Establishment of a laboratory ecosystem and computational simulation for C. elegans and E. coli – a new platform to investigate the relationships between individual traits and the emergent property of population dynamics. Biological systems are characterized by levels of organization that proceed from microscopically small to immense, and every level displays emergent properties. Atoms combine to form simple molecules, such as H2O, and properties such as polarity emerge that are not displayed by atoms alone. Simple molecules combine to form complex macromolecules, such as DNA, which displays the fascinating emergent property of self-replication that is the essence of life. Macromolecules assemble to form organelles and cells, which display emergent properties such as ion gradients. Cells assemble to form organs and organisms, which display emergent properties such as blood pressure. These levels of organization encompass the fields of biochemistry, cell biology, physiology and developmental biology. In the next level of biological organization, organisms assemble to form populations, which display the emergent property of population dynamics. Whereas individual organisms are born and die, when these individuals assemble in populations, the age-structure and total number of organisms fluctuates over time. Finally, populations of different species assemble to form complex ecosystems, a level of organization encompassed by the field of ecology 18,19. Populations with their emergent property of population dynamics are an interdisciplinary level of organization, because it bridges the traits of individual organisms, the domain of physiology and developmental biology, with the behavior of populations of organisms, the domain of ecology. A key objective in biology is to understand how the properties of the assembled parts determine the nature of emergent properties at the next level.
Population dynamics is of fundamental importance, because when the number of organisms in the population fluctuates to zero, the population is extinct. Extinction is a crisis in the modern world due to human activity. More fundamentally, extinction of populations and species is a driving force in evolution. Thus, it is of considerable value to develop approaches to study extinction. There are two basic approaches to experimentally address population dynamics and extinction: field studies of wild ecosystems and laboratory ecosystems. While field studies are by definition relevant to natural conditions, they suffer from practical limitations. For example, many species are impossible to reliably track because they are too small or hard to observe, wild populations exist in complex ecosystems affected by many variables, and manipulation of these ecosystems may not be possible or ethical. Laboratory ecosystems represent a reductionist approach to the problem of the ecosystem complexity rooted in the idea that fundamental aspects of population dynamics will apply to small populations in a laboratory. Because they can be readily manipulated and exhaustively analyzed, laboratory ecosystems overcome the major limitations of field studies 20–23. Of course, laboratory ecosystems have their own limitations. They lack the complexity of the natural world, and laboratory conditions can be highly artificial.
The continuity of populations depends on the replacement of the ancestor generations by future generations. In principle, population dynamics is a straightforward function of birth and death, which has led to extensive modeling based on equations. However, modeling birth and death is far from straightforward, since these outcomes depend on complex interactions between individual organisms and the environment. Commonly used matrix models simulate populations as birth and death rates and neglect the adaptive behavior of the individual. To address the complexity of modeling birth and death, we developed an agent based model. This approach is ideal for this purpose, since the rules that govern the behavior of individuals can include complex interactions between the stage of the animals and environmental conditions, which is not possible with mathematical equations. The behavior of the individual worms is based on measured traits of individual C. elegans in the laboratory. Although all worms operate by the same rules, each displays a unique life trajectory including growth rates, time in the dauer stage, and reproductive output, etc., depending on the fluctuations in the environment during its life. This allows a realistic simulation of in silico worms and their population dynamics. Furthermore, this is a sturdy platform to investigate in silico mutant worms that have properties distinct from wild-type worms.
We reasoned that specific traits of individual organisms determine population dynamic behavior when these organisms assemble, and that rules that govern the interface between the level of individual organisms and the level of population dynamics could be elucidated by combining a simple laboratory ecosystem and computational simulation. To bridge the gap between laboratory experiments of isolated individuals and complex natural ecosystems, we developed a laboratory ecosystem with just two species: C. elegans and its food source E. coli. A complementary computational model that simulates C. elegans population dynamics as a flux system based on measured individual traits adds data depth and predictive power. Controlled laboratory ecosystem have been previously established, mainly with plankton-algae ecosystems in large water tanks 24. These have been used to investigate multiple topics such as prey evolution 22, steady state biomass levels 8, or toxic effects of heavy metals 24. Although the zooplankton species Daphnia magna is used as a model organism 25, it is rarely used in aging studies. By contrast, C. elegans is a premier model organism for studies of development, physiology and aging 26. It can be reliably measured in different environmental conditions such as variable food concentrations. In addition, the COPAS biosort is an automated counting machine developed specifically for C. elegans that makes it possible to perform high throughput monitoring of population dynamics. The experimental system described here is distinct from previous laboratory ecosystems in several respects. (1) The C. elegans laboratory ecosystem was designed with the goal of creating a complementary agent-based model, so it well suited for this purpose. (2) The simulation outputs include intuitive graphical representations of the C. elegans life cycle, conceptualized as a flux system. Thus, the simulation outputs integrate the development and physiology of individuals with the properties of the population. (3) The simulation was designed to make it convenient to analyze in silico mutant worms, creating a platform that complements the large collections of C. elegans mutants that can be analyzed in the laboratory ecosystem.
