Check out our Editors-in-Chief’s selection of papers from the [September] issue of PLOS Computational Biology.
Emergent mechanics of actomyosin drive punctuated contractions and shape network morphology in the cell cortex
Recent genetic and mechanical studies of embryonic development have revealed a critical role for intracellular scaffolds in generating the shape of the embryo and constructing internal organs. In this paper Callie J. Miller and colleagues developed computer simulations of these scaffolds, composed of filamentous actin (F-actin, a rod-like protein polymer), and mini-thick filaments, composed of non-muscle myosin II, which form a two-headed spring-like complex of motor proteins that can walk on and remodel F-actin networks. Using simulations of these dynamic interactions, the authors carried out virtual experiments where they found they could change the physics and chemistry of F-actin polymers, their associated myosin motors and cross-linkers, and observe the changes in scaffolds that emerge.
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-timescale dynamics by atomistic molecular simulations and Markov state modelling
The oncogene KRAS is frequently mutated in various cancers. When the amino acid glycine 12 is mutated, KRAS protein acquires oncogenic properties that result in tumor cell-growth and cancer progression. These mutations prevail especially in the pancreatic ductal adenocarcinoma, which is a cancer with an exceptionally dismal prognosis. To date, there is a limited understanding of the consequences of different mutations at position 12, which could have major implications for future drug therapies targeting KRAS mutant-harboring tumors. In this study, Tatu Pantsar and colleagues made a critical assessment of the observed frequency of KRAS G12X mutations and the underlying causes of these frequencies. They also assessed KRAS G12X mutant discrepancies at an atomic level by utilizing state-of-the-art molecular dynamics simulations. The team found that the dynamics of the mutants not only differ from the wild-type protein, but there are also profound differences among the different mutants.
Multiscale analysis of autotroph-heterotroph interactions in a high-temperature microbial community
Microbial communities often display emergent properties, such as enhanced productivity, stability, and robustness, compared to their component populations in isolation. However, determining the governing principles of these emergent properties can be elusive due to the complexities of interpreting and integrating genomic and geochemical data sets collected at largely different observational scales. Here, Kristopher A. Hunt and colleagues use multiscale, metagenome-enabled modelling of an Fe(II)-oxidizing community to extract information regarding biomass productivity limitations, relative population abundance, total biomass concentration, and electron acceptor uptake rates.
Modelling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data
With an aging global population, the prevalence of Alzheimer’s disease (AD) is rapidly increasing, creating a heavy burden on public healthcare systems. It is, therefore, critical to identify those most likely to decline towards AD, in an effort to implement preventative treatments and interventions. However, predictions are complicated by the substantial heterogeneity present in the clinical presentation in the prodromal stages of AD. Longitudinal data comprising cognitive assessments, magnetic resonance images, along with genetic and demographic information can help model and predict the symptom progression patterns at the single subject level. Additionally, recent advances in machine-learning techniques provide the computational framework for extracting combinatorial longitudinal and multimodal feature sets. To this end, Nikhil Bhagwat and colleagues have used multiple AD datasets consisting of 1000 subjects with longitudinal visits spanning up to six years for 1) modelling stable versus declining clinical symptom trajectories and 2) predicting these trajectories using data from both baseline and a follow-up visit within one year.
Optimal multi-source forecasting of seasonal influenza
In the United States, seasonal influenza causes thousands of deaths and hundreds of thousands of hospitalizations. The annual timing and burden of the flu season vary considerably with the severity of the circulating viruses. Epidemic forecasting can inform early and effective countermeasures to limit the human toll of severe seasonal and pandemic influenza. With a growing toolkit of sophisticated statistical methods and the recent explosion of influenza-related data, it’s now possibly to systematically match models to data to achieve timely and accurate warning as flu epidemics emerge, peak and subside. Here, Zeynep Ertem and colleagues introduce a framework for identifying optimal combinations of data sources and show that public health surveillance data and electronic health records collectively forecast seasonal influenza better than any single data source alone and better than an influenza-related search engine and social media data.