Resolving the Rules of Robustness and Resilience in Biology Across Scales

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.

Fig. 1

Network theory can be applied to describe systems across biological levels of organization, and models linking these nested networks will ultimately allow us to understand and predict how biological systems respond to changing conditions over time and space

Fig. 2

Schematic of the properties relating to robustness and resilience of biological systems based on a network science framework. (A) In this model, robustness and resilience are emergent properties (blue circles) of the dynamic workings of networks that have redundancy, diversity, and connectivity, which includes functional feedbacks and lines of communication among nodes. (B) Examples of networks that represent redundant, connected, and diverse topologies are shown. By defining systems using this framework, biologists can use unifying experimental, mathematical, computational, and engineering approaches to understand how systems interact across levels of biological organization and respond to perturbations

Fig. 3

Cellular metabolic network of Escherichia coli illustrating the characteristics of essential and nonessential metabolites obtained from a combined gene knock-out, computational modeling, and physiological analysis (adapted from Kim et al. 2007)..