Optimal control of protein copy number
Cell-cell communication is often achieved by secreted signaling molecules that bind membrane-bound receptors. A common class of such receptors are G-protein coupled receptors, where extracellular binding induces changes on the membrane affinity near the receptor for certain cytosolic proteins, effectively altering their chemical potential. We analyze the minimum-dissipation schedules for dynamically changing chemical potential to induce steady-state changes in protein copy-number distributions, and illustrate with analytic solutions for linear chemical reaction networks. Protocols that change chemical potential on biologically relevant timescales are experimentally accessible using optogenetic manipulations, and our framework provides non-trivial predictions about functional dynamical cell-cell interactions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Single-molecular quantification of flowering control proteins within nuclear condensates in live whole Arabidopsis root
Here we describe the coupled standardisation of two complementary fluorescence imaging techniques and apply it to liquid-liquid phase separated condensates formed from an EGFP fluorescent reporter of Flowering Control Lo…
Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets
The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferati…
Decision MakingTowards fully automated protein structure elucidation with NMR spectroscopy
Nuclear magnetic resonance (NMR) spectroscopy is one of the leading techniques for protein studies. The method features a number of properties, allowing to explain macromolecular interactions mechanistically and resolve …
Combinatorial OptimizationA preparative mass spectrometer to deposit intact large native protein complexes
Electrospray ion-beam deposition (ES-IBD) is a versatile tool to study structure and reactivity of molecules from small metal clusters to large protein assemblies. It brings molecules gently into the gas phase where they…
3D Structure from 2D Microscopy images using Deep Learning
Understanding the structure of a protein complex is crucial indetermining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are…
Deep Learning