Solutions
For educators
A course in metabolic engineering needs a pathway students care about and tools they will meet again. Capsaicin supplies the first; COBRApy, ESM-2 and the rest supply the second.
Free
CPU engines
1,000
worked designs
25
founding constructs
No install
browser only
01
The problem
Teaching computational biology runs into the same wall every term. The real tools require environments students cannot install reliably, the toy datasets do not behave like real ones, and by the time everyone’s dependencies work the window for teaching the actual concept has closed.
Motivation is the other half. Flux balance analysis on a generic model is abstract. Flux balance analysis on the question of how hot a pepper can be made, in a specific plant, with a real budget constraint — that one students argue about.
02
What the platform does about it
Nothing to install
Everything runs in a browser. No conda, no CUDA, no laptop that will not cooperate on the one afternoon the lab session happens.
The teaching engines are free
Flux analysis, annotation, alignment, codon work, primer design, assembly and export are all CPU engines and cost nothing. A whole course can run without a budget line.
A thousand worked designs
The catalog is 1,000 constructs across 65 organisms with the reasoning attached. Students can take apart a finished design instead of only producing incomplete ones.
A pathway you can hold
Two converging branches, one junction, and about ninety percent of flux control on a single side. Small enough to teach in a week, real enough to be worth teaching.
Reproducibility by construction
Every run stores its inputs, parameters and engine version. Students learn to work in a way that can be checked, which is a habit that transfers to everything they do afterwards.
Work you can actually review
A submitted project is a readable chain of runs and decisions. You can see where the reasoning went wrong, not just that the final number differed from expectation.
03
A representative module
- Course
- Upper-level metabolic engineering
- Reference set
- Founding ten peppers
- Constructs
- 25, totalling 31,911 bp
- Guide RNAs
- 24
- Week 1
- Read the pathway, browse the genome
- Week 2
- Flux analysis, feasibility of a target
- Week 3
- Variant scan, then construct and export
The founding ten are a well-sized teaching set: twenty-five constructs and twenty-four guides in 31,911 base pairs, complete enough to exercise every stage and small enough that a student can hold the whole thing in mind. Opening one and asking why each decision was made is a better first exercise than starting from nothing.
The assignment that works best is deliberately constrained. Pick a host, set a target, defend it with a flux solution, then explain what the model says has to change. Most students discover their first target was infeasible, which is the lesson — and it costs nothing to discover, because the engine that tells them is free.
04
Getting started
Students create their own free accounts and have immediate access to the catalog and every CPU engine. Nothing needs to be provisioned, and the pathway and genome data are browsable without designing anything at all, which suits a lecture as well as a lab.
If a module reaches structure prediction or variant scanning, those are GPU engines on prepaid credits — $25 for 250 credits, one credit for roughly one Standard job — so a demonstration run for a whole class is a small, predictable cost.
Ready to run it
Try this in the Splice Platform.
CPU engines are free to run. GPU engines run on prepaid credits.
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