python, return immediately with a run handle, or react to signed lifecycle events.
One runtime. Two explicit execution modes.
Same runtime, same package refs, same results contract. BioWorld.run() stays local, MIT-licensed, and free; Client and AsyncClient opt into managed execution. Nothing silently moves your work to the cloud.
Local runtime: BioWorld.run()
Compose and execute on your machine or in your own CI, with no hosted-service dependency.
from biosimulant import BioWorld
world = BioWorld()
world.add_biomodule("growth", Growth()) # your module, SBML, ONNX…
world.run(duration=24.0) # runs on your machine
print(world.get_outputs("growth"))Managed API: Client.run()
Submit the same ref as a durable job with managed CPU/GPU, artifacts, provenance, and lifecycle events.
from biosimulant import Client
client = Client() # BIOSIMULANT_API_KEY
client.run( # durable managed job
"demi/microbiology-hello-world-growth@1.0.0",
inputs={"initial_cells": 10, "available_food": 80},
)Pay for the compute a run uses.
Choose an API-ready lab and compute profile before submission. Credits are reserved up front, settled from measured execution time, and unused reserved credits are returned.
Talk to us about your simulation workflow
Biosimulant Studio is for teams building reusable virtual simulations across research and engineering.
- Research teams building reusable virtual experiments instead of one-off simulations.
- Simulation groups that need inspectable workflows, reproducible runs, and shared review loops.
Contact
Start the conversation
Tell us what you are simulating, how your team works today, and where Biosimulant might fit.
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