In silico models of cancer

Building bones for blood and beyond: the growing field of bone marrow niche model development

The bone marrow (BM) niche is a complex microenvironment that provides the signals required for regulation of hematopoietic stem cells (HSCs) and the process of hematopoiesis they are responsible for. Bioengineered models of the BM niche incorporate various elements of the in vivo BM microenvironment, including cellular components, soluble factors, a three-dimensional environment, mechanical stimulation of included cells, and perfusion. Recent advances in the bioengineering field have resulted in a spate of new models that shed light on BM function and are approaching precise imitation of the BM niche. These models promise to improve our understanding of the in vivo microenvironment in health and disease. They also aim to serve as platforms for HSC manipulation or as preclinical models for screening novel therapies for BM-associated disorders and diseases.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

Agent-based approaches for biological modeling in oncology: A literature review

CONTEXT

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions.

METHODOLOGY

This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023.

RESULTS

Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Comprehensive exploration of Biochanin A as an oncotherapeutics potential in the treatment of multivarious cancers with molecular insights

Cancer is considered a leading cause of mortality. This rising cancer death rate and several existing limitations like side effects, poor efficacies, and high cost of the present chemotherapeutic agents have increased the demand for more potent and alternative cancer treatments. This review elucidated a brief overview of Biochanin A (BCA) and its potentiality on various cancers with details of anticancer mechanism. According to our review, a number of studies including in silico, in vitro, pre-clinical, and clinical trials have tested to evaluate the efficacy of BCA. This compound is effective against 15 types of cancer, including breast, cervical, colorectal, gastric, glioblastoma, liver, lung, melanoma, oral, osteosarcoma, ovarian, pancreatic, pharynx, prostate, and umbilical vein cancer. The general anticancer activities of this compound are mediated via several molecular processes, including regulation of apoptosis, cell proliferation, metastasis and angiogenesis, signaling, enzymatic pathways, and other mechanisms. Targeting both therapeutic and oncogenic proteins, as well as different pathways, makes up the molecular mechanism underlying the anticancer action. Many signaling networks and their components, such as EFGR, PI3K/Akt/mTOR, MAPK, MMP-2, MMP-9, PARP, Caspase-3/8/9, Bax, Bcl2, PDL-1, NF-κB, TNF-α, IL-6, JAK, STAT3, VEGFR, VEGF, c-MY, Cyclin B1, D1, E1 and CDKs, Snail, and E-cadherin proteins, can be regulated in cancer cells by BCA. Such kind of anticancer properties of BCA could be a result of its correct structural chemistry. The use of BCA-based therapies as nano-carriers for the delivery of chemotherapeutic medicines has the potential to be very effective. This natural compound synergises with other natural compounds and standard drugs, including sorafenib, 5-fluorouracil, temozolomide, doxorubicin, apigenin, and genistein. Moreover, proper use of this compound can reverse multidrug resistance through numerous mechanisms. BCA has better drug-likeness and pharmacokinetic properties and is nontoxic (eye, liver, kidney, skin, cardio) in human bodies. As having a wide range of cancer-fighting mechanisms, synergistic effects, and good pharmacokinetic properties, BCA can be used as a supplementary food until standard drugs are available at pharma markets.

Unveiling the potential anti-cancer activity of calycosin against multivarious cancers with molecular insights: A promising frontier in cancer research

BACKGROUND

Calycosin may be a potential candidate regarding chemotherapeutic agent, because already some studies against multivarious cancer have been made with this natural compound.

AIM

This review elucidated a brief overview of previous studies on calycosin potential effects on various cancers and its potential mechanism of action.

METHODOLOGY

Data retrieved by systematic searches of Google Scholar, PubMed, Science Direct, Web of Science, and Scopus by using keywords including calycosin, cancer types, anti-cancer mechanism, synergistic, and pharmacokinetic and commonly used tools are BioRender, ChemDraw Professional 16.0, and ADMETlab 2.0.

