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Vera-Licona, P.

Publications and source records attributed to Vera-Licona, P..

5 recordsLinked to original sources

Cancer Cell Adaptation to Cycling Hypoxia: Integrating Mathematical Modeling with Experimental Validation of HIF-1 Dynamics

The adaptive response of cancer cells to hypoxia, a key microenvironmental factor in solid tumors, is intricately orchestrated by Hypoxia-inducible factor 1 (HIF1). Evidence is accumulating on oxygen dynamics in tumor cores, showing that hypoxia is frequently unstable, or cycling, associated with specific phenotypic outcomes for the cancer. Transcriptomic analysis show that most gene expression changes in cycling hypoxia lie in between the change caused by stable hypoxia, suggesting multi-cycle averaging of dosage in the oxygen tension, and likely HIF-1 induced transcription. However, small subset of genes show an oscillation/cycling hypoxia specific response, suggesting that the transcriptional machinery of these genes may interpret cycling HIF-1 activity differently from stably high HIF-1 activity. Here, we model a gene regulatory circuit, the incoherent feed-forward loops (IFFLs) to explore parameter regimes where oscillatory specific transcription is plausible. In these IFFL models, HIF-1 regulates gene transcription of a target gene directly, as well indirectly via another transcription factor with an opposite effect on gene transcription. We have identified several plausible parameter regimes where oscillation specific gene expression can occur, with potentially significant effect on cancer growth and progression. Finally, we experimentally confirmed that HIF-1 can form IFFLs with two key transcription factors p53, and Notch1, resulting in cycling hypoxia specific gene expression linked to breast cancer progression and poor prognosis. Our models mechanistically reveal how temporal fluctuations in the tumor microenvironment may directly inform downstream transcription, identify hitherto unknown HIF-1 driven mechanism of cancer progression contributing to emergent tumor heterogeneity.

bioinformatics↗

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↗

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↗

Benchmarking Time-Series Data Discretization on Inference Methods

The rapid development in quantitatively measuring DNA, RNA, and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider 4 published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed.\n\nAvailabilityAll the datasets, reverse-engineering methods and source code used in this paper are available in Vera-Liconas lab Github repository: https://github.com/VeraLiconaResearchGroup/Benchmarking_TSDiscretizations

systems biology↗

COMPUTING SIGNAL TRANSDUCTION IN SIGNALING NETWORKS MODELED AS BOOLEAN NETWORKS, PETRI NETS AND HYPERGRAPHS

Mathematical frameworks circumventing the need of mechanistic detail to build models of signal transduction networks include graphs, hypergraphs, Boolean Networks, and Petri Nets. Predicting how a signal transduces in a signaling network is essential to understand cellular functions and disease. Different formalisms exist to describe how a signal transduces in a given intracellular signaling network represented in the aforementioned modeling frameworks: elementary signaling modes, T-invariants, extreme pathway analysis, elementary flux modes, and simple paths. How do these formalisms compare?\n\nWe present an overview of how signal transduction networks have been modelled using graphs, hypergraphs, Boolean Networks, and Petri Nets in the literature. We provide a review of the different formalisms for capturing signal transduction in a given model of an intracellular signaling network. We also discuss the existing translations between the different modeling frameworks, and the relationships between their corresponding signal transduction representations that have been described in the literature. Furthermore, as a new formalism of signal transduction, we show how minimal functional routes proposed for signaling networks modeled as Boolean Networks can be captured by computing topological factories, a methodology found in the metabolic networks literature. We further show that in the case of signaling networks represented with an acyclic B-hypergraph structure, the definitions are equivalent. In signaling networks represented as directed graphs, it has been shown that computations of elementary modes via its incidence matrix correspond to computations of simple paths and feedback loops. We show that computing elementary modes based on the incidence matrix of a B-hypergraph fails to capture minimal functional routes.

systems biology↗