C. elegans is an example of a species that is difficult to analyze in a natural ecosystem because of its small size and subterranean lifestyle. C. elegans can be recovered from nature, but the process is time consuming and does not support direct measurements of population dynamics. It is hypothesized that wild C. elegans populations undergo boom-bust cycles 14. A cycle begins when a dauer enters a new food patch, such as a rotten apple or wood. The dauer transitions into a larva, matures, and reproduces to initiate a new population. This population proliferates until the food source is exhausted, leading to the generation of many dauers. These dauers must disperse to find a new food patch to restart the cycle. Galimov and Gems (2020) used a computational approach to test the hypothesis that programmed death is an adaptive strategy for C. elegans to secure food for clonal progeny 27. This computational simulation models single boom-bust cycles on a predefined single food patch. The authors concluded that adult death has fitness advantages defined as amount of dauers produced in a single boom-bust cycle. The laboratory ecosystem described here is a liquid culture that involves regular addition of E. coli as a food source. During the initialization phase, the population expands rapidly since food is abundant, similar to what is hypothesized to occur when a dauer disperses to a new food patch. This phase is characterized by adults, eggs, and larval stages. When bacterial food is depleted, the stage composition changes and is characterized by parlads and dauers, similar to what is hypothesized to occur when a food patch is exhausted. Thus, the laboratory ecosystem appears to model key features of the boom-bust cycle that is proposed to occur in the wild. In addition, the laboratory ecosystem and simulation could be adapted to specifically model the episodic food cycle proposed to occur in the wild. The current system cannot model dispersal of dauers to new food sources, since the simulation is a single food environment. However, the agent based model could be adapted to have multiple food sources separated in space, so in principle dauer dispersal could be incorporated into an expanded model.
For animals living in a population, dying of old age depends on conditions. To begin to elucidate how aging and lifespan influence population dynamics, we identified environmental and intrinsic factors that influence whether animals in a population die of old age. In a controlled laboratory setting, individual C. elegans become frail and die of old age. While it is not possible to directly determine if this occurs in the wild, it has been suggested to be unlikely 28–31. In the laboratory ecosystem that we analyzed, our simulation modeling indicates that adults typically die of starvation and culling rather than old age. We used the simulation to identify conditions where adults do die of old age. One key factor is progeny number, which we manipulated by stage specific culling. Interestingly, old age as a cause of adult death displays tipping point behavior – it rarely occurs with high levels of progeny but can become frequent when progeny levels are reduced to a critical level. The tipping point suggests the populations can exist in two states. State 1 is characterized by frequent episodes of starvation and an abundance of dauers; state 2 is characterized by a stable food supply and an absence of dauers. This result may be related to observations in the wild - abrupt shifts of ecosystems from one state to another state have been observed and described 32,33. A second key factor is adult culling. As expected, when adult culling increases, fewer adults die of old age. This factor did not display tipping point behavior but was relatively continuous. The third key factor was maximum adult lifespan. In silico worms with a 25-day maximum lifespan died of old age more frequently, whereas in silico worms with a maximum adult lifespan of 60 days died of old age less frequently. Thus, conditions that promote adults dying of old age include, (1) reproductive restraint, which leads to food stability and minimizes death from starvation, (2) infrequent adult culling, and (3) a short maximum adult lifespan. By contrast, conditions that inhibit adults from dying of old age include (1) abundant reproduction, which leads to food instability and death from starvation, (2) frequent adult culling, and (3) a long maximum adult lifespan.
We speculate that these results could be relevant to the natural world, and shifting environmental conditions might cause populations to alternate between time periods when few or no adults die of old age and time periods when many adults die of old age. The factors defined here provide a framework that can explain diverse animals that die of old age in the wild (Table 1). For example, elephants are intrinsically long-lived animals that have been observed to have aging as a cause of adult death in nature. Our model predicts that elephants must have a low level of adult culling and a small number of juvenile animals. Indeed, elephants make very few progeny, and their large size makes them essentially immune to predation34–36. Mayflies have a very short intrinsic lifespan and have been observed to have aging as a cause of adult death in nature. These adults do not feed, so they are immune to starvation, and even though they are subject to high levels of adult culling, the lifespan is so short they can still frequently die of old age 37,38. Our future goal is to combine this powerful experimental platform with the advanced tools of C. elegans genetics to bridge the gap between individual traits and the behavior of populations and expand our understanding of “eco-devo” 39.