Gel Time Engineering in Bacteria-Embedded Silk Hydrogels

Hydrogels embedded with living bacteria offer a pathway to soft materials with living capabilities. In theory, such materials can produce and secrete bio-active substances; sense and respond to environmental signals including bio-active substances; and in general are capable of exhibiting complex temporal and programmable responses to many stimuli of interest. Engineering bacteria-embedded living hydrogels—wherein the activity of the embedded microorganisms must be balanced against the desired mechanical properties of the resultant hydrogel—can be a difficult task. Not many tools exist to screen large numbers of formulations for desired properties. In this work, we employ techniques of microrheology—especially the newly developed technique of differential dynamic microscopy, with its capability to quantify the viscoelastic characteristics of soft materials in an automatable fashion—to rapidly screen formula variants for gelation within a chosen timeframe. Given the large number of formula variants that could impact gelation coupled with the potential adverse impact of those formulations on the activity of the embedded microorganisms, we augment automated microrheology with machine learning algorithms tailored to rapidly converge on areas in the formulation space where gelation within a chosen timeframe of 5-15 minutes is likely. This work demonstrates a new approach to engineering soft living materials when the gel time of the formulas and the activity of the embedded biocomponents is critical to application success.