Abstract
Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.
Highlights
Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells
Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation
Attractors can be visualized in the form of attractor landscapes whereas the cell fates associated with each attractor can be viewed as cell fate landscapes (Fig. 1j)
Summary
Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. The leading tools for computing attractor states include BoolNet[36], The Cell Collective[37] and CellNetAnalyzer[38] These tools fail to associate biologically relevant network states with emergent cell fates. It is the lack of this functionality which further impedes cell fate reprogramming in light of molecular cues from attractor states. To address this need, we propose ATLANTIS, a MATLAB toolbox for determining and reprogramming cell fates using attractor landscape analysis of biomolecular networks. A list of these features along with a comparison of ATLANTIS with existing tools is provided in Supplementary Information (Comparison of Features - ATLANTIS vs. Other Tools)
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