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Abstract This perspective derives from the presentations and discussions on mechanobiology at the 2025 Cellular and Molecular Bioengineering Conference in San Diego. Mechanobiological processes play critical roles in tissue development, regeneration, and disease progression. Recent advances in engineering, biology, and medicine have enabled the translation of mechanobiology discoveries into clinical practice, giving rise to the emerging field of mechanomedicine. The development and application of engineering technology and tools have provided new insights into how mechanical cues regulate immune cell response, stem cell differentiation, cell migration, and cell metabolism. In this perspective, we highlight exciting discoveries and innovative tools in mechanobiology research, and discuss challenges that must be overcome to truly bridge the gap between mechanobiology and mechanomedicine. Graphical Abstract 

Environmental stress forces populations to move away from oppressive regions and look for desirable environments. Different species can respond to the same spatial distributions of resources and toxicants with distinct movement strategies. However, the optimal behavioral strategy may differ when resources and stressors occur simultaneously or if they are distributed in different patterns. We compared the total abundance of two strains ofCaenorhabditis eleganswith different locomotion speeds as they forage in various spatial distributions of resources and toxicants. Informed by the experimental observations, we proposed a new two‐state population model, wherein nutrient uptake and reproduction are modeled separately, as driven by the spatial distribution of resources and toxicants. We found that fast movers had an advantage when either the toxicant coverage or the overlap between toxicants and resources was increased. Also, to assess the effectiveness of designing refuges to conserve species in stressful cases, we compared different preferences of locations of refuge areas according to movement strategies. Our mathematical model explained that fast movement enables individuals to consume resources at one location and reproduce at a separate location to avoid the toxicant‐induced reduction in reproduction rate, which underlined its observed advantage in certain experimental settings. This work provided a better model to predict how species with different movement strategies respond to environmental stressors in natural systems. 

Abstract Habitat loss and fragmentation have independent impacts on biodiversity; thus, field studies are needed to distinguish their impacts. Moreover, species with different locomotion rates respond differently to fragmentation, complicating direct comparisons of the effects of habitat loss and fragmentation across differing taxa and landscapes. To overcome these challenges, we combined mechanistic mathematical modeling and laboratory experiments to compare how species with different locomotion rates were affected by low (∼80% intact) and high (∼30% intact) levels of habitat loss. In our laboratory experiment, we usedCaenorhabditis elegansstrains with different locomotion rates and subjected them to the different levels of habitat loss and fragmentation by placingEscherichia coli(C. elegansfood) over different proportions of the Petri dish. We developed a partial differential equation model that incorporated spatial and biological phenomena to predict the impacts of habitat arrangement on populations. Only species with low rates of locomotion declined significantly in abundance as fragmentation increased in areas with low (p = 0.0270) and high (p = 0.0243) levels of habitat loss. Despite that species with high locomotion rates changed little in abundance regardless of the spatial arrangement of resources, they had the lowest abundance and growth rates in all environments because the negative effect of fragmentation created a mismatch between the population distribution and the resource distribution. Our findings shed new light on incorporating the role of locomotion in determining the effects of habitat fragmentation. 

Abstract Randomization inference is a powerful tool in early phase vaccine trials when estimating the causal effect of a regimen against a placebo or another regimen. Randomization-based inference often focuses on testing either Fisher’s sharp null hypothesis of no treatment effect for any participant or Neyman’s weak null hypothesis of no sample average treatment effect. Many recent efforts have explored conducting exact randomization-based inference for other summaries of the treatment effect profile, for instance, quantiles of the treatment effect distribution function. In this article, we systematically review methods that conduct exact, randomization-based inference for quantiles of individual treatment effects (ITEs) and extend some results to a special case where naïve participants are expected not to exhibit responses to highly specific endpoints. These methods are suitable for completely randomized trials, stratified completely randomized trials, and a matched study comparing two non-randomized arms from possibly different trials. We evaluate the usefulness of these methods using synthetic data in simulation studies. Finally, we apply these methods to HIV Vaccine Trials Network Study 086 (HVTN 086) and HVTN 205 and showcase a wide range of application scenarios of the methods.Rcode that replicates all analyses in this article can be found in first author’s GitHub page athttps://github.com/Zhe-Chen-1999/ITE-Inference.