Interactive labs

Stop reading about biotech. Run it.

Ten working simulators built on the real algorithms and the real equations. Design a CRISPR guide against a sequence you paste in. Compute a primer's melting temperature with nearest-neighbor thermodynamics. Watch dynamic programming fill an alignment matrix cell by cell. Nothing here is a canned animation. Every number on the screen is computed from your input.

10interactive labs
~3hof guided work
0canned answers
Diagram of the central dogma showing DNA replication, transcription to RNA, and translation to protein with the enzymes involved01
Molecular BiologyFoundational12 min

Central Dogma Engine

DNA → mRNA → protein, and what happens when you break it

Transcribe and translate a real gene, shift the reading frame, then mutate any base and watch the tool classify the damage.

TranscriptionTranslationReading framesSilent / missense / nonsenseFrameshift+1
The same physical event, one base changing, can be harmless or catastrophic. The genetic code's structure, not the mutation itself, decides which.
Open lab
Schematic of the Cas9 protein complexed with guide RNA bound to target DNA at a PAM site02
Genetic EngineeringAdvanced15 min

CRISPR Guide RNA Designer

Scan for PAMs, score guides, and find the cut site

Paste a target sequence, scan both strands for NGG PAM sites, score every candidate guide against transparent design rules, and visualize the double-strand break.

PAM recognitionProtospacer designGC contentOff-target riskBlunt cut site+1
Cas9 finds the PAM before it ever checks your guide. No PAM, no cut, which is the first and hardest constraint in genome editing.
Open lab
Diagram of PCR showing denaturation, primer annealing, and extension repeated over cycles to amplify DNA03
Lab TechniqueAdvanced15 min

PCR & Primer Design Lab

Design primers, compute Tm properly, and run the thermocycler

Design forward and reverse primers against a template, compute melting temperature by both the Wallace rule and nearest-neighbor thermodynamics, then run the amplification.

Reverse complementNearest-neighbor TmGC clampPrimer dimersAnnealing temperature+1
The reverse primer is the reverse complement of the template, the single most common conceptual error in molecular biology, and one you can only unlearn by doing it.
Open lab
Photograph of DNA bands in an agarose gel glowing under UV light04
Lab TechniqueFoundational12 min

Gel Electrophoresis Simulator

Digest DNA, run the gel, read the bands

Cut a plasmid with real restriction enzymes, load the fragments into lanes beside a ladder, and run a gel whose migration is computed from the physics.

Restriction digestLog-linear migrationDNA ladderAgarose %Band intensity+1
Migration distance scales with the log of fragment size, not size itself, which is why a gel resolves 500 bp from 600 bp beautifully and 9 kb from 10 kb not at all.
Open lab
Graph of reaction velocity versus substrate concentration showing the hyperbolic Michaelis-Menten saturation curve05
PharmacologyAdvanced18 min

Enzyme Kinetics Simulator

Michaelis–Menten, and how inhibitors give themselves away

Tune Vmax, Km, and inhibitor concentration across four inhibition mechanisms, and watch each one produce its own unmistakable signature on a Lineweaver–Burk plot.

Michaelis–MentenKm and VmaxCompetitive inhibitionNon-competitiveUncompetitive+2
You can determine where a drug binds without ever seeing it bind, the geometry of the double-reciprocal plot tells you the mechanism.
Open lab
Diagram of a Y-shaped antibody showing heavy chains, light chains, and antigen-binding sites06
PharmacologyAdvanced18 min

Dose–Response & IC50 Lab

Potency is not efficacy, and the curve proves it

Fit sigmoidal dose–response curves, compare compounds head to head, add experimental noise, and compute a therapeutic index against a toxicity curve.

Hill equationIC50 / EC50Potency vs efficacyCooperativityCurve fitting+1
A compound can be ten times more potent and still be the worse drug. Potency moves the curve left; efficacy sets how high it goes, and only one of those decides whether the patient gets better.
Open lab
Multiple sequence alignment of histone proteins across species showing highly conserved residues07
BioinformaticsAdvanced20 min

Sequence Alignment Visualizer

Watch dynamic programming actually happen

Run Needleman–Wunsch and Smith–Waterman over two sequences, then hover any cell in the scoring matrix to see exactly how its value was derived and trace the optimal path.

Dynamic programmingGlobal vs local alignmentTracebackGap penaltiesBLOSUM62+1
BLAST is not magic. It is this matrix, filled in cell by cell, and once you have watched the traceback, homology search stops being a black box.
Open lab
Graph of Hardy-Weinberg genotype frequencies as a function of allele frequency08
EvolutionAdvanced18 min

Population Genetics Simulator

Evolution as arithmetic you can run

Break Hardy–Weinberg one assumption at a time, add selection, drift, mutation, migration, and run replicate populations to watch identical starting conditions diverge.

Hardy–WeinbergSelection coefficientsGenetic driftHeterozygote advantageFounder effect+1
Run twenty identical populations and they end up in different places. Drift is not a correction to evolution, in a small population, it is the dominant force.
Open lab
Map of the pBR322 plasmid showing antibiotic resistance genes, origin of replication, and restriction sites09
Genetic EngineeringAdvanced20 min

Molecular Cloning Designer

Get a gene into a vector without wrecking it

Choose restriction enzymes, check sticky-end compatibility, ligate an insert into a plasmid, and screen the colonies you actually produced.

Restriction enzymesSticky vs blunt endsDirectional cloningLigationAntibiotic selection+1
Cut with one enzyme and your vector re-circularizes on itself and your insert goes in backwards. Cut with two, and there is only one way in. That asymmetry is why directional cloning exists.
Open lab
Circular diagram of the drug discovery pipeline from target identification through clinical trials to approval10
Biotech IndustryAdvanced20 min

Drug Development Pipeline

Why a drug costs billions and probably fails anyway

Set success probabilities, costs, and timelines for each clinical phase, then compute the true cost per approved drug and the risk-adjusted NPV that decides go/no-go.

Probability of successAttritionPhase II failureCapitalized costRisk-adjusted NPV+1
The billion-dollar price tag is not the cost of the drug that worked. It is the cost of the drug that worked plus the nine that didn't, and that division is the whole business model.
Open lab