Biology subjects

Stumpf, M.

Publications and source records attributed to Stumpf, M..

6 recordsLinked to original sources

bioRxiv2022

Quantification of Early Gait Development: Expanding the Application of Catwalk Technology to an Infant Rhesus Macaque Model

BackgroundUnderstanding gait development is essential for identifying motor impairments in neurodevelopmental disorders. Defining typical gait development in a rhesus macaque model is critical prior to characterizing abnormal gait. The goal of this study was to 1) explore the feasibility of using the Noldus Catwalk to assess gait in infant rhesus macaques and 2) provide preliminary normative data of gait development during the first month of life. New methodThe Noldus Catwalk was used to assess gait speed, dynamic and static paw measurements, and interlimb coordination in twelve infant rhesus macaques at 14, 21, and 28 days of age. All macaque runs were labeled as a diagonal or non-diagonal walking pattern. ResultsInfant rhesus macaques primarily used a diagonal (mature) walking pattern as early as 14 days of life. Ten infant rhesus macaques (83.3%) were able to successfully walk across the Noldus Catwalk at 28 days of life. Limited differences in gait parameters were observed between timepoints because of the variability within the group at 14, 21, and 28 days. Comparison with existing methods: No prior gait analysis system has been used to provide objective quantification of gait parameters for infant macaques. ConclusionsThe Catwalk system can be utilized to quantify gait in infant rhesus macaques less than 28 days old. Future applications to infant rhesus macaques could provide a better understanding of gait development and early differences within various neurodevelopmental disorders. Highlights- Infant rhesus macaque gait parameters can successfully be captured by the Catwalk - At 14 days of life, macaques were consistently using a diagonal walking pattern - Limited developmental change occurs in gait parameters over the first month of life - Infant macaque gait parameters had high within group variability at each timepoint Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=190 HEIGHT=200 SRC="FIGDIR/small/516450v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@6185edorg.highwire.dtl.DTLVardef@9fa472org.highwire.dtl.DTLVardef@e54919org.highwire.dtl.DTLVardef@12231cc_HPS_FORMAT_FIGEXP M_FIG C_FIG Note: Brown and grey-colored macaques were created in biorender

animal behavior and cognition↗

bioRxiv2020

Development of antibody-dependent cell cytotoxicity function in HIV-1 antibodies

A prerequisite for the design of an HIV vaccine that elicits protective antibodies is understanding the developmental pathways that result in desirable antibody features. The development of antibodies that mediate antibody-dependent cellular cytotoxicity (ADCC) is particularly relevant because such antibodies have been associated with HIV protection in humans. We reconstructed the developmental pathways of six human HIV-specific ADCC antibodies using longitudinal antibody sequencing data. Most of the inferred naive antibodies did not mediate detectable ADCC. Gain of antigen binding and ADCC function typically required mutations in complementarity determining regions of one or both chains. Enhancement of ADCC potency often required additional mutations in framework regions. Antigen binding affinity and ADCC activity were correlated, but affinity alone was not sufficient to predict ADCC potency. Thus, elicitation of human ADCC antibodies may require mutations that first enable high affinity antigen recognition, followed by mutations that optimize factors contributing to functional ADCC activity.

immunology↗

bioRxiv2019

GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using i) standard rejection ABC or ABC-SMC, or ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.\n\nURL: https://github.com/tanhevg/GpABC.jl

systems biology↗

bioRxiv2018

Signalling pathways drive heterogeneity of ground state pluripotency

Pluripotent stem cells (PSCs) can self-renew indefinitely while maintaining the ability to generate all cell types of the body. This plasticity is proposed to require heterogeneity in gene expression, driving a metastable state which may allow flexible cell fate choices. Contrary to this, naive PSC grown in fully defined 2i environmental conditions, containing small molecule inhibitors of MEK and GSK3 kinases, show homogenous pluripotency and lineage marker expression. However, here we show that 2i induces greater genome-wide heterogeneity than traditional serum-containing growth environments at the population level across both male and female PSCs. This heterogeneity is dynamic and reversible over time, consistent with a dynamic metastable equilibrium of the pluripotent state. We further show that the 2i environment causes increased heterogeneity in the calcium signalling pathway at both the population and single-cell level. Mechanistically, we identify loss of robustness regulators in the form of negative feedback to the upstream EGF receptor. Our findings advance the current understanding of the plastic nature of the pluripotent state and highlight the role of signalling pathways in the control of transcriptional heterogeneity. Furthermore, our results have critical implications for the current use of kinase inhibitors in the clinic, where inducing heterogeneity may increase the risk of cancer metastasis and drug resistance.

molecular biology↗

bioRxiv2018

Parametric and Non-parametric Gradient Matching for Network Inference

Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. To avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately. Based on this gradient matching approach, we evaluate the fits of parametric as well as non-parametric candidate models to the data under various settings for different inference objectives. We also use model averaging, based on the Bayesian Information Criterion (BIC), in order to combine the different inferences. We found that parametric methods can provide comparable, and often improved inference compared to non-parametric methods; the latter, however, require no kinetic information and are computationally more efficient.\n\nThe code used in this work is available at https://github.com/ld2113/Final-Project.

systems biology↗

bioRxiv2016

Network inference and hypotheses-generation from single-cell transcriptomic data using multivariate information measures

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

systems biology↗