RESUMO
Multi-agent biohybrid microrobotic systems, owing to their small size and distributed nature, offer powerful solutions to challenges in biomedicine, bioremediation, and biosensing. Synthetic biology enables programmed emergent behaviors in the biotic component of biohybrid machines, expounding vast potential benefits for building biohybrid swarms with sophisticated control schemes. The design of synthetic genetic circuits tailored toward specific performance characteristics is an iterative process that relies on experimental characterization of spatially homogeneous engineered cell suspensions. However, biohybrid systems often distribute heterogeneously in complex environments, which will alter circuit performance. Thus, there is a critically unmet need for simple predictive models that describe emergent behaviors of biohybrid systems to inform synthetic gene circuit design. Here, we report a data-driven statistical model for computationally efficient recapitulation of the motility dynamics of two types of Escherichia coli bacteria-based biohybrid swarms-NanoBEADS and BacteriaBots. The statistical model was coupled with a computational model of cooperative gene expression, known as quorum sensing (QS). We determined differences in timescales for programmed emergent behavior in BacteriaBots and NanoBEADS swarms, using bacteria as a comparative baseline. We show that agent localization and genetic circuit sensitivity strongly influence the timeframe and the robustness of the emergent behavior in both systems. Finally, we use our model to design a QS-based decentralized control scheme wherein agents make independent decisions based on their interaction with other agents and the local environment. We show that synergistic integration of synthetic biology and predictive modeling is requisite for the efficient development of biohybrid systems with robust emergent behaviors.
RESUMO
Persistent cell migration can occur due to anisotropy in the extracellular matrix (ECM), the gradient of a chemo-effector, or a combination of both. Through a variety of in vitro platforms, the contributions of either stimulus have been extensively studied, while the combined effect of both cues remains poorly described. Here, we report an integrative microfluidic chemotaxis assay device that enables the study of single cell chemotaxis on ECM-mimicking, aligned, and suspended nanofibers. Using this assay, we evaluated the effect of fiber spacing on the morphology and chemotaxis response of embryonic murine NIH/3T3 fibroblasts in the presence of temporally invariant, linear gradients of platelet-derived growth factor-BB (PDGF-BB). We found that the strength of PDGF-mediated chemotaxis response depends on not only the gradient slope but also the cell morphology. Low aspect ratio (3.4 ± 0.2) cells on flat substrata exhibited a chemotaxis response only at a PDGF-BB gradient of 0-10 ng mL-1. However, high aspect ratio (19.1 ± 0.7) spindle-shaped cells attached to individual fibers exhibited maximal chemotaxis response at a ten-fold shallower gradient of 0-1 ng mL-1, which was robustly maintained up to 0-10 ng mL-1. Quadrilateral-shaped cells of intermediate aspect ratio (13.6 ± 0.8) attached to two fibers exhibited a weaker response compared to the spindle-shaped cells, but still stronger compared to cells attached to 2D featureless substrata. Through pharmacological inhibition, we show that the mesenchymal chemotaxis pathway is conserved in cells on fibers. Altogether, our findings show that chemotaxis on ECM-mimicking fibers is modulated by fiber spacing-driven cell shape and can be significantly different from the behavior observed on flat 2D substrata. We envisage that this microfluidic platform will have wide applicability in understanding the combined role of ECM architecture and chemotaxis in physiological and pathological processes.