Estimating Attractor Reachability in Asynchronous Logical Models

Computational Analysis of Cytokine Release Following Bispecific T-Cell Engager Therapy: Applications of a Logic-Based Model

Bispecific T-cell engaging therapies harness the immune system to elicit an effective anticancer response. Modulating the immune activation avoiding potential adverse effects such as cytokine release syndrome (CRS) is a critical aspect to realizing the full potential of this therapy. The use of suitable exogenous intervention strategies to mitigate the CRS risk without compromising the antitumoral capability of bispecific antibody treatment is crucial. To this end, computational approaches can be instrumental to systematically exploring the effects of combining bispecific antibodies with CRS intervention strategies. Here, we employ a logical model to describe the action of bispecific antibodies and the complex interplay of various immune system components and use it to perform simulation experiments to improve the understanding of the factors affecting CRS. We performed a sensitivity analysis to identify the comedications that could ameliorate CRS without impairing tumor clearance. Our results agree with publicly available experimental data suggesting anti-TNF and anti-IL6 as possible co-treatments. Furthermore, we suggest anti-IFNγ as a suitable candidate for clinical studies.

Dynamical modeling of miR-34a, miR-449a, and miR-16 reveals numerous DDR signaling pathways regulating senescence, autophagy, and apoptosis in HeLa cells

Transfection of tumor suppressor miRNAs such as miR-34a, miR-449a, and miR-16 with DNA damage can regulate apoptosis and senescence in cancer cells. miR-16 has been shown to influence autophagy in cervical cancer. However, the function of miR-34a and miR-449a in autophagy remains unknown. The functional and persistent G1/S checkpoint signaling pathways in HeLa cells via these three miRNAs, either synergistically or separately, remain a mystery. As a result, we present a synthetic Boolean network of the functional G1/S checkpoint regulation, illustrating the regulatory effects of these three miRNAs. To our knowledge, this is the first synthetic Boolean network that demonstrates the advanced role of these miRNAs in cervical cancer signaling pathways reliant on or independent of p53, such as MAPK or AMPK. We compared our estimated probability to the experimental data and found reasonable agreement. Our findings indicate that miR-34a or miR-16 may control senescence, autophagy, apoptosis, and the functional G1/S checkpoint. Additionally, miR-449a can regulate just senescence and apoptosis on an individual basis. MiR-449a can coordinate autophagy in HeLa cells in a synergistic manner with miR-16 and/or miR-34a.

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.

FPGA Accelerated Analysis of Boolean Gene Regulatory Networks

Boolean models are a powerful abstraction for qualitative modeling of gene regulatory networks. With the recent availability of advanced high-throughput technologies, Boolean models have increasingly grown in size and complexity, posing a challenge for existing software simulation tools that have not scaled at the same speed. Field Programmable Gate Arrays (FPGAs) are powerful reconfigurable integrated circuits that can offer massive performance improvements. Due to their highly parallel nature, FPGAs are well suited to simulate complex molecular networks. We present here a new simulation framework for Boolean models, which first converts the model to Verilog, a standardized hardware description language, and then connects it to an execution core that runs on an FPGA coherently attached to a POWER8 processor. We report an order of magnitude speedup over a multi-threaded software simulation tool running on the same processor on a selection of Boolean models. Analysis on a T-cell large granular lymphocyte leukemia (T-LGL) demonstrates that our framework achieves consistent performance improvements resulting in new biological insights. In addition, we show that our solution allows to perform attractor detection at an unprecedented speed, exhibiting a speedup ranging from one to three orders of magnitude compared to alternative software solutions.

Reconciling qualitative, abstract, and scalable modeling of biological networks

Predicting biological systems' behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.

A Boolean Logical model for Reprogramming of Testes-derived male Germline Stem Cells into Germline pluripotent stem cells

BACKGROUND AND OBJECTIVE

Male germline stem (GS) cells are responsible for the maintenance of spermatogenesis throughout the adult life of males. Upon appropriate in vitro culture conditions, these GS cells can undergo reprogramming to become germline pluripotent stem (GPS) cells with the loss of spermatogenic potential. In recent years, voluminous data of gene transcripts in GS and GPS cells have become available. However, the mechanism of reprogramming of GS cells into GPS cells remains elusive. This study was designed to develop a Boolean logical model of gene regulatory network (GRN) that might be involved in the reprogramming of GS cells into GPS cells.

METHODS

The gene expression profile of GS and GPS cells (GSE ID: GSE11274 and GSE74151) were analyzed using R Bioconductor to identify differentially expressed genes (DEGs) and were functionally annotated with DAVID server. Potential pluripotent genes among the DEGs were then predicted using a combination of machine learning [Support Vector Machine (SVM)] and BLAST search. Protein isoforms were identified by pattern matching with UniProt database with in-house scripts written in C++. Both linear and non-linear interaction maps were generated using the STRING server. CellNet is used to study the relationship of GRNs between the GS and GPS cells. Finally, the GRNs involving all the genes from integrated methods and literature was constructed and qualitative modelling for reprogramming of GS to GPS cells were done by considering the discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation tool.

