More Like this

1. Reintegrating Biology Through the Nexus of Energy, Information, and Matter

   https://doi.org/10.1093/icb/icab174  

   Hoke, Kim L; Zimmer, Sara L; Roddy, Adam B; Ondrechen, Mary Jo; Williamson, Craig E; Buan, Nicole R (August 2021, Integrative and Comparative Biology)

   null (Ed.)

   Synopsis Information, energy, and matter are fundamental properties of all levels of biological organization, and life emerges from the continuous flux of matter, energy, and information. This perspective piece defines and explains each of the three pillars of this nexus. We propose that a quantitative characterization of the complex interconversions between matter, energy, and information that comprise this nexus will help us derive biological insights that connect phenomena across different levels of biological organization. We articulate examples from multiple biological scales that highlight how this nexus approach leads to a more complete understanding of the biological system. Metrics of energy, information, and matter can provide a common currency that helps link phenomena across levels of biological organization. The propagation of energy and information through levels of biological organization can result in emergent properties and system-wide changes that impact other hierarchical levels. Deeper consideration of measured imbalances in energy, information, and matter can help researchers identify key factors that influence system function at one scale, highlighting avenues to link phenomena across levels of biological organization and develop predictive models of biological systems. 

   more » « less
2. Resolving the Rules of Robustness and Resilience in Biology Across Scales

   https://doi.org/10.1093/icb/icab183  

   Crespi, Erica; Burnap, Robert; Chen, Jing; Das, Moumita; Gassman, Natalie; Rosa, Epaminondas; Simmons, Rebecca; Wada, Haruka; Wang, Zhen Q; Xiao, Jie; et al (August 2021, Integrative and Comparative Biology)

   Abstract Why do some biological systems and communities persist while others fail? Robustness, a system's stability, and resilience, the ability to return to a stable state, are key concepts that span multiple disciplines within and outside the biological sciences. Discovering and applying common rules that govern the robustness and resilience of biological systems is a critical step toward creating solutions for species survival in the face of climate change, as well as the for the ever-increasing need for food, health, and energy for human populations. We propose that network theory provides a framework for universal scalable mathematical models to describe robustness and resilience and the relationship between them, and hypothesize that resilience at lower organization levels contribute to robust systems. Insightful models of biological systems can be generated by quantifying the mechanisms of redundancy, diversity, and connectivity of networks, from biochemical processes to ecosystems. These models provide pathways towards understanding how evolvability can both contribute to and result from robustness and resilience under dynamic conditions. We now have an abundance of data from model and non-model systems and the technological and computational advances for studying complex systems. Several conceptual and policy advances will allow the research community to elucidate the rules of robustness and resilience. Conceptually, a common language and data structure that can be applied across levels of biological organization needs to be developed. Policy advances such as cross-disciplinary funding mechanisms, access to affordable computational capacity, and the integration of network theory and computer science within the standard biological science curriculum will provide the needed research environments. This new understanding of biological systems will allow us to derive ever more useful forecasts of biological behaviors and revolutionize the engineering of biological systems that can survive changing environments or disease, navigate the deepest oceans, or sustain life throughout the solar system. 

   more » « less
3. Dynamic coexistence driven by physiological transitions in microbial communities

   https://doi.org/10.1073/pnas.2405527122  

   Narla, Avaneesh V; Hwa, Terence; Murugan, Arvind (April 2025, Proceedings of the National Academy of Sciences)

   Microbial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes in exponential growth often modify their environments so strongly that they are forced out of the growth state into stressed, nongrowing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory. Here, we introduce a phenomenological model, the Community State Model, to gain insight into the dynamic coexistence of microbes due to changes in their physiological states during cyclic succession. Our model specifies the growth preference of each species along a global ecological coordinate, taken to be the biomass density of the community, but is otherwise agnostic to specific interactions (e.g., nutrient starvation, stress, aggregation), in order to focus on self-consistency conditions on combinations of physiological states, “community states,” in a stable ecosystem. We identify three key features of such dynamical communities that contrast starkly with steady-state communities: enhanced community stability through staggered dominance of different species in different community states, increased tolerance of community diversity to fast growing species dominating distinct community states, and increased requirement of growth dominance by late-growing species. These features, derived explicitly for simplified models, are proposed here as principles aiding the understanding of complex dynamical communities. Our model shifts the focus of ecosystem dynamics from bottom–up studies based on fixed, idealized interspecies interaction to top–down studies based on accessible macroscopic observables such as growth rates and total biomass density, enabling quantitative examination of community-wide characteristics. 

   more » « less
4. Characterizing biological responses to climate variability and extremes to improve biodiversity projections

   https://doi.org/10.1371/journal.pclm.0000226  

   Buckley, Lauren B.; Carrington, Emily; Dillon, Michael E.; García-Robledo, Carlos; Roberts, Steven B.; Wegrzyn, Jill L.; Urban, Mark C. (June 2023, PLOS Climate)

   Moura, Mario R. (Ed.)

   Projecting ecological and evolutionary responses to variable and changing environments is central to anticipating and managing impacts to biodiversity and ecosystems. Current modeling approaches are largely phenomenological and often fail to accurately project responses due to numerous biological processes at multiple levels of biological organization responding to environmental variation at varied spatial and temporal scales. Limited mechanistic understanding of organismal responses to environmental variability and extremes also restricts predictive capacity. We outline a strategy for identifying and modeling the key organismal mechanisms across levels of biological organization that mediate ecological and evolutionary responses to environmental variation. A central component of this strategy is quantifying timescales and magnitudes of climatic variability and how organisms experience them. We highlight recent empirical research that builds this information and suggest how to design future experiments that can produce more generalizable principles. We discuss how to create biologically informed projections in a feasible way by combining statistical and mechanistic approaches. Predictions will inform both fundamental and practical questions at the interface of ecology, evolution, and Earth science such as how organisms experience, adapt to, and respond to environmental variation at multiple hierarchical spatial and temporal scales. 

   more » « less
5. Characterizing biological responses to climate variability and extremes to improve biodiversity projections

   Buckley, L.; Carrington, E.; Dillon, M.E.; Garcia-Robledo, C; Roberts, S.B; Wegrzyn, J.; Urban, M.C. (June 2023, PLOS climate)

   Projecting ecological and evolutionary responses to variable and changing environments is central to anticipating and managing impacts to biodiversity and ecosystems. Current model- ing approaches are largely phenomenological and often fail to accurately project responses due to numerous biological processes at multiple levels of biological organization responding to environmental variation at varied spatial and temporal scales. Limited mechanistic under- standing of organismal responses to environmental variability and extremes also restricts predictive capacity. We outline a strategy for identifying and modeling the key organismal mechanisms across levels of biological organization that mediate ecological and evolutionary responses to environmental variation. A central component of this strategy is quantifying timescales and magnitudes of climatic variability and how organisms experience them. We highlight recent empirical research that builds this information and suggest how to design future experiments that can produce more generalizable principles. We discuss how to create biologically informed projections in a feasible way by combining statistical and mechanistic approaches. Predictions will inform both fundamental and practical questions at the interface of ecology, evolution, and Earth science such as how organisms experience, adapt to, and respond to environmental variation at multiple hierarchical spatial and temporal scales. 

   more » « less