The aim of this study was to explore the sensitizing potency of several known skin allergens, focusing on their capacity to induce allergic contact dermatitis (ACD). Our approach involves different bioinformatic tools, including UISS-TOX, a simulation platform designed to predict the immune response following allergen exposure. Using eight well-characterized skin sensitizers, including pyridine and hexyl salicylate, we evaluated docking interactions with keratin and Toll-like receptors (TLRs), and we predicted B-cell epitopes, providing insights into potential antigenic sites. The results were integrated into UISS-TOX simulations to observe T helper cell and cytokine dynamics over time. The simulations revealed distinct Th1-mediated responses consistent with ACD, enabling not only the prediction of skin sensitizer potency but also the differentiation of response intensities among the sensitizers. Pyridine, for instance, demonstrated a higher Th1 activation and associated cytokine release than hexyl salicylate, aligning with its stronger sensitizing profile in literature. This study underscores UISS-TOX’s potential as a reliable in silico method for allergenicity prediction, aligning with New Approach Methodologies and reducing the need for animal testing.

This study explored the possibility to adapt the physiologically based kinetic (PBK) model, originally developed for long-chain and long half-life per- and polyfluoroalkyl substances (PFASs) by the European Food Safety Authority (EFSA), to assess three short-chain and short half-life PFASs namely perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA) and perfluorobutanesulfonic acid (PFBS). The aim was to estimate the plasma concentration of short-chain PFASs following repeated oral exposure and use this data to inform the Universal Immune System Simulator (UISS) model to predict effect on antibody response. In parallel, in vitro to in vivo extrapolation (QIVIVE) was conducted using values obtained from in vitro experiments. Results show the feasibility of applying established long-chain PFASs kinetic models to short-chain PFASs and to investigate their potential health impacts.

Influenza remains a significant public health concern, with annual vaccine formulations traditionally developed based on the most prevalent virus strains from the previous year. This process often results in mismatches between the vaccine and circulating strains, limiting efficacy. In this study, we utilize the UISS-FLU simulator to compare the effects of a novel influenza vaccine formulation, developed in our previous research, against this year's standard vaccine. We aim to evaluate the potential advantages of our formulation in terms of immunogenic response and protective efficacy. By leveraging in silico methodologies, we can enhance vaccine design and optimization, allowing for a more adaptive and responsive approach to influenza immunization. Our findings will provide insights into the future of vaccine development, showcasing how computational tools can improve public health outcomes by potentially yielding a universal vaccine solution.

In 2019, humanity witnessed one of the most violent and dangerous pandemics ever seen: SARS-CoV-2 emerged suddenly, spreading rapidly across the world. The speed of its spread and the severity of its infections—with clinical symptoms ranging from flu-like manifestations to various forms of pneumonia, and even death due to acute respiratory distress syndrome (ARDS)—made SARS-CoV-2 one of the deadliest members of the coronavirus family. This triggered an urgent, global search for new technologies capable of curbing its spread and mitigating its effects as quickly as possible. In this context, mRNA vaccines established themselves as the most promising solution for a rapid response, even though the long-term effects of using this technology in vaccines remained unclear. Here, we shed light on the mechanism of action and potential side effects of mRNA vaccines, using the Universal Immune System Simulator (UISS) for in silico simulation of the human immune response.

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease characterized by the immune system attacking the body’s own tissue, losing its ability to distinguish between foreign pathogens and healthy cells. This condition affects approximately 5 million individuals worldwide, leading to widespread tissue and organ damage, including the skin, joints, kidneys, cardiovascular system, and central nervous system. The pathology of SLE is influenced by a variety of predisposing factors such as genetic and epigenetic changes, environmental triggers, infections, and hormonal imbalances. One subtype of SLE is Cutaneous Lupus Erythematosus (CLE), where skin involvement serves as a significant manifestation of the disease. CLE is characterized by distinct serological and histological features, with ultraviolet (UV) radiation being a primary environmental trigger exacerbating disease severity. Epidemiological data indicate an annual incidence of approximately 4 cases per 100,100 persons for CLE, while 70–80% of SLE patients report skin involvement. In this work, we extend the capabilities of the Universal Immune System Simulator (UISS), an agent-based modeling framework, by incorporating a disease layer to simulate SLE progression with a focus on CLE. This enhancement emphasizes the role of keratinocytes in disease development, offering deeper insights into the mechanisms underlying CLE pathology.

The assessment of the allergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

We present here the results obtained by applying the Universal Immune System Simulator (UISS) computational platform in one declinations developed for COVID19 disease (UISS-COVID19) to model the efficacy, demonstrated during the SAVE-MORE study, of anakinra drug on patients affected by a severe illness induced by SARS-COV-2. The goal is to further validate the model capabilities and to confirm through computer simulations the biological hypotheses on the mechanisms of actions involved with the use of anakinra drug. UISS, and in silico models in general, have the potential to be integrated in future clinical trial pipelines and to be adopted as technologies to support clinical trials to evaluate the effectiveness of any drug or or xenobiotic.

