Biology subjects

Villalobos, C. A.

Publications and source records attributed to Villalobos, C. A..

5 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

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↗

bioRxiv2024

An engineered prodrug selectively suppresses β-lactam resistant bacteria in a mixed microbial setting

The rise of {beta}-lactam resistance necessitates new strategies to combat bacterial infections. We purposefully engineered the {beta}-lactam prodrug AcephPT to exploit {beta}-lactamase activity to selectively suppress resistant bacteria producing extended-spectrum-{beta}-lactamases (ESBLs). Selective targeting of resistant bacteria requires avoiding interaction with penicillin-binding proteins, the conventional targets of {beta}-lactam antibiotics, while maintaining recognition by ESBLs to activate AcephPT only in resistant cells. Computational approaches provide a rationale for structural modifications to the prodrug to achieve this biased activity. We show AcephPT selectively suppresses gram-negative ESBL-producing bacteria in clonal populations and in mixed microbial cultures, with effective selectivity for both lab strains and clinical isolates expressing ESBLs. Time-course NMR experiments confirm hydrolytic activation of AcephPT exclusively by ESBL-producing bacteria. In mixed microbial cultures, AcephPT suppresses proliferation of ESBL-producing strains while sustaining growth of {beta}-lactamase-non-producing bacteria, highlighting its potential to combat {beta}-lactam resistance while promoting antimicrobial stewardship. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/606422v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@cd7f9aorg.highwire.dtl.DTLVardef@1747bc6org.highwire.dtl.DTLVardef@6a3419org.highwire.dtl.DTLVardef@825aa6_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2020

Optogenetic activation of the inhibitory nigro-collicular circuit evokes orienting movements in mice

In contrast to predictions from the current model of basal ganglia (BG) function, we report here that increasing inhibition from the BG to the superior colliculus (SC) through the substantia nigra (nigra) using in vivo optogenetic activation of GABAergic terminals in mice, produces contralateral orienting movements. Orienting movements resulting from activation of inhibitory nigral terminals are unexpected because decreases and not increases, in nigral activity are generally associated with orienting movements. To determine how orienting movements may result from activation of inhibitory terminals, we performed a series of slice experiments and found that the same optogenetic stimulation of nigral terminals used in vivo, evoked post-inhibitory rebound depolarization and spiking in SC output neurons in vitro. Only high frequency (100Hz) stimulation evoked contralateral movements in vivo and triggered rebound spiking in vitro. The latency of orienting movements relative to the stimulation in vivo was similar to the latency of rebound spiking in vitro. Taken together, our results point toward a novel hypothesis that inhibition from the BG may play an active rather than passive role in the generation of orienting movements in mice.

neuroscience↗