Many interactions in microbial consortia or tissues of multicellular organisms rely on networks of metabolite exchanges. To predict community function and composition beyond statistical correlations, one can use genome-scale metabolic models. However, comprehensive model analysis via metabolic pathways is a major challenge because pathway counts grow combinatorially with model size. Here, we define minimal pathways that yield compact representations of metabolic network capabilities. They generalize existing pathway concepts by allowing inhomogeneous constraints and targeted analysis of subnetworks, and we show how to enumerate and sample them efficiently via iterative minimization and pathway graphs. This enables applications such as assessing quantitative gene essentiality in the central metabolism of Escherichia coli, predicting metabolite exchanges associated with homeostasis and health in a host-microbe model of the human gut, and designing butyrate-producing microbial communities. Minimal pathways enable scalable analysis of metabolic subnetworks such as metabolite exchanges in uni- and multicellular systems. A record of this paper’s transparent peer review process is included in the supplemental information.

Host-microbiota mutualism is rooted in the exchange of dietary and metabolic molecules. Microbial diversity broadens the metabolite pool, with each taxon contributing distinct compounds in varying proportions. In the human microbiome, high variability in consortial composition is largely compensated by similar metabolic functions across different taxa. However, the extent of compensation in lower diversity mouse models, and whether vivaria are metabolically equivalent, is unknown. We provide a searchable resource of microbiome composition variability across 51 murine vivaria and 12 wild mouse colonies worldwide, with vivarium-specific variants mapped according to predicted 3D structures for each microbial species. Our matched metabolomics data show that realized metabolic potential has relatively low variability, providing functional evidence for metabolic compensation. Additionally, variability is related to taxonomic composition rather than vivarium, revealing taxa-metabolite associations that are potentially relevant to phenotypic differences between vivaria. Collectively, this resource offers tools to strengthen microbiome studies and collaborative science.

Microorganisms growing together within a community can either cooperate by exchanging nutrients or compete for the same nutrients. The resulting complex network of interactions leads to the emergence of various community functions. However, metabolic interactions are difficult to identify experimentally, and current computational predictions assume a community objective. Here, we propose a community objective-free computational method using a constraint-based metabolic model for predicting the minimal exchanges of nutrients between species coexisting in a chemostat at steady state. For a toy model, we showed that the species can be involved in different metabolic strategies, depending on the scarcity of resources and on their biomasses, and that alternate steady states exist. We then extended our method to larger-scale metabolic models and predicted the minimal interactions in a community of two amino acid auxotrophic E. coli strains. Overall, our approach proves promising for better identifying community interactions, for example, in the gut microbiome.

Optimal experimental design (OED) aims to design more effective experiments and thereby save resources. Applications of OED most often design experiments for model parameter estimation, but rarely for model selection. In addition, few OED methods exist for biological systems with considerable cell-to-cell variability, where population models such as non-linear mixed effect (NLME) models can help elucidate sources of variability. Here, we address this gap with an OED method for designing dynamic inputs selecting between NLME models. Specifically, we propose a novel utility function for NLME model discrimination based on the separation of predicted population distributions. Our utility provides an interpretable output: a separability score denotes the expected number of pairwise model separations after conducting the experiment. We demonstrate our approach to optimal input design by separating candidate models for the variance components in a simple gene expression circuit. We show that with the suggested optimal design we can separate 4 out of 5 candidate models from one another. We consider this proof of principle as a first step towards designing more efficient experiments to elucidate the mechanisms of cell-to-cell variability. We envisage future extensions to larger model selection problems in systems and synthetic biology.

MR1T cells are a recently found class of T cells that recognize antigens presented by the major histocompatibility complex-I–related molecule MR1 in the absence of microbial infection. The nature of the self-antigens that stimulate MR1T cells remains unclear, hampering our understanding of their physiological role and therapeutic potential. By combining genetic, pharmacological, and biochemical approaches, we found that carbonyl stress and changes in nucleobase metabolism in target cells promote MR1T cell activation. Stimulatory compounds formed by carbonyl adducts of nucleobases were detected within MR1 molecules produced by tumor cells, and their abundance and antigenicity were enhanced by drugs that induce carbonyl accumulation. Our data reveal carbonyl-nucleobase adducts as MR1T cell antigens. Recognizing cells under carbonyl stress allows MR1T cells to monitor cellular metabolic changes with physiological and therapeutic implications.

Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

BACKGROUND:Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically.RESULTS:Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor). Furthermore, investigating a realistic synthetic community, where the two involved strains exhibit no growth in isolation, but grow as a community, we predict multiple modes of cooperation, even without an explicit cooperation mechanism.CONCLUSIONS:Steady state GSM modelling of microbial communities relies both on assumed decision making principles and environmental assumptions. In principle, dynamic flux balance analysis addresses both. In practice, our methods that address the steady state directly may be preferable, especially if the community is expected to display multiple steady states.

BACKGROUND:Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability.RESULTS:Here, we present a computational framework and an efficient algorithm to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a Non-linear Mixed-Effects (NLME) framework into a Markov Chain Monte-Carlo (MCMC) algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability.CONCLUSION:We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.

Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes in Escherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.

Kinesins are microtubule-dependent motor proteins, some of which moonlight as microtubule polymerases, such as the yeast protein Kip2. Here, we show that the CLIP-170 ortholog Bik1 stabilizes Kip2 at microtubule ends where the motor domain of Kip2 promotes microtubule polymerization. Live-cell imaging and mathematical estimation of Kip2 dynamics reveal that disrupting the Kip2-Bik1 interaction aborts Kip2 dwelling at microtubule ends and abrogates its microtubule polymerization activity. Structural modeling and biochemical experiments identify a patch of positively charged residues that enables the motor domain to bind free tubulin dimers alternatively to the microtubule shaft. Neutralizing this patch abolished the ability of Kip2 to promote microtubule growth both in vivo and in vitro without affecting its ability to walk along microtubules. Our studies suggest that Kip2 utilizes Bik1 as a cofactor to track microtubule tips, where its motor domain then recruits free tubulin and catalyzes microtubule assembly.

For virus infection of new host cells, the disassembly of the protective outer protein shell (capsid) is a critical step, but the mechanisms and host-virus interactions underlying the dynamic, active, and regulated uncoating process are largely unknown. Here, we develop an experimentally supported, multiscale kinetics model that elucidates mechanisms of influenza A virus (IAV) uncoating in cells. Biophysical modeling demonstrates that interactions between capsid M1 proteins, host histone deacetylase 6 (HDAC6), and molecular motors can physically break the capsid in a tug-of-war mechanism. Biochemical analysis and biochemical-biophysical modeling identify unanchored ubiquitin chains as essential and allow robust prediction of uncoating efficiency in cells. Remarkably, the different infectivity of two clinical strains can be ascribed to a single amino acid variation in M1 that affects binding to HDAC6. By identifying crucial modules of viral infection kinetics, the mechanisms and models presented here could help formulate novel strategies for broad-range antiviral treatment.

Methylmalonyl-coenzyme A (CoA) mutase (MMUT)-type methylmalonic aciduria is a rare inherited metabolic disease caused by the loss of function of the MMUT enzyme. Patients develop symptoms resembling those of primary mitochondrial disorders, but the underlying causes of mitochondrial dysfunction remain unclear. Here, we examined environmental and genetic interactions in MMUT deficiency using a combination of computational modeling and cellular models to decipher pathways interacting with MMUT. Immortalized fibroblast (hTERT BJ5ta) MMUT-KO (MUTKO) clones displayed a mild mitochondrial impairment in standard glucose-based medium, but they did not to show increased reliance on respiratory metabolism nor reduced growth or viability. Consistently, our modeling predicted MUTKO specific growth phenotypes only for lower extracellular glutamine concentrations. Indeed, two of three MMUT-deficient BJ5ta cell lines showed a reduced viability in glutamine-free medium. Further, growth on 183 different carbon and nitrogen substrates identified increased NADH (nicotinamide adenine dinucleotide) metabolism of BJ5ta and HEK293 MUTKO cells compared with controls on purine- and glutamine-based substrates. With this knowledge, our modeling predicted 13 reactions interacting with MMUT that potentiate an effect on growth, primarily those of secondary oxidation of propionyl-CoA, oxidative phosphorylation and oxygen diffusion. Of these, we validated 3-hydroxyisobutytyl-CoA hydrolase (HIBCH) in the secondary propionyl-CoA oxidation pathway. Altogether, these results suggest compensation for the loss of MMUT function by increasing anaplerosis through glutamine or by diverting flux away from MMUT through the secondary propionyl-CoA oxidation pathway, which may have therapeutic relevance.

Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct gene expression phenotypes in Escherichia coli . These genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.

Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking—assigning identical cells on consecutive images to a track—is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (Trac X ) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae , we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

Metabolic network analysis is an accessible and versatile modeling approach for biology that has taken much inspiration from electric circuit analysis. After introducing its main concepts, we focus on numerical tools, such as optimization and sampling, to predict cellular features and behaviors at a large scale. Optimization approaches exploit that metabolic networks are shaped by evolution and are, thus, assumed to embed a fitness condition reflecting the environment that they evolved in. In the past ten years, there is a trend to generalize metabolic network analysis to consortia of interacting species. This raises technical questions on, for example, optimality in consortia but also more general ones on metabolic coevolution, information exchange, and adaptation. This suggests and allows us to explore interesting analogies to technological systems, specifically to smart grids.

This special issue brings together leading experts in the fields of systems and synthetic biology to provide a quantitative and analytical perspective of the state-of-the-art developments.

Systems biology is a field of biology that was propelled into existence due to the development of methods to generate massively parallel experimental biological data, starting with genome sequencing projects approximately 30 years ago. Transcending the focus on single biological components, the goal of systems biology is to reverseengineermechanisms of biological systems to enhance our quantitative holistic understanding of biology and to translate this understanding to applications such as novel therapeutic approaches. As such, systems biology is the science counterpart to synthetic biology described below. A hallmark of biological systems is their sheer complexity, as evidenced by the existence of thousands of components and millions of interactions between them even in the simplest of living cells. While this degree of complexity is paralleled by some engineered systems, the other hallmark, large uncertainty, distinguishes biological systems from their human-made counterparts. In addition to complexity, limited observability, lower accuracy of measurements, and uncertain mechanisms make reverse-engineering in biology highly challenging.

Abstract Background Influenza vaccination efficacy is reduced after hematopoietic stem cell transplantation (HSCT) and patient factors determining vaccination outcomes are still poorly understood. Methods We investigated the antibody response to seasonal influenza vaccination in 135 HSCT patients and 69 healthy volunteers (HVs) in a prospective observational multicenter cohort study. We identified patient factors associated with hemagglutination inhibition titers against A/California/2009/H1N1, A/Texas/2012/H3N2, and B/Massachusetts/2012 by multivariable regression on the observed titer levels and on seroconversion/seroprotection categories for comparison. Results Both regression approaches yielded consistent results but regression on titers estimated associations with higher precision. HSCT patients required 2 vaccine doses to achieve average responses comparable to a single dose in HVs. Prevaccination titers were positively associated with time after transplantation, confirming that HSCT patients can elicit potent antibody responses. However, an unrelated donor, absolute lymphocyte counts below the normal range, and treatment with calcineurin inhibitors lowered the odds of responding. Conclusions HSCT patients show a highly heterogeneous vaccine response but, overall, patients benefited from the booster shot and can acquire seroprotective antibodies over the years after transplantation. Several common patient factors lower the odds of responding, urging identification of additional preventive strategies in the poorly responding groups. Clinical Trials Registration NCT03467074.

Summary MR1T lymphocytes are a recently identified population of T cells that recognize unknown self-antigens presented by the non-polymorphic MHC-I-related molecule, MR1. MR1T cells can kill tumor cells and modulate the functions of other immune cells with promising therapeutic applications. By integrating genetic, pharmacological and biochemical approaches we identified carbonyl stress and alterations of nucleobase metabolism in tumor target cells that promote recognition by MR1 T cells. We dissected these pathways and found that nucleobase adduct-containing metabolites are self-antigens stimulating MR1T cells. Several nucleobase adducts are presented by MR1 molecules and stimulate individual MR1T cells. Our data suggest that MR1T cells are surveyor of cellular metabolic alterations occurring in conditions of metabolic stress, such as cancer, and lay the groundwork for the development of novel HLA-unrestricted T cell-based therapies.

Microtubule plus-end tracking proteins (+TIPs) control microtubule specialization and are as such essential for cell division and morphogenesis. Here we investigated interactions and functions of the budding yeast Kar9 network consisting of the core +TIP proteins Kar9 (functional homologue of APC, MACF and SLAIN), Bim1 (orthologous to EB1) and Bik1 (orthologous to CLIP-170). A multivalent web of redundant interactions links the three +TIPs together to form a ‘+TIP body’ at the end of chosen microtubules. This body behaves as a liquid condensate that allows it to persist on both growing and shrinking microtubule ends, and to function as a mechanical coupling device between microtubules and actin cables. Our study identifies nanometre-scale condensates as effective cellular structures and underlines the power of dissecting the web of low-affinity interactions driving liquid–liquid phase separation in order to establish how condensation processes support cell function.