The Role of Criticality of Gene Regulatory Networks on Emergent Properties of Biological Systems

The relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single cell level has been vigorously studied. However, the relationship between the criticality of GRNs and properties of multicellular organisms at a higher level has not been fully explored. Here we aim at revealing potential roles of the criticality of GRNs at a multicellular and hierarchical level, using a random Boolean network as a GRN. We perform three studies. Firstly, we propose a GRN-based morphogenetic model, and delve into the role of the criticality of GRNs in morphogenesis at a multicellular level. Secondly, we include an evolutionary context in our morphogenetic model by introducing genetic perturbations (e.g., mutations) to GRNs, and examine whether the role of the criticality of GRNs can be maintained even in the presence of the evolutionary perturbations. Also, we look into what the resulting morphologies are like and what kind of biological implications they have from the epigenetic viewpoint in morphology. Lastly, we present multilayer GRNs consisting of an intercellular layer and an intracellular layer. A network in an intercellular layer represents interactions between cells, and a network in an intracellular layer means interactions between genes. All the nodes of an intercellular network have identical intracellular GRNs. We investigate how the criticality of GRNs affects the robustness and evolvability of the multilayer GRNs at a hierarchical level, depending on cellular topologies and the number of links of an intercellular network. From the three studies, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies at a multicellular level, and generated robust and evolvable multilayer GRNs most frequently at a hierarchical level. Our findings indicate that the roles of the criticality of GRNs are hard to be discovered through the single-cell-level studies. It justifies the value of our research on the relationship between criticality of GRNs and properties of organisms in the context of multicellular settings

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network

Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.Comment: 22 pages, 10 figure

Bespoke Nanoparticle Synthesis and Chemical Knowledge Discovery Via Autonomous Experimentations

The optimization of nanomaterial synthesis using numerous synthetic variables is considered to be extremely laborious task because the conventional combinatorial explorations are prohibitively expensive. In this work, we report an autonomous experimentation platform developed for the bespoke design of nanoparticles (NPs) with targeted optical properties. This platform operates in a closed-loop manner between a batch synthesis module of NPs and a UV- Vis spectroscopy module, based on the feedback of the AI optimization modeling. With silver (Ag) NPs as a representative example, we demonstrate that the Bayesian optimizer implemented with the early stopping criterion can efficiently produce Ag NPs precisely possessing the desired absorption spectra within only 200 iterations (when optimizing among five synthetic reagents). In addition to the outstanding material developmental efficiency, the analysis of synthetic variables further reveals a novel chemistry involving the effects of citrate in Ag NP synthesis. The amount of citrate is a key to controlling the competitions between spherical and plate-shaped NPs and, as a result, affects the shapes of the absorption spectra as well. Our study highlights both capabilities of the platform to enhance search efficiencies and to provide a novel chemical knowledge by analyzing datasets accumulated from the autonomous experimentations

Measuring the Local Twist Angle and Layer Arrangement in Van der Waals Heterostructures

The properties of Van der Waals heterostructures are determined by the twist angle and the interface between adjacent layers as well as their polytype and stacking. Here we describe the use of spectroscopic Low Energy Electron Microscopy (LEEM) and micro Low Energy Electron Diffraction ({\mu}LEED) methods to measure these properties locally. We present results on a MoS$_{2}$/hBN heterostructure, but the methods are applicable to other materials. Diffraction spot analysis is used to assess the benefits of using hBN as a substrate. In addition, by making use of the broken rotational symmetry of the lattice, we determine the cleaving history of the MoS$_{2}$ flake, i.e., which layer stems from where in the bulk

Non-Abelian topological defects and strain mapping in 2D moir\'e materials

We present a general method to analyze the topological nature of the domain boundary connectivity that appeared in relaxed moir\'e superlattice patterns at the interface of 2-dimensional (2D) van der Waals (vdW) materials. At large enough moir\'e lengths, all moir\'e systems relax into commensurated 2D domains separated by networks of dislocation lines. The nodes of the 2D dislocation line network can be considered as vortex-like topological defects. We find that a simple analogy to common topological systems with an $S^1$ order parameter, such as a superconductor or planar ferromagnet, cannot correctly capture the topological nature of these defects. For example, in twisted bilayer graphene, the order parameter space for the relaxed moir\'e system is homotopy equivalent to a punctured torus. Here, the nodes of the 2D dislocation network can be characterized as elements of the fundamental group of the punctured torus, the free group on two generators, endowing these network nodes with non-Abelian properties. Extending this analysis to consider moir\'e patterns generated from any relative strain, we find that antivortices occur in the presence of anisotropic heterostrain, such as shear or anisotropic expansion, while arrays of vortices appear under twist or isotropic expansion between vdW materials. Experimentally, utilizing the dark field imaging capability of transmission electron microscopy (TEM), we demonstrate the existence of vortex and antivortex pair formation in a moir\'e system, caused by competition between different types of heterostrains in the vdW interfaces. We also present a methodology for mapping the underlying heterostrain of a moir\'e structure from experimental TEM data, which provides a quantitative relation between the various components of heterostrain and vortex-antivortex density in moir\'e systems.Comment: 15 pages with 11 figure