School of Biological Sciences, Washington State University, Pullman, WA, 99164, USA.
Microbiology and Molecular Genetics, Oklahoma State University, Stillwater, OK, 74078, USA.
Integr Comp Biol. 2022 Feb 5;61(6):2163-2179. doi: 10.1093/icb/icab183.
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.
为什么有些生物系统和群落能够持续存在,而有些则会失败?鲁棒性(robustness),即系统的稳定性,以及弹性(resilience),即系统回到稳定状态的能力,是跨越生物科学内外多个学科的关键概念。发现和应用支配生物系统鲁棒性和弹性的通用规则,是在面对气候变化时为物种生存创造解决方案的关键一步,也是满足人类对食物、健康和能源日益增长的需求的关键一步。我们提出,网络理论为描述鲁棒性和弹性及其之间关系的通用可扩展数学模型提供了一个框架,并假设较低组织层次的弹性有助于形成鲁棒系统。通过量化网络的冗余、多样性和连通性的机制,从生化过程到生态系统,可以生成具有洞察力的生物系统模型。这些模型提供了理解在动态条件下可进化性如何既能促成鲁棒性和弹性,又能从中产生的途径。我们现在拥有大量来自模型和非模型系统的数据,以及研究复杂系统的技术和计算进步。一些概念和政策上的进步将使研究界能够阐明鲁棒性和弹性的规则。从概念上讲,需要开发一种可应用于生物组织各级别的通用语言和数据结构。政策上的进步,如跨学科的资助机制、获得负担得起的计算能力的机会,以及将网络理论和计算机科学纳入标准生物学课程,将为研究提供所需的环境。对生物系统的这种新理解将使我们能够对生物行为进行更有用的预测,并彻底改变能够在不断变化的环境或疾病中生存、在最深的海洋中航行或在整个太阳系中维持生命的生物系统的工程设计。