RESULTS

Based on our review, calycosin is available in nature and effective against around 15 different types of cancer. Generally, the anti-cancer mechanism of this compound is mediated through a variety of processes, including regulation of apoptotic pathways, cell cycle, angiogenesis and metastasis, oncogenes, enzymatic pathways, and signal transduction process. These study conducted in various study models, including in silico, in vitro, preclinical and clinical models. The molecular framework behind the anti-cancer effect is targeting some oncogenic and therapeutic proteins and multiple signaling cascades. Therapies based on nano-formulated calycosin may make excellent nanocarriers for the delivery of this compound to targeted tissue as well as particular organ. This natural compound becomes very effective when combined with other natural compounds and some standard drugs. Moreover, proper use of this compound can reverse resistance to existing anti-cancer drugs through a variety of strategies. Calycosin showed better pharmacokinetic properties with less toxicity in human bodies.

CONCLUSION

Calycosin exhibits excellent potential as a therapeutic drug against several cancer types and should be consumed until standard chemotherapeutics are available in pharma markets.

Computational modeling of in-stent restenosis: Pharmacokinetic and pharmacodynamic evaluation

Persistence of the pathology of in-stent restenosis even with the advent of drug-eluting stents warrants the development of highly resolved in silico models. These computational models assist in gaining insights into the transient biochemical and cellular mechanisms involved and thereby optimize the stent implantation parameters. Within this work, an already established fully-coupled Lagrangian finite element framework for modeling the restenotic growth is enhanced with the incorporation of endothelium-mediated effects and pharmacological influences of rapamycin-based drugs embedded in the polymeric layers of the current generation drug-eluting stents. The continuum mechanical description of growth is further justified in the context of thermodynamic consistency. Qualitative inferences are drawn from the model developed herein regarding the efficacy of the level of drug embedment within the struts as well as the release profiles adopted. The framework is then intended to serve as a tool for clinicians to tune the interventional procedures patient-specifically.

Antiproliferative and Apoptosis Effects of Hybrid Varieties of Vitis vinifera L. Sweet Sapphire and Sweet Surprise on Human Prostate Cancer Cells Using In Vitro and In Silico Approaches

OBJECTIVE

Grape hybrids are characterized by different chemical compositions; often with high hybrids are characterized by different chemical compositions, often with a high phenolic content and a specific profile of anthocyanins. The aim of study was to characterize the constituents of hybrid Vitis vinifera L. varieties Sweet Sapphire (SA) and Sweet Surprise (SU) extracts and their influence on apoptosis induction and antiproliferative effects on human prostate cancer cells.

METHODS

We used the MTT assay to evaluate the cytotoxic effect of extracts of SA and SU, on the prostate adenocarcinoma cell lines PC-3 and DU-145. To analyze the inhibiting impact by flow cytometry, used 24 and 48 hours. Anthocyanins were quantified by liquid chromatography and analysed by their absorption rate, hepatotoxicity, blood concentration, blood-brain barrier passage ability and maximum recommended dose by in silico approaches.

RESULTS

Our results showed that malvidin derivatives present the highest content in both cultivars. We identified 14.46mg/100g malvidin-3-O-glycoside in SA and 2.76 mg/100 g in SU. A reduction in cell viability of DU-145 (45 and 65%) and PC-3 (63 and 67%) cells after 48h treatment with SA and SU, respectively, was found via MTT assay. Flow cytometry showed that the treatment with extracts from SA and SU had an inhibitory impact on cell development due to G2/M arrest and caused a rise in apoptotic cells compared to control group. None of the anthocyanin presented hepatotoxicity as well as blood-brain barrier passage ability. Peonidin 3-O-glucoside had the lower maximum recommended dose as well as the highest intestinal absorption rate. However, delphinidin 3-O-glucoside had the highest blood concentration values.

CONCLUSION

The findings of this study highlight the potential of hybrid Vitis vinifera L. varieties as an important source of natural antioxidants and their protective effect against prostate cancer cells as well as elucidate in part their anthocyanin's metabolism.

