Systems modelling methodology for the analysis of apoptosis signal transduction and cell death decisions

Toward a predictive understanding of epithelial cell death

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning

Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.

Mathematical model of STAT signalling pathways in cancer development and optimal control approaches

In various diseases, the STAT family display various cellular controls over various challenges faced by the immune system and cell death programs. In this study, we investigate how an intracellular signalling network (STAT1, STAT3, Bcl-2 and BAX) regulates important cellular states, either anti-apoptosis or apoptosis of cancer cells. We adapt a mathematical framework to illustrate how the signalling network can generate a bi-stability condition so that it will induce either apoptosis or anti-apoptosis status of tumour cells. Then, we use this model to develop several anti-tumour strategies including IFN-β infusion. The roles of JAK-STATs signalling in regulation of the cell death program in cancer cells and tumour growth are poorly understood. The mathematical model unveils the structure and functions of the intracellular signalling and cellular outcomes of the anti-tumour drugs in the presence of IFN-β and JAK stimuli. We identify the best injection order of IFN-β and DDP among many possible combinations, which may suggest better infusion strategies of multiple anti-cancer agents at clinics. We finally use an optimal control theory in order to maximize anti-tumour efficacy and minimize administrative costs. In particular, we minimize tumour volume and maximize the apoptotic potential by minimizing the Bcl-2 concentration and maximizing the BAX level while minimizing total injection amount of both IFN-β and JAK2 inhibitors (DDP).

Defining rules for cancer cell proliferation in TRAIL stimulation

Owing to their self-organizing evolutionary plasticity, cancers remain evasive to modern treatment strategies. Previously, for sensitizing tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-resistant human fibrosarcoma (HT1080), we developed and validated a dynamic computational model that showed the inhibition of protein kinase (PK)C, using bisindolylmaleimide (BIS) I, enhances apoptosis with 95% cell death. Although promising, the long-term effect of remaining ~ 5% cells is a mystery. Will they remain unchanged or are they able to proliferate? To address this question, here we adopted a discrete spatiotemporal cellular automata model utilizing simple rules modified from the famous "Conway's game of life". Based on three experimental initializations: cell numbers obtained from untreated (high), treatment with TRAIL only (moderate), and treatment with TRAIL and BIS I (low), the simulations show cell proliferation in time and space. Notably, when all cells are fixed in their initial space, the proliferation is rapid for high and moderate cell numbers, however, slow and steady for low number of cells. However, when mesenchymal-like random movement was introduced, the proliferation becomes significant even for low cell numbers. Experimental verification showed high proportion of mesenchymal cells in TRAIL and BIS I treatment compared with untreated or TRAIL only treatment. In agreement with the model with cell movement, we observed rapid proliferation of the remnant cells in TRAIL and BIS I treatment over time. Hence, our work highlights the importance of mesenchymal-like cellular movement for cancer proliferation. Nevertheless, re-treatment of TRAIL and BIS I on proliferating cancers is still largely effective.

In silico modelling of apoptosis induced by photodynamic therapy

Photodynamic therapy (PDT) is an emergent technique used for the treatment of several diseases. After PDT, cells die by necrosis, apoptosis or autophagy. Necrosis is produced immediately during photodynamic therapy by high concentration of reactive oxygen species, apoptosis and autophagy are triggered by mild or low doses of light and photosensitizer. In this work we model the cell response to low doses of PDT assuming a bi-dimensional matrix of interacting cells. For each cell of the matrix we simulate in detail, with the help of the Gillespie's algorithm, the two main chemical pathways leading to apoptosis. We unveil the role of both pathways in the cell death rate of the tumor, as well as the relevance of several molecules in the process. Our model suggests values of concentrations for several species of molecules to enhance the effectiveness of PDT.

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

INTRODUCTION

Drug-induced liver injury (DILI) is a significant threat to human health and a major problem in drug development. It is hard to predict due to its idiosyncratic nature and which does not show up in animal trials. Hepatic adaptive stress response pathway activation is generally observed in drug-induced liver injury. Dynamical pathway modeling has the potential to foresee adverse effects of drugs before they go in trial. Ordinary differential equation modeling can offer mechanistic insight, and allows us to study the dynamical behavior of stress pathways involved in DILI. Areas covered: This review provides an overview on the progress of the dynamical modeling of stress and death pathways pertinent to DILI, i.e. pathways relevant for oxidative stress, inflammatory stress, DNA damage, unfolded proteins, heat shock and apoptosis. We also discuss the required steps for applying such modeling to the liver. Expert opinion: Despite the strong progress made since the turn of the century, models of stress pathways have only rarely been specifically applied to describe pathway dynamics for DILI. We argue that with minor changes, in some cases only to parameter values, many of these models can be repurposed for application in DILI research. Combining both dynamical models with in vitro testing might offer novel screening methods for the harmful side-effects of drugs.

