Supplementary Figure Legend from In silico Modeling and In vivo Efficacy of Cancer-Preventive Vaccinations

Background SARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for coronaviruses. To promptly and rapidly respond to pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. Results We present an in silico platform that showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approach to design an effective vaccine, based on monoclonal antibody. Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2. Conclusions In silico trials are showing to be powerful weapons in predicting immune responses of potential candidate vaccines. Here, UISS has been extended to be used as an in silico trial platform to speed-up and drive the discovery pipeline of vaccine against SARS-CoV-2.

BACKGROUND:In 2018, about 10 million people were found infected by tuberculosis, with approximately 1.2 million deaths worldwide. Despite these numbers have been relatively stable in recent years, tuberculosis is still considered one of the top 10 deadliest diseases worldwide. Over the years, Mycobacterium tuberculosis has developed a form of resistance to first-line tuberculosis treatments, specifically to isoniazid, leading to multi-drug-resistant tuberculosis. In this context, the EU and Indian DBT funded project STriTuVaD-In Silico Trial for Tuberculosis Vaccine Development-is supporting the identification of new interventional strategies against tuberculosis thanks to the use of Universal Immune System Simulator (UISS), a computational framework capable of predicting the immunity induced by specific drugs such as therapeutic vaccines and antibiotics.RESULTS:Here, we present how UISS accurately simulates tuberculosis dynamics and its interaction within the immune system, and how it predicts the efficacy of the combined action of isoniazid and RUTI vaccine in a specific digital population cohort. Specifically, we simulated two groups of 100 digital patients. The first group was treated with isoniazid only, while the second one was treated with the combination of RUTI vaccine and isoniazid, according to the dosage strategy described in the clinical trial design. UISS-TB shows to be in good agreement with clinical trial results suggesting that RUTI vaccine may favor a partial recover of infected lung tissue.CONCLUSIONS:In silico trials innovations represent a powerful pipeline for the prediction of the effects of specific therapeutic strategies and related clinical outcomes. Here, we present a further step in UISS framework implementation. Specifically, we found that the simulated mechanism of action of RUTI and INH are in good alignment with the results coming from past clinical phase IIa trials.

As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing–remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.

This paper deals with the medical treatment of tumors which are able to escape the immune system surveillance. A mathematical model for the tumor immune system competition is generalized by introducing a therapy which helps the immune system cells to recognize the tumor cells. The mathematical model consists into a system of two ordinary differential equations modeling the time evolution of the density of the immune system cells and the density of the tumor cells, respectively, and their competition. An optimal control strategy is introduced in order to reduce the percentage of tumor cells which are able to escape the immune system action. Specifically the control represents the percentage of effect that the therapy has on the tumor escape phenomenon production. The therapy lasts for a given period; the results, however, are not interval-dependent. The objective function to be maximized is based on the benefit related to T-cell counts less the systemic cost of the therapy. The model is mathematically analyzed and the existence of the optimal control is investigated by using the Pontryagins Maximum Principle.

In this work, we present the application of a computational modeling infrastructure named UISS (Universal Immune System Simulator) able to simulate the main features and dynamics of the immune system activities. We provide an extended version of UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. We simulated MS patients with different relapsing-remitting courses. Even though the model can be further personalized employing immunological parameters and genetic information, based on the available data, we obtained simulation scenarios for each patient who matched the real clinical and MRI history. UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.

Tuberculosis is one of the world's deadliest diseases that infects one third of the world's population, mostly in developing countries. However, tuberculosis is becoming again very dangerous also for developed countries, due to the increased mobility of the world population, and the appearance of several new bacterial strains that are multi-drug resistant. With the aim to help in finding new therapeutic interventions against tuberculosis, we present the application of a computational modeling infrastructure named UISS (Universal Immune System Simulator) able to simulate the main features and dynamics of the immune system activities. We show a further development of UISS to consider the underlying tuberculosis pathogenesis and its interaction with the host immune system. Even though the model can be further personalized employing immunological parameters and genetic information, based on the available data, we obtained simulation scenarios able to reproduce persistent latent infection or the development of active disease. In particular, UISS is able to simulate those mechanisms in which M. tuberculosis is involved in the early influx of alveolar macrophages and recruited neutrophils until the formation of the tuberculous granuloma, at both cellular and molecular levels.