Three-dimensional in vitro culture models in oncology research

Cancer is a multifactorial disease that is responsible for 10 million deaths per year. The intra- and inter-heterogeneity of malignant tumors make it difficult to develop single targeted approaches. Similarly, their diversity requires various models to investigate the mechanisms involved in cancer initiation, progression, drug resistance and recurrence. Of the in vitro cell-based models, monolayer adherent (also known as 2D culture) cell cultures have been used for the longest time. However, it appears that they are often less appropriate than the three-dimensional (3D) cell culture approach for mimicking the biological behavior of tumor cells, in particular the mechanisms leading to therapeutic escape and drug resistance. Multicellular tumor spheroids are widely used to study cancers in 3D, and can be generated by a multiplicity of techniques, such as liquid-based and scaffold-based 3D cultures, microfluidics and bioprinting. Organoids are more complex 3D models than multicellular tumor spheroids because they are generated from stem cells isolated from patients and are considered as powerful tools to reproduce the disease development in vitro. The present review provides an overview of the various 3D culture models that have been set up to study cancer development and drug response. The advantages of 3D models compared to 2D cell cultures, the limitations, and the fields of application of these models and their techniques of production are also discussed.

Role of Persistent Organic Pollutants in Breast Cancer Progression and Identification of Estrogen Receptor Alpha Inhibitors Using In-Silico Mining and Drug-Drug Interaction Network Approaches

Simple Summary

The role of persistent organic pollutants (POPs) in breast cancer progression and their bioaccumulation in adipose tissue has been reported. We used a computational approach to study molecular interactions of POPs with breast cancer proteins and identified natural and synthetic compounds to inhibit these interactions. Moreover, for comparative analysis, standard drugs and screened compounds were also docked against estrogen receptor alpha (ERα) and identification of the finest inhibitor was performed using in-silico mining and drug-drug interaction (DDI) network approaches. Based on scoring values, short-chained chlorinated paraffins demonstrated strong interactions with ERα compared to organo-chlorines and PCBs. Synthetic and natural compounds demonstrating strong associations with the active site of the ERα protein could be potential candidates to treat breast cancer specifically caused by POPs and other organic toxins and can be used as an alternative to standard drugs.

Abstract

The strong association between POPs and breast cancer in humans has been suggested in various epidemiological studies. However, the interaction of POPs with the ERα protein of breast cancer, and identification of natural and synthetic compounds to inhibit this interaction, is mysterious yet. Consequently, the present study aimed to explore the interaction between POPs and ERα using the molecular operating environment (MOE) tool and to identify natural and synthetic compounds to inhibit this association through a cluster-based approach. To validate whether our approach could distinguish between active and inactive compounds, a virtual screen (VS) was performed using actives (627 compounds) as positive control and decoys (20,818 compounds) as a negative dataset obtained from DUD-E. Comparatively, short-chain chlorinated paraffins (SCCPs), hexabromocyclododecane (HBCD), and perfluorooctanesulfonyl fluoride (PFOSF) depicted strong interactions with the ERα protein based on the lowest-scoring values of −31.946, −18.916, −17.581 kcal/mol, respectively. Out of 7856 retrieved natural and synthetic compounds, sixty were selected on modularity bases and subsequently docked with ERα. Based on the lowest-scoring values, ZINC08441573, ZINC00664754, ZINC00702695, ZINC00627464, and ZINC08440501 (synthetic compounds), and capsaicin, flavopiridol tectorgenin, and ellagic acid (natural compounds) showed incredible interactions with the active sites of ERα, even more convening and resilient than standard breast cancer drugs Tamoxifen, Arimidex and Letrozole. Our findings confirm the role of POPs in breast cancer progression and suggest that natural and synthetic compounds with high binding affinity could be more efficient and appropriate candidates to treat breast cancer after validation through in vitro and in vivo studies.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

Modeling Cancer Dynamics

Cancer is a complex disease that continues to pose formidable challenges to therapeutic interventions. An increased understanding of cancer complexity and in particular tumor growth dynamics is critical to the development of more effective therapies. In this respect, an agent-based model of tumor growth is explored with the consideration of cancer reprograming of metabolism and the immune response, to seek insight about the coupling between these two key determinants of tumor growth dynamics. Ultimately, this exploration is intended to inform the development of therapies that can induce a more effective immune response despite the metabolic constraints of the tumor microenvironment.