Cell-to-cell variability in cell death: can systems biology help us make sense of it all?

One of the most common observations in cell death assays is that not all cells die at the same time, or at the same treatment dose. Here, using the perspective of the systems biology of apoptosis and the context of cancer treatment, we discuss possible sources of this cell-to-cell variability as well as its implications for quantitative measurements and computational models of cell death. Many different factors, both within and outside of the apoptosis signaling networks, have been correlated with the variable responses to various death-inducing treatments. Systems biology models offer us the opportunity to take a more synoptic view of the cell death process to identify multifactorial determinants of the cell death decision. Finally, with an eye toward 'systems pharmacology', we discuss how leveraging this new understanding should help us develop combination treatment strategies to compel cancer cells toward apoptosis by manipulating either the biochemical state of cancer cells or the dynamics of signal transduction.

From computational modelling of the intrinsic apoptosis pathway to a systems-based analysis of chemotherapy resistance: achievements, perspectives and challenges in systems medicine

Our understanding of the mitochondrial or intrinsic apoptosis pathway and its role in chemotherapy resistance has increased significantly in recent years by a combination of experimental studies and mathematical modelling. This combined approach enhanced the quantitative and kinetic understanding of apoptosis signal transduction, but also provided new insights that systems-emanating functions (i.e., functions that cannot be attributed to individual network components but that are instead established by multi-component interplay) are crucial determinants of cell fate decisions. Among these features are molecular thresholds, cooperative protein functions, feedback loops and functional redundancies that provide systems robustness, and signalling topologies that allow ultrasensitivity or switch-like responses. The successful development of kinetic systems models that recapitulate biological signal transduction observed in living cells have now led to the first translational studies, which have exploited and validated such models in a clinical context. Bottom-up strategies that use pathway models in combination with higher-level modelling at the tissue, organ and whole body-level therefore carry great potential to eventually deliver a new generation of systems-based diagnostic tools that may contribute to the development of personalised and predictive medicine approaches. Here we review major achievements in the systems biology of intrinsic apoptosis signalling, discuss challenges for further model development, perspectives for higher-level integration of apoptosis models and finally discuss requirements for the development of systems medical solutions in the coming years.

Systems biology of death receptor networks: live and let die

The extrinsic apoptotic pathway is initiated by death receptor activation. Death receptor activation leads to the formation of death receptor signaling platforms, resulting in the demolition of the cell. Despite the fact that death receptor-mediated apoptosis has been studied to a high level of detail, its quantitative regulation until recently has been poorly understood. This situation has dramatically changed in the last years. Creation of mathematical models of death receptor signaling led to an enormous progress in the quantitative understanding of the network regulation and provided fascinating insights into the mechanisms of apoptosis control. In the following sections, the models of the death receptor signaling and their biological implications will be addressed. Central attention will be given to the models of CD95/Fas/APO-1, an exemplified member of the death receptor signaling pathways. The CD95 death-inducing signaling complex (DISC) and regulation of CD95 DISC activity by its key inhibitor c-FLIP, have been vigorously investigated by modeling approaches, and therefore will be the major topic here. Furthermore, the non-linear dynamics of the DISC, positive feedback loops and bistability as well as stoichiometric switches in extrinsic apoptosis will be discussed. Collectively, this review gives a comprehensive view how the mathematical modeling supported by quantitative experimental approaches has provided a new understanding of the death receptor signaling network.

Biomedical Perspectives of Polyaniline Based Biosensors

Electrically conducting polymers (ECPs) are finding applications in various fields of science owing to their fascinating characteristic properties such as binding molecules, tuning their properties, direct communication to produce a range of analytical signals and new analytical applications. Polyaniline (PANI) is one such ECP that has been extensively used and investigated over the last decade for direct electron transfer leading towards fabrication of mediator-less biosensors. In this review article, significant attention has been paid to the various polymerization techniques of polyaniline as a transducer material, and their use in enzymes/biomolecules immobilization methods to study their bio-catalytic properties as a biosensor for potential biomedical applications.