Understanding how complex phenotypes emerge from large-scale biochemical networks necessitates accurate representation of biochemical reactions that extend beyond mass and charge balances. Atom transition maps provide atom-level representation of biochemical reactions that facilitate discovery of design principles in biochemical networks and, in combination with labeling experiments, enable flux estimation. In this systematic review, we categorize, compare, and contrast the existing computational approaches for the prediction of atom transition maps both on the level of individual reactions as well as entire biochemical networks. Emphasis is placed on the growing number of approaches that predict atom transition maps using deep learning alone or in combination with conventional optimization-based approaches. We stress the problems of data leakage typically arising in prediction of atom transition maps using the existing machine and deep learning approaches for reactions that share mechanisms. Importantly, we illustrate the severity of the problem by re-examining representative deep learning models for the prediction of atom transition maps on a unified test set of reactions. The review of these advances alongside our comparative analysis points at open problems in generation of atom transition maps and paves the way for future research in this area.
Alternative splicing (AS) generates transcript and protein isoforms, but its evolutionary significance cannot be inferred from isoform number alone. This review uses a comparative framework based on recent literature to separate splice-variant origin, functional consequence, and evolutionary retention in plants and animals. We synthesize evidence from long-read sequencing, single-cell isoform profiling, pangenomics, proteogenomics, and perturbation studies. Animal studies emphasize exon-level switches in neural, cardiac, immune, and germline programs, whereas plant studies highlight intron retention, exitron splicing, environmental responses, and polyploidy-associated regulation. These are tendencies shaped by biological and sampling differences, not exclusive properties of either kingdom. Three worked examples illustrate how reproducible regulation, conservation or population data, molecular consequences, and targeted perturbation support different levels of inference. Functional effects alone do not establish historical adaptation, and conservation alone does not identify the trait under selection. The framework helps prioritize isoforms for crop improvement, biomarkers, and RNA intervention. Clinical splice modulation and field-evaluated natural rice alleles demonstrate translational potential, while highlighting the distinction between validated outcomes and prospective engineering. Future studies should connect isoform-resolved measurements to causal perturbation and fitness or agronomic performance across relevant environments.