Time-lapse imaging of HeLa spheroids in soft agar culture provides virtual inner proliferative activity

Cancer is a complex disease caused by multiple types of interactions. To simplify and normalize the assessment of drug effects, spheroid microenvironments have been utilized. Research models that involve agent measurement with the examination of clonogenic survival by monitoring culture process with image analysis have been developed for spheroid-based screening. Meanwhile, computer simulations using various models have enabled better predictions for phenomena in cancer. However, user-based parameters that are specific to a researcher’s own experimental conditions must be inputted. In order to bridge the gap between experimental and simulated conditions, we have developed an in silico analysis method with virtual three-dimensional embodiment computed using the researcher’s own samples. The present work focused on HeLa spheroid growth in soft agar culture, with spheroids being modeled in silico based on time-lapse images capturing spheroid growth. The spheroids in silico were optimized by adjusting the growth curves to those obtained from time-lapse images of spheroids and were then assigned virtual inner proliferative activity by using generations assigned to each cellular particle. The ratio and distribution of the virtual inner proliferative activities were confirmed to be similar to the proliferation zone ratio and histochemical profiles of HeLa spheroids, which were also consistent with those identified in an earlier study. We validated that time-lapse images of HeLa spheroids provided virtual inner proliferative activity for spheroids in vitro. The present work has achieved the first step toward an in silico analysis method using computational simulation based on a researcher’s own samples, helping to bridge the gap between experiment and simulation.

Upcoming Revolutionary Paths in Preclinical Modeling of Pancreatic Adenocarcinoma

To date, PDAC remains the cancer having the worst prognosis with mortality rates constantly on the rise. Efficient cures are still absent, despite all attempts to understand the aggressive physiopathology underlying this disease. A major stumbling block is the outdated preclinical modeling strategies applied in assessing effectiveness of novel anticancer therapeutics. Current in vitro preclinical models have a low fidelity to mimic the exact architectural and functional complexity of PDAC tumor found in human set, due to the lack of major components such as immune system and tumor microenvironment with its associated chemical and mechanical signals. The existing PDAC preclinical platforms are still far from being reliable and trustworthy to guarantee the success of a drug in clinical trials. Therefore, there is an urgent demand to innovate novel in vitro preclinical models that mirrors with precision tumor-microenvironment interface, pressure of immune system, and molecular and morphological aspects of the PDAC normally experienced within the living organ. This review outlines the traditional preclinical models of PDAC namely 2D cell lines, genetically engineered mice, and xenografts, and describing the present famous approach of 3D organoids. We offer a detailed narration of the pros and cons of each model system. Finally, we suggest the incorporation of two off-center newly born techniques named 3D bio-printing and organs-on-chip and discuss the potentials of swine models and in silico tools, as powerful new tools able to transform PDAC preclinical modeling to a whole new level and open new gates in personalized medicine.

Engineering Patient-on-a-Chip Models for Personalized Cancer Medicine

Traditional in vitro and in vivo models typically used in cancer research have demonstrated a low predictive power for human response. This leads to high attrition rates of new drugs in clinical trials, which threaten cancer patient prognosis. Tremendous efforts have been directed towards the development of a new generation of highly predictable pre-clinical models capable to reproduce in vitro the biological complexity of the human body. Recent advances in nanotechnology and tissue engineering have enabled the development of predictive organs-on-a-chip models of cancer with advanced capabilities. These models can reproduce in vitro the complex three-dimensional physiology and interactions that occur between organs and tissues in vivo, offering multiple advantages when compared to traditional models. Importantly, these models can be tailored to the biological complexity of individual cancer patients resulting into biomimetic and personalized cancer patient-on-a-chip platforms. The individualized models provide a more accurate and physiological environment to predict tumor progression on patients and their response to drugs. In this chapter, we describe the latest advances in the field of cancer patient-on-a-chip, and discuss about their main applications and current challenges. Overall, we anticipate that this new paradigm in cancer in vitro models may open up new avenues in the field of personalized - cancer - medicine, which may allow pharmaceutical companies to develop more efficient drugs, and clinicians to apply patient-specific therapies.

In silico disease model: from simple networks to complex diseases

In this chapter we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.

