Ghim, C.-M. | bioRxiv Science

bioRxiv2025

Long-term innovative potential of genetic research and its suppression

Long-term innovation depends not only on discovering new elements but also on facilitating new recombination at the time of their co-availability. This notion of combinatorial innovation raises the question about whether scientific exploration and industry research persist over similar time horizons to meet innovative opportunities. Here, we analyze about 20 million papers and US, Chinese, and European patents to compare the courses of scientific and industrial activities in genetic research, a domain of far-reaching societal importance. We observe that research on new genes has declined since the early 2000s, but the exploration of novel gene combinations remains active for biotechnology innovation. Yet, fields of highly practical or commercial focus are less likely to test novel gene combinations. Furthermore, industry R&D on each gene tends to decline earlier than scientific interest in that gene. After this decline of the industrial momentum, continuous scientific research still sustains exploratory and innovative opportunities, involving novel gene combinations, clinical trials, and technologically-impactful work. Based on a hypothetical scenario of over-aligned science and industry, we estimate that up to 42.2-74.4% of the combinational opportunities would be unexplored if scientific research becomes hindered due to industrys retreat. This study suggests the potential complementary roles of scientific and industrial research with different temporal modes in innovation cycles, calling for balanced policies to foster innovation ecosystems.

scientific communication and education↗

bioRxiv2022

Revised Michaelis-Menten rate law with time-varying molecular concentrations

The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

biophysics↗