Chemicals such as trimellitic anhydride and toluene diisocyanate can induce allergic reactions, leading to conditions like occupational asthma and rhinitis. Although they manifest differently, these sensitizers possess shared characteristics, notably their low molecular weight and the capacity to trigger immune responses upon protein binding. This study utilizes the Universal Immune System Simulator to simulate immune reactions to these chemicals. Results indicate that the Universal Immune System Simulator can closely replicate trimellitic anhydride induced immune reactions, primarily showing Th2-type responses with cytokine patterns typical of allergies. These findings underscore the Universal Immune System Simulator's potential in predicting immune reactions to certain chemicals, offering insights into the distinctions between skin and respiratory sensitizers. Upcoming research seeks to further elucidate the immunotoxic pathways of these agents, enabling differentiation based on unique immune reactions.

The study used the Universal Immune System Simulator (UISS) to create a virtual model of cutaneous leishmaniasis (CL), a disease caused by Leishmania parasites transmitted through sandfly bites. CL can manifest in various forms, impacting millions of people globally. Current treatments face challenges, including toxicity and a lack of preventive vaccines. Researchers explored the immune response intricacies in CL, focusing on the balance between M1 and M2 macrophages, which significantly affect disease outcomes. The UISS accurately simulated the immune response, representing interactions between digital patients and Leishmania parasites. The simulation represented a scenario where the importance of M2 macrophages and Th2 cells in CL immunopathology is emphasized. These findings demonstrate UISS's potential in understanding complex host-pathogen interactions, paving the way for targeted therapies and innovative treatments in leishmaniasis.

The Universal Immune System Simulator offers a state-of-the-art platform for simulating the intricate dynamics of the immune system. This study presents a comparative simulation analysis using a representative digital patient exposed to a generic bacterial challenge under two distinct conditions. In the baseline scenario, the patient's immune response is observed without interventions. Conversely, the second scenario introduces the application of the " Variable Approach Technique®," a method designed to enhance both muscle functionality and consequently lymphatic flow. This technique, by augmenting muscle activity, acts as a catalyst for efficient lymphatic circulation, driving the rapid mobilization of immune cells to the infection site. Preliminary observations indicate that the synergistic effect of improved muscle and lymphatic functionality, achieved through the Variable Approach Technique, leads to quicker bacterial containment, efficient toxin removal, and an expedited overall immune response. By contrasting these scenarios, this simulation highlights the potential benefits of optimizing the muscular and lymphatic systems in bolstering the body's defenses against bacterial infections. The findings offer a compelling case for the real-world medical applications of such interventions, emphasizing the transformative potential of the Variable Approach Technique in immune system modulation and enhanced disease resistance.

The emergence of Variants of Concern in infectious diseases, particularly in the context of viruses like SARS-CoV-2, has highlighted the critical importance of continuous prediction and monitoring, showcasing the pivotal role of computational biology in addressing the challenges posed by these emerging infectious diseases. This study advocates for implementing a computational approach able to predict the next SARS-CoV-2 variant of concern (VOC). To that end, inspired by natural selection principles, we used the Genetic Algorithm (GA) as it offers a potent framework for optimizing complex problems. We initiated our investigation with the Wuhan spike protein sequence since it is critical target for variant surveillance and used as reference input. Subsequently, we systematically introduced specific mutations into this sequence to make the initial population. Computational modeling generated three-dimensional structures of the mutated spike within the SARS-CoV-2 ACE2 to evaluate the best candidate of each generation. These were later evaluated by predicting their Gibbs free energy (ΔG values) to evaluate the stability and interactions of these mutants, providing insights into their potential effects on viral behavior and the emergence of VOC. Our analysis demonstrates that the ΔG of our predicted variant closely compares to the delta variant, indicating a similar thermodynamic profile in their interactions. Moreover, our finding indicates that the transmission potential of the new variant is nearly on par with that of the delta variants. Additional factors will be taken into account to evaluate the overall importance of our predicted variant, and we will undertake further research and analysis to comprehend its real-world consequences and potential advantages or drawbacks.

Accurate segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) scans is crucial for clinical diagnosis and effective treatment planning. In this work, we investigate the effectiveness of Diffusion Models (DM) in achieving pixel-wise segmentation of MS lesions. DM significantly improves segmentation sensitivity, especially in regions with subtle abnormalities. We conducted extensive experiments using the magnetic resonance volumes from a public dataset, encompassing various imaging modalities. Our analysis demonstrated how DM can achieve performance levels that are on par with state-of-the-art techniques, as evidenced by a mean Dice coefficient comparable to the best existing methods. Furthermore, some variants of standard DM exhibits robustness across various imaging modalities, showcasing its versatility in clinical settings.