Tumor Clearance Analysis on a Cancer Chemo-Immunotherapy Mathematical Model

Mathematical models may allow us to improve our knowledge on tumor evolution and to better comprehend the dynamics between cancer, the immune system and the application of treatments such as chemotherapy and immunotherapy in both short and long term. In this paper, we solve the tumor clearance problem for a six-dimensional mathematical model that describes tumor evolution under immune response and chemo-immunotherapy treatments. First, by means of the localization of compact invariant sets method, we determine lower and upper bounds for all cells populations considered by the model and we use these results to establish sufficient conditions for the existence of a bounded positively invariant domain in the nonnegative orthant by applying LaSalle's invariance principle. Then, by exploiting a candidate Lyapunov function we determine sufficient conditions on the chemotherapy treatment to ensure tumor clearance. Further, we investigate the local stability of the tumor-free equilibrium point and compute conditions for asymptotic stability and tumor persistence. All conditions are given by inequalities in terms of the system parameters, and we perform numerical simulations with different values on the chemotherapy treatment to illustrate our results. Finally, we discuss the biological implications of our work.

Mathematical modeling of tumor-immune cell interactions

The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.

Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors

Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to the understanding of the dose-response relationships, but they also bring challenges to properly parameterizing and validating such models. In this study, we developed a workflow to detect CD8+ T cells from whole slide imaging data, and quantify the spatial heterogeneity using multiple metrics by applying spatial point pattern analysis and morphometric analysis. The results indicate a higher intra-tumoral heterogeneity compared with the heterogeneity across patients. By comparing the baseline metrics with PD-1 blockade treatment outcome, our results indicate that the number of high-density T cell clusters of both circular and elongated shapes are higher in patients who responded to the treatment. This methodology can be applied to quantitatively characterize the tumor microenvironment, including immuno-architecture, and its heterogeneity for different cancer types.

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.

The Multitarget Activity of Natural Extracts on Cancer: Synergy and Xenohormesis

It is estimated that over 60% of the approved drugs and new drug developments for cancer and infectious diseases are from natural origin. The use of natural compounds as a potential source of antitumor agents has been deeply studied in many cancer models, both in vitro and in vivo. Most of the Western medicine studies are based on the use of highly selective pure compounds with strong specificity for their targets such as colchicine or taxol. Nevertheless, approximately 60% of fairly specific drugs in their initial research fail because of toxicity or ineffectiveness in late-stage preclinical studies. Moreover, cancer is a multifaceted disease that in most cases deserves a polypharmacological therapeutic approach. Complex plant-derived mixtures such as natural extracts are difficult to characterize and hardly exhibit high pharmacological potency. However, in some cases, these may provide an advantage due to their multitargeted mode of action and potential synergistic behavior. The polypharmacology approach appears to be a plausible explanation for the multigargeted mechanism of complex natural extracts on different proteins within the same signalling pathway and in several biochemical pathways at once. This review focuses on the different aspects of natural extracts in the context of anticancer activity drug development, with special attention to synergy studies and xenohormesis.

Simulation of Multispecies Desmoplastic Cancer Growth via a Fully Adaptive Non-linear Full Multigrid Algorithm

A fully adaptive non-linear full multigrid (FMG) algorithm is implemented to computationally simulate a model of multispecies desmoplastic tumor growth in three spatial dimensions. The algorithm solves a thermodynamic mixture model employing a diffuse interface approach with Cahn-Hilliard-type fourth-order equations that are coupled, non-linear, and numerically stiff. The tumor model includes extracellular matrix (ECM) as a major component with elastic energy contribution in its chemical potential term. Blood and lymphatic vasculatures are simulated via continuum representations. The model employs advection-reaction-diffusion partial differential equations (PDEs) for the cell, ECM, and vascular components, and reaction-diffusion PDEs for the elements diffusing from the vessels. This study provides the details of the numerical solution obtained by applying the fully adaptive non-linear FMG algorithm with finite difference method to solve this complex system of PDEs. The results indicate that this type of computational model can simulate the extracellular matrix-rich desmoplastic tumor microenvironment typical of fibrotic tumors, such as pancreatic adenocarcinoma.

The biology and mathematical modelling of glioma invasion: a review

Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.

