NetEvo: A computational framework for the evolution of dynamical complex networks

NetEvo is a computational framework designed to help understand the evolution of dynamical complex networks. It provides flexible tools for the simulation of dynamical processes on networks and methods for the evolution of underlying topological structures. The concept of a supervisor is used to bring together both these aspects in a coherent way. It is the job of the supervisor to rewire the network topology and alter model parameters such that a user specified performance measure is minimised. This performance measure can make use of current topological information and simulated dynamical output from the system. Such an abstraction provides a suitable basis in which to study many outstanding questions related to complex system design and evolution

Assaying protein palmitoylation in plants

<p>Abstract</p> <p>Background</p> <p>Protein S-acylation (also known as palmitoylation) is the reversible post-translational addition of acyl lipids to cysteine residues in proteins through a thioester bond. It allows strong association with membranes. Whilst prediction methods for S-acylation exist, prediction is imperfect. Existing protocols for demonstrating the S-acylation of plant proteins are either laborious and time consuming or expensive.</p> <p>Results</p> <p>We describe a biotin switch method for assaying the S-acylation of plant proteins. We demonstrate the technique by showing that the heterotrimeric G protein subunit AGG2 is S-acylated as predicted by mutagenesis experiments. We also show that a proportion of the Arabidopsis alpha-tubulin subunit pool is S-acylated <it>in planta</it>. This may account for the observed membrane association of plant microtubules. As alpha-tubulins are ubiquitously expressed they can potentially be used as a positive control for the S-acylation assay regardless of the cell type under study.</p> <p>Conclusion</p> <p>We provide a robust biotin switch protocol that allows the rapid assay of protein S-acylation state in plants, using standard laboratory techniques and without the need for expensive or specialised equipment. We propose alpha-tubulin as a useful positive control for the protocol.</p

Towards an engineering theory of evolution

Effective biological engineering requires the acknowledgement of evolution and its consideration during the design process. In this perspective, the authors present the concept of the evotype to reason about and shape the evolutionary potential of natural and engineered biosystems

Understanding metabolic flux behaviour in whole-cell model output

Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design

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Summary Rapid tip growth allows for efficient development of highly elongated cells (e.g. neuronal axons, fungal hyphae and pollen tubes) and requires an elaborate spatiotemporal regulation of the growing region. Here, we use the pollen tube as a model to investigate the mechanism regulating the growing region. ROPs (Rho-related GTPases from plants) are essential for pollen tip growth and display oscillatory activity changes in the apical plasma membrane (PM). By manipulating the ROP activity level, we showed that the PM distribution of ROP activity as an apical cap determines the tip growth region and that efficient tip growth requires an optimum level of the apical ROP1 activity. Excessive ROP activation induced the enlargement of the tip growth region, causing growth depolarization and reduced tube elongation. Time-lapse analysis suggests that the apical ROP1 cap is generated by lateral propagation of a localized ROP activity. Subcellular localization and gain-and loss-of-function analyses suggest that RhoGDI-and RhoGAP-mediated global inhibition limits the lateral propagation of apical ROP1 activity. We propose that the balance between the lateral propagation and the global inhibition maintains an optimal apical ROP1 cap and generates the apical ROP1 activity oscillation required for efficient pollentube elongation

Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks

We develop methods to decipher the rules controlling how small structures cluster and connect in complex networks.</jats:p

BSim 2.0:An Advanced Agent-Based Cell Simulator

Agent-based models (ABMs) provide a number of advantages relative to traditional continuum modeling approaches, permitting incorporation of great detail and realism into simulations, allowing in silico tracking of single-cell behaviors and correlation of these with emergent effects at the macroscopic level. In this study we present BSim 2.0, a radically new version of BSim, a computational ABM framework for modeling dynamics of bacteria in typical experimental environments including microfluidic chemostats. This is facilitated through the implementation of new methods including cells with capsular geometry that are able to physically and chemically interact with one another, a realistic model of cellular growth, a delay differential equation solver, and realistic environmental geometries

Cheetah:a computational toolkit for cybergenetic control

Abstract Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah’s core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah’s segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells