Cutting the wires: modularization of cellular networks for experimental design.

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future.

In silico feedback for in vivo regulation of a gene expression circuit.

We show that difficulties in regulating cellular behavior with synthetic biological circuits may be circumvented using in silico feedback control. By tracking a circuit's output in Saccharomyces cerevisiae in real time, we precisely control its behavior using an in silico feedback algorithm to compute regulatory inputs implemented through a genetically encoded light-responsive module. Moving control functions outside the cell should enable more sophisticated manipulation of cellular processes whenever real-time measurements of cellular variables are possible.

Discretely calibrated regulatory loops controlled by ppGpp partition gene induction across the 'feast to famine' gradient in Escherichia coli.

Bacteria comprehensively reorganize their global gene expression when faced with starvation. The alarmone ppGpp facilitates this massive response by co-ordinating the downregulation of genes of the translation apparatus, and the induction of biosynthetic genes and the general stress response. Such a large reorientation requires the activities of multiple regulators, yet the regulatory network downstream of ppGpp remains poorly defined. Transcription profiling during isoleucine depletion, which leads to gradual starvation (over > 100 min), allowed us to identify genes that required ppGpp, Lrp and RpoS for their induction and to deduce the regulon response times. Although the Lrp and RpoS regulons required ppGpp for their activation, they were not induced simultaneously. The data suggest that metabolic genes, i.e. those of the Lrp regulon, require only a low level of ppGpp for their induction. In contrast, the RpoS regulon was induced only when high levels of ppGpp accumulated. We tested several predictions of a model that explains how bacteria allocate transcriptional resources between metabolism and stress response by discretely tuning two regulatory circuits to different levels of ppGpp. The emergent regulatory structure insures that stress survival circuits are only triggered if homeostatic metabolic networks fail to compensate for environmental deficiencies.

Stochastic dynamics of genetic networks: modelling and parameter identification.

MOTIVATION: Identification of regulatory networks is typically based on deterministic models of gene expression. Increasing experimental evidence suggests that the gene regulation process is intrinsically random. To ensure accurate and thorough processing of the experimental data, stochasticity must be explicitly accounted for both at the modelling stage and in the design of the identification algorithms. RESULTS: We propose a model of gene expression in prokaryotes where transcription is described as a probabilistic event, whereas protein synthesis and degradation are captured by first-order deterministic kinetics. Based on this model and assuming that the network of interactions is known, a method for estimating unknown parameters, such as synthesis and binding rates, from the outcomes of multiple time-course experiments is introduced. The method accounts naturally for sparse, irregularly sampled and noisy data and is applicable to gene networks of arbitrary size. The performance of the method is evaluated on a model of nutrient stress response in Escherichia coli.

The global, ppGpp-mediated stringent response to amino acid starvation in Escherichia coli.

The stringent response to amino acid starvation, whereby stable RNA synthesis is curtailed in favour of transcription of amino acid biosynthetic genes, is controlled by the alarmone ppGpp. To elucidate the extent of gene expression effected by ppGpp, we designed an experimental system based on starvation for isoleucine, which could be applied to both wild-type Escherichia coli and the multiauxotrophic relA spoT mutant (ppGpp(0)). We used microarrays to profile the response to amino acid starvation in both strains. The wild-type response included induction of the general stress response, downregulation of genes involved in production of macromolecular structures and comprehensive restructuring of metabolic gene expression, but not induction of amino acid biosynthesis genes en masse. This restructuring of metabolism was confirmed using kinetic Biolog assays. These responses were profoundly altered in the ppGpp(0) strain. Furthermore, upon isoleucine starvation, the ppGpp(0) strain exhibited a larger cell size and continued growth, ultimately producing 50% more biomass than the wild-type, despite producing a similar amount of protein. This mutant phenotype correlated with aberrant gene expression in diverse processes, including DNA replication, cell division, and fatty acid and membrane biosynthesis. We present a model that expands and functionally integrates the ppGpp-mediated stringent response to include control of virtually all macromolecular synthesis and intermediary metabolism.