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.

PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell.

Pharmacokinetics and pharmacodynamics (PKPD) are key considerations in any study of molecular therapies. It is thus imperative to factor their effects into any in silico model of biological tissue involving such therapies. Furthermore, creating 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 & Implementation: The source code for PhysiPKPD is located here: https://github.com/drbergman/PhysiPKPD.

OCSANA+: optimal control and simulation of signaling networks from network analysis.

SUMMARY: OCSANA+ is a Cytoscape app for identifying nodes to drive the system toward 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 network's structure. OCSANA+ includes an update to 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 network's topology. OCSANA+ is implemented as a Cytoscape app to enable a user interface for running analyses and visualizing results. AVAILABILITY AND IMPLEMENTATION: OCSANA+ 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptome dataset for RNA-seq analysis of axolotl embryonic oropharyngeal endoderm explants.

Animal nutrition and toxin deterrence rely on the ability to taste, which occurs through columnar taste cells clustered within taste buds. Taste buds in mammals are located within specialized tissues, called papillae. However, taste buds in fish and amphibians, such as axolotls (Ambystoma mexicanum), are not housed in papillae, rather they are embedded within the pharyngeal epithelium. This simplified tissue level organization, along with the ability of cultured oropharyngeal explants from early embryos to produce taste buds on the same time-line as embryos, make the axolotl an excellent model to identify molecules specifically involved in taste bud cell differentiation. We performed de novo transcriptomic analysis on RNA sequences from three different stages of oropharyngeal explants: stages 37/38, 39, and 41. RNA-seq data from 17 total samples representing these stages were pooled to generate a de novo assembly of the transcriptome using a Trinity pipeline. From 27.9Gb of raw sequences, we identified 21,244 transcripts. To our knowledge, this is the first published assembly of axolotl oropharyngeal endoderm explants. This data and transcriptome assembly relate to the research article "Transcriptome Analysis of Axolotl Oropharyngeal Explants During Taste Bud Differentiation Stages" (Kohli et al. 2020). This RNA-seq data and transcriptome assembly provide information on genes expressed in the oropharyngeal endoderm and will be valuable in the identification of taste bud development genes.

Transcriptome analysis of axolotl oropharyngeal explants during taste bud differentiation stages.

The Mexican salamander, Ambystoma mexicanum (Axolotl), is an excellent vertebrate model system to understand development and regeneration. Studies in axolotl embryos have provided important insights into taste bud development. Taste bud specification and determination occur in the oropharyngeal endoderm of axolotl embryos during gastrulation and neurulation, respectively, whereas taste bud innervation and taste cell differentiation occur later in development. Axolotl embryos are amenable to microsurgery, and tissue explants develop readily in vitro. We performed RNA-seq analysis to investigate the differential expression of genes in oropharyngeal explants at several stages of taste cell differentiation. Since the axolotl genome has only recently been sequenced, we used a Trinity pipeline to perform de novo assembly of sequencing reads. Linear models for RNA-seq data were used to identify differentially expressed genes. We found 1234 unique genes differentially expressed during taste cell differentiation stages. We validated four of these genes using RTqPCR and performed GO functional analysis. The differential expression of these genes suggests that they may play a role in taste cell differentiation in axolotls.