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Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-y, Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality., We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503)., info:eu-repo/semantics/publishedVersion

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome., Mechanistic insight into the regulation of transcriptional modules remains scarce. Here, the authors identify statistically independent gene sets by applying independent component analysis to a high-quality E. coli RNA-seq data compendium and find that most gene sets represent the effects of specific transcriptional regulators.

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the ‘y-ome’) which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with ‘y’ is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

Gene replacement and pre-mRNA splicing modifier therapies represent breakthrough gene targeting treatments for the neuromuscular disease spinal muscular atrophy (SMA), but mechanisms underlying variable efficacy of treatment are incompletely understood. Our examination of severe infantile onset human SMA tissues obtained at expedited autopsy revealed persistence of developmentally immature motor neuron axons, many of which are actively degenerating. We identified similar features in a mouse model of severe SMA, in which impaired radial growth and Schwann cell ensheathment of motor axons began during embryogenesis and resulted in reduced acquisition of myelinated axons that impeded motor axon function neonatally. Axons that failed to ensheath degenerated rapidly postnatally, specifically releasing neurofilament light chain protein into the blood. Genetic restoration of survival motor neuron protein (SMN) expression in mouse motor neurons, but not in Schwann cells or muscle, improved SMA motor axon development and maintenance. Treatment with small-molecule SMN2 splice modifiers beginning immediately after birth in mice increased radial growth of the already myelinated axons, but in utero treatment was required to restore axonal growth and associated maturation, prevent subsequent neonatal axon degeneration, and enhance motor axon function. Together, these data reveal a cellular basis for the fulminant neonatal worsening of patients with infantile onset SMA and identify a temporal window for more effective treatment. These findings suggest that minimizing treatment delay is critical to achieve optimal therapeutic efficacy.

Mesoplasma florum, a fast‐growing near‐minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein‐coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species‐specific constraints to produce the functional iJL208 genome‐scale model (GEM) of metabolism. Genome‐wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI‐syn3.0 and its parent JCVI‐syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping‐stone toward model‐driven whole‐genome engineering., The first genome‐scale metabolic model for the near‐minimal bacterium Mesoplasma florum is reported. Comparing the model‐driven prediction of a M. florum genome reduction scenario to a closely related minimal cell reveals key features of a minimal gene set.

A genome contains the information underlying an organism's form and function. Yet, we lack formal framework to represent and study this information. Here, we introduce the Bitome, a matrix composed of binary digits (bits) representing the genomic positions of genomic features. We form a Bitome for the genome of Escherichia coli K-12 MG1655. We find that: (i) genomic features are encoded unevenly, both spatially and categorically; (ii) coding and intergenic features are recapitulated at high resolution; (iii) adaptive mutations are skewed towards genomic positions with fewer features; and (iv) the Bitome enhances prediction of adaptively mutated and essential genes. The Bitome is a formal representation of a genome and may be used to study its fundamental organizational properties.

Background Stable isotope tracing has become an invaluable tool for probing the metabolism of biological systems. However, data analysis and visualization from metabolic tracing studies often involve multiple software packages and lack pathway architecture. A deep understanding of the metabolic contexts from such datasets is required for biological interpretation. Currently, there is no single software package that allows researchers to analyze and integrate stable isotope tracing data into annotated or custom-built metabolic networks. Results We built a standalone web-based software, Escher-Trace, for analyzing tracing data and communicating results. Escher-Trace allows users to upload baseline corrected mass spectrometer (MS) tracing data and correct for natural isotope abundance, generate publication quality graphs of metabolite labeling, and present data in the context of annotated metabolic pathways. Here we provide a detailed walk-through of how to incorporate and visualize 13C metabolic tracing data into the Escher-Trace platform. Conclusions Escher-Trace is an open-source software for analysis and interpretation of stable isotope tracing data and is available at https://escher-trace.github.io/.

