ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming

Shah, Osama Shiraz; Chaudhary, Muhammad Faizyab Ali; Awan, Hira Anees; Fatima, Fizza; Arshad, Zainab; Bibi, Amina; Ahmed, Maria; Hameed, Hadia; Furqan, Muhammad; Khalid, Shareef; Faisal, Amir; Chaudhary, Safee Ullah

doi:10.1038/s41598-018-22031-3

Cited by 15 publications

(22 citation statements)

References 65 publications

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“…For models of regulatory networks controlling cell fates, it is of a real interest to identify the model attractors, as well as quantify their reachability over the whole state space or from specific initial conditions. In particular, the impact of model perturbations (e.g., corresponding to observed mutations) on attractors and their basins of attraction has been used to better understand the fates of tumor cells (Huang et al, 2009 ; Kim et al, 2017 ; Shah et al, 2018 ). Most studies rely on Boolean models under a synchronous updating scheme.…”

Section: Discussionmentioning

confidence: 99%

Estimating Attractor Reachability in Asynchronous Logical Models

et al. 2018

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Logical models are well-suited to capture salient dynamical properties of regulatory networks. For networks controlling cell fate decisions, cell fates are associated with model attractors (stable states or cyclic attractors) whose identification and reachability properties are particularly relevant. While synchronous updates assume unlikely instantaneous or identical rates associated with component changes, the consideration of asynchronous updates is more realistic but, for large models, may hinder the analysis of the resulting non-deterministic concurrent dynamics. This complexity hampers the study of asymptotical behaviors, and most existing approaches suffer from efficiency bottlenecks, being generally unable to handle cyclical attractors and quantify attractor reachability. Here, we propose two algorithms providing probability estimates of attractor reachability in asynchronous dynamics. The first algorithm, named Firefront, exhaustively explores the state space from an initial state, and provides quasi-exact evaluations of the reachability probabilities of model attractors. The algorithm progresses in breadth, propagating the probabilities of each encountered state to its successors. Second, Avatar is an adapted Monte Carlo approach, better suited for models with large and intertwined transient and terminal cycles. Avatar iteratively explores the state space by randomly selecting trajectories and by using these random walks to estimate the likelihood of reaching an attractor. Unlike Monte Carlo simulations, Avatar is equipped to avoid getting trapped in transient cycles and to identify cyclic attractors. Firefront and Avatar are validated and compared to related methods, using as test cases logical models of synthetic and biological networks. Both algorithms are implemented as new functionalities of GINsim 3.0, a well-established software tool for logical modeling, providing executable GUI, Java API, and scripting facilities.

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Section: Discussionmentioning

confidence: 99%

Estimating Attractor Reachability in Asynchronous Logical Models

et al. 2018

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“…Attractor can be calculated by the built GRN, and this article introduces the method of attractor calculation based on Boolean network. Because the Boolean model is a mature technique that can abstract the dynamical regulatory relationship between biomolecules in cells 42. Although the Boolean model is a mathematical model with low complexity, it is able to capture the basic characteristics of GRNs and has been widely used as an appropriate method to perform the system-level results of biomolecular networks 43–45.…”

Section: The Pipeline Of Attractor Analysis and The Common Methods Ofmentioning

confidence: 99%

<p>Attractor – a new turning point in drug discovery</p>

Hou¹,

Li²,

Jia³

et al. 2019

DDDT

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Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.

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“…The Boolean network models were analyzed using Deterministic Analysis (DA) [65,95] performed in TISON, an in-house web-based multi-scale modeling platform for in silico systems oncology. The DA pipeline was derived from ATLANTIS [96]. (Table S22).…”

Section: Deterministic Analysismentioning

confidence: 99%

“…The fixed node states file contained fixed values for generating environmental conditions such as normal, stress, or cancer conditions. The cell fate classification file was used to the map network states onto the biological cell fates in the light of particular cell fate markers[96]…”

mentioning

confidence: 99%

In silico Drosophila Patient ModelReveals Optimal Combinatorial Therapies for Colorectal Cancer

Gondal

Butt

Shah

et al. 2020

Preprint

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In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data can aid in the development of novel personalized cancer therapeutics strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). The network models were validated against cell-type-specific RNA-seq gene expression data from the FlyGut-seq database and through three literature-based case studies on colorectal cancer. The results obtained from the study help elucidate cell fate evolution in colorectal tumorigenesis, validate cytotoxicity of nine FDA-approved cancer drugs, and devise optimal personalized drug treatment combinations. The proposed personalized therapeutics approach also helped identify synergistic combinations of chemotherapy (paclitaxel) with targeted therapies (pazopanib, or ruxolitinib) for treating colorectal cancer. In conclusion, this work provides a novel roadmap for decoding colorectal tumorigenesis and in the development of personalized cancer therapeutics through a DPM.

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ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming

Cited by 15 publications

References 65 publications

Estimating Attractor Reachability in Asynchronous Logical Models

Estimating Attractor Reachability in Asynchronous Logical Models

<p>Attractor – a new turning point in drug discovery</p>

In silico Drosophila Patient ModelReveals Optimal Combinatorial Therapies for Colorectal Cancer

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