ENKIE: a package for predicting enzyme kinetic parameter values and their uncertainties.

Motivation: Relating metabolite and enzyme abundances to metabolic fluxes requires reaction kinetics, core elements of dynamic and enzyme cost models. However, kinetic parameters have been measured only for a fraction of all known enzymes, and the reliability of the available values is unknown.

Results: The ENzyme KInetics Estimator (ENKIE) uses Bayesian Multilevel Models to predict value and uncertainty of KM and kcat parameters.

Nucleobase adducts bind MR1 and stimulate MR1-restricted T cells.

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.

A quantitative model for virus uncoating predicts influenza A infectivity.

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.

Functional comparison of metabolic networks across species.

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.

Multivalency ensures persistence of a +TIP body at specialized microtubule ends.

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.

Cellular and computational models reveal environmental and metabolic interactions in MMUT-type methylmalonic aciduria.

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.

eQuilibrator 3.0: a database solution for thermodynamic constant estimation.

eQuilibrator (equilibrator.weizmann.ac.

A rationally engineered decoder of transient intracellular signals.

Cells can encode information about their environment by modulating signaling dynamics and responding accordingly. Yet, the mechanisms cells use to decode these dynamics remain unknown when cells respond exclusively to transient signals. Here, we approach design principles underlying such decoding by rationally engineering a synthetic short-pulse decoder in budding yeast.

Probabilistic thermodynamic analysis of metabolic networks.

Motivation: Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism's potential or actual metabolic operations.

Results: We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network.

Efficient manipulation and generation of Kirchhoff polynomials for the analysis of non-equilibrium biochemical reaction networks.

Kirchhoff polynomials are central for deriving symbolic steady-state expressions of models whose dynamics are governed by linear diffusion on graphs. In biology, such models have been unified under a common linear framework subsuming studies across areas such as enzyme kinetics, G-protein coupled receptors, ion channels and gene regulation. Due to 'history dependence' away from thermodynamic equilibrium, these models suffer from a (super) exponential growth in the size of their symbolic steady-state expressions and, respectively, Kirchhoff polynomials.

TopoFilter: a MATLAB package for mechanistic model identification in systems biology.

Background: To develop mechanistic dynamic models in systems biology, one often needs to identify all (or minimal) representations of the biological processes that are consistent with experimental data, out of a potentially large set of hypothetical mechanisms. However, a simple enumeration of all alternatives becomes quickly intractable when the number of model parameters grows. Selecting appropriate dynamic models out of a large ensemble of models, taking the uncertainty in our biological knowledge and in the experimental data into account, is therefore a key current problem in systems biology.

Controlling cell-to-cell variability with synthetic gene circuits.

Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset.

BioSwitch: a tool for the detection of bistability and multi-steady state behaviour in signalling and gene regulatory networks.

Motivation: Multi-steady state behaviour, and in particular multi-stability, provides biological systems with the capacity to take reliable decisions (such as cell fate determination). A problem arising frequently in systems biology is to elucidate whether a signal transduction mechanism or a gene regulatory network has the capacity for multi-steady state behaviour, and consequently for a switch-like response to stimuli. Bifurcation diagrams are a powerful instrument in non-linear analysis to study the qualitative and quantitative behaviour of equilibria including bifurcation into different equilibrium branches and bistability.

Publisher Correction: Microbial network disturbances in relapsing refractory Crohn's disease.

Owing to an error during typesetting, a number of references were deleted from the Methods reference list. This altered all of the references in the Methods section and some of the references in Extended Data Fig. 5, making them inaccurate.

Microbial network disturbances in relapsing refractory Crohn's disease.

Inflammatory bowel diseases (IBD) can be broadly divided into Crohn's disease (CD) and ulcerative colitis (UC) from their clinical phenotypes. Over 150 host susceptibility genes have been described, although most overlap between CD, UC and their subtypes, and they do not adequately account for the overall incidence or the highly variable severity of disease. Replicating key findings between two long-term IBD cohorts, we have defined distinct networks of taxa associations within intestinal biopsies of CD and UC patients.

Integrating -omics data into genome-scale metabolic network models: principles and challenges.

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired.

Predicting ligand-dependent tumors from multi-dimensional signaling features.

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations.

BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data.

Motivation: Photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) is an experimental method based on next-generation sequencing for identifying the RNA interaction sites of a given protein. The method deliberately inserts T-to-C substitutions at the RNA-protein interaction sites, which provides a second layer of evidence compared with other CLIP methods. However, the experiment includes several sources of noise which cause both low-frequency errors and spurious high-frequency alterations.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations.

Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome.

Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is often complicated because ubiquitous feedback loops obscure causal relationships. Consequently, the endogenous inputs of many nutrient signaling pathways remain unknown.