untangle.bio is an AI-native platform for downstream process design in biotechnology. Generate optimal purification routes, run real-time simulations, and perform techno-economic analysis — all in one workspace.
Get up and running with untangle.bio in under 5 minutes. The platform follows a simple workflow: define your feed, set target products, generate routes, and analyze results.
Set flow rate, components, and properties. Choose from 70+ molecules or add custom components.
AI explores thousands of combinations using genetic algorithms and expert rules.
Compare yield, purity, and CAPEX across routes. Export optimized designs.
Pro tip: Start with the Balanced optimization mode for your first project. It provides a good mix of yield and purity while keeping costs reasonable.
untangle.bio is a conceptual process design tool, intended for early-stage route screening and feasibility assessment — not for detailed engineering or final process validation. Understanding what the simulator does and does not model will help you interpret results correctly.
Engineering interpretation required. Results should be treated as indicative order-of-magnitude estimates. Promising routes identified by untangle.bio should be validated with detailed process modelling, pilot-scale experiments, and consultation with separation specialists before making engineering or investment decisions.
untangle.bio follows a proven engineering workflow that mirrors how process engineers actually work — from initial feed characterization to final economic evaluation.
Define your input stream with volumetric flow rate and component specifications. The platform includes an extensive molecule database with physical properties for accurate modeling:
Select one or multiple target products from your feed components. untangle.bio optimizes routes for maximum recovery and purity of specified products, with support for complex multi-product separations.
The AI engine generates thousands of candidate routes using a diversity-preserving genetic algorithm. Set constraints and optimization goals:
Run rigorous mass balances with stream-level tracking of concentrations, pH, and flow rates. The simulation engine handles:
Alongside the automated route generators, you can build and edit process flowsheets entirely by hand — drag nodes onto the canvas, wire them together, and run the simulation yourself. This is useful when you want to test a specific sequence, reproduce a literature process, or make targeted modifications to a generated route.
Drag a Feed Stream node from the left palette onto the canvas. Double-click it to open the feed configuration dialog. Set the volumetric flow rate, temperature, and pH, then add your components — either from the built-in molecule database or as custom entries with manually entered properties.
Drag one or more unit operation nodes from the palette. Available operations are grouped by category:
Double-click any unit operation to configure its parameters (MWCO, pH target, wash volume, etc.).
Press C (or click the Connect button in the toolbar) to enter Connect Mode. In this mode, hovering over a node reveals its connection handles. Click and drag from one handle to another to draw a stream edge.
| Handle | Position | Meaning |
|---|---|---|
output |
Right side of feed node | Feed stream outlet — connect to the first unit operation's input |
input |
Left side of unit operation | Main process inlet |
light |
Right side of unit operation | Light-phase outlet — permeate, filtrate, mother liquor, volatiles |
heavy |
Bottom of unit operation | Heavy-phase outlet — retentate, concentrate, solid, crystals |
dilution |
Top of filtration nodes | Auxiliary water inlet for diafiltration — connect a Wash Water node here |
Tip: Press V to return to Select Mode for moving nodes around. Use Ctrl + Z / Y for undo/redo.
Every outlet of every unit operation must terminate at either a Product node or a Waste node — the simulator validates this before running. Drag these from the palette (Sources & Sinks section) and connect them to the appropriate outlets.
To model diafiltration or pH adjustment, drag reagent feed nodes from the palette and connect them to the appropriate inlets:
dilution handle on any filtration nodePress F5 or click the Solve button in the toolbar. The simulator performs a steady-state mass balance through every node in sequence, propagating concentrations, flow rates, and pH along every stream. Results appear as labels on stream edges and as summary panels on each unit operation node.
Validation errors: If the simulator reports dangling outlets or unconnected streams, check that every outlet handle on every unit operation is connected to either a downstream node, a Product node, or a Waste node. Unconnected outlets prevent the simulation from running.
Accurate feed characterization is critical for reliable route optimization. untangle.bio provides comprehensive tools for defining complex biotechnology feeds.
The platform includes 70+ pre-characterized molecules across key categories:
Database integration: Clicking any molecule automatically populates all relevant properties for separation modeling, including molecular weight, charge, and transport properties.
untangle.bio uses advanced algorithms to explore the vast space of possible purification sequences and identify optimal routes based on your criteria.
The platform employs a diversity-preserving genetic algorithm optimized for breadth rather than convergence:
All generated routes pass through 13 expert rules that eliminate physically impossible or economically infeasible combinations:
The simulation engine performs rigorous mass and energy balances with real-time validation of process feasibility and stream compatibility.
untangle.bio uses a mass-flow-based approach for accurate modeling:
// Convert to mass flows
mass_flow = concentration × volumetric_flow
// Apply separation efficiency
retained_mass = mass_flow × rejection_coefficient
permeate_mass = mass_flow × (1 - rejection_coefficient)
// Enforce conservation
total_out = retained_mass + permeate_mass
assert(total_out == mass_flow_in)pH is tracked throughout the entire process with buffer capacity weighting:
Built-in cost estimation provides immediate economic feedback on route alternatives using industry-standard methodologies.