The immune system is a critical regulator of tumor biology with the capacity to support or inhibit tumor onset, growth, invasion and metastasis. Medical approaches designed to harness the immune system are the focus of several recent promising therapeutic approaches for cancer patients. However, not all tumors appear to respond to these immunomodulatory schemes. A major challenge for cancer immunotherapy, therefore, lies in understanding resistance mechanisms for selecting patients who are most likely to benefit. Moreover, tumors may also escape elimination either by recruiting immunosuppressive leukocytes which orchestrate a microenvironment that spoils the productivity of an anti-tumor immune response or underexpose cell-surface molecules. Therefore, predicting the clinical benefit of T cell immunotherapy is likely to require an understanding of each of these steps and their combinations as it relates to a patient's individual tumor. Trial and errors approaches should be avoided in the clinical practice. Computer simulations based on mathematical model can help physician in evaluating in advance the effects of a given therapy on a class of patients. This paper describes a simple tumor escape mathematical model as first contribution toward a simulation based personalized medical treatment.

Background Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease. Result Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive.

BACKGROUND:Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN.RESULTS:We propose a novel methodological approach that is based on high level PN, and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features.CONCLUSIONS:The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra cellular) models and cellular (population) models built upon the same technique and software.

The central nervous system is the most complex network of the human body. The existence and functionality of a large number of molecular species in human brain are still ambiguous and mostly unknown, thus posing a challenge to Science and Medicine. Neurological diseases inherit the same level of complexity, making effective treatments difficult to be found. Multiple sclerosis (MS) is a major neurological disease that causes severe inabilities and also a significant social burden on health care system: between 2 and 2.5 million people are affected by it, and the cost associated with it is significantly higher as compared with other neurological diseases because of the chronic nature of the disease and to the partial efficacy of current therapies. Despite difficulties in understanding and treating MS, many computational models have been developed to help neurologists. In the present work, we briefly review the main characteristics of MS and present a selection criteria of modeling approaches.

MOTIVATION Vaccines represent the most effective and cost-efficient weapons against a wide range of diseases. Nowadays new generation vaccines based on subunit antigens reduce adverse effects in high risk individuals. However, vaccine antigens are often poor immunogens when administered alone. Adjuvants represent a good strategy to overcome such hurdles, indeed they are able to: enhance the immune response; allow antigens sparing; accelerate the specific immune response; and increase vaccine efficacy in vulnerable groups such as newborns, elderly or immuno-compromised people. However, due to safety concerns and adverse reactions, there are only a few adjuvants approved for use in humans. Moreover, in practice current adjuvants sometimes fail to confer adequate stimulation. Hence, there is an imperative need to develop novel adjuvants that overcome the limitations of the currently available licensed adjuvants. RESULTS We developed a computational framework that provides a complete pipeline capable of predicting the best citrus-derived adjuvants for enhancing the immune system response using, as a target disease model, influenza A infection. In silico simulations suggested a good immune efficacy of specific citrus-derived adjuvant (Beta Sitosterol) that was then confirmed in vivoAvailability: The model is available visiting the following URL: http://vaima.dmi.unict.it/AdjSim CONTACT francesco.pappalardo@unict.it; fp@francescopappalardo.net.

