A major goal of systems biology is to understand how interactions of individual components give rise to emergent properties. The reductionist approach of systems analysis relies on amassing data on each individual component and then uniting the details back into the whole. While thorough, this approach becomes progressively more difficult to implement as the number and complexity of components increases. It is becoming increasingly important to apply a holistic approach to synthesize our understanding into parsimonious models integrating molecular and cellular details into quantifiable phenotypic behavior. The Caenorhabditis elegans reproductive system offers an ideal test case for such a model as it has been extensively studied using genetic and biochemical approaches, has a readily quantifiable output, and can easily be probed by exploiting its temperature sensitivity.
We quantify the non-stationary rate of egg laying over time at 6 temperatures using 4,343 individual worms and 191,659 embryos. This data set contains an unprecedented amount of information to train and test our model against the distribution of reproductive outputs. Under ambient conditions, 20°C, brood size distribution for individual worms is normal, whereas prolonged exposure to elevated temperatures can be best described by a combination of two Gaussian distributions. This implies individuals adopt one of two states in their egg laying behavior.
We derive a cellular-scale model using constraints from tissue structure, cellular states, and a minimal set of molecular information to recreate the time-dependent distributions of eggs laid among individual worms. At the model’s core are the first principels of process engineering, mass action kinetics and species conservation. Remarkably the reproductive system bears a striking similarity to production processes engineered by humans. We predict the flow of gametes (oocytes and sperm) through a series of cellular states that signify different stages in development, fertilization, or signaling behavior. Using this framework, it becomes clear that the two distributions in our data set arise due to changes in the upstream development of oocytes. Furthermore, the model suggests a novel protective signaling behavior for stress conditions that prevents waste of the limiting resource, sperm, until the worms return to more favorable conditions.
This model captures the temperature-dependent dynamics of a process that incorporates information over several molecular, cellular and organismal scales. Additionally, we uncover the potential for two novel temperature-dependent switches in two different areas of the reproductive system: the upstream development of oocytes and the regulation of a sperm derived signaling molecule. We argue that top-down mechanistic models are necessary to bridge the gap between scales in biological processes.
See more of this Group/Topical: Topical A: Systems Biology