Miniel Mahfoud, I. E. | Search bioRxiv

bioRxiv2024

A Linear Mixed Effects Model for Evaluating Synthetic Gene Circuits

A significant advancement in synthetic biology is the development of synthetic gene circuits with predictive Boolean logic. However, there is no universally accepted or applied statistical test to analyze the performance of these circuits. Many basic statistical tests fail to capture the predicted logic (OR, AND, etc.) and most studies neglect statistical analysis entirely. As synthetic gene circuits shift toward advanced applications, primarily in computing, biosensing, and human health, it is critical to standardize the statistical methods used to evaluate gate success. Here, we propose the application of a linear mixed effects model to analyze and quantify genetic Boolean logic gate performance. First, we analyzed 144 currently published Boolean logic gates for trends and used unsupervised machine learning (k-means clustering) to validate the statistical model. Next, we utilized the model to generate estimates for the fixed effect of the ON state, {beta}, as a general descriptor of the Boolean nature of a circuit and used Monte Carlo simulations to recommend sample sizes for evaluating gate performance. Finally, we examined {beta} as a holistic metric for circuit performance using a series of nested repressor OR gates with intentionally degraded performance. We observed a linear correlation between {beta} and the predicted translation rate, highlighting the use of {beta} for the forward design of new Boolean gates. In summary, we utilized a linear mixed effects model to describe synthetic gene circuits and determined that the fixed effect, {beta}, is an appropriate descriptor of gate behavior that can be used to statistically evaluate performance. Significance statementThere is no standard method for statistically evaluating the success of biological Boolean logic gates and common statistical tests are not used or incorrectly applied. We propose the use of a linear mixed effects model to estimate the value for {beta}, a parameter which describes the difference between ON and OFF gate states. A null hypothesis of {beta} = 0 can then be tested to determine if there is a statistically significant difference between the two states.

synthetic biology↗

bioRxiv2021

Transcriptional Regulation of Synthetic Polymer Networks

Individual cells direct non-equilibrium processes through coordinated signal transduction and gene expression, allowing for dynamic control over multicellular, system-wide behavior. This behavior extends to remodeling the extracellular polymer matrix that encases biofilms and tissues, where constituent cells dictate spatiotemporal network properties including stiffness, pattern formation, and transport properties. The majority of synthetic polymer networks cannot recreate these phenomena due to their lack of autonomous centralized actuators (i.e., cells). In addition, non-living polymer networks that perform computation are generally restricted to a few inputs (e.g., light, pH, enzymes), limiting the logical complexity available to a single network chemistry. Toward synergizing the advantages of living and synthetic systems, engineered living materials leverage genetic and metabolic programming to establish control over material-wide properties. Here we demonstrate that a bacterial metal respiration mechanism, extracellular electron transfer (EET), can control metal-catalyzed radical cross-linking of polymer networks. Linking metabolic electron flux to a synthetic redox catalyst allows dynamic, tunable, and predictable control over material formation and bulk polymer network mechanics using genetic circuits. By programming key EET genes with transcriptional Boolean logic, we rationally design computational networks that sense-and-respond to multiple inputs in biological contexts. Finally, we capitalize on the wide reactivity of EET and redox catalyses to predictably control another class of living synthetic materials using copper(I) alkyne-azide cycloaddition click chemistry. Our results demonstrate the utility of EET as a bridge for controlling abiotic materials and how the design rules of synthetic biology can be applied to emulate physiological behavior in polymer networks.

synthetic biology↗