Development of Life Science Research in the Future: From Expanding Omics to Predictive, Integrative, and Causal Biology

Abstract

Life science research is entering a phase in which biological discovery is no longer driven by a single dominant methodology, but by the convergence of many molecular, cellular, spatial, computational, and engineering approaches. The original omics revolution began with genomics and was rapidly expanded by transcriptomics, proteomics, metabolomics, and epigenomics. It has now matured into a broader ecosystem that includes single-cell omics, spatial omics, multi-omics integration, perturbation-driven profiling, organoid-based modeling, and artificial intelligence-assisted inference. Together, these developments are changing not only what can be measured, but also how biology is conceptualized. Instead of describing cells and tissues through static averages, the field is moving toward dynamic, mechanistic, and predictive frameworks that capture heterogeneity, context, and causality. This transition will influence basic biology, translational research, precision medicine, agriculture, environmental health, and bioengineering. At the same time, several less common or underused approaches, including glycomics, fluxomics, degradomics, exposomics, culturomics, and immunopeptidomics, are becoming increasingly important because they address dimensions of biology that remain incompletely explained by mainstream methods, yet their adoption has been slowed by technical difficulty, cost, poor standardization, and limited computational infrastructure (Cummings and Pierce, 2014; Winter and Krömer, 2013; Lagier et al., 2015; Xue et al., 2019; Savickas et al., 2020; Flender et al., 2025).
The next stage of life science research will likely be defined by seven interconnected shifts: wider use of single-cell and spatial measurements; routine multi-omic integration across time and scale; stronger coupling of perturbation with readout; greater use of organoids and organ-on-chip systems; adoption of biological foundation models; increased emphasis on rare, low-abundance, or environmentally driven signals; and a move from descriptive atlases toward actionable predictions. However, these opportunities are accompanied by major challenges. These include data quality problems, batch effects, the dominance of correlative over causal reasoning, unequal access to expensive platforms, reproducibility concerns, poor interoperability between datasets, underrepresentation of rare cell states and minority populations, ethical issues around data governance, and the risk that artificial intelligence may amplify rather than solve biological bias (Ma et al., 2020; Miao et al., 2021; Nam et al., 2024; Baek et al., 2025; Guo et al., 2025). This review examines the development of omics research, describes mainstream and underused omics fields, predicts future directions, and discusses the problems, challenges, and advantages that are likely to shape the next era of life science.