RESULTS

Through the use of machine learning and logical modeling, the present study identified 3585 DEGs and 221 novel pluripotent genes including Tet1, Cdh1, Tfap2c, Etv4, Etv5, Prdm14, and Prdm10 in GPS cells. Pathway analysis revealed that important signaling pathways such as core pluripotency network, PI3K-Akt, WNT, GDNF and BMP4 signalling pathways were important for the reprogramming of GS cells to GPS cells. On the other hand, CellNet analysis of GRNs of GS and GPS cells revealed that GS cells were similar to gonads whereas GPS cells were similar to ESCs in gene expression profile. A logical regulatory model was developed, which showed that TGFβ negatively regulated the reprogramming of the GS to GPS cells, as confirmed by perturbations studies.

CONCLUSION

The study identified novel pluripotent genes involved in the reprogramming of GS cells into GPS cells. A multivalued logical model of cellular reprogramming is proposed, which suggests that reprogramming of GS cells to GPS cells involves signalling pathways namely LIF, GDNF, BMP4, and TGFβ along with some novel pluripotency genes.

Basins of Attraction, Commitment Sets, and Phenotypes of Boolean Networks

The attractors of Boolean networks and their basins have been shown to be highly relevant for model validation and predictive modeling, e.g., in systems biology. Yet, there are currently very few tools available that are able to compute and visualize not only attractors but also their basins. In the realm of asynchronous, non-deterministic modeling not only is the repertoire of software even more limited, but also the formal notions for basins of attraction are often lacking. In this setting, the difficulty both for theory and computation arises from the fact that states may be elements of several distinct basins. In this paper, we address this topic by partitioning the state space into sets that are committed to the same attractors. These commitment sets can easily be generalized to sets that are equivalent w.r.t. the long-term behaviors of pre-selected nodes which leads us to the notions of markers and phenotypes which we illustrate in a case study on bladder tumorigenesis. For every concept, we propose equivalent CTL model checking queries and an extension of the state of the art model checking software NuSMV is made available that is capable of computing the respective sets. All notions are fully integrated as three new modules in our Python package PyBoolNet, including functions for visualizing the basins, commitment sets, and phenotypes as quotient graphs and pie charts.

Hybrid Epithelial-Mesenchymal Phenotypes Are Controlled by Microenvironmental Factors

Epithelial-to-mesenchymal transition (EMT) has been associated with cancer cell heterogeneity, plasticity, and metastasis. However, the extrinsic signals supervising these phenotypic transitions remain elusive. To assess how selected microenvironmental signals control cancer-associated phenotypes along the EMT continuum, we defined a logical model of the EMT cellular network that yields qualitative degrees of cell adhesions by adherens junctions and focal adhesions, two features affected during EMT. The model attractors recovered epithelial, mesenchymal, and hybrid phenotypes. Simulations showed that hybrid phenotypes may arise through independent molecular paths involving stringent extrinsic signals. Of particular interest, model predictions and their experimental validations indicated that: (i) stiffening of the extracellular matrix was a prerequisite for cells overactivating FAK_SRC to upregulate SNAIL and acquire a mesenchymal phenotype and (ii) FAK_SRC inhibition of cell-cell contacts through the receptor-type tyrosine-protein phosphatases kappa led to acquisition of a full mesenchymal, rather than a hybrid, phenotype. Altogether, these computational and experimental approaches allow assessment of critical microenvironmental signals controlling hybrid EMT phenotypes and indicate that EMT involves multiple molecular programs. SIGNIFICANCE: A multidisciplinary study sheds light on microenvironmental signals controlling cancer cell plasticity along EMT and suggests that hybrid and mesenchymal phenotypes arise through independent molecular paths.

Dynamics of the feedback loops required for the phenotypic stabilization in the epithelial-mesenchymal transition

The epithelial-mesenchymal transition (EMT) is a complex mechanism in which cells undergo a transition from epithelial to mesenchymal phenotypes (there is also an intermediary hybrid state) in response to microenvironmental alterations and aberrant stimuli triggered by molecules such as TGF-β. Recent studies in breast cancer progression reported new feedback loops and new participant molecules such as microRNAs 340 and 1199. In this work, we propose a logical model of EMT contemplating the influence of these new published molecules on the regulatory core of EMT. The model results were compared with theoretical and experimental data for the human breast epithelial cell line MCF10A presenting excellent agreement. We propose that the miRNAs 340 and 1199 should be considered phenotypic stability factors of the hybrid state based on the positive feedback loops they form with ZEB1. In addition, the model allows the prediction of phenotype probabilities at the coexistence region. For the tristable dynamics when epithelial, hybrid, and mesenchymal phenotypes coexist, we found that the hybrid state is the most probable, agreeing with experiments. Our results highlight new mechanisms related to the EMT dynamics in response to TGF-β stimulus in epithelial breast cells and might help the design of therapeutic strategies for breast cancer.