Rapid rebinding of molecular interaction partners that are in close proximity after dissociation leads to a dissociation and association kinetics that can profoundly differ from predictions based on bulk reaction models. The cause of this effect can be traced back to the non-Markovian character of the ligand's rebinding time probability density function, reflecting the fact that, for a certain time span, the ligand still 'remembers' the receptor it was bound to previously. In this manuscript, we explore the consequences of the hypothesis that initial binding and consecutive rebinding give rise to a bond lifetime density that is non-Markovian as well. We study the combined effect of the two non-Markovian waiting time probability densities and show that even for very short times the decay of the fraction of occupied receptors deviates from an exponential. For long times, dissociation is slower than an exponential and the fate of the the steady-state bound receptor fraction critically depends on the extent of the deviation, relative to the rebinding time density, from the Markovian limit: The population of occupied receptors may either decay completely or assume a non-vanishing value for small and strong deviations, respectively. Furthermore, we point out the important role played by fractional calculus and demonstrate that the short- and long-time dynamics of the occupied receptors can be naturally expressed as well as easily obtained in terms of fractional differential equations involving the Riemann-Liouville derivative. Our analysis shows that cells may exploit receptor memory as mechanism to dynamically widen the range of potential response-patterns to a given signal.
GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery
The State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China（中国科学院上海药物研究所国家药物研究重点实验室）；School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA（卡内基梅隆大学计算机科学学院）；College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan（湖南大学计算机科学与电子工程学院）；Lingang Laboratory, Shanghai, China（上海灵冈实验室）；Research Center for Medicinal Structural Biology, National Research Center for Translational Medicine at Shanghai, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China（上海 translational 医学研究中心，国家医学基因组学重点实验室，瑞金医院，上海交通大学医学院，上海，中国）；School of Pharmacy, Fudan University, Shanghai 201203, China（复旦大学药学院）
Simulating Organogenesis in COMSOL: Phase-Field Based Simulations of Embryonic Lung Branching Morphogenesis
D-BSSE, ETH Zurich（巴塞尔生物系统学院，苏黎世联邦理工学院）；Swiss Institute of Bioinformatics (SIB)（瑞士生物信息学研究所）；Institute of Scientific Computing, TU Dresden（德累斯顿工业大学科学计算研究所）；Faculty of Informatics/Mathematics, HTW Dresden（德累斯顿应用科技大学信息/数学学院）
