Hypothesis-generating concept model · not calibrated

ECM mechanical state × microgroove guidance

Does the surrounding gingival ECM state change how microgrooves organize fibroblast alignment, inward migration, and matrix deposition after premature GBR membrane exposure? This model screens that interaction and helps prioritize groove depths for a first wet-lab comparison; it does not predict a final membrane.

Soft-tissue module · gingival fibroblasts · not calibrated

Comparison scenarios

Every condition change restarts the same seed at day 0.

day 0.00 / 14.00
fibroblast (margin, not yet in defect) fibroblast inside defect · higher-stiffness ECM fibroblast inside defect · lower-stiffness ECM deposited ECM (persists after cell moves on) groove axis (when on)

A · Groove

An axis-only cue: it can align cells without directing them inward. Depth ranks starting conditions for later testing; width and pitch are not represented.

Depth (applies to both grooved conditions below)

B · ECM mechanical state

A fixed state of the surrounding gingival matrix—not the membrane's bulk stiffness and not a timed intervention.

Nematic alignment index
+0
order-parameter points, not %: −100 = perpendicular to groove axis · 0 = isotropic · +100 = parallel to groove axis
Net inward migration
+0%
mean reduction in distance to the defect band vs. settled starting position
ECM infill
0%
grid-sampled against deposited ECM marks only

Compare groove with its matched flat reference—not with zero. ECM infill counts persistent deposited marks, not cells passing through the defect.

From model to experiment

Three short views keep the decision logic, translation path, and evidence boundary separate.

At each depth, compare groove with its matched flat condition in both ECM states. The key result is whether the groove effect changes with ECM state—not which single animation scores highest.

Interaction(d) = [Groove(d) − Flat]higher ECM − [Groove(d) − Flat]lower ECM
Robust guidance

Similar groove effect

A comparable effect in both ECM states suggests ECM-robust guidance, even when the interaction is near zero.

State dependence

Different groove effect

A repeatable difference between ECM states supports the interaction hypothesis.

Candidate rule

Read all three metrics

Prioritize depths that improve organization without sacrificing inward migration or matrix infill across matched seeds.

Depth is a screening input, not an optimum. Its strength mapping is phenomenological and rises monotonically by construction. A deep groove may therefore increase modeled alignment while still being a poor experimental candidate.