Breast cancer is among the leading causes of mortality in women of all ages worldwide. Prevention and timely diagnosis can make the difference between life and death for most patients, and it is for this reason that today there is a great deal of focus on raising public awareness about the possibility of using the various techniques of preventive screening available today. In cases where surgery is necessary, beyond the purely medical aspects, it is important to take into account also the psychological aspects, which are sometimes underestimated with negative consequences for the patient. However, it is very difficult to determine a priori what the final outcome of the intervention will be, especially with regard to the level of personal satisfaction of patients. The aim of this work is to identify a possible correlation between the commonly used measurements of breast morphology, necessary for the assessment of any surgical procedure, and the personal satisfaction level of the patients, based on the results of anonymous questionnaires, through the use of machine learning techniques.

When it was first introduced in 2000, reverse vaccinology was defined as an in silico approach that begins with the pathogen's genomic sequence. It concludes with a list of potential proteins with a possible, but not necessarily, list of peptide candidates that need to be experimentally confirmed for vaccine production. During the subsequent years, reverse vaccinology has dramatically changed: now it consists of a large number of bioinformatics tools and processes, namely subtractive proteomics, computational vaccinology, immunoinformatics, and in silico related procedures. However, the state of the art of reverse vaccinology still misses the ability to predict the efficacy of the proposed vaccine formulation. Here, we describe how to fill the gap by introducing an advanced immune system simulator that tests the efficacy of a vaccine formulation against the disease for which it has been designed. As a working example, we entirely apply this advanced reverse vaccinology approach to design and predict the efficacy of a potential vaccine formulation against influenza H5N1. Climate change and melting glaciers are critical due to reactivating frozen viruses and emerging new pandemics. H5N1 is one of the potential strains present in icy lakes that can raise a pandemic. Investigating structural antigen protein is the most profitable therapeutic pipeline to generate an effective vaccine against H5N1. In particular, we designed a multi-epitope vaccine based on predicted epitopes of hemagglutinin and neuraminidase proteins that potentially trigger B-cells, CD4, and CD8 T-cell immune responses. Antigenicity and toxicity of all predicted CTL, Helper T-lymphocytes, and B-cells epitopes were evaluated, and both antigenic and non-allergenic epitopes were selected. From the perspective of advanced reverse vaccinology, the Universal Immune System Simulator, an in silico trial computational framework, was applied to estimate vaccine efficacy using a cohort of 100 digital patients.

The COVID-19 pandemic motivated an intense debate over high transmissibility and unavailability of effective vaccine to cover all existent variants, and also has raised critical questions, such as concerns about new mutations and genetic recombination that could lead to novel variants of concerns. The density of mutation observed in the different residue indices of spike protein sequence, may correlate to the speed of virus distribution. Therefore, predicting an accurate determination of mutation rates is essential to comprehend this virus evolution and assess the risk of emergent infectious disease. The current study predicts the mutations that may be cause of new variants of concerns using a genetic algorithm approach. In this regard, we mutated randomly the wild-type sequence of SARS-CoV-2 spike protein to generate first 100 different sequences (initial population) that were modelled individually and used to evaluate their discrete optimized protein energy score. After applying cross-over and breeding 200 new generations, one of the sequences with the lowest discrete optimized protein energy score was identified and chosen for a further analysis to realize whether this sequence is potential for being the next variant of concern.

Cutaneous leishmaniasis (CL) is the most common form of leishmaniasis, an infectious disease caused by the Leishmania parasite and transmitted by phlebotomine sandflies. CL is manifested as skin lesions, which typically evolve to ulcerative lesions and may last for months or even years, if not treated. Currently available treatments against CL involve the use of particularly toxic and overpriced drugs, which, among other things, require long times of administration and do not always lead to the complete recovery of the patient. The use of in silico technologies, as the Universal Immune System Simulator (UISS), may be helpful in finding new and potentially more effective drugs against CL.

The Universal Immune System Simulator (UISS) is a computational framework based on agent-based modelling (ABM) paradigm that has been specifically developed for simulating the immune system behaviour in presence of diseases and treatments. It has a long history of development, ranging from its initial applications into the field of tumor immunology and then moving towards wide disease modelling scenarios such as influenza, Multiple Sclerosis and atherosclerosis. Recently, inside the STriTuVaD H2020 EU project, it has been specialized to simulate tuberculosis dynamics and its interaction with the immune system, including the efficacy of the combined action of various treatments such as isoniazid and novel vaccines. TB simulation entitles large scale (e.g., tissue to organ scale) simulations over a wide digital population cohort. The computational costs of running large scale simulations are prohibitive using traditional forms of CPU simulation. This paper considers the use of parallel to gpu-based computing approaches via an agent-based domain independent complex systems simulator, FLAME GPU. Integration of FLAME GPU with UISS enables the simulation of larger, more complex problem domains. The combined UISS-FLAMEGPU simulator provides vastly increased performance characteristics for large problems, with a speedup of 4.22x for a typical tuberculosis model simulating 128 microlitres. FLAME GPU abstracts away a significant portion of the normal programming that would be required to effectively parallelise a model of this complexity. Adaptations were made to increase performance, such as message mutation and parallelisation of certain algorithms.

