Biology subjects

Veliz-Cuba, A.

Publications and source records attributed to Veliz-Cuba, A..

4 recordsLinked to original sources

bioRxiv2023

Modularity of biological systems: a link between structure and function

This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.

systems biology↗

bioRxiv2023

Model-free Identification of Phenotype-Relevant Variables From Dose Response Data

Complex phenotypic changes occur during development and in response to injury and disease. Identifying key regulators of phenotypic change is a shared aim of many different fields of research, including life history, tissue regeneration, and cancer. These examples of phenotypic change involve coordinated changes in cellular behaviors and associated changes in gene expression which if disrupted, can alter and even block completion of the phenotypic transition. Experimental treatments that effectively block the completion of a phenotypic transition can be quantitatively manipulated to identify key regulatory genes through changes in transcriptional dose response. In this paper, we present a "model free" approach to identify "bifurcation-like" behaviors of key regulatory genes by tracking spikes in their transcriptional sensitivities. Genes that exhibit such behavior are predicted to comprise nodes in subnetworks or modules that regulate the phenotypic transition. We applied our method to an in-silico data set where we also studied the impact of noise in the predictions. We also applied the method to a gene expression data that were collected during tail regeneration in axolotls. Our code for gene identification, which can be extended more generally to other component variables of complex phenotypic change, is freely available via the following GitHub site github.com/alanavc/id-vars-from-resp-data.

developmental biology↗

bioRxiv2022

Building model prototypes from time-course data

A primary challenge in building predictive models from temporal data is selecting the appropriate model topology and the regulatory functions that describe the data. Software packages are available for equation learning of continuous models, but not for discrete models. In this paper we introduce a method for building model prototypes. These model prototypes consist of a wiring diagram and a set of discrete functions that can explain the time course data. The method takes as input a collection of time course data or discretized measurements over time. After network inference, we use our toolbox to simulate the prototype model as a stochastic Boolean model. Our method provides a model that can qualitatively reproduce the patterns of the original data and can further be used for model analysis, making predictions, and designing interventions. We applied our method to a time-course, gene-expression data that were collected during salamander tail regeneration under control and intervention conditions. The inferred model captures important regulations that were previously validated in the research literature and gives novel interactions for future testing. The toolbox for inference and simulations is freely available at github.com/alanavc/prototype-model.

systems biology↗

bioRxiv2019

Performance of normative and approximate evidence accumulation on the dynamic clicks task

The aim of a number of psychophysics tasks is to uncover how mammals make decisions in a world that is in flux. Here we examine the characteristics of ideal and near-ideal observers in a task of this type. We ask when and how performance depends on task parameters and design, and, in turn, what observer performance tells us about their decision-making process. In the dynamic clicks task subjects hear two streams (left and right) of Poisson clicks with different rates. Subjects are rewarded when they correctly identify the side with the higher rate, as this side switches unpredictably. We show that a reduced set of task parameters defines regions in parameter space in which optimal, but not near-optimal observers, maintain constant response accuracy. We also show that for a range of task parameters an approximate normative model must be finely tuned to reach near-optimal performance, illustrating a potential way to distinguish between normative models and their approximations. In addition, we show that using the negative log-likelihood and the 0/1-loss functions to fit these types of models is not equivalent: the 0/1-loss leads to a bias in parameter recovery that increases with sensory noise. These findings suggest ways to tease apart models that are hard to distinguish when tuned exactly, and point to general pitfalls in experimental design, model fitting, and interpretation of the resulting data.

neuroscience↗