Developing effective drugs for Alzheimer’s disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood–brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs.

Current spreading could be improved by using Ag-mesh-combined graphene sheets due to dramatically reducing the sheet resistance.

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:We analyzed the relationships between backscattering coefficients of wheat measured by COSMO-SkyMed SAR and biophysical measurements such as biomass, vegetation water content, and soil moisture over an entire wheat growth period. Backscattering coefficients increased until DOY 129 and then decreased along with fresh weight, dry weight, and vegetation water content. Correlation analysis between backscattering and wheat growth parameters revealed that backscatter correlated well with fresh weight (r=0.88), vegetation water content (r=0.87), and dry weight (r=0.80), while backscatter did not correlated with soil moisture (r=0.18). Prediction equations for estimation of wheat growth parameters from the backscattering coefficients were developed.

Motivation Cellular behavior is determined by complex nonlinear interactions between numerous intracellular molecules that are often represented by Boolean network models. To achieve a desired cellular behavior with minimal intervention, we need to identify optimal control targets that can drive heterogeneous cellular states to the desired phenotypic cellular state with minimal node intervention. Previous attempts to realize such global stabilization were based solely on either network structure information or simple linear dynamics. Other attempts based on nonlinear dynamics are not scalable. Results Here, we investigate the underlying relationship between structurally identified control targets and optimal global stabilizing control targets based on nonlinear dynamics. We discovered that optimal global stabilizing control targets can be identified by analyzing the dynamics between structurally identified control targets. Utilizing these findings, we developed a scalable global stabilizing control framework using both structural and dynamic information. Our framework narrows down the search space based on strongly connected components and feedback vertex sets then identifies global stabilizing control targets based on the canalization of Boolean network dynamics. We find that the proposed global stabilizing control is superior with respect to the number of control target nodes, scalability, and computational complexity. Availability We provide a GitHub repository that contains the DCGS framework written in Python as well as biological random Boolean network datasets (https://github.com/sugyun/DCGS). Supplementary information Supplementary data are available at Bioinformatics online.