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As agricultural biotechnology continues to develop solutions for addressing crop pests through newly expressed proteins from novel source organisms, with different modes or sites of action and/or different spectra of activity, the safety of these proteins will be assessed. The results of hazard-identification and characterization studies for the insecticidal protein IPD079Ea, which is derived from a fern (Ophioglossum pendulum) and active against the maize pest western corn rootworm (Diabrotica virgifera virgifera, Coleoptera: Chrysomelidae) are provided. Collectively these results indicate that IPD079Ea is unlikely to present a hazard to human or animal health and support the safety of genetically modified maize expressing IPD079Ea.

Circadian timekeeping by intracellular molecular clocks is evident widely in prokaryotes and eukaryotes. The clockworks are driven by autoregulatory feedback loops that lead to oscillating levels of components whose maxima are in fixed phase relationships with one another. These phase relationships are the key metric characterizing the operation of the clocks. In this study, we built a mathematical model from the regulatory structure of the intracellular circadian clock in mice and identified its parameters using an iterative evolutionary strategy, with minimum cost achieved through conformance to phase separations seen in cell-autonomous oscillators. The model was evaluated against the experimentally observed cell-autonomous circadian phenotypes of gene knockouts, particularly retention of rhythmicity and changes in expression level of molecular clock components. These tests reveal excellent de novo predictive ability of the model. Furthermore, sensitivity analysis shows that these knockout phenotypes are robust to parameter perturbation.

Classical sensitivity analysis is routinely used to identify points of fragility or robustness in biochemical networks. However, intracellular systems often contain components that number in the thousands to tens or less and consequently motivate a stochastic treatment. Although methodologies exist to quantify sensitivities in stochastic models, they differ substantially from those used in deterministic regimes. Therefore it is not possible to tell whether observed differences in sensitivity measured in deterministic and stochastic elaborations of the same network are the result of methodology or model form. The authors introduce here a distribution-based methodology to measure sensitivity that is equally applicable in both regimes, and demonstrate its use and applicability on a sophisticated mathematical model of the mouse circadian clock that is available in both deterministic and stochastic variants. The authors use the method to produce sensitivity measurements on both variants. They note that the rank-order sensitivity of the clock to parametric perturbations is extremely well conserved across several orders of magnitude. The data show that the clock is fragile to perturbations in parameters common to the cellular machinery ('global' parameters) and robust to perturbations in parameters that are clock-specific ('local' parameters). The sensitivity measure can be used to reduce the model from its original 73 ordinary differential equations (ODEs) to 18 ODEs and to predict the degree to which parametric perturbation can distort the phase response curve of the clock. Finally, the method is employed to evaluate the effect of transcriptional and translational noise on clock function. [Includes supplementary material].

Publisher Summary This chapter focuses on system's analysis tools and its application to the study of biological systems through mathematical modeling. The tools from classical sensitivity analysis are outlined, and described with application to the unraveling of design principles in complex biophysical networks, particularly with regard to robustness. Applications to optimized experimental design, and hypothesis discrimination are also discussed. In the field of systems biology, sensitivity analysis has been used in a number of applications, including optimized design of synthetic circuits, design of experiments for optimal parameter estimation, and robustness analysis to provide insights into design principles. Examples of how these sensitivity analysis implications and extensions have already been applied to biological systems are provided in this chapter. Sensitivity analysis investigates the changes in a system's behavior in response to infinitesimal parametric perturbations. Results from UPR sensitivity comparisons showed the greatest disparity between the BiP-heterologous unfolded protein binding rate sensitivities for the two models. Sensitivity analysis is also used to explore a stochastic, oscillatory system: the mouse circadian rhythm. The fundamental sensitivity analysis concept, or definition, is quite basic: perturbations in model parameters will affect model outputs. However, its implications, extensions, and applications are profound and numerous in the field of systems biology.

The reductionist approaches of molecular and cellular biology have produced revolutionary advances in our understanding of biological function and information processing. The difficulty associated with relating molecular components to their systemic function led to the development of systems biology, a relatively new field that aims to establish a bridge between molecular level information and systems level understanding. The novelty of systems biology lies in the emphasis on analyzing complexity in networked biological systems using integrative rather than reductionist approaches. By its very nature, systems biology is a highly interdisciplinary field that requires the effective collaboration of scientists and engineers with different technical backgrounds, and the interdisciplinary training of students to meet the rapidly evolving needs of academia, industry, and government. This chapter summarizes state-of-the-art developments of automatic control in systems biology with substantial theoretical background and illustrative examples.

Micro RNAs comprise a large family of small, functional RNAs with important roles in the regulation of protein coding genes in animals and plants. Here we show that human and mouse miRNA22 precursor molecules are subject to posttranscriptional modification by A-to-I RNA editing in vivo. The observed editing events are predicted to have significant implications for the biogenesis and function of miRNA22 and might point toward a more general role for RNA editing in the regulation of miRNA gene expression.