Review of the systems biology of the immune system using agent-based models

Arginine vasopressin hormone receptor antagonists in experimental autoimmune encephalomyelitis rodent models: A new approach for human multiple sclerosis treatment

Multiple sclerosis (MS) is a chronic demyelinating and neurodegenerative disease that affects the central nervous system. MS is a heterogeneous disorder of multiple factors that are mainly associated with the immune system including the breakdown of the blood-brain and spinal cord barriers induced by T cells, B cells, antigen presenting cells, and immune components such as chemokines and pro-inflammatory cytokines. The incidence of MS has been increasing worldwide recently, and most therapies related to its treatment are associated with the development of several secondary effects, such as headaches, hepatotoxicity, leukopenia, and some types of cancer; therefore, the search for an effective treatment is ongoing. The use of animal models of MS continues to be an important option for extrapolating new treatments. Experimental autoimmune encephalomyelitis (EAE) replicates the several pathophysiological features of MS development and clinical signs, to obtain a potential treatment for MS in humans and improve the disease prognosis. Currently, the exploration of neuro-immune-endocrine interactions represents a highlight of interest in the treatment of immune disorders. The arginine vasopressin hormone (AVP) is involved in the increase in blood−brain barrier permeability, inducing the development and aggressiveness of the disease in the EAE model, whereas its deficiency improves the clinical signs of the disease. Therefore, this present review discussed on the use of conivaptan a blocker of AVP receptors type 1a and type 2 (V1a and V2 AVP) in the modulation of immune response without completely depleting its activity, minimizing the adverse effects associated with the conventional therapies becoming a potential therapeutic target in the treatment of patients with multiple sclerosis.

Biology and medicine in the landscape of quantum advantages

Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

Unveiling the Bio-corona Fingerprinting of Potential Anticancer Carbon Nanotubes Coupled with D-Amino Acid Oxidase

The oxidation therapy, based on the controlled production of Reactive Oxygen Species directly into the tumor site, was introduced as alternative antitumor approach. For this purpose, d-amino acid oxidase (DAAO) from the yeast Rhodotorula gracilis, an enzyme able to efficiently catalyze the production of hydrogen peroxide from d-amino acids, was adsorbed onto multi-walled carbon nanotubes (MWCNTs), previously functionalized with polylactic-co-glycolic acid (PLGA) or polyethylene glycol (PEG) at different degrees to reduce their toxicity, to be targeted directly into the tumor. In vitro activity and cytotoxicity assays demonstrated that DAAO-functionalized nanotubes (f-MWCNTs) produced H2O2 and induced toxic effects to selected tumor cell lines. After incubation in human plasma, the protein corona was investigated by SDS-PAGE and mass spectrometry analysis. The enzyme nanocarriers generally seemed to favor their biocompatibility, promoting the interaction with dysopsonins. Despite this, PLGA or high degree of PEGylation promoted the adsorption of immunoglobulins with a possible activation of immune response and this effect was probably due to PLGA hydrophobicity and dimensions and to the production of specific antibodies against PEG. In conclusion, the PEGylated MWCNTs at low degree seemed the most biocompatible nanocarrier for adsorbed DAAO, preserving its anticancer activity and forming a bio-corona able to reduce both defensive responses and blood clearance.

Modeling viral dynamics in SARS-CoV-2 infection based on differential equations and numerical analysis

Computational modeling and simulation of viral dynamics would explain the pathogenesis for any virus. Such computational attempts have been successfully made to predict and control HIV-1 or hepatitis B virus. However, the dynamics for SARS-CoV-2 has not been adequately investigated. The purpose of this research is to propose different SARS-CoV-2 dynamics models based on differential equations and numerical analysis towards distilling the models to explain the mechanism of SARS-CoV-2 pathogenesis. The proposed four models formalize the dynamical system of SARS-CoV-2 infection, which consists of host cells and viral particles. These models undergo numerical analysis, including sensitivity analysis and stability analysis. Based on the sensitivity indices of the four models' parameters, the four models are simplified into two models. In advance of the following calibration experiments, the eigenvalues of the Jacobian matrices of these two models are calculated, thereby guaranteeing that any solutions are stable. Then, the calibration experiments fit the simulated data sequences of the two models to two observed data sequences, SARS-CoV-2 viral load in mild cases and that in severe cases. Comparing the estimated parameters in mild cases and severe cases indicates that cell-to-cell transmission would significantly correlate to the COVID-19 severity. These experiments for modeling and simulation provide plausible computational models for the SARS-CoV-2 dynamics, leading to further investigation for identifying the essential factors in severe cases.

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.

Multiscale modeling in disease

Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

COMPLEX BIOLOGICAL IMMUNE SYSTEM THROUGH THE EYES OF DUAL-PHASE EVOLUTION

Dual-phase evolution (DPE) and the network theory help to analyze prominent properties of the complex adaptive systems (CASs) such as emergence and self-organization that are caused due to the phase transitions. These transitions are observed because of the increase and decrease in the number of system components and their interactions. The immune system, which is one of the CASs, provides an adaptive response to the foreign molecules. Prior to this response, the immune system is present in the circulation state and during the response, it moves into the growth state, where the number of immune cells and their cell–cell contacts increase rapidly. The phase transitions from the circulation state to the growth state and then back to the circulation state cause the emergence and self-organization of the immune system, respectively. There is a need to understand these complex cellular dynamics during the immune response. In this paper, we have proposed an integrated model of DPE, network theory, and the immune system that has helped to understand and analyze the phases and properties of the immune system. Analysis of the growth phase network is provided and it is concluded that this network exhibits scale-free nature following power law for the degree distribution of nodes.