The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.

In Silico Trials methodologies will play a growing and fundamental role in the development and de-risking of new medical devices in the future. While the regulatory pathway for Digital Patient and Personal Health Forecasting solutions is clear, it is more complex for In Silico Trials solutions, and therefore deserves a deeper analysis. In this position paper, we investigate the current state of the art towards the regulatory system for in silico trials applied to medical devices while exploring the European regulatory system toward this topic. We suggest that the European regulatory system should start a process of innovation: in principle to limit distorted quality by different internal processes within notified bodies, hence avoiding that the more innovative and competitive companies focus their attention on the needs of other large markets, like the USA, where the use of such radical innovations is already rapidly developing.

Multiple sclerosis is an autoimmune inflammatory disease of the central nervous system with a relapsing or progressive course, potentially leading to severe disability. In this manuscript, a well-known sensitivity analysis technique, Latin Hypercube Sampling along with Partial Rank Correlation Coefficient, was applied to an Agent-Based Model in silico trial used in Multiple Sclerosis context to evaluate the correlation of vitamin D dosage with the number of oligodendrocytes and the thymus efficiency with regulatory T cells.

Technological advances in fields such as computer modelling and simulation are playing a fundamental role, during the COVID-19 pandemic, that has been afflicting us for 2 years now. These methodologies may help in the development, assessment and monitoring for better prevention, diagnosis, treatment and generation of specific therapeutic strategies. In this perspective, in silico platforms are emerging thanks to their ability to predict the efficacy and safety of new therapies and vaccines. Here, our integrated bioinformatics pipeline for evaluating the elicited response of human immune system against every pathogen is applied to predict the cross-reactive immunity induced by pre-existing vaccinations against SARS-CoV-2 UK variants.

SUMMARY: Although several bioinformatics tools have been developed to examine signaling pathways, little attention has been given to ever long-distance crosstalk mechanisms. Here, we developed PETAL, a Python tool that automatically explores and detects the most relevant nodes within a KEGG pathway, scanning and performing an in-depth search. PETAL can contribute to discovering novel therapeutic targets or biomarkers that are potentially hidden and not considered in the network under study. AVAILABILITYAND IMPLEMENTATION: PETAL is a freely available open-source software. It runs on all platforms that support Python3. The user manual and source code are accessible from https://github.com/Pex2892/PETAL.

Abstract Background Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated. Results This study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a+ vector. Conclusions The proposed “Recombinant multi-epitope vaccine” was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions.

BACKGROUND: Nowadays, the inception of computer modeling and simulation in life science is a matter of fact. This is one of the reasons why regulatory authorities are open in considering in silico trials evidence for the assessment of safeness and efficacy of medicinal products. In this context, mechanistic Agent-Based Models are increasingly used. Unfortunately, there is still a lack of consensus in the verification assessment of Agent-Based Models for regulatory approval needs. VV&UQ is an ASME standard specifically suited for the verification, validation, and uncertainty quantification of medical devices. However, it can also be adapted for the verification assessment of in silico trials for medicinal products. RESULTS: Here, we propose a set of automatic tools for the mechanistic Agent-Based Model verification assessment. As a working example, we applied the verification framework to an Agent-Based Model in silico trial used in the COVID-19 context. CONCLUSIONS: Using the described verification computational workflow allows researchers and practitioners to easily perform verification steps to prove Agent-Based Models robustness and correctness that provide strong evidence for further regulatory requirements.

BACKGROUND: Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. RESULTS: We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. CONCLUSIONS: The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data.

The term "In Silico Trial" indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative.

The application of Agent Based Models (ABMs) in biology and immunology has recently come to the fore, thanks to their ability to accurately describe complex biological behaviors, rules, and interactions, without the need to use complex mathematical formalisms. However, even if there is a growing interest in applying such methodologies to improve and speed up the research of novel pharmaceutical products, verification and validation procedures voted at assessing ABMs credibility are far from being well-established. We present Verify, the first toolbox of instruments selected and designed for the verification of discrete-time models, with a focus on agent-based approaches. The toolbox has a friendly GUI, does not require the installation of any additional software, and can easily find possible numerical errors and incongruences that may affect such models.

One of the main problems in the fight against cancer is represented by the immunosuppression it causes. Several research lines have been undertaken to address this issue; one of the most promising is represented by oncolytic viruses. Oncolytic viruses are designed from live attenuated recombinant viruses and are designed to preferentially infect cancer cells, within which they replicate causing their lysis and enhance, through various mechanisms, innate and adaptive immunity. In collaboration with the Etna Biotech company, which was involved in designing five protein constructs composed of five different variants of a monoclonal antibody linked to a viral surface protein, we worked on the modeling and evaluation of these proteins associated with the Measles Virus. The software we used was Swiss-model (https://swissmodel.expasy.org/), a bioinformatics webserver dedicated to homology modeling of 3D protein structures, and Pro-Q (https://proq.bioinfo.se/ProQ/ProQ.html) to perform quality control. We shared our results with the company, which completed laboratory tests for two of the five constructs, confirming our predictions. Transfection experiments are currently underway with two other constructs, while the last one has not yet been subjected to such investigations.

An ever-increasing number of tools and databases offers pieces of evidence and knowledge about gene function annotations, protein interactions, and experimentally validated biological pathways. These resources represent an excellent and essential instrument to facilitate pathway analysis. Among them, particular attention should be given to the one capable of finding alternative pathways or cross-talks events and potential drug target candidates. Here, we evaluated PETAL predictive capability, a Python tool that automatically explores and scans the relevant nodes within a KEGG pathway. Starting from three specific cancer scenarios (chronic myelogenous leukemia, non-small cell lung cancer, and head and neck squamous cell carcinoma) and related literature results about potential driver genes of drug resistance to EGFR tyrosine kinases inhibitors (SNCA, BCL-6 and YAP-1), we used PETAL to test its capability to identify in parallel these potential target genes involved in tumor progression and EGFR inhibitors resistance. By searching in-depth for ancestor and descendent nodes of SNCA, BCL-6 and YAP-1, across the EGFR pathway, we found that PETAL was able to detect the same targets investigated in the recent literature. Finally, this retrospective work emphasizes that PETAL could represent a powerful tool that can be used to improve the understanding of complex biological pathways and speed-up the identification of potential biomarkers and therapeutical candidates in cancer potentially in any other disease.

BACKGROUND: The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial. RESULTS: One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject. CONCLUSIONS: We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration.

The 3rd edition of the computational methods for the immune system function workshop has been held in San Diego, CA, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) from November 18 to 21, 2019. The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. Among these, the best 10 papers have been selected and extended for presentation in this special issue. The covered topics range from computer-aided identification of T cell epitopes to the prediction of heart rate variability to prevent brain injuries, from In Silico modeling of Tuberculosis and generation of digital patients to machine learning applied to predict type-2 diabetes risk.

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.

Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models.

Intracellular biochemical reactions emerge from the interaction among multiple extracellular signaling components. Considering the number, type and connections of the signaling components represents a needed step to characterize, identify and describe potential targets for a clinical purpose. However, it is increasingly documented that the presence of sub-types of signaling proteins, branching and crosstalk may lead to very variable outcomes in the same path, which is not always well defined experimentally. For this reason, we present an improved version of the algorithm based on the MapReduce paradigm to facilitate the discovery of new therapeutic targets. Our algorithm allows you to scan and perform a search in depth of biological pathway in order to analyze less recurrent and therefore non-trivial paths. These routes represent a chain of biochemical interactions among different biological actors that can be represented by quite distant nodes along the pathway. This type of analysis can also be performed manually, but with high execution times due to the large amount of pathways and genes present. Thus, our tool performs exhaustive analysis in an automated way, drastically reducing the time required. Our proposal allows us to discover the genes far from the initial target genes, also showing the number of occurrences of a given path found within the set of biological pathways analyzed during the simulation.

EC funded STriTuVaD project aims to test, through a phase IIb clinical trial, two of the most advanced therapeutic vaccines against tuberculosis. In parallel, we have extended the Universal Immune System Simulator to include all relevant determinants of such clinical trial, to establish its predictive accuracy against the individual patients recruited in the trial, to use it to generate digital patients and predict their response to the HRT being tested, and to combine them to the observations made on physical patients using a new in silico-augmented clinical trial approach that uses a Bayesian adaptive design. This approach, where found effective could drastically reduce the cost of innovation in this critical sector of public healthcare. One of the most challenging task is to develop a methodology to reproduce biological diversity of the subjects that have to be simulated, i.e., provide an appropriate strategy for the generation of libraries of digital patients. This has been achieved through the the creation of the initial immune system repertoire in a stochastic way, and though the identification of a “vector of features” that combines both biological and pathophysiological parameters that personalize the digital patient to reproduce the physiology and the pathophysiology of the subject.

It is well known that the response of individuals to disease varies, either because of unpredictable exogenous events, such as possibly unknown environmental effects, or just because of endogenous factors, i.e. different genetic background. In particular, when a treatment effectiveness has to be validated, the individual variability should be taken into account by exploiting stochastic models. Relapsing Remitting Multiple Sclerosis (RRMS) is an unpredictable and complex disease, whose random behaviour perfectly fits the study with stochastic models. RRMS is the most common form of Multiple Sclerosis (MS), an immune-mediated inflammatory disease of the central nervous system, characterized by alternate episodes of symptom exacerbation (relapses) with periods of disease stability (remission). Several treatments were proposed to contrast the disease progression. Among these, Daclizumab initially exhibited promising results. However, due to the risk of serious side effects the treatment has been retired. We propose a stochastic and an hybrid extension, based on a generalization of the high level Petri Net formalism, of an existing model of Daclizumab effects on RRMS. The model is developed to investigate the complex mechanisms and unpredictable behaviour characterizing the RRMS disease and its relapsing, especially under the Daclizumab administration.

Tuberculosis (TB) is one of the deadliest diseases worldwide, with 1,5 million fatalities every year along with potential devastating effects on society, families and individuals. To address this alarming burden, vaccines can play a fundamental role, even though to date no fully effective TB vaccine really exists. Current treatments involve several combinations of antibiotics administered to TB patients for up to two years, leading often to financial issues and reduced therapy adherence. Along with this, the development and spread of drug-resistant TB strains is another big complicating matter. Faced with these challenges, there is an urgent need to explore new vaccination strategies in order to boost immunity against tuberculosis and shorten the duration of treatment. Computational modeling represents an extraordinary way to simulate and predict the outcome of vaccination strategies, speeding up the arduous process of vaccine pipeline development and relative time to market. Here, we present EU - funded STriTuVaD project computational platform able to predict the artificial immunity induced by RUTI and ID93/GLA-SE, two specific tuberculosis vaccines. Such an in silico trial will be validated through a phase 2b clinical trial. Moreover, STriTuVaD computational framework is able to inform of the reasons for failure should the vaccinations strategies against M. tuberculosis under testing found not efficient, which will suggest possible improvements.

Thyroid cancer is the commonest endocrine malignancy. Mutation in the BRAF serine/threonine kinase is the most frequent genetic alteration in thyroid cancer. Target therapy for advanced and poorly differentiated thyroid carcinomas include BRAF pathway inhibitors. Here, we evaluated the role of MAP3K8 expression as a potential driver of resistance to BRAF inhibition in thyroid cancer. By analyzing Gene Expression Omnibus data repository, across all thyroid cancer histotypes, we found that MAP3K8 is up-regulated in poorly differentiated thyroid carcinomas and its expression is related to a stem cell like phenotype and a poorer prognosis and survival. Taken together these data unravel a novel mechanism for thyroid cancer progression and chemo-resistance and confirm previous results obtained in cultured thyroid cancer stem cells.

We present here a novel algorithm based on a MapReduce approach to facilitate the discovery of novel therapeutic targets. The proposed algorithm has been enabled to scan a set biological pathways in order to discover non-trivial (less common) routes. Such routes represent a chain of biochemical interactions among different biological actors. These actors can be represented by quite distant nodes along the devised pathway. Our approach detects nodes that are far from the initial target nodes, also showing the number of times that a given route has been found inside the selected set of biological pathways.

Innovations in information and communication technology infuse all branches of science, including life sciences. Nevertheless, healthcare is historically slow in adopting technological innovation, compared with other industrial sectors. In recent years, new approaches in modelling and simulation have started to provide important insights in biomedicine, opening the way for their potential use in the reduction, refinement and partial substitution of both animal and human experimentation. In light of this evidence, the European Parliament and the United States Congress made similar recommendations to their respective regulators to allow wider use of modelling and simulation within the regulatory process. In the context of in silico medicine, the term 'in silico clinical trials' refers to the development of patient-specific models to form virtual cohorts for testing the safety and/or efficacy of new drugs and of new medical devices. Moreover, it could be envisaged that a virtual set of patients could complement a clinical trial (reducing the number of enrolled patients and improving statistical significance), and/or advise clinical decisions. This article will review the current state of in silico clinical trials and outline directions for a full-scale adoption of patient-specific modelling and simulation in the regulatory evaluation of biomedical products. In particular, we will focus on the development of vaccine therapies, which represents, in our opinion, an ideal target for this innovative approach.

MOTIVATION: Val600Glu (V600E) mutation is the most common BRAF mutation detected in thyroid cancer. Hence, recent research efforts have been performed trying to explore several inhibitors of the V600E mutation-containing BRAF kinase as potential therapeutic options in thyroid cancer refractory to standard interventions. Among them, vemurafenib is a selective BRAF inhibitor approved by Food and Drug Administration for clinical practice. Unfortunately, vemurafenib often displays limited efficacy in poorly differentiated and anaplastic thyroid carcinomas probably because of intrinsic and/or acquired resistance mechanisms. In this view, cancer stem cells (CSCs) may represent a possible mechanism of resistance to vemurafenib, due to their self-renewal and chemo resistance properties. RESULTS: We present a computational framework to suggest new potential targets to overcome drug resistance. It has been validated with an in vitro model based upon a spheroid-forming method able to isolate thyroid CSCs that may mimic resistance to vemurafenib. Indeed, vemurafenib did not inhibit cell proliferation of BRAF V600E thyroid CSCs, but rather stimulated cell proliferation along with a paradoxical over-activation of ERK and AKT pathways. The computational model identified a fundamental role of mitogen-activated protein kinase 8 (MAP3K8), a serine/threonine kinase expressed in thyroid CSCs, in mediating this drug resistance. To confirm model prediction, we set a suitable in vitro experiment revealing that the treatment with MAP3K8 inhibitor restored the effect of vemurafenib in terms of both DNA fragmentation and poly (ADP-ribose) polymerase cleavage (apoptosis) in thyroid CSCs. Moreover, MAP3K8 expression levels may be a useful marker to predict the response to vemurafenib. AVAILABILITY AND IMPLEMENTATION: The model is available in GitHub repository visiting the following URL: https://github.com/francescopappalardo/MAP3K8-Thyroid-Spheres-V-3.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: DNA methylation is an epigenetic mechanism of genomic regulation involved in the maintenance of homeostatic balance. Dysregulation of DNA methylation status is one of the driver alterations occurring in neoplastic transformation and cancer progression. The identification of methylation hotspots associated to gene dysregulation may contribute to discover new prognostic and diagnostic biomarkers, as well as, new therapeutic targets. RESULTS: We present EpiMethEx (Epigenetic Methylation and Expression), a R package to perform a large-scale integrated analysis by cyclic correlation analyses between methylation and gene expression data. For each gene, samples are segmented according to the expression levels to select genes that are differentially expressed. This stratification allows to identify CG methylation probesets modulated among gene-stratified samples. Subsequently, the methylation probesets are grouped by their relative position in gene sequence to identify wide genomic methylation events statically related to genetic modulation. CONCLUSIONS: The beta-test study showed that the global methylation analysis was in agreement with scientific literature. In particular, this analysis revealed a negative association between promoter hypomethylation and overexpression in a wide number of genes. Less frequently, this overexpression was sustained by intragenic hypermethylation events.

The 2nd Computational Methods for the Immune System function Workshop has been held in Madrid in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, from December 3 to 6, 2018. The workshop has been obtained 100% more submissions in respect to the first edition, highlighting a growing interest for the treated topics. The best papers (9) have been selected for extension in this special issue, with themes about immune system and disease simulation, computer-aided design of novel candidate vaccines, methods for the analysis of immune system involved diseases based on statistical methods, meta-heuristics and game theory, and modelling strategies for improving the simulation of the immune system dynamics.

BACKGROUND: Tuberculosis (TB) represents a worldwide cause of mortality (it infects one third of the world's population) affecting mostly developing countries, including India, and recently also developed ones due to the increased mobility of the world population and the evolution of different new bacterial strains capable to provoke multi-drug resistance phenomena. Currently, antitubercular drugs are unable to eradicate subpopulations of Mycobacterium tuberculosis (MTB) bacilli and therapeutic vaccinations have been postulated to overcome some of the critical issues related to the increase of drug-resistant forms and the difficult clinical and public health management of tuberculosis patients. The Horizon 2020 EC funded project "In Silico Trial for Tuberculosis Vaccine Development" (STriTuVaD) to support the identification of new therapeutic interventions against tuberculosis through novel in silico modelling of human immune responses to disease and vaccines, thereby drastically reduce the cost of clinical trials in this critical sector of public healthcare. RESULTS: We present the application of the Universal Immune System Simulator (UISS) computational modeling infrastructure as a disease model for TB. The model is capable to simulate the main features and dynamics of the immune system activities i.e., the artificial immunity induced by RUTI® vaccine, a polyantigenic liposomal therapeutic vaccine made of fragments of Mycobacterium tuberculosis cells (FCMtb). Based on the available data coming from phase II Clinical Trial in subjects with latent tuberculosis infection treated with RUTI® and isoniazid, we generated simulation scenarios through validated data in order to tune UISS accordingly to STriTuVaD objectives. The first case simulates the establishment of MTB latent chronic infection with some typical granuloma formation; the second scenario deals with a reactivation phase during latent chronic infection; the third represents the latent chronic disease infection scenario during RUTI® vaccine administration. CONCLUSIONS: The application of this computational modeling strategy helpfully contributes to simulate those mechanisms involved in the early stages and in the progression of tuberculosis infection and to predict how specific therapeutical strategies will act in this scenario. In view of these results, UISS owns the capacity to open the door for a prompt integration of in silico methods within the pipeline of clinical trials, supporting and guiding the testing of treatments in patients affected by tuberculosis.

The developing of novel prophylactic and therapeutic vaccine candidates in the field of cancer immunology brought to very promising results against tumors, entitling full protection with reduced amount of the typical side effects of the actual conventional treatments. However, such treatments required a constant, life-long, administration procedure to keep protection. As both the period of protection and the relative number of administrations grow, the problem of finding the best administration protocol, in time and dosage, becomes more and more complex. Such a problem cannot be usually solved in in vivo experiments, as the costs in terms of time, money, and people would be prohibitive. We propose a hybrid approach that integrates machine learning and parallel genetic algorithms to enhance the research in silico of optimal administration protocols for a cancer vaccine. A neural network is used to improve both crossover and mutation operators. Preliminary results suggest that the use of such could bring to better administration protocols using a similar computational effort.

Personalized target therapies represent one of the possible treatment strategies to fight the ongoing battle against cancer. New treatment interventions are still needed for an effective and successful cancer therapy. In this scenario, we simulated and analyzed the dynamics of BRAF V600E melanoma patients treated with BRAF inhibitors in order to find potentially interesting targets that may make standard treatments more effective in particularly aggressive tumors that may not respond to selective inhibitor drugs. To this aim, we developed a continuous Petri Net model that simulates fundamental signalling cascades involved in melanoma development, such as MAPK and PI3K/AKT, in order to deeply analyze these complex kinase cascades and predict new crucial nodes involved in melanomagenesis. The model pointed out that some microRNAs, like hsa-mir-132, downregulates expression levels of p120RasGAP: under high concentrations of p120RasGAP, MAPK pathway activation is significantly decreased and consequently also PI3K/PDK1/AKT activation. Furthermore, our analysis carried out through the Genomic Data Commons (GDC) Data Portal shows the evidence that hsa-mir-132 is significantly associated with clinical outcome in melanoma cancer genomic data sets of BRAF-mutated patients. In conclusion, targeting miRNAs through antisense oligonucleotides technology may suggest the way to enhance the action of BRAF-inhibitors.

Vaccines represent nowadays one of the most efficient weapons against foreign pathogens. To be effective, vaccines need a proper administration strategy that requires multiple administrations in order to ensure the acquisition of immunological memory. Vaccination schedules are usually based on past experience, and economical, ethical and time constraints have limited the research for better combinations of timing and dosage. We present here a computational approach based on the use of stochastic optimization techniques and validated “in silico” models to study and optimize vaccination protocols. We use Simulated Annealing in conjunction with a validated agent based model to suggest some interventions that may improve the efficacy of a candidate vaccine composed by influenza-A virosome and natural citrus-derived adjuvants to prevent influenza-A infection.

Immune system processes can be simulated using both system dynamics (SD) models based on differential equations and Agent-based (AB) models. The two approaches are intrinsically different but some methodologies have been developed to convert SD models into AB models with a variable degree of success. However, until now none of such methods have considered the use of scale factors in SD to AB model conversion. In this work, we revisited a well know SD model describing the interaction between effector T cells and tumor cells that was previously shown unsuitable for AB modeling. We introduced non-dimensional scaling factors in AB modeling and compared AB and AD simulations through a sensitivity analysis. Under this scenario, we obtained AB models that could successfully reproduce SD simulations with a reasonable number of agents and a stochastic behavior that did not compromise computer resources. In general, our results justify the introduction of non-dimensional scaling factors to reproduce SD simulations with AB models.

BACKGROUND: Human papillomavirus infection is a global social burden that, every year, leads to thousands new diagnosis of cancer. The introduction of a protocol of immunization, with Gardasil and Cervarix vaccines, has radically changed the way this infection easily spreads among people. Even though vaccination is only preventive and not therapeutic, it is a strong tool capable to avoid the consequences that this pathogen could cause. Gardasil vaccine is not free from side effects and the duration of immunity is not always well determined. This work aim to enhance the effects of the vaccination by using a new class of adjuvants and a different administration protocol. Due to their minimum side effects, their easy extraction, their low production costs and their proven immune stimulating activity, citrus-derived molecules are valid candidates to be administered as adjuvants in a vaccine formulation against Hpv. RESULTS: With the aim to get a stronger immune response against Hpv infection we built an in silico model that delivers a way to predict the best adjuvants and the optimal means of administration to obtain such a goal. Simulations envisaged that the use of Neohesperidin elicited a strong immune response that was then validated in vivo. CONCLUSIONS: We built up a computational infrastructure made by a virtual screening approach able to preselect promising citrus derived compounds, and by an agent based model that reproduces HPV dynamics subject to vaccine stimulation. This integrated methodology was able to predict the best protocol that confers a very good immune response against HPV infection. We finally tested the in silico results through in vivo experiments on mice, finding good agreement.

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: [email protected]; [email protected].

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.