Biology subjects

Dorfan, Y.

Publications and source records attributed to Dorfan, Y..

4 recordsLinked to original sources

bioRxiv2026

Assessing soluble and insoluble calcium sources for growth, biofilm formation, and biomineralization in Bacillus subtilis.

Biofilms formed by soil microbes hold immense potential for bioremediation, carbon dioxide sequestration, and the development of sustainable cementitious materials. However, quantifying the complex temporal coupling among bacterial growth, extracellular matrix (ECM) production, and mineralization dynamics remains a significant challenge due to the inherent nonlinearity of these processes and signal noise in high-throughput assays. To address this, we utilized an automated kinetic framework to evaluate the biomineralization competence of Bacillus subtilis under varying calcium regimes. Our results demonstrate that calcium carbonate promotes microbial growth as effectively as soluble calcium acetate, indicating that B. subtilis actively solubilizes the crystalline powder. Despite this growth efficacy, calcium carbonate was an inadequate source for macro-calcite production compared to organic salts. By quantifying expression from the sinI promoter, which initiates SinI production to activate matrix synthesis, we suggested that calcium-acetate-driven extracellular matrix (ECM) expression significantly enhances the structural template required for robust biomineralization. Kinetic expression analysis and consolidation assays indicate that overproduced ECM partially mitigates crystalline calcite growth defects, offering a baseline for utilizing mineral-rich construction waste ImportanceBiomineralization in B. subtilis has been studied as a potential avenue for developing engineered living materials. However, organic calcium salts, such as calcium acetate and calcium lactate, typically yield higher bioconversion efficiencies than certain inorganic sources often used in construction. This advantage has been largely attributed to the metabolic utilization of the organic anion as a carbon source, thereby promoting local alkalinity. Here, our experiments indicate that this limitation is influenced by structural and regulatory constraints rather than purely chemical barriers. Moreover, we explored a targeted synthetic biology approach to address this by genetically decoupling matrix production from native environmental sensing, thereby engineering strains with increased matrix production that can partially overcome the limitations of non-inductive substrates. This approach can contribute to the development of sustainable construction technologies, moving toward actively programmed Engineered Living Materials (ELMs).

microbiology↗

bioRxiv2023

Disentangling gene expression burden identifies generalizable phenotypes induced by synthetic gene networks

Large-scale genetic circuits are rapidly becoming critical components for the next generation of biotechnologies and living therapeutics. However, the relationship between synthetic and host gene expression is poorly understood. To reveal the impact of genetic circuits on their host, we measure the transcriptional response of wild-type and engineered E. coli MG1655 subject to seven genomically integrated circuits and two plasmid-based circuits across 4 growth time points and 4 circuit input states resulting in 1007 transcriptional profiles. We train a classifier to distinguish profiles from wild-type or engineered strains and use the classifier to identify synthetic construct burdened genes, i.e., genes whose dysregulation is dependent on the presence of a genetic circuit and not what is encoded on the circuit. We develop a deep learning architecture, capable of disentangling influence of combinations of perturbations, to model the impact that synthetic genes have on their host. We use the model to hypothesize a generalizable, synthetic cell state phenotype and validate the phenotype through antibiotic challenge experiments. The synthetic cell state results in increased resistance to {beta}-lactam antibiotics in gram-negative bacteria. This work enhances our understanding of circuit impact by quantifying the disruption of host biological processes and can guide the design of robust genetic circuits with minimal burden or uncover novel biological circuits and phenotypes.

systems biology↗

bioRxiv2022

Investigating And Modeling the Factors that Effects the Performance of Genetic Circuits

Over the past two decades, synthetic biology has yielded ever more complex genetic circuits able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a scale step to the design-build-test workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof-of-concept, we designed and tested a genetic circuit under different temperatures, mediums, inducer concentrations, and bacterial growth phases. We determined that the circuits performance is dramatically altered when these factors differ from the optimal lab conditions. Based on these results, a scaling effort, coupled with a learning process, proceeded to generate model predictions for the genetic circuits performance under untested conditions, which is currently lacking in synthetic biology application design. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on a scale step is needed to ensure correct and robust performances.

synthetic biology↗

bioRxiv2021

Prediction of Whole-Cell Transcriptional Response with Machine Learning

Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that takes the cell response to single perturbations as the input and predicts the whole cell transcriptional response to the combination of inducers. We find that the HRM is able to qualitatively predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of known prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). This tool will significantly reduce the number of high-throughput sequencing experiments that need to be run to characterize the transcriptional impact of the combination of perturbations on the host.

bioinformatics↗