인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Computational electrophysiology models are beginning to emerge as digital‐twin–oriented representations of cancer cells, offering mechanistic insights that complement traditional patch‐clamp experiments. In this study, we evaluate the ability of the earliest in‐silico cancer electrophysiology model, an ion channel model based on Hidden Markov state transitions, to reproduce drug‐modulated current densities in A549 lung adenocarcinoma cells. Using independent experimental data from Glaser et al. (2021), we characterised Ca 2 + ‐activated K + channels, KCa1.1 and KCa3.1, in wild‐type (WT) and erlotinib‐resistant (ER) A549 cells under baseline conditions, as well as after activation with 1‐EBIO (3‐ethyl‐1H‐benzimidazol‐2‐one) and inhibition with paxilline and senicapoc. The in‐silico model reproduced the qualitative order of current responses under all pharmacological conditions, quantitatively matching the paxilline‐ and senicapoc‐blocked states while remaining within biologically reasonable channel expression limits. Reproducing 1‐EBIO activation required higher‐than‐physiological effective channel numbers, indicating that ligand‐dependent gating is not fully represented. Nevertheless, the model captured the overall electrophysiological behaviour of both WT and ER cells and successfully distinguished their phenotypes. In summary, the in‐silico model already enables mechanistic interpretation of electrophysiological phenotypes and drug‐modulated responses. With continued refinement, including the incorporation of ligand‐modulated gating, improved calcium‐feedback dynamics, and formal uncertainty quantification, this model has the potential to evolve into a predictive digital twin platform supporting ion‐channel pharmacology, therapy optimisation and precision oncology.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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