Biology subjects

Marazzi, L.

Publications and source records attributed to Marazzi, L..

5 recordsLinked to original sources

bioRxiv2022

Diversity-stability relationships become decoupled across spatial scales: a synthesis of organism and ecosystem types

The relationship between biodiversity and stability, or its inverse, temporal variability, is multidimensional and complex. Temporal variability in aggregate properties, like total biomass or abundance, is typically lower in communities with higher species diversity (i.e., the diversity-stability relationship or DSR). Recent work has shown that, at broader spatial extents, regional-scale aggregate variability is also lower with higher regional diversity (in plant systems) and with lower spatial synchrony. However, it is not yet clear whether regional DSRs hold across a broad range of organisms and ecosystem types. Furthermore, focusing exclusively on aggregate properties of communities may overlook potentially destabilizing compositional shifts. To test these questions, we compiled a large collection of long-term spatial metacommunity data spanning a wide range of taxonomic groups (e.g., birds, fish, plants, invertebrates) and ecosystem types (e.g., deserts, forests, oceans). We applied a newly developed quantitative framework for jointly analyzing aggregate and compositional variability across scales. We quantified DSRs for composition and total abundance in local communities and metacommunities. At the local scale, compositional DSRs suggested that higher local () diversity was associated with lower variability in animal populations but higher variability in plant populations, while aggregate DSRs supported the classic stabilizing effects of diversity. Spatial synchrony differed among taxa (birds had the lowest, plants the highest), suggesting differences in stabilization by spatial processes. Spatial synchrony declined with higher diversity among sites ({beta}) for both compositional and aggregate properties. However, at the regional ({gamma}) scale, we found no aggregate DSR, but a positive compositional DSR. Across a broader range of taxa, our results suggest that high {gamma}-diversity does not consistently stabilize aggregate properties at regional scales without sufficient spatial {beta}-diversity to reduce spatial synchrony. Open research statementAll data sets are accessible via the Environmental Data Initiative, and a specific data package of the data sets used in this analysis will be made publicly available (doi: pending). Citations to original sources are included in Appendix S1. Code to reproduce the analyses is found in a Zenodo archive (doi: pending) of the GitHub repository for this project (https://github.com/sokole/ltermetacommunities/tree/master/Manuscripts/MS3).

ecology↗

bioRxiv2022

PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell

Pharmacokinetics and pharmacodynamics are key considerations in any study of molecular therapies. It is thus imperative to factor their effects in to any in silico model of biological tissue involving such therapies. Furthermore, creation of a standardized and flexible framework will benefit the community by increasing access to such modules and enhancing their communicability. PhysiCell is an open source physics-based cell simulator, i.e. a platform for modeling biological tissue, that is quickly being adopted and utilized by the mathematical biology community. We present here PhysiPKPD, an open source PhysiCell-based package that allows users to include PKPD in PhysiCell models. Availability & ImplementationThe source code for PhysiPKPD is located here: https://github.com/drbergman/PhysiPKPD.

systems biology↗

bioRxiv2022

NETISCE: A Network-Based Tool for Cell Fate Reprogramming

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using Signal Flow Analysis and Feedback Vertex Set Control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.

systems biology↗

bioRxiv2021

The dual nature of metacommunity variability

There is increasing interest in measuring ecological stability to understand how communities and ecosystems respond to broad-scale global changes. One of the most common approaches is to quantify the variation through time in community or ecosystem aggregate attributes (e.g., total biomass), referred to as aggregate variability. It is now widely recognized that aggregate variability represents only one aspect of communities and ecosystems, and compositional variability, the changes in the relative frequency of species in an assemblage, is equally important. Recent contributions have also begun to explore ecological stability at regional spatial scales, where interconnected local communities form metacommunities, a key concept in managing complex landscapes. However, the conceptual frameworks and measures of ecological stability in space have only focused on aggregate variability, leaving a conceptual gap. Here, we address this gap with a novel framework for quantifying the aggregate and compositional variability of communities and ecosystems through space and time. We demonstrate that the compositional variability of a metacommunity depends on the degree of spatial synchrony in compositional trajectories among local communities. We then provide a conceptual framework in which compositional variability of (i) the metacommunity through time and (ii) among local communities combine into four archetype scenarios: spatial stasis (low/low); spatial synchrony (high/low); spatial asynchrony (high/high) and spatial compensation (low/high). We illustrate this framework based on numerical examples and a case study of a macroalgal metacommunity in which low spatial synchrony reduced variability in aggregate biomass at the metacommunity scale, while masking high spatial synchrony in compositional trajectories among local communities. Finally, we discuss the role of dispersal, environmental heterogeneity, species interactions and suggest future avenues. We believe this framework will be helpful for considering both aspects of variability simultaneously which is important to better understand ecological stability in natural and complex landscapes in response to environmental changes.

ecology↗

bioRxiv2019

OCSANA+: Optimal Control and Simulation of Signaling Networks from Network Analysis

SummaryOCSANA+ is a Cytoscape app for identifying nodes to drive the system towards a desired long-term behavior, prioritizing combinations of interventions in large scale complex networks, and estimating the effects of node perturbations in signaling networks, all based on the analysis of the networks structure. OCSANA+ includes an update to OCSANA (optimal combinations of interventions from network analysis) software tool with cutting-edge and rigorously tested algorithms, together with recently-developed structure-based control algorithms for non-linear systems and an algorithm for estimating signal flow. All these algorithms are based on the networks topology. OCSANA+ is implemented as a Cytoscape app to enable a user interface for running analyses and visualizing results. Availability and ImplementationOCSANA+ app and its tutorial can be downloaded from the Cytoscape App Store or https://veraliconaresearchgroup.github.io/OCSANA-Plus/. The source code and computations are available in https://github.com/VeraLiconaResearchGroup/OCSANA-Plus_SourceCode.

systems biology↗