High levels of stress during pregnancy increase the chances of having a premature or low-birthweight baby. Perceived self-reported stress does not often capture or align with the physiological and behavioral response. But what if there was a self-report measure that could better capture the physiological response? Current perceived stress self-report assessments require users to answer multi-item scales at different time points of the day. Reducing it to one question, using microinteraction-based ecological momentary assessment (micro-EMA, collecting a single in situ self-report to assess behaviors) allows us to identify smaller or more subtle changes in physiology. It also allows for more frequent responses to capture perceived stress while at the same time reducing burden on the participant. We propose a framework for selecting the optimal micro-EMA that combines unbiased feature selection and unsupervised Agglomerative clustering. We test our framework in 18 women performing 16 activities in-lab wearing a Biostamp, a NeuLog, and a Polar chest strap. We validated our results in 17 pregnant women in real-world settings. Our framework shows that the question "How worried were you?" results in the highest accuracy when using a physiological model. Our results provide further in-depth exposure to the challenges of evaluating stress models in real-world situations.

Glyburide (also known as glibenclamide) is a second-generation sulfonylurea drug that inhibits sulfonylurea receptor 1 (Sur1) at nanomolar concentrations. Long used to target KATP (Sur1–Kir6.2) channels for the treatment of diabetes mellitus type 2, glyburide was recently repurposed to target Sur1–transient receptor potential melastatin 4 (Trpm4) channels in acute central nervous system injury. Discovered nearly two decades ago, SUR1–TRPM4 has emerged as a critical target in stroke, specifically in large hemispheric infarction, which is characterized by edema formation and life-threatening brain swelling. Following ischemia, SUR1–TRPM4 channels are transcriptionally upregulated in all cells of the neurovascular unit, including neurons, astrocytes, microglia, oligodendrocytes and microvascular endothelial cells. Work by several independent laboratories has linked SUR1–TRPM4 to edema formation, with blockade by glyburide reducing brain swelling and death in preclinical models. Recent work showed that, following ischemia, SUR1–TRPM4 co-assembles with aquaporin-4 to mediate cellular swelling of astrocytes, which contributes to brain swelling. Additionally, recent work linked SUR1–TRPM4 to secretion of matrix metalloproteinase-9 (MMP-9) induced by recombinant tissue plasminogen activator in activated brain endothelial cells, with blockade of SUR1–TRPM4 by glyburide reducing MMP-9 and hemorrhagic transformation in preclinical models with recombinant tissue plasminogen activator. The recently completed GAMES (Glyburide Advantage in Malignant Edema and Stroke) clinical trials on patients with large hemispheric infarctions treated with intravenous glyburide (RP-1127) revealed promising findings with regard to brain swelling (midline shift), MMP-9, functional outcomes and mortality. Here, we review key elements of the basic science, preclinical experiments and clinical studies, both retrospective and prospective, on glyburide in focal cerebral ischemia and stroke.

A critical question for theories of spatial vision concerns the nature of the inputs to perception. The action-specific account asserts that information related to action, specifically a perceiver's ability to perform the intended action, is one of these sources of information. This claim challenges assumptions about the mind in general and perception in particular, and not surprisingly, has been met with much resistance. Alternative explanations include that these effects are due to response bias, rather than genuine differences in perception. Using a paradigm in which ease to block a ball impacts estimated speed of the ball, participants were given explicit feedback about their perceptual judgements to test the response bias alternative. Despite the feedback, the action-specific effect still persisted, thus ruling out a response-bias interpretation. Coupled with other research ruling out additional alternative explanations, the current findings offer an important step towards the claim that a person's ability to act truly influences spatial perception.

Evidence suggests that novel enzyme functions evolved from low‐level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems‐level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non‐native substrates in Escherichia coli K‐12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non‐native substrates—D‐lyxose, D‐2‐deoxyribose, D‐arabinose, m‐tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high‐efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome‐scale model simulations of metabolism with enzyme promiscuity.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains—with diverse metabolic deficiencies—were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities., Author summary Many basic characteristics underlying the establishment of cooperative growth in bacterial communities have not been studied in detail. The presented work sought to understand the adaptation of syntrophic communities by first employing a new computational method to generate a comprehensive catalog of E. coli auxotrophic mutants. Many of the knockouts in the catalog had the predicted effect of disabling a major biosynthetic process. As a result, these strains were predicted to be capable of growing when supplemented with many different individual metabolites (i.e., a non-specific auxotroph), but the strains would require a high amount of metabolic cooperation to grow in community. Three such non-specific auxotroph mutants from this catalog were co-cultured with a proven auxotrophic partner in vivo and evolved via adaptive laboratory evolution. In order to successfully grow, each strain in co-culture had to evolve under a pressure to grow cooperatively in its new niche. The non-specific auxotrophs further had to adapt to significant homeostatic changes in cell’s metabolic state caused by knockouts in metabolic genes. The genomes of the successfully growing communities were sequenced, thus providing unique insights into the genetic changes accompanying the formation and optimization of the viable communities. A computational model was further developed to predict how finite protein availability, a fundamental constraint on cell metabolism, could impact the composition of the community (i.e., the relative abundances of each community member).

Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat)., Author summary The formulation of phenotypic predictions by genome-scale models (GEMs) is dependent on the specified objective. The idea of a biomass objective function (BOF) is to represent all metabolites necessary for cells to double so that optimizing the BOF is equivalent to optimizing growth. Knowledge of the qualitative and quantitative organism’s composition (i.e. which metabolites are necessary for growth and in what proportion) is critical for accurate predictions. We implemented BOFdat with the idea that experimental data should drive the definition of the biomass composition. As omic datasets become more available, the possibility of integrating them to obtain a condition-specific biomass composition is in reach and therefore one of the main features of BOFdat. While major macromolecules, coenzymes, and inorganic ions are ubiquitous components across species, several species-specific components exist in the cell that should be accounted for in the BOF. To identify these, we implemented an approach that minimizes the error between experimental essentiality data and GEM-driven prediction. Hence BOFdat provides an unbiased, data-driven approach to defining BOF that has the potential to improve the quality of new genome-scale models and greatly decrease the time required to generate a new reconstruction.

Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach., Author summary This study reconciles multiple existing microbial rate-yield tradeoff theories with experimental data. There is great interest in developing quantitative descriptions of the relationship between growth rate and growth yield [1]. However, some reported experiments [2–4] in the literature do not agree with existing theories [5–7]. Specifically, overflow metabolism in E. coli can either be coupled [5, 8] or decoupled [2–4] from growth rate. We found that adaptive laboratory evolution (ALE) experiments of E. coli reveal a two-dimensional rate-yield tradeoff in adapted strains where the dimensions are (i) a tradeoff between growth rate and growth yield, previously reported by [5], and (ii) a tradeoff between substrate uptake rate and growth yield. The appearance of this two-dimensional tradeoff during adaptation suggests that microorganisms adapting to new environments are subject to a more complex set of rate-yield tradeoffs than previously reported [5, 6]. In this study, the two-dimensional rate-yield tradeoff is quantitatively explained through our multi-scale modeling approach, combining a previously reported small-scale proteome allocation model [5] with a genome-scale model of metabolism and gene-expression (ME-model) [9]. The modeling approach is also instrumental to future studies.

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called i JL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in i JL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection.Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

Genome-scale models (GEMs) rely on a biomass objective function (BOF) to predict phenotype from genotype. Here we present BOFdat, a Python package that offers functions to generate biomass objective function stoichiometric coefficients (BOFsc) from macromolecular cell composition and relative abundances of macromolecules obtained from omic datasets. Growth-associated and non-growth associated maintenance (GAM and NGAM) costs can also be calculated by BOFdat.BOFdat is freely available on the Python Package Index (pip install BOFdat). The source code and an example usage (Jupyter Notebook and example files) are available on GitHub (https://github.com/jclachance/BOFdat). The documentation and API are available through ReadTheDocs (https://bofdat.readthedocs.io).Contactjean-christophe.lachance@usherbrooke.ca, zaking@eng.ucsd.edu, pierre-etienne.jacques@usherbrooke.ca

As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.

The visual system is influenced by action. Objects that are easier to reach or catch look closer and slower, respectively. Here, we describe evidence for one action-specific effect, and show that none of the six pitfalls can account for the results. Vision is not an isolate module, as shown by this top-down effect of action on perception.

With the rapid adoption of computational tools in the life sciences, scientists are taking on the challenge of developing their own software libraries and releasing them for public use. This trend is being accelerated by popular technologies and platforms, such as GitHub, Jupyter, R/Shiny, that make it easier to develop scientific software and by open-source licenses that make it easier to release software. But how do you build a software library that people will use? And what characteristics do the best libraries have that make them enduringly popular? Here, we provide a reference guide, based on our own experiences, for developing software libraries along with real-world examples to help provide context for scientists who are learning about these concepts for the first time. While we can only scratch the surface of these topics, we hope that this article will act as a guide for scientists who want to write great software that is built to last.