Mathematical Modeling of p53 Pathways

Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review

BACKGROUND AND OBJECTIVE

Mechanistic-based Model simulations (MM) are an effective approach commonly employed, for research and learning purposes, to better investigate and understand the inherent behavior of biological systems. Recent advancements in modern technologies and the large availability of omics data allowed the application of Machine Learning (ML) techniques to different research fields, including systems biology. However, the availability of information regarding the analyzed biological context, sufficient experimental data, as well as the degree of computational complexity, represent some of the issues that both MMs and ML techniques could present individually. For this reason, recently, several studies suggest overcoming or significantly reducing these drawbacks by combining the above-mentioned two methods. In the wake of the growing interest in this hybrid analysis approach, with the present review, we want to systematically investigate the studies available in the scientific literature in which both MMs and ML have been combined to explain biological processes at genomics, proteomics, and metabolomics levels, or the behavior of entire cellular populations.

METHODS

Elsevier Scopus®, Clarivate Web of Science™ and National Library of Medicine PubMed® databases were enquired using the queries reported in Table 1, resulting in 350 scientific articles.

RESULTS

Only 14 of the 350 documents returned by the comprehensive search conducted on the three major online databases met our search criteria, i.e. present a hybrid approach consisting of the synergistic combination of MMs and ML to treat a particular aspect of systems biology.

CONCLUSIONS

Despite the recent interest in this methodology, from a careful analysis of the selected papers, it emerged how examples of integration between MMs and ML are already present in systems biology, highlighting the great potential of this hybrid approach to both at micro and macro biological scales.

Protein persulfidation: Rewiring the hydrogen sulfide signaling in cell stress response

The past few decades have witnessed significant progress in the discovery of hydrogen sulfide (H₂S) as a ubiquitous gaseous signaling molecule in mammalian physiology, akin to nitric oxide and carbon monoxide. As the third gasotransmitter, H₂S is now known to exert a wide range of physiological and cytoprotective functions in the biological systems. However, endogenous H₂S concentrations are usually low, and its potential biologic mechanisms responsible have not yet been fully clarified. Recently, a growing body of evidence has demonstrated that protein persulfidation, a posttranslational modification of cysteine residues (RSH) to persulfides (RSSH) elicited by H₂S, is a fundamental mechanism of H₂S-mediated signaling pathways. Persulfidation, as a biological switch for protein function, plays an important role in the maintenance of cell homeostasis in response to various internal and external stress stimuli and is also implicated in numerous diseases, such as cardiovascular and neurodegenerative diseases and cancer. In this review, the biological significance of protein persulfidation by H₂S in cell stress response is reviewed providing a framework for understanding the multifaceted roles of H₂S. A mechanism-guided perspective can help open novel avenues for the exploitation of therapeutics based on H₂S-induced persulfidation in the context of diseases.

Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.

Mathematical Models of Death Signaling Networks

This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms that constitute the cell death network. We define the cell death network as a comprehensive decision-making mechanism that controls multiple death execution molecular circuits. This network involves multiple feedback and feed-forward loops and crosstalk among different cell death-regulating pathways. While substantial progress has been made in characterizing individual cell death execution pathways, the cell death decision network is poorly defined and understood. Certainly, understanding the dynamic behavior of such complex regulatory mechanisms can be only achieved by applying mathematical modeling and system-oriented approaches. Here, we provide an overview of mathematical models that have been developed to characterize different cell death mechanisms and intend to identify future research directions in this field.

MicroRNA-520c-3p impacts sphingolipid metabolism mediating PI3K/AKT signaling in NSCLC: Systems perspective

Increasing research suggests that sphingolipid metabolism is essential for the progression and metastasis of cancer. The underlying mechanistic insight into the dysregulation of sphingolipid metabolism affecting pathways is poorly investigated. As a result, the goal of the current study was to glean knowledge from the systems biology approach to investigate how the sphingolipid metabolism affects the signal transduction network in non-small cell lung cancer (NSCLC), the most common type of cancer in terms of occurrence and death globally. Our paper includes system-level models representing the diseased and healthy states elucidating that sphingolipids and its enzymes mediate PI3K/AKT pathway. Notably, its activation of downstream signaling mediators has led to cancer growth. Considering the critical role of sphingolipids in NSCLC, our study advocates the target CERS6 which can be potentially inhibited using hsa-miR-520c-3p to combat NSCLC for future precision medicine.

