Emerging technologies have enabled the acquisition of large genomics and proteomics data sets. However, current methodologies for analysis do not permit interpretation of the data in ways that unravel cellular networking. We propose a quantitative method for determining functional interactions in cellular signaling and gene networks. It can be used to explore cell systems at a mechanistic level or applied within a “modular” framework, which dramatically decreases the number of variables to be assayed. This method is based on a mathematical derivation that demonstrates how the topology and strength of network connections can be retrieved from experimentally measured network responses to successive perturbations of all modules. Importantly, our analysis can reveal functional interactions even when the components of the system are not all known. Under these circumstances, some connections retrieved by the analysis will not be direct but correspond to the interaction routes through unidentified elements. The method is tested and illustrated by using computer-generated responses of a modeled mitogen-activated protein kinase cascade and gene network.

Introduction: Cells use multilayered regulatory systems to respond adequately to changing environments or perturbations. Failure in regulation underlies cellular malfunctioning, loss of fitness, or disease. How molecular components dynamically interact to give rise to robust and adaptive responses is not well understood. Here, we studied how the model eukaryote Saccharomyces cerevisiae can cope with transition to high glucose levels, a failure of which results in metabolic malfunctioning and growth arrest. Methods: We combined experimental and modeling approaches to unravel the mechanisms used by yeast to cope with sudden glucose availability. We studied growth characteristics and metabolic state at population and single-cell levels (through flow cytometry and colony plating) of the wild type and of mutants unable to transit properly to excess glucose; such mutants are defective in trehalose synthesis, a disaccharide associated with stress resistance. Dynamic ¹³ C tracer enrichment was used to estimate dynamic intracellular fluxes immediately after glucose addition. Mathematical modeling was used to interpret and generalize results and to suggest subsequent experiments. Results: The failure to cope with glucose is caused by imbalanced reactions in glycolysis, the essential pathway in energy metabolism in most organisms. In the failure mode, the first steps of glycolysis carry more flux than the downstream steps, resulting in accumulating intermediates at constant low levels of adenosine triphosphate (ATP) and inorganic phosphate. We found that cells with such an unbalanced glycolysis coexist with vital cells with normal glycolytic function. Spontaneous, nongenetic metabolic variability among individual cells determines which state is reached and consequently which cells survive. In mutants of trehalose metabolism, only 0.01% of the cells started to grow on glucose; in the wild type, the success rate was still only 93% (i.e., 7% of wild-type yeast did not properly start up glycolysis). Mathematical models predicted that the dynamics of inorganic phosphate is a key determinant in successful transition to glucose, and that phosphate release through ATP hydrolysis reduces the probability of reaching an imbalanced state. ¹³ C-labeling experiments confirmed the hypothesis that trehalose metabolism constitutes a futile cycle that would provide proper phosphate balance: Upon a glucose pulse, almost 30% of the glucose is transiently shuttled into trehalose metabolism. Discussion: Our work reveals how cell fate can be determined by glycolytic dynamics combined with cell heterogeneity purely at the metabolic level. Specific regulatory mechanisms are required to initiate the glycolytic pathway; in yeast, trehalose cycling pushes glycolysis transiently into the right direction, after which cycling stops. The coexistence of two modes of glycolysis—an imbalanced state and the normal functional state—arises from the fundamental design of glycolysis. This makes the imbalanced state a generic risk for humans as well, extending our fundamental knowledge of this central pathway that is dysfunctional in diseases such as diabetes and cancer.

Maximization of growth rate is an important fitness strategy for bacteria. Bacteria can achieve this by expressing proteins at optimal concentrations, such that resources are not wasted. This is exemplified for Escherichia coli by the increase of its ribosomal protein‐fraction with growth rate, which precisely matches the increased protein synthesis demand. These findings and others have led to the hypothesis that E. coli aims to maximize its growth rate in environments that support growth. However, what kind of regulatory strategy is required for a robust, optimal adjustment of the ribosome concentration to the prevailing condition is still an open question. In the present study, we analyze the ppGpp‐controlled mechanism of ribosome expression used by E. coli and show that this mechanism maintains the ribosomes saturated with its substrates. In this manner, overexpression of the highly abundant ribosomal proteins is prevented, and limited resources can be redirected to the synthesis of other growth‐promoting enzymes. It turns out that the kinetic conditions for robust, optimal protein‐partitioning, which are required for growth rate maximization across conditions, can be achieved with basic biochemical interactions. We show that inactive ribosomes are the most suitable ‘signal’ for tracking the intracellular nutritional state and for adjusting gene expression accordingly, as small deviations from optimal ribosome concentration cause a huge fractional change in ribosome inactivity. We expect to find this control logic implemented across fast‐growing microbial species because growth rate maximization is a common selective pressure, ribosomes are typically highly abundant and thus costly, and the required control can be implemented by a small, simple network.

A central focus in studies of microbial communities is the elucidation of the relationships between genotype, phenotype, and dynamic community structure. Here, we present a new computational method called community flux balance analysis (cFBA) to study the metabolic behavior of microbial communities. cFBA integrates the comprehensive metabolic capacities of individual microorganisms in terms of (genome-scale) stoichiometric models of metabolism, and the metabolic interactions between species in the community and abiotic processes. In addition, cFBA considers constraints deriving from reaction stoichiometry, reaction thermodynamics, and the ecosystem. cFBA predicts for communities at balanced growth the maximal community growth rate, the required rates of metabolic reactions within and between microbes and the relative species abundances. In order to predict species abundances and metabolic activities at the optimal community growth rate, a nonlinear optimization problem needs to be solved. We outline the methodology of cFBA and illustrate the approach with two examples of microbial communities. These examples illustrate two useful applications of cFBA. Firstly, cFBA can be used to study how specific biochemical limitations in reaction capacities cause different types of metabolic limitations that microbial consortia can encounter. In silico variations of those maximal capacities allow for a global view of the consortium responses to various metabolic and environmental constraints. Secondly, cFBA is very useful for comparing the performance of different metabolic cross-feeding strategies to either find one that agrees with experimental data or one that is most efficient for the community of microorganisms.