Equipment costs are scaled using the power law, with an exponent that varies by operation type — generally following the "six-tenths rule" but calibrated individually to each technology class:
CAPEX = Base_Cost × (Flow_Rate / Reference_Rate)^n
Total_CAPEX = Σ(Equipment_Cost × Lang_Factor)The exponent n is not a fixed 0.6 for all equipment — it is calibrated per technology class. Chromatography columns and membrane systems (area-limited equipment) scale more favourably than thermal or cryogenic systems. As a rough guide: membrane and column operations sit in the lower range (~0.55–0.65), mechanical separators in the middle, and drying operations — especially freeze drying — at the higher end (~0.70–0.75).
Lang factors (1.5–3.0×) account for installation, instrumentation, and auxiliary equipment based on operation complexity. All base costs are referenced at 100 L/hr feed rate (2026 USD).
Variable costs include utilities, consumables, and labor:
untangle.bio supports complex separations where multiple valuable products are simultaneously recovered from a single feed stream through branching routes. Each product is tracked individually for yield and purity and exits at a dedicated product node.
At every two-outlet unit operation, each product is assigned to whichever physical stream carries more of its mass — heavy (retentate/solid) or light (permeate/filtrate). Products that end up in different streams at the same step are considered separated at that step and branch into their own product nodes. Products that remain together continue downstream together.
Design constraint: For N selected products, the route must produce exactly N distinct product nodes — each product must exit through a unique (step, outlet) combination. Routes that fail to separate all products are automatically rejected.
The multi-product generator uses a property-driven constructive search — not a genetic algorithm. It analyses the physical differences between products and systematically builds separation sequences that exploit the largest differences first.
For every pair of target products, the algorithm calculates key physical differences that determine which separation technologies can distinguish them:
Properties are sourced from the molecule database, falling back to component data entered in the feed stream.
Each separation technology is scored for every product pair based on how well it exploits the available property differences:
| Technology | Applicable when | Score scales with |
|---|---|---|
| Ultrafiltration / Nanofiltration | Meaningful MW difference between soluble products | Magnitude of MW ratio |
| Ion exchange | Products carry different net charges | Magnitude of charge difference |
| Size exclusion chromatography | Large MW difference between soluble products | Magnitude of MW ratio |
| Reverse phase | Meaningful hydrophobicity difference; smaller molecules only | Magnitude of log P difference |
| Crystallization | Meaningful solubility difference between products | Magnitude of solubility ratio |
| Drying (spray / freeze / vacuum) | One product is volatile, the other is not | Boiling point difference |
| Centrifugation / Depth filtration / MF | At least one product is a particulate (cells, spores, etc.) | Always strongly preferred — particle/soluble separation is highly selective |
| Affinity chromatography | One product is a protein, the other is not | Fixed baseline score |
Routes are built using two complementary strategies, run in parallel:
Strategy 1 — Sequential greedy: Product pairs are sorted by overall separability score. Starting from the most separable pair, the algorithm picks the top-scoring operation for that pair, then finds the best next operation for any remaining unseparated products. Produces focused 2-step routes.
Strategy 2 — Exhaustive permutations: All orderings of products are tried, with each position in the sequence assigned the highest-scored operation for that product pair. For 3 products this explores all A→B→C orderings across all scored technology combinations.
Both strategies generate outlet handle variants (trying both heavy and light at each two-outlet step). Non-drying routes are evaluated first. Up to 2,500 candidates are produced and deduplicated.
Every candidate route is passed through the full mass-balance simulation engine, then subjected to a series of hard rejection gates in order:
Streaming results: Routes are yielded to the UI as soon as each simulation completes — you see results appear live without waiting for all candidates to finish.
The platform incorporates decades of downstream processing knowledge through 13 expert rules that prevent infeasible designs.
The built-in molecule database currently covers a limited set of common biotech components — proteins, sugars, organic acids, amino acids, salts, alcohols, and cell types. It is actively being expanded over time based on user feedback and real-world process cases.
For testing purposes: If your molecule is not in the database yet, you can add it manually directly in the feed stream dialog. Enter the component name and as many physical properties as you know (MW, charge, solubility, pKa, log P, etc.). The simulator will use whatever properties you provide — missing values are handled gracefully, though accuracy improves with more complete data.
Note that molecules added this way are local to your simulation only — they are not automatically added to the central database. To request a molecule be added for all users, reach out via LinkedIn.
The database is continuously expanding. If you work with a molecule that is missing, reach out on LinkedIn — feedback from practitioners directly shapes what gets added next.
Ready to start designing processes? Launch the workspace and begin optimizing your downstream operations.