Landeros, A. | bioRxiv Science

bioRxiv2023

Nuclear lamin A-associated proteins are required for centromere assembly

Many Lamin A-associated proteins (LAAPs) that are key constituents of the nuclear envelope (NE), assemble at the "core" domains of chromosomes during NE reformation and mitotic exit. However, the identity and function of the chromosomal core domains remain ill-defined. Here, we show that a distinct section of the core domain overlaps with the centromeres/kinetochores of chromosomes during mitotic telophase. The core domain can thus be demarcated into a kinetochore proximal core (KPC) on one side of the segregated chromosomes and the kinetochore distal core (KDC) on the opposite side, close to the central spindle. We next tested if centromere assembly is connected to NE re-formation. We find that centromere assembly is markedly perturbed after inhibiting the function of LMNA and the core-localized LAAPs, BANF1 and Emerin. We also find that the LAAPs exhibit multiple biochemical interactions with the centromere and inner kinetochore proteins. Consistent with this, normal mitotic progression and chromosome segregation was severely impeded after inhibiting LAAP function. Intriguingly, the inhibition of centromere function also interferes with the assembly of LAAP components at the core domain, suggesting a mutual dependence of LAAP and centromeres for their assembly at the core domains. Finally, we find that the localization of key proteins involved in the centromeric loading of CENP-A, including the Mis18 complex and HJURP were markedly affected in LAAP-inhibited cells. Our evidence points to a model where LAAP assembly at the core domain serves a key function in loading new copies of centromeric proteins during or immediately after mitotic exit.

cell biology↗

bioRxiv2021

Differential Methods for Assessing Sensitivity in Biological Models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex-perturbation sensitivity analysis, and forward-mode sensitivity analysis. We then carry out four instructive case studies. (i) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (ii) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (iii) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (iv) a discrete birth-death-migration model illustrates how the complex-perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward-mode automatic differentiation has the quickest computation time, while the complex-perturbation method is the simplest to implement and the most generalizable. Author SummaryOver the past few decades, mathematical modeling has become an indispensable tool in the biologists toolbox. From deterministic to stochastic to statistical models, computational modeling is ubiquitous in almost every field of biology. Because model parameter estimates are often noisy or depend on poorly understood interactions, it is crucial to examine how both quantitative and qualitative predictions change as parameter estimates change, especially as the number of parameters increase. Sensitivity analysis is the process of understanding how a models behavior depends on parameter values. Sensitivity analysis simultaneously quantifies prediction certainty and clarifies the underlying biological mechanisms that drive computational models. While sensitivity analysis is universally recognized to be an important step in modeling, it is often unclear how to best leverage the available differential sensitivity methods. In this manuscript we explain and compare various differential sensitivity methods in the hope that best practices will be widely adopted. In particular, we stress the relative advantages of existing software and their limitations. We also present a new numerical technique for computing differential sensitivity.

systems biology↗