A Novel Approach to Comparative RNA-Seq Does Not Support a Conserved Set of Orthologs Underlying Animal Regeneration.

Molecular studies of animal regeneration typically focus on conserved genes and signaling pathways that underlie morphogenesis. To date, a holistic analysis of gene expression across animals has not been attempted, as it presents a suite of problems related to differences in experimental design and gene homology. By combining orthology analyses with a novel statistical method for testing gene enrichment across large data sets, we are able to test whether tissue regeneration across animals shares transcriptional regulation.

Hard Limits and Performance Tradeoffs in a Class of Antithetic Integral Feedback Networks.

Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism called antithetic integral feedback, which can be implemented using the binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control.

Architectural Principles for Characterizing the Performance of Antithetic Integral Feedback Networks.

As we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply mathematical tools from the theory of control and dynamical systems to yield practical insights into the architecture and function of a particular class of biological feedback circuit. Specifically, we show that it is possible to analytically characterize both the operating regime and performance tradeoffs of an antithetic integral feedback circuit architecture.

Evaluation of Hansen et al.: Nuance Is Crucial in Comparisons of Noise.

One snapshot of the peer review process for "Cytoplasmic Amplification of Transcriptional Noise Generates Substantial Cell-to-Cell Variability" (Hansen et al., 2018).

There's (still) plenty of room at the bottom.

Motifs, circuits, and networks are core conceptual elements in modern systems and synthetic biology. While there are still undoubtedly more fascinating computations to discover at network level, there are also rich computations that we are only beginning to uncover within the diverse molecules that constitute the networks. Here we explore some work, both new and old, that showcases the incredible computational capacity of seemingly simple molecular mechanisms.

Modeling and analysis of modular structure in diverse biological networks.

Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of well-defined and meaningful smaller modules. While engineered networks are designed and refined by humans with particular goals in mind, biological networks are created by the selective pressures of evolution.

Allosteric proteins as logarithmic sensors.

Many sensory systems, from vision and hearing in animals to signal transduction in cells, respond to fold changes in signal relative to background. Responding to fold change requires that the system senses signal on a logarithmic scale, responding identically to a change in signal level from 1 to 3, or from 10 to 30. It is an ongoing search in the field to understand the ways in which a logarithmic sensor can be implemented at the molecular level.

Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.

An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling.