Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights

There is an ongoing debate on the therapeutic potential of vaso-modulatory interventions against glioma invasion. Prominent vasculature-targeting therapies involve functional tumour-associated blood vessel deterioration and normalisation. The former aims at tumour infarction and nutrient deprivation medi- ated by vascular targeting agents that induce occlusion/collapse of tumour blood vessels. In contrast, the therapeutic intention of normalising the abnormal structure and function of tumour vascular net- works, e.g. via alleviating stress-induced vaso-occlusion, is to improve chemo-, immuno- and radiation therapy efficacy. Although both strategies have shown therapeutic potential, it remains unclear why they often fail to control glioma invasion into the surrounding healthy brain tissue. To shed light on this issue, we propose a mathematical model of glioma invasion focusing on the interplay between the mi- gration/proliferation dichotomy (Go-or-Grow) of glioma cells and modulations of the functional tumour vasculature. Vaso-modulatory interventions are modelled by varying the degