A growth model of human papillomavirus type 16 designed from cellular automata and agent-based models

Theoretical Epidemiology Needs Urgent Attention in China

The mathematical method to which theoretical epidemiology belongs is one of the three major methodologies in epidemiology. It is of great value in diagnosing infectious disease epidemic trends and evaluating the effectiveness of prevention and control measures. This paper aims to summarize the brief history of the development of theoretical epidemiology, common types of mathematical models, and key steps to develop a mathematical model. It also provides some thoughts and perspectives on the development and application of theoretical epidemiology in China.

Assessing the impact of HIV treatment interruptions using stochastic cellular Automata

Chronic HIV infection causes a progressive decrease in the ability to maintain homeostasis resulting, after some time, in eventual break down of immune functions. Recent clinical research has shed light on a significant contribution of the lymphatic tissues, where HIV causes accumulation of collagen, (fibrosis). Specifically, where tissue is populated by certain types of functional stromal cells designated Fibroblastic Reticular Cells (FRCs), these have been found to play a crucial role in balancing out apoptosis and regeneration of naïve T-cells through 2-way cellular signaling. Tissue fibrosis not only impedes this signaling, effectively reducing T-cell levels through increased apoptosis of cells of both T- and FRC type but has been found to be irreversible by current HIV standard treatment (cART). While the therapy aims to block the viral lifecycle, cART-associated increase of T-cell levels in blood appears to conceal existing FRC impairment through fibrosis. This hidden impairment can lead to adverse consequences if treatment is interrupted, e.g. due to poor adherence (missing doses) or through periods recovering from drug toxicities. Formal clinical studies on treatment interruption have indicated possible adverse effects, but quantification of those effects in relation to interruption protocol and patient predisposition remains unclear. Accordingly, the impact of treatment interruption on lymphatic tissue structure and T-cell levels is explored here by means of computer simulation. A novel Stochastic Cellular Automata model is proposed, which utilizes all sources of clinical detail available to us (though sparse in part) for model parametrization. Sources are explicitly referenced and conflicting evidence from previous studies explored. The main focus is on (i) spatial aspects of collagen build up, together with (ii) collagen increase after repeated treatment interruptions to explore the dynamics of HIV-induced fibrosis and T-cell loss.

In silico/In vivo analysis of high-risk papillomavirus L1 and L2 conserved sequences for development of cross-subtype prophylactic vaccine

Human papillomavirus (HPV) is the most common sexually transmitted infection in the world and the main cause of cervical cancer. Nowadays, the virus-like particles (VLPs) based on L1 proteins have been considered as the best candidate for vaccine development against HPV infections. Two commercial HPV (Gardasil and Cervarix) are available. These HPV VLP vaccines induce genotype-limited protection. The major impediments such as economic barriers especially gaps in financing obstructed the optimal delivery of vaccines in developing countries. Thus, many efforts are underway to develop the next generation of vaccines against other types of high-risk HPV. In this study, we developed DNA constructs (based on L1 and L2 genes) that were potentially immunogenic and highly conserved among the high-risk HPV types. The framework of analysis include (1) B-cell epitope mapping, (2) T-cell epitope mapping (i.e., CD4⁺ and CD8⁺ T cells), (3) allergenicity assessment, (4) tap transport and proteasomal cleavage, (5) population coverage, (6) global and template-based docking, and (7) data collection, analysis, and design of the L1 and L2 DNA constructs. Our data indicated the 8-epitope candidates for helper T-cell and CTL in L1 and L2 sequences. For the L1 and L2 constructs, combination of these peptides in a single universal vaccine could involve all world population by the rate of 95.55% and 96.33%, respectively. In vitro studies showed high expression rates of multiepitope L1 (~57.86%) and L2 (~68.42%) DNA constructs in HEK-293T cells. Moreover, in vivo studies indicated that the combination of L1 and L2 DNA constructs without any adjuvant or delivery system induced effective immune responses, and protected mice against C3 tumor cells (the percentage of tumor-free mice: ~66.67%). Thus, the designed L1 and L2 DNA constructs would represent promising applications for HPV vaccine development.

Modeling the dynamics of chromosomal alteration progression in cervical cancer: A computational model

Computational modeling has been applied to simulate the heterogeneity of cancer behavior. The development of Cervical Cancer (CC) is a process in which the cell acquires dynamic behavior from non-deleterious and deleterious mutations, exhibiting chromosomal alterations as a manifestation of this dynamic. To further determine the progression of chromosomal alterations in precursor lesions and CC, we introduce a computational model to study the dynamics of deleterious and non-deleterious mutations as an outcome of tumor progression. The analysis of chromosomal alterations mediated by our model reveals that multiple deleterious mutations are more frequent in precursor lesions than in CC. Cells with lethal deleterious mutations would be eliminated, which would mitigate cancer progression; on the other hand, cells with non-deleterious mutations would become dominant, which could predispose them to cancer progression. The study of somatic alterations through computer simulations of cancer progression provides a feasible pathway for insights into the transformation of cell mechanisms in humans. During cancer progression, tumors may acquire new phenotype traits, such as the ability to invade and metastasize or to become clinically important when they develop drug resistance. Non-deleterious chromosomal alterations contribute to this progression.

Modeling cell adhesion and proliferation: a cellular-automata based approach

BACKGROUND

Cell adhesion is a process that involves the interaction between the cell membrane and another surface, either a cell or a substrate. Unlike experimental tests, computer models can simulate processes and study the result of experiments in a shorter time and lower costs. One of the tools used to simulate biological processes is the cellular automata, which is a dynamic system that is discrete both in space and time.

METHOD

This work describes a computer model based on cellular automata for the adhesion process and cell proliferation to predict the behavior of a cell population in suspension and adhered to a substrate. The values of the simulated system were obtained through experimental tests on fibroblast monolayer cultures.

RESULTS

The results allow us to estimate the cells settling time in culture as well as the adhesion and proliferation time. The change in the cells morphology as the adhesion over the contact surface progress was also observed. The formation of the initial link between cell and the substrate of the adhesion was observed after 100 min where the cell on the substrate retains its spherical morphology during the simulation. The cellular automata model developed is, however, a simplified representation of the steps in the adhesion process and the subsequent proliferation.

CONCLUSION

A combined framework of experimental and computational simulation based on cellular automata was proposed to represent the fibroblast adhesion on substrates and changes in a macro-scale observed in the cell during the adhesion process. The approach showed to be simple and efficient.

Agent-based modeling of the immune system: NetLogo, a promising framework

Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.