# Virtual Cell

ELEM Biotech plans to invest US$1 million in generating and openly sharing perturbation data for training and validating virtual cell models, and is asking researchers worldwide which experiments should be measured first. Submissions must name concrete cell, gene and perturbation combinations along with sampling times, readouts, controls and independent biological replicates, building on the already open-source ELEM-1200RS and the upcoming ELEM-600PR. Its automated platform can prepare up to 100,000 arrayed genetically perturbed cell samples per month, but that is sample production capacity, not 100,000 distinct genes measured monthly.

ELEM Biotech plans to invest US$1 million in generating and openly sharing perturbation data for training and validating virtual cell models, and is asking researchers worldwide which experiments should be measured first. Submissions must name concrete cell, gene and perturbation combinations along with sampling times, readouts, controls and independent biological replicates, building on the already open-source ELEM-1200RS and the upcoming ELEM-600PR. Its automated platform can prepare up to 100,000 arrayed genetically perturbed cell samples per month, but that is sample production capacity, not 100,000 distinct genes measured monthly.

Deep Genomics subtracts the whole-transcriptome perturbation response that virtual-cell models usually try to predict, treating it as noise, and trains a sequence model, DeepRNAi, only on the direct effects between an siRNA and its target transcript. Direct effects account for about 1% of all siRNA-RNA interactions, and uncalibrated virtual-cell objectives had treated that signal as a rounding error. On a held-out-transcript split, DeepRNAi performs about the same as on seen transcripts, while a conventional machine-learning approach without a direct-effect focus loses roughly 50% of its above-baseline performance.

Deep Genomics subtracts the whole-transcriptome perturbation response that virtual-cell models usually try to predict, treating it as noise, and trains a sequence model, DeepRNAi, only on the direct effects between an siRNA and its target transcript. Direct effects account for about 1% of all siRNA-RNA interactions, and uncalibrated virtual-cell objectives had treated that signal as a rounding error. On a held-out-transcript split, DeepRNAi performs about the same as on seen transcripts, while a conventional machine-learning approach without a direct-effect focus loses roughly 50% of its above-baseline performance.

The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.

The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.