SNAIL driven by a feed forward loop motif promotes TGFβinduced epithelial to mesenchymal transition

Epithelial to Mesenchymal Transition (EMT) plays an important role in tissue regeneration, embryonic development, and cancer metastasis. Several signaling pathways are known to regulate EMT, among which the modulation of TGFβ(Transforming Growth Factor-β) induced EMT is crucial in several cancer types. Several mathematical models were built to explore the role of core regulatory circuit of ZEB/miR-200, SNAIL/miR-34 double negative feedback loops in modulating TGFβinduced EMT. Different emergent behavior including tristability, irreversible switching, existence of hybrid EMT states were inferred though these models. Some studies have explored the role of TGFβreceptor activation, SMADs nucleocytoplasmic shuttling and complex formation. Recent experiments have revealed that MDM2 along with SMAD complex regulates SNAIL expression driven EMT. Encouraged by this, in the present study we developed a mathematical model for p53/MDM2 dependent TGFβinduced EMT regulation. Inclusion of p53 brings in an additional mechanistic perspective in exploring the EM transition. The network formulated comprises a C1FFL moderating SNAIL expression involving MDM2 and SMAD complex, which functions as a noise filter and persistent detector. The C1FFL was also observed to operate as a coincidence detector driving the SNAIL dependent downstream signaling into phenotypic switching decision. Systems modelling and analysis of the devised network, displayed interesting dynamic behavior, systems response to various inputs stimulus, providing a better understanding of p53/MDM2 dependent TGF-βinduced Epithelial to Mesenchymal Transition.

Combined multiple transcriptional repression mechanisms generate ultrasensitivity and oscillations

Transcriptional repression can occur via various mechanisms, such as blocking, sequestration and displacement. For instance, the repressors can hold the activators to prevent binding with DNA or can bind to the DNA-bound activators to block their transcriptional activity. Although the transcription can be completely suppressed with a single mechanism, multiple repression mechanisms are used together to inhibit transcriptional activators in many systems, such as circadian clocks and NF-κB oscillators. This raises the question of what advantages arise if seemingly redundant repression mechanisms are combined. Here, by deriving equations describing the multiple repression mechanisms, we find that their combination can synergistically generate a sharply ultrasensitive transcription response and thus strong oscillations. This rationalizes why the multiple repression mechanisms are used together in various biological oscillators. The critical role of such combined transcriptional repression for strong oscillations is further supported by our analysis of formerly identified mutations disrupting the transcriptional repression of the mammalian circadian clock. The hitherto unrecognized source of the ultrasensitivity, the combined transcriptional repressions, can lead to robust synthetic oscillators with a previously unachievable simple design.

Mathematical Modelling of p53 Signalling during DNA Damage Response: A Survey

No gene has garnered more interest than p53 since its discovery over 40 years ago. In the last two decades, thanks to seminal work from Uri Alon and Ghalit Lahav, p53 has defined a truly synergistic topic in the field of mathematical biology, with a rich body of research connecting mathematic endeavour with experimental design and data. In this review we survey and distill the extensive literature of mathematical models of p53. Specifically, we focus on models which seek to reproduce the oscillatory dynamics of p53 in response to DNA damage. We review the standard modelling approaches used in the field categorising them into three types: time delay models, spatial models and coupled negative-positive feedback models, providing sample model equations and simulation results which show clear oscillatory dynamics. We discuss the interplay between mathematics and biology and show how one informs the other; the deep connections between the two disciplines has helped to develop our understanding of this complex gene and paint a picture of its dynamical response. Although yet more is to be elucidated, we offer the current state-of-the-art understanding of p53 response to DNA damage.

p53: A Key Protein That Regulates Pulmonary Fibrosis

Pulmonary fibrosis is a progressively aggravating lethal disease that is a serious public health concern. Although the incidence of this disease is increasing, there is a lack of effective therapies. In recent years, the pathogenesis of pulmonary fibrosis has become a research hotspot. p53 is a tumor suppressor gene with crucial roles in cell cycle, apoptosis, tumorigenesis, and malignant transformation. Previous studies on p53 have predominantly focused on its role in neoplastic disease. Following in-depth investigation, several studies have linked it to pulmonary fibrosis. This review covers the association between p53 and pulmonary fibrosis, with the aim of providing novel ideas to improve the clinical diagnosis, treatment, and prognosis of pulmonary fibrosis.