Cross-domain structural pattern
Proteome-metabolome dynamics are modeled as continuous interplay via Neural ODEs. Rather than requiring pre-specified mechanistic knowledge, NODEs infer latent interaction structure directly from time-series multiomics data. Learned continuous dynamics enable simulation of temporal trajectories and anticipation of intervention effects, capturing complex feedback between protein expression and metabolite concentrations without manually encoding pathway topology.
view paper→Environmental state dominates progression dynamics over intrinsic mutation rates in spatially structured systems: supportive environments amplify weak growth signals into invasive spread, while inhibitory environments suppress even high-mutation populations.
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