Computational and Systems Biology

eLife assessment

This important study combines virology experiments and mathematical modeling to determine the nuclear export rate of each of the eight RNA segments of the influenza A virus, leading to the proposal that a specific retention of mRNA within the nucleus delays the expression of antigenic viral proteins. The proposed model for explaining the differential rate of export is compelling, going beyond the state of the art, but the experimental setup is incomplete and would benefit from additional approaches. The insight so far is interesting, but because in the end it is left as an observation, the overall advance remains limited.

eLife assessment

In this valuable paper, the authors use an existing theoretical framework relying on information theory and maximum entropy inference in order to quantify how much information single cells can carry, taking into account their internal state. They reanalyze experimental data in this light. Despite some limitations of the data, the study convincingly highlights the difference between single-cell and population channel capacities. This result should be of interest to the quantitative biology community, as it contributes to explaining why channel capacities are apparently low in cells.

eLife assessment

This study presents fundamental findings on the role of transcription readout and intron retention in transposon expression during aging in mammals. The evidence supporting the claims of the authors is compelling, strongly supporting the authors' claims. The work will be of interest to scientists studying aging, transcription regulation, and epigenetics.

eLife assessment

Focusing mainly on var genes, the investigators performed comprehensive computational analyses of gene expression in malaria parasites isolated from patients and assessed changes that occur as these parasites adapt to in vitro culture conditions. The study provides an improved computational pipeline for monitoring var gene expression, and importantly, the study documents changes in expression of the core genome and thus provides insights into metabolic adaptations that parasites undergo while transitioning to culture conditions. The findings are important for their technical advances that are more rigorous than the current state-of-the-art. The solid data analyses, broadly support the claims with only minor weaknesses, tell us to be cautious when interpreting results obtained only from cultured parasites.

eLife assessment

In this interesting manuscript, Pietsch et al. develop innovative machine learning approaches for automated analysis of budding yeast live-cell imaging data obtained with a dedicated microfluidic device that retains mother cells. Developing such tools is crucial to enable high-throughput image analysis. These methods will be useful for researchers studying these cells, and may also inspire similar approaches for other types of cells.

eLife assessment

This valuable manuscript presents a new approach to transform multi-omics datasets into images and to exploit Deep Learning methods for image analysis of the transformed datasets. As an example, the method is applied to multi-omics datasets on different cancers. While the evidence in this specific case is solid, whether the method is working as advertised in other settings is not yet known.

eLife assessment

This work presents an important online platform designed to facilitate the exploration of genes and genetic pathways implicated in human aging. Leveraging a new inference methodology, the tool enables the identification and visualization of key genes and tissues impacted by aging, facilitating scientific discovery. The methods and analyses are convincing and will be broadly used by scientists aiming to mine human aging RNA-seq data.

eLife assessment

This study presents Bactabolize, a valuable tool for the rapid genome-scale reconstruction of bacteria and the prediction of growth phenotypes. Using validated methodology, the tool relies on a reference pan-genome model to create strain-specific draft metabolic models, as demonstrated in this study using Klebsiella pneumoniae. While the evidence in this specific case is solid, validation across diverse bacterial species is yet to be confirmed.

eLife assessment

This fundamental study provides a framework for leveraging systems genetics data to dissect mechanisms of gut physiology. The authors provide compelling analyses to highlight diverse modes of interrogating intestinal inflammation, dietary response, and consequent impacts on inflammatory bowel disease. As a resource, it will have great utility for linking genetic variation and diet to gut-related pathophysiologies.

eLife assessment

This fundamental study leverages natural genetic diversity in mice to discover candidate genes for insulin sensitivity, followed by experimental identification of compounds that can modulate insulin sensitivity, and finally initial mechanistic investigation of the mode of action. The generalized approach presented here – the integration of systems genetics data with drug discovery – supported by compelling evidence, will be a guide for others who seek to translate insights from mammalian genetics to drug discovery.

eLife assessment

This is an important study that investigates the impact of tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia. Through a combination of pre-clinical in vivo measurements, clinical data, and computational modeling, the authors present solid evidence regarding the heterogeneous effects of TKIs in patients and how the response to treatment may be improved. With the assumptions about differences between normal and leukemic cells addressed, this study would be of interest to those working in the fields of mathematical oncology and cancer biology.

eLife assessment

The authors have developed a useful and user-friendly software to analyse gene expression data from four datasets representing premalignant lung lesions. This software would be of interest to those working in lung cancer and specifically the pre-malignant space. The major strength is the ease of use while the major limitation is the inability for the user to integrate other datasets.

eLife assessment

This important paper reports recent improvements and extensions to stdpopsim, a community-driven resource that is built on top of powerful software for performing simulations of population genomic data and provides a catalog of species with curated genomic parameters and demographic models. In addition to describing the new features and species in stdpopsim, the authors provide a set of practical guidelines for implementing realistic simulations. Overall, this convincing manuscript serves as an excellent overview of the utility, challenges, common pitfalls, and best practices of population genomic simulations. It will be of broad interest to population, evolutionary, and ecological geneticists studying humans, model organisms, or non-model organisms.

eLife assessment

This study presents a useful tool called Phantasus, a web application to analyze gene expression data generated by microarray or RNA-seq technologies. The web application will help biologists end users, and non-bioinformatics experts to analyze new data or replicate transcriptomic studies. Local use of the Phantasus through its Bioconductor package reveals an incomplete functionality concerning the current best practices in analyzing bulk RNA-seq data.

eLife assessment

In this important work, the authors present a sequence-based approach using transfer learning and Restricted Boltzmann Machines to predict antigen immunogenicity and specificity. The evidence and methodology are compelling. This work should be of interest to immunologists, computational biologists, and biophysicists.

eLife assessment:

This manuscript describes development of a new algorithm for integrative analysis of multi-omics data. This work should be of potential interest to scientists performing bioinformatic pathway discovery in multi-omic datasets especially those that relate to signaling.

eLife assessment

The study presents analyses linking cell-types to monogenic disorders using over-expression of known disease-associated genes in single-cell data to identify 110 disease-affected cell types for 714 Mendelian diseases. Overall this important study combines multiple data analyses to quantify the connection between cell types and human disorders. While some of the analyses are compelling, updates to the method are needed to ensure that statistical inference is appropriately stringent and rigorous.

eLife assessment

This manuscript presents an important mathematical analysis on metabolic "co-substrates" and how their cycling can affect metabolic fluxes. Through mathematical analysis of simple network motifs, it shows the impact on constraining metabolic fluxes and the applied mathematical modeling/simulation approaches and the statistical analysis to compare predictions with data from previous studies offer convincing support for the potential biological relevance of co-substrate cycling. The work will be of interest to researchers who study microbial metabolism and metabolic engineering. However, part of this analysis remains unclear and would benefit from clarification.

eLife assessment

This study systematically integrates multi-omics data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy and presents findings that have focused importance and scope. The methods, data, and analyses as described now only partially support the primary claims.

eLife assessment

This fundamental work advances our understanding of how bacteria evolve to resist drugs used for cancer treatment and how this could potentially affect drug efficacy and treatment outcome. The data were collected and analyzed using a solid methodology and can be used as a starting point for functional studies of the interaction between microbiome interactions and cancer drug treatment. The findings will be of broad interest to microbiologists and organismal biologists interested in the role of microbiomes in drug responses.