MOTIVATION:Many important problems in cell biology require dense non-linear interactions between functional modules to be considered. The importance of computer simulation in understanding cellular processes is now widely accepted, and a variety of simulation algorithms useful for studying certain subsystems have been designed. Expansion of hematopoietic stem and progenitor cells (HSC/HPC) in ex vivo culture with cytokines and small molecules is a method to increase the restricted numbers of stem cells found in umbilical cord blood (CB), while also enhancing the content of early engrafting neutrophil and platelet precursors. The efficacy of the expanded product depends on the composition of the cocktail of cytokines and small molecules used for culture. Testing the influence of a cytokine or small molecule on the expansion of HSC/HPC is a laborious and expensive process. We therefore developed a computational model based on cellular signaling interactions that predict the influence of a cytokine on the survival, duplication and differentiation of the CD133(+) HSC/HPC subset from human umbilical CB. RESULTS:We have used results from in vitro expansion cultures with different combinations of one or more cytokines to develop an ordinary differential equation model that includes the effect of cytokines on survival, duplication and differentiation of the CD133(+) HSC/HPC. Comparing the results of in vitro and in silico experiments, we show that the model can predict the effect of a cytokine on the fold expansion and differentiation of CB CD133(+) HSC/HPC after 8-day culture on a 3D scaffold. Supplementary data are available at Bioinformatics online.

Multiple sclerosis is a disease of the central nervous system that involves the destruction of the insulating sheath of axons, causing severe disabilities. Since the etiology of the disease is not yet fully understood, the use of novel techniques that may help to understand the disease, to suggest potential therapies and to test the effects of candidate treatments is highly advisable.To this end we developed an agent based model that demonstrated its ability to reproduce the typical oscillatory behavior observed in the most common form of multiple sclerosis, relapsing-remitting multiple sclerosis. The model has then been used to test the potential beneficial effects of vitamin D over the disease.Many scientific studies underlined the importance of the blood-brain barrier and of the mechanisms that influence its permeability on the development of the disease. In the present paper we further extend our previously developed model with,a mechanism that mimics the blood-brain barrier behavior. The goal of our work is to suggest the best strategies to follow for developing new potential treatments that intervene in the blood-brain barrier.Results suggest that the best treatments should potentially prevent the opening of the blood-brain barrier, as treatments that help in recovering the blood-brain barrier functionality could be less effective. (C) 2015 Elsevier B.V. All rights reserved.

Multiple sclerosis is an appalling disease which destroys the insulatic covers of the connections between neurons, causing disability and death. Its etiology is not yet fully understood and causes remain still unknown; therefore mathematical and computational models can help to test and verify biological hypoteses, and to understand the immune mechanisms involved. We present an extension of an agent based model that demonstrated able to describe the most common form of multiple sclerosis (relapsing-remitting) coupled with the presence of an important external factor that can improve the disease course: Vitamin D. We investigate the role of Vitamin D by a qualitative point of view, taking into account its well known immunomodulatory mechanisms.

Excepting the Peripheral and Central Nervous Systems, the Immune System is the most complex of somatic systems in higher animals. This complexity manifests itself at many levels from the molecular to that of the whole organism. Much insight into this confounding complexity can be gained through computational simulation. Such simulations range in application from epitope prediction through to the modelling of vaccination strategies. In this review, we evaluate selectively various key applications relevant to computational vaccinology: these include technique that operates at different scale that is, from molecular to organisms and even to population level.

It is coming nowadays more clear that in order to obtain a unified description of the different mechanisms governing the behavior and causality relations among the various parts of a living system, the development of comprehensive computational and mathematical models at different space and time scales is required. This is one of the most formidable challenges of modern biology characterized by the availability of huge amount of high throughput measurements. In this paper we draw attention to the importance of multiscale modeling in the framework of studies of biological systems in general and of the immune system in particular.

Ontology construction is a time consuming and labor intensive task. It may take many months to construct an ontology as according to standard practices each concept must have synonyms, domain specific definition, unique identifier and references. Current practices of ontology construction require manual data input to feed this data via programs such as Protégé etc. We designed a small application that speeds up the development of new ontologies. It provides an easy to use and convenient interface that allows to theoretically build an ontology within few days. The output of our program can be easily opened and then used into a standard ontology editor like Protégé. Availability: The software is freely available visiting this link: http://www. francescopappalardo.net/ontofast.zip.

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.