This review discusses recent computational approaches aimed at enhancing the precision and efficiency of the CRISPR-Cas9 gene editing system. These approaches leverage data-driven biophysical modeling, integrating insights from high-throughput experimental datasets and atomistic molecular dynamics simulations to elucidate the underlying molecular mechanisms. We evaluate these computational frameworks in terms of their ability to accurately predict CRISPR-Cas9 cleavage efficiency. Importantly, we highlight how the synergy between computational modeling and experimental validation accelerates the development of more robust predictive tools, while also minimizing the reliance on costly trial-and-error strategies in single-guide RNA (sgRNA) design. Looking ahead, the implementation of closed-loop feedback systems, where computational predictions guide experiments and experimental outcomes refine models, will be essential for realizing the full potential of CRISPR-based therapeutics and applications.
Antibodies are key immune system proteins that recognize and neutralize antigens through their variable domains, which contain complementarity-determining regions (CDRs). Knowledge of antibody-antigen structures is essential for understanding immune recognition and guiding therapeutic antibody development. High-resolution experimental methods such as X-ray crystallography, NMR, and cryo-EM are resource-intensive and impractical for all antibodies due to the vast diversity of antigen-specific antibodies. As a result, computational structure prediction methods have become increasingly valuable for modeling antibody-antigen complexes. This study provides an overview of commonly used computational approaches for predicting antibody-antigen structures, including molecular docking and recent machine learning-driven approaches.
With the rapid development of genome sequencing technology, genomic sequence analysis has become an important field in modern biological research. However, sequencing errors, repetitive regions, and complex biological processes often lead to missing or ambiguous bases in genomic sequences, which are typically represented by non-standard symbols (such as R, Y, S, W, K, etc.). These issues severely affect the accuracy of genomic data, especially in tasks such as gene assembly and variant detection. To address this issue, this study proposes an encoding method based on asymmetric covariance natural vectors to characterize genomic sequences and predict ambiguous bases using Gated Recurrent Unit (GRU). Experimental results demonstrate that, compared with traditional encoding methods (such as one-hot encoding), the asymmetric covariance natural vector can more effectively utilize the information surrounding missing nucleotides for prediction, showing significant advantages in recovering nucleotides missing from intermediate positions. Additionally, this method also performs well on the SARS-CoV-2 Alpha variant dataset, with an error rate of only 1.09% in predicting non-standard bases during the encoding recovery process, further validating its effectiveness and potential for practical genomic data analysis.
Estimation of stochastic systems on very large networks is intractable computationally. Graphon theory provides limit objects for infinite sequences of graphs by mapping adjacency matrices to the unit square, enabling the modelling of dynamical systems on arbitrarily large graphs via functional analytic methods. In previous work (Dunyak and Caines, 2022, 2023, 2024), Q-noise was used to extend stochastic systems on large graphs to stochastic systems in Hilbert spaces on graphons. In this paper, the linear system state estimation problem on large networks and their graphon limits are analyzed, and the Separation Principle of control and estimation is introduced. Convergence of finite network linear system state estimates and the corresponding Kalman filter systems to their graph limit counterparts is established. Subject to the new hypothesis of Q-observability, the Separation Principle is extended to the case of control via finite-dimensional approximating subsystems. This control method is demonstrated on an Erdos-Renyi graph system.
