Biology subjects

Simsek, E.

Publications and source records attributed to Simsek, E..

6 recordsLinked to original sources

bioRxiv2025

Simulation-templated photorealistic prediction of bacterial patterns

Pattern formation underlies biological organization across scales, but predicting experimentally observed patterns remains difficult because mechanistic models and data-driven generative models fail in complementary ways. Coarse-grained mechanistic models can encode causal constraints and global morphology, yet they omit fine-scale features such as texture, color gradients, and stochastic replicate-to-replicate variation. In contrast, generative image models can produce realistic images but are not inherently grounded in the biophysical rules that shape real patterns. Here, we introduce a simulation-conditioned generative framework that uses mechanistic simulations as spatial priors for generating biologically realistic pattern data. As a concrete test, we use a synthetic-to-real inverse task to show that these generated patterns preserve information needed for inference on real experimental images, beyond merely reproducing plausible visual appearance. Using branching colony expansion of Pseudomonas aeruginosa as a model system, we combine a coarse-grained PDE model with latent representations from a foundation image model and a conditional diffusion model. The resulting framework preserves the global structures imposed by simulation while restoring experimentally observed fine-scale morphology and stochastic variability. A model trained exclusively on simulation-conditioned synthetic patterns transfers without fine-tuning to real experimental patterns, enabling inference of initial seeding configurations from experimental colony morphology. Together, these results establish simulation-conditioned generative modeling as a strategy for converting coarse mechanistic models into scientifically structured synthetic data, enabling inference tasks on real biological patterns where experimental data are scarce.

systems biology↗

bioRxiv2025

A foundation model for microbial growth dynamics

Microbial growth dynamics contain rich information about microbial populations, which support applications from antibiotic testing to microbiome engineering. However, the high dimensionality of growth data and the scarcity of large, task-specific datasets have limited generalizable modeling analysis across systems. Here, we develop a foundation model for microbial growth dynamics. It is a large-scale, self-supervised representation model trained on [~]370,000 experimental and simulated growth curves spanning diverse microbial species, environmental conditions, and community contexts. The model learns lower-dimensional latent embeddings that capture essential dynamical features of raw growth data and enable accurate reconstruction of these data. The concise representations enhance predictive performance in diverse downstream applications. Using these embedding, we achieve few-shot learning for antibiotic classification and concentration prediction, accurate forecasting of simulated and experimental communities, and inference of total abundance from relative-abundance data. By extracting transferable representations from heterogeneous datasets, our model provides a general framework for analyzing and predicting microbial community dynamics from limited measurements.

synthetic biology↗

bioRxiv2025

Linear scaling reveals low-dimensional structure in observable microbial dynamics

Microbial communities often exhibit apparently complex dynamics driven by myriad interactions among community members and with their environments. Yet, practical modeling and control are often based on limited number of observables, raising a fundamental question: to what extent are these observed dynamics predictable given unobserved background complexity? Here, we report an emergent simplicity that the temporal dynamics of observable microbial populations can be captured by low- dimensional representations. Using variational autoencoders (VAEs), we define a critical latent dimension (Ec) that quantifies the minimal number of variables required to represent observable microbial dynamics. We find that Ec scales linearly with the number of observables, despite the complexity of unobserved background dynamics. This principle holds across simulations of ecological, spatial, and gene-transfer models, experiments with engineered and environment-derived communities, and human microbiomes. Our findings establish a scaling law for microbial community dynamics and demonstrate observable dynamics alone contain sufficient information for prediction and control, even without full knowledge of the community.

systems biology↗

bioRxiv2025

Transposon-plasmid nesting enables fast response to fluctuating environments

Mobile genetic elements (MGEs) play a critical role in shaping the response and evolution of microbial populations and communities. Despite distinct maintenance mechanisms, different types of MGEs can form nested structures. Using bioinformatics analysis of 14,338 plasmids in the NCBI RefSeq database, we found transposons to be widespread and significantly enriched on plasmids relative to chromosomes, highlighting the prevalence of transposon-plasmid nesting. We hypothesized that this nested structure provides unique adaptive advantages by combining transposition-driven genetic mobility with plasmid-mediated copy number amplification. Using engineered transposon systems, we demonstrated that nesting enables rapid and tunable responses of transposon-encoded genes in fluctuating environments. Specifically, transposition maintains a reservoir of the encoded genes, while plasmid copy number fluctuations further amplify the dynamic range of gene dosage, thus enhancing the response speed and stability of transposon-encoded traits. Our findings demonstrate an adaptive benefit of transposon-plasmid nesting and provide insights into their ecological persistence and evolutionary success.

systems biology↗

bioRxiv2025

Keystone engineering enables collective range expansion in microbial communities

Keystone engineers profoundly influence microbial communities by altering their shared environment, often by modifying key resources. Here, we show that in an antibiotic-treated microbial community, bacterial spread is controlled by keystone engineering affecting dispersal--an effect hidden in well-mixed environments. Focusing on two pathogens, non-motile Klebsiella pneumoniae and motile Pseudomonas aeruginosa, we found that both tolerate a {beta}-lactam antibiotic, with Pseudomonas being more resilient and dominating in well-mixed cultures. During range expansion, however, the antibiotic inhibits Pseudomonas ability to spread unless it is near Klebsiella--Klebsiella degrades the antibiotic to create a "clear zone" that allows Pseudomonas to expand, at the expense of Klebsiellas own growth, thus acting as a keystone engineer. As Pseudomonas spreads, it competitively suppresses Klebsiella. Our modeling and experimental analyses reveal that this keystone effect operates at a millimeter scale. We also observed similar keystone engineering by a Bacillus species isolated from a hospital sink, in both pairwise and eight-member bacterial communities with its co-isolates. These findings suggest that spatially explicit experiments are essential to understand certain keystone engineering mechanisms and have implications for surface-associated microbial communities like biofilms, as well as for diagnosing and treating polymicrobial infections involving drug-degrading, non-motile (e.g., Klebsiella), and drug-tolerant, motile (e.g., Pseudomonas) bacteria.

ecology↗

bioRxiv2022

Mapping single-cell responses to population-level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta-lactam antibiotics often leads to two salient population-level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population-level responses emerge from collective single-cell responses. During beta-lactam treatment, it is well recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the probability of cell lysis increases with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single-cell lysis probabilities can give rise to population-level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time-kill curve emerges from single cells and allows the representation of both single-and population-level responses with universal parameters.

microbiology↗