https://ieeexplore.ieee.org/abstract/document/8721451
In the present paper from the Del Vecchio lab, the authors expose retroactivity as the main cause that accounts for the breakdown of modularity in biomolecular circuits, defining two different types of retroactivity in biomolecular systems (the one due of the sequestration of a protein of a circuit by a downstream protein/module and the one corresponding to the burden to the common pool of resources that a new component poses to the system, being this type one that appears even if the modules of a network are not connected). The paper details a number of useful tools to account mathematically for these phenomena (including a set of rules to translate burden retroactivity into internal interaction equivalent edges of a network and a biochemical equivalent to Thevenin theorem that allows us to study whole circuits as a single black box with an output). It also highights design strategies to limit the effect of retroactivity (use of insulator motifs in local interactions and implementation of negative feedback loops for attenuating the burden-based retroactivity).
Incompatibility of the Schrödinger equation with Langevin and Fokker-Planck equations
https://link.springer.com/article/10.1007/BF02059525
Quantum mechanics posits that the wave function of a one-particle system evolves with time according to the Schrödinger equation, and furthermore has a square modulus that serves as a probability density function for the position of the particle. It is natural to wonder if this stochastic characterization of the particle's position can be framed as a univariate continuous Markov process, sometimes also called a classical diffusion process, whose temporal evolution is governed by the classically transparent equations of Langevin and Fokker-Planck. It is shown here that this cannot generally be done in a consistent way, despite recent suggestions to the contrary.
A universal biomolecular integral feedback controller for robust perfect adaptation
The authors demonstrate the application of their antithetic integral controller (AIC) to fix the levels of certain proteins in E coli, and provide theoretical results showing that the AIC is, in some sense, a minimal molecular integral feedback controller.
The experimental results are undeniably impressive, but perhaps gloss over a number of subtleties. In particular, the underlying reactions show some important differences with respect to the ideal reaction equations of the AIC, and not all aspects of this are clearly addressed. In addition, the perturbation is necessarily of a particular kind in order for the control to work.
Weight-agnostic neural networks
The authors explore the idea of creating artificial neural network structures with topologies suitable for a problem without explicit weight training. The networks are evolved from a set of minimal networks by adding random connections, nodes, and activation functions, and selecting the best performing networks for further evolution. The performance is measured by sampling a shared weight from a uniform distribution for all network connections and averaged over multiple samples.
The networks are tested on continuous control tasks as well as multi-label classification. In control tasks, the architectures outperform a fixed topology for random, random shared, and tuned shared weights and achieve comparable performance with fine-tuned weights. For classification, random shared weights result in equal performance to linear regression.
None of the results are close to any state-of-the-art performance but the general idea is interesting and might be useful in designing computational networks in settings where the ability to tune weights is limited.
Magnetic quadrupole assemblies with arbitrary shapes and magnetizations
Unlike magnetic dipole particles that can only assembled into 2D chain structures, magnetic quadrupole particles have potential to be assembled into arbitrary 2D patterns. By placing two magnetic particles in 3D-printed square case, a magnetic quadrupole was assembled with small residual dipole moment that allows final 2D patterns to align with external magnetic field with specific angle.
DNA-Mediated Proximity-Based Assembly Circuit for Actuation of Biochemical Reactions
DNA strand displacement allows he design of complex networks capable of producing exotic dynamics. However, the toolbox for output transducer is still quite limited. In the present paper, Won Oh et al. demonstrate the experimental viability of a new transducer method: Enzyme/Cofactor co-localization. The method consists of and invader and target strands functionalised with an enzyme and an enzymatic cofactor, respectively. During the system initial state, the cofactor is hidden inside a hairpin to avoid the interaction between enzyme and cofactor, cancelling the enzymatic activity.
In the described system the enzyme is bound to a DNA sequence complementary to the target enzyme containing the cofactor. After the DNA system is triggered by a defined input, the enzyme-functionalised strand is able to form a duplex with the cofactor strand, co-localizing both molecules. The catalytic activity after the co-localization increases up to 110-fold from the initial activity for the tested enzyme (Glucose-6-phosphate deshydrogenase) producing a discernible signal.
This new transduction method will allow to further increase the capabilities of DNA logic circuits to regulate in a direct way molecular processes, outside and inside living systems.
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