A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH

Cancer results from a complex interplay of different biological, chemical, and physical phenomena that span a wide range of time and length scales. Computational modeling may help to unfold the role of multiple evolving factors that exist and interact in the tumor microenvironment. Understanding these complex multiscale interactions is a crucial step towards predicting cancer growth and in developing effective therapies. We integrate different modeling approaches in a multiscale, avascular, hybrid tumor growth model encompassing tissue, cell, and sub-cell scales. At the tissue level, we consider the dispersion of nutrients and growth factors in the tumor microenvironment, which are modeled through reaction-diffusion equations. At the cell level, we use an agent based model (ABM) to describe normal and tumor cell dynamics, with normal cells kept in homeostasis and cancer cells differentiated apoptotic, hypoxic, and necrotic states. Cell movement is driven by the balance of a variety of forces according to Newton's second law, including those related to growth-induced stresses. Phenotypic transitions are defined by specific rule of behaviors that depend on microenvironment stimuli. We integrate in each cell/agent a branch of the epidermal growth factor receptor (EGFR) pathway. This pathway is modeled by a system of coupled nonlinear differential equations involving the mass laws of 20 molecules. The rates of change in the concentration of some key molecules trigger proliferation or migration advantage response. The bridge between cell and tissue scales is built through the reaction and source terms of the partial differential equations. Our hybrid model is built in a modular way, enabling the investigation of the role of different mechanisms at multiple scales on tumor progression. This strategy allows representating both the collective behavior due to cell assembly as well as microscopic intracellular phenomena described by signal transduction pathways. Here, we investigate the impact of some mechanisms associated with sustained proliferation on cancer progression. Specifically, we focus on the intracellular proliferation/migration-advantage-response driven by the EGFR pathway and on proliferation inhibition due to accumulation of growth-induced stresses. Simulations demonstrate that the model can adequately describe some complex mechanisms of tumor dynamics, including growth arrest in avascular tumors. Both the sub-cell model and growth-induced stresses give rise to heterogeneity in the tumor expansion and a rich variety of tumor behaviors.

In-silico modeling of granulomatous diseases

PURPOSE OF REVIEW

The pathogenesis of genetically complex granulomatous diseases, such as sarcoidosis and latent tuberculosis, remains largely unknown. With the recent advent of more powerful research tools, such as genome-wide expression platforms, comes the challenge of making sense of the enormous data sets so generated. This manuscript will provide demonstrations of how in-silico (computer) analysis of large research data sets can lead to novel discoveries in the field of granulomatous lung disease.

RECENT FINDINGS

The application of in-silico research tools has led to novel discoveries in the fields of noninfectious (e.g., sarcoidosis) and infectious granulomatous diseases. Computer models have identified novel disease mechanisms and can be used to perform 'virtual' experiments rapidly and at low cost compared with conventional laboratory techniques.

SUMMARY

Granulomatous lung diseases are extremely complex, involving dynamic interactions between multiple genes, cells, and molecules. In-silico interpretation of large data sets generated from new research platforms that are capable of comprehensively characterizing and quantifying pools of biological molecules promises to rapidly accelerate the rate of scientific discovery in the field of granulomatous lung disorders.

Model of vascular desmoplastic multispecies tumor growth

We present a three-dimensional nonlinear tumor growth model composed of heterogeneous cell types in a multicomponent-multispecies system, including viable, dead, healthy host, and extra-cellular matrix (ECM) tissue species. The model includes the capability for abnormal ECM dynamics noted in tumor development, as exemplified by pancreatic ductal adenocarcinoma, including dense desmoplasia typically characterized by a significant increase of interstitial connective tissue. An elastic energy is implemented to provide elasticity to the connective tissue. Cancer-associated fibroblasts (myofibroblasts) are modeled as key contributors to this ECM remodeling. The tumor growth is driven by growth factors released by these stromal cells as well as by oxygen and glucose provided by blood vasculature which along with lymphatics are stimulated to proliferate in and around the tumor based on pro-angiogenic factors released by hypoxic tissue regions. Cellular metabolic processes are simulated, including respiration and glycolysis with lactate fermentation. The bicarbonate buffering system is included for cellular pH regulation. This model system may be of use to simulate the complex interactions between tumor and stromal cells as well as the associated ECM and vascular remodeling that typically characterize malignant cancers notorious for poor therapeutic response.

In silico modeling for tumor growth visualization

BACKGROUND

Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research.

RESULTS

We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ .

CONCLUSION

In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.

Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer

Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

The Tumor Microenvironment as a Barrier to Cancer Nanotherapy

Although extensive research effort and resources have been dedicated to the development of nanotherapeutics to treat cancer, few formulations have reached clinical application. A major reason is that the large number of parameters available to tune nanotherapy characteristics coupled with the variability in tumor tissue precludes evaluation of complex interactions through experimentation alone. In order to optimize the nanotechnology design and gain further insight into these phenomena, mathematical modeling and computational simulation have been applied to complement empirical work. In this chapter, we discuss modeling work related to nanotherapy and the tumor microenvironment. We first summarize the biology underlying the dysregulated tumor microenvironment, followed by a description of major nano-scale parameters. We then present an overview of the mathematical modeling of cancer nanotherapy, including evaluation of nanotherapy in multi-dimensional tumor tissue, coupling of nanotherapy with vascular flow, modeling of nanotherapy in combination with in vivo imaging, modeling of nanoparticle transport based on in vitro data, modeling of vasculature-bound nanoparticles, evaluation of nanotherapy using pharmacokinetic modeling, and modeling of nano-based hyperthermia. We conclude that an even tighter interdisciplinary effort between biological, material, and physical scientists is needed in order to eventually overcome the tumor microenvironment barrier to successful nanotherapy.

Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor

Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment.

A biophysical model of cell evolution after cytotoxic treatments: Damage, repair and cell response

We present a theoretical agent-based model of cell evolution under the action of cytotoxic treatments, such as radiotherapy or chemotherapy. The major features of cell cycle and proliferation, cell damage and repair, and chemical diffusion are included. Cell evolution is based on a discrete Markov chain, with cells stepping along a sequence of discrete internal states from 'normal' to 'inactive'. Probabilistic laws are introduced for each type of event a cell can undergo during its life: duplication, arrest, senescence, damage, reparation, or death. We adjust the model parameters on a series of cell irradiation experiments, carried out in a clinical LINAC, in which the damage and repair kinetics of single- and double-strand breaks are followed. Two showcase applications of the model are then presented. In the first one, we reconstruct the cell survival curves from a number of published low- and high-dose irradiation experiments. We reobtain a very good description of the data without assuming the well-known linear-quadratic model, but instead including a variable DSB repair probability. The repair capability of the model spontaneously saturates to an exponential decay at increasingly high doses. As a second test, we attempt to simulate the two extreme possibilities of the so-called 'bystander' effect in radiotherapy: the 'local' effect versus a 'global' effect, respectively activated by the short-range or long-range diffusion of some factor, presumably secreted by the irradiated cells. Even with an oversimplified simulation, we could demonstrate a sizeable difference in the proliferation rate of non-irradiated cells, the proliferation acceleration being much larger for the global than the local effect, for relatively small fractions of irradiated cells in the colony.

Evaluation of uptake and distribution of gold nanoparticles in solid tumors

Although nanotherapeutics offer a targeted and potentially less toxic alternative to systemic chemotherapy in cancer treatment, nanotherapeutic transport is typically hindered by abnormal characteristics of tumor tissue. Once nanoparticles targeted to tumor cells arrive in the circulation of tumor vasculature, they must extravasate from irregular vessels and diffuse through the tissue to ideally reach all malignant cells in cytotoxic concentrations. The enhanced permeability and retention effect can be leveraged to promote extravasation of appropriately sized particles from tumor vasculature; however, therapeutic success remains elusive partly due to inadequate intra-tumoral transport promoting heterogeneous nanoparticle uptake and distribution. Irregular tumor vasculature not only hinders particle transport but also sustains hypoxic tissue kregions with quiescent cells, which may be unaffected by cycle-dependent chemotherapeutics released from nanoparticles and thus regrow tumor tissue following nanotherapy. Furthermore, a large proportion of systemically injected nanoparticles may become sequestered by the reticuloendothelial system, resulting in overall diminished efficacy. We review recent work evaluating the uptake and distribution of gold nanoparticles in pre-clinical tumor models, with the goal to help improve nanotherapy outcomes. We also examine the potential role of novel layered gold nanoparticles designed to address some of these critical issues, assessing their uptake and transport in cancerous tissue.

An in silico model to demonstrate the effects of Maspin on cancer cell dynamics

Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support. This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell-extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research.

Time-Delayed Models of Gene Regulatory Networks

We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems.

Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy). The mean volume at the beginning of radiotherapy and the end of radiotherapy was on average 23.7 cm(3) (range: 12.7-44.4 cm(3)) and 8.6 cm(3) (range: 3.6-17.1 cm(3)), respectively. Two different tumor dynamics were taken into account in the model: the viable (active) and the necrotic cancer cells. However, according to the results of a preliminary volume regression analysis, we assumed a short dead cell resolving time and the model was simplified to the active tumor volume. Model learning was performed both on the complete patient cohort (cohort-based model learning) and on each single patient (patient-specific model learning). The fitting results (mean error: ∼ 16% and ∼ 6% for the cohort-based model and patient-specific model, respectively) highlighted the model ability to quantitatively reproduce tumor regression. Volume prediction errors of about 18% on average were obtained using cohort-based model computed on all but 1 patient at a time (leave-one-out technique). Finally, a sensitivity analysis was performed and the data uncertainty effects evaluated by simulating an average volume perturbation of about 1.5 cm(3) obtaining an error increase within 0.2%. In conclusion, we showed that simple time-continuous models can represent tumor regression curves both on a patient cohort and patient-specific basis; this discloses the opportunity in the future to exploit such models to predict how changes in the treatment schedule (number of fractions, doses, intervals among fractions) might affect the tumor regression on an individual basis.

Web-based workflow planning platform supporting the design and execution of complex multiscale cancer models

Significant Virtual Physiological Human efforts and projects have been concerned with cancer modeling, especially in the European Commission Seventh Framework research program, with the ambitious goal to approach personalized cancer simulation based on patient-specific data and thereby optimize therapy decisions in the clinical setting. However, building realistic in silico predictive models targeting the clinical practice requires interactive, synergetic approaches to integrate the currently fragmented efforts emanating from the systems biology and computational oncology communities all around the globe. To further this goal, we propose an intelligent graphical workflow planning system that exploits the multiscale and modular nature of cancer and allows building complex cancer models by intuitively linking/interchanging highly specialized models. The system adopts and extends current standardization efforts, key tools, and infrastructure in view of building a pool of reliable and reproducible models capable of improving current therapies and demonstrating the potential for clinical translation of these technologies.

An open data ecosystem for cell migration research

Cell migration research has recently become both a high content and a high throughput field thanks to technological, computational, and methodological advances. Simultaneously, however, urgent bioinformatics needs regarding data management, standardization, and dissemination have emerged. To address these concerns, we propose to establish an open data ecosystem for cell migration research.

Contribution of bioinformatics prediction in microRNA-based cancer therapeutics

Despite enormous efforts, cancer remains one of the most lethal diseases in the world. With the advancement of high throughput technologies massive amounts of cancer data can be accessed and analyzed. Bioinformatics provides a platform to assist biologists in developing minimally invasive biomarkers to detect cancer, and in designing effective personalized therapies to treat cancer patients. Still, the early diagnosis, prognosis, and treatment of cancer are an open challenge for the research community. MicroRNAs (miRNAs) are small non-coding RNAs that serve to regulate gene expression. The discovery of deregulated miRNAs in cancer cells and tissues has led many to investigate the use of miRNAs as potential biomarkers for early detection, and as a therapeutic agent to treat cancer. Here we describe advancements in computational approaches to predict miRNAs and their targets, and discuss the role of bioinformatics in studying miRNAs in the context of human cancer.

Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack

The post-genomic era promises to pave the way to a personalized understanding of disease processes, with technological and analytical advances helping to solve some of the world's health challenges. Despite extraordinary progress in our understanding of cancer pathogenesis, the disease remains one of the world's major medical problems. New therapies and diagnostic procedures to guide their clinical application are urgently required. OncoTrack, a consortium between industry and academia, supported by the Innovative Medicines Initiative, signifies a new era in personalized medicine, which synthesizes current technological advances in omics techniques, systems biology approaches, and mathematical modeling. A truly personalized molecular imprint of the tumor micro-environment and subsequent diagnostic and therapeutic insight is gained, with the ultimate goal of matching the "right" patient to the "right" drug and identifying predictive biomarkers for clinical application. This comprehensive mapping of the colon cancer molecular landscape in tandem with crucial, clinical functional annotation for systems biology analysis provides unprecedented insight and predictive power for colon cancer management. Overall, we show that major biotechnological developments in tandem with changes in clinical thinking have laid the foundations for the OncoTrack approach and the future clinical application of a truly personalized approach to colon cancer theranostics.

Simulating cancer growth with multiscale agent-based modeling

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.