Predicting infectious disease epidemics, probing gene regulatory networks with memory functions, explaining microbial growth/yield trade-offs
Check out our Editors-in-Chief’s selection of papers from the February issue of PLOS Computational Biology:
Prediction of infectious disease epidemics via weighted density ensembles
Public health agencies such as the US Centers for Disease Control and Prevention would like to have as much information as possible when planning interventions intended to reduce and prevent the spread of infectious disease. For instance, accurate and reliable predictions of the timing and severity of the influenza season could help with planning how many influenza vaccine doses to produce and by what date they will be needed. Many different mathematical and statistical models have been proposed to model influenza and other infectious diseases, and these models have different strengths and weaknesses. In particular, one or another of these model specifications is often better tha n the others in different seasons, at different times within the season, and for different prediction targets (such as different measures of the timing or severity of the influenza season). In this article, Evan L. Ray and Nicholas G. Reich explore ensemble methods that combine predictions from multiple “component” models. They find that these ensemble methods do about as well as the best of the component models in terms of aggregate performance across multiple seasons, but that the ensemble methods have more consistent performance across different seasons. This improved consistency is valuable for planners who need predictions that can be trusted under all circumstances.
Shown are steady state concentrations of the four molecular species against neural tube position, with unstable steady states marked dashed. Colours in (D, E) identify genes/proteins in the same way as in the labelling of the illustration (B) and of the network nodes in (C). This colour code is used throughout the paper unless otherwise noted. Image Credit: Peter Sollich.
Memory functions reveal structural properties of gene regulatory networks
Gene regulatory networks are essential for cell fate specification and function. But the recursive links that comprise these networks often make determining their properties and behaviour complicated; computational models of these networks can also be difficult to decipher. To reduce the complexity of such models Peter Sollich and colleagues employ a Zwanzig-Mori projection approach. This allows a system of ordinary differential equations, representing a network, to be reduced to an arbitrary subnetwork consisting of part of the initial network, with the rest of the network (bulk) captured by memory functions. These memory functions account for the bulk by describing signals that return to the subnetwork after some time, having passed through the bulk. They show how this approach can be used to simplify analysis and to probe the behaviour of a gene regulatory network. Applying the method to a transcriptional network in the vertebrate neural tube reveals previously unappreciated properties of the network. By taking advantage of the structure of the memory functions they identify interactions within the network that are unnecessary for sustaining correct patterning. Upon further investigation the authors find that these interactions are important for conferring robustness to variation in initial conditions. Taken together they demonstrate the validity and applicability of the Zwanzig-Mori projection approach to gene regulatory networks.
Metabolic enzyme cost explains variable trade-offs between microbial growth rate and
Image Credit: Ikram Blilou (KAUST)
yield
When cells compete for nutrients, those that grow faster and produce more offspring per time are favored by natural selection. In contrast, when cells need to maximize the cell number at a limited nutrient supply, fast growth does not matter and an efficient use of nutrients (i.e. high biomass yield) is essential. This raises a basic question about metabolism: can cells achieve high growth rates and yields simultaneously, or is there a conflict between the two goals? Using a new modeling method called Enzymatic Flux Cost Minimization (EFCM), Wolfram Liebermeister and colleagues predict cellular growth rates and find that growth rate/yield trade-offs and the ensuing preference for enzyme-efficient or substrate-efficient metabolic pathways are not universal, but depend on growth conditions such as external glucose and oxygen concentrations.