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Managing the overwhelming numbers of molecular states and interactions is a fundamental obstacle to building predictive models of biological systems. Here we introduce the Network-Free Stochastic Simulator (NFsim), a general-purpose modeling platform that overcomes the combinatorial nature of molecular interactions. Unlike standard simulators that represent molecular species as variables in equations, NFsim uses a biologically intuitive representation: objects with binding and modification sites acted on by reaction rules. During simulations, rules operate directly on molecular objects to produce exact stochastic results with performance that scales independently of the reaction network size. Reaction rates can be defined as arbitrary functions of molecular states to provide powerful coarse-graining capabilities, for example to merge Boolean and kinetic representations of biological networks. NFsim enables researchers to simulate many biological systems that were previously inaccessible to general-purpose software, as we illustrate with models of immune system signaling, microbial signaling, cytoskeletal assembly and oscillating gene expression.

In E. coli, chemotactic behavior exhibits perfect adaptation that is robust to changes in the intracellular concentration of the chemotactic proteins, such as CheR and CheB. However, the robustness of the perfect adaptation does not explicitly imply a robust chemotactic response. Previous studies on the robustness of the chemotactic response relied on swarming assays, which can be confounded by processes besides chemotaxis, such as cellular growth and depletion of nutrients. Here, using a high-throughput capillary assay that eliminates the effects of growth, we experimentally studied how the chemotactic response depends on the relative concentration of the chemotactic proteins. We simultaneously measured both the chemotactic response of E. coli cells to l-aspartate and the concentrations of YFP-CheR and CheB-CFP fusion proteins. We found that the chemotactic response is fine-tuned to a specific ratio of [CheR]/[CheB] with a maximum response comparable to the chemotactic response of wild-type behavior. In contrast to adaptation in chemotaxis, that is robust and exact, capillary assays revealed that the chemotactic response in swimming bacteria is fined-tuned to wild-type level of the [CheR]/[CheB] ratio. [ABSTRACT FROM AUTHOR]

The chemotaxis network that governs the motion of Escherichia coli has long been studied to gain a general understanding of signal transduction. Although this pathway is composed of just a few components, it exhibits some essential characteristics of biological complexity, such as adaptation and response to environmental signals. In studying intracellular networks, most experiments and mathematical models have assumed that network properties can be inferred from population measurements. However, this approach masks underlying temporal fluctuations of intracellular signalling events. We have inferred fundamental properties of the chemotaxis network from a noise analysis of behavioural variations in individual bacteria. Here we show that certain properties established by population measurements, such as adapted states, are not conserved at the single-cell level: for timescales ranging from seconds to several minutes, the behaviour of non-stimulated cells exhibit temporal variations much larger than the expected statistical fluctuations. We find that the signalling network itself causes this noise and identify the molecular events that produce it. Small changes in the concentration of one key network component suppress temporal behavioural variability, suggesting that such variability is a selected property of this adaptive system. [ABSTRACT FROM AUTHOR]

The rotary flagellar motor of Escherichia coli bacterium switches stochastically between the clockwise (CW) and counterclockwise (CCW) direction. We found that the CW and CCW intervals could be described by a gamma distribution, suggesting the existence of hidden Markov steps preceding each motor switch. Power spectra of time series of switching events exhibited a peaking frequency instead of the Lorentzian profile expected from standard kinetic two-state models. Our analysis indicates that the number of hidden steps may be a key dynamical parameter underlying the switching process in a single bacterial motor as well as in large cooperative molecular systems., Molecular and Cellular Biology, Physics