Collaborative Research: MODULUS: Data-Driven Discovery for Mechanisms of Nuclear Dynamics and Scaling
Collaborative Research: MODULUS: Data-Driven Discovery for Mechanisms of Nuclear Dynamics and Scaling
批准号:
2052640
负责人:
Jesse Gatlin
金额:
$37.58万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-01 至 2024-08-31
中文摘要
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英文摘要
This award supports research on the fundamental biological question of scaling – how cells and organelles regulate size relative to the whole organism. The nucleus is a particular organelle where scaling is tightly regulated and aberrations are often hallmarks of disease. Consequently, a better understanding of these fundamental mechanisms may lead to novel treatments. The Principal Investigators (PIs) will iterate between wet-lab experiments and mathematical modeling to uncover the principles underpinning scaling in the model system Xenopus Laevis (frog). The PIs posit that the dynamics of nuclear growth and ultimately the steady-state size of the organelle depend upon multiple, interdependent processes including (i) nuclear pore complex (NPC) mediated import of soluble proteins, (ii) osmotic/oncotic pressure differences, (iii) microtubule-dependent transport of vesicular membrane building blocks, and (iv) regulated exchange between the outer nuclear membrane and the contiguous endoplasmic reticulum. Using parameter estimation and Bayesian model selection criteria, The PIs will delineate the roles played by each of these processes. The outcomes of this award will provide a framework for data driven discovery in a vast range of biological problems. This award will enable the training of the next generation of biophysical researchers who will be immersed in both theoretical and experimental environments.The PIs will determine the mechanisms of nuclear scaling using the model system X. laevis within an iterative experimental and modeling approach. On the experimental end, the PIs will combine microfluidics, photolabile hydrogels, and cell-free cytoplasmic extracts derived from X. laevis eggs. This platform enables recapitulation of nuclear assembly and growth under controlled experimental conditions in which variables such as cytoplasmic volume, cytoplasmic shape, and cytoplasmic composition can be independently modulated. To complement this experimental framework, the PIs will develop a hierarchy of mathematical models ranging from analytically tractable (single nucleus, radially symmetric), to computationally demanding (multiple interacting nuclei, complex cell geometry). These models will take the form of free boundary partial differential equations (reaction-advection-diffusion PDEs) and describe the transport of proteins by unbiased diffusion, directed motion along microtubules and active fluid flow. To incorporate surface details of the nuclear envelope, particularly the arrangement and dynamics of NPCs during nuclear growth, macroscopic laws describing rates of import and growth will be obtained using asymptotic analysis and homogenization theory. Broader impacts arising from this award include 1) cross disciplinary training of graduate students in both experimental and modeling through rotations at U. Wyoming and Notre Dame 2) two international workshops aimed at cementing connections between biological and mathematical scientists and further dissemination of research methodologies and tools.This project is jointly funded by the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program, the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Molecular and Cellular Biosciences (MCB) through the Systems and Synthetic Biology program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
Mathematical modeling accurately predicts the dynamics and scaling of nuclear growth in discrete cytoplasmic volumes
数学模型准确预测离散细胞质体积中核生长的动态和缩放
DOI:
10.1016/j.jtbi.2021.110936
发表时间:
2022
期刊:
Journal of Theoretical Biology
影响因子:
2
作者:
[Leech, V., Hazel, J.W., Gatlin, J.C., Lindsay, A.E., Manhart, A.]
通讯作者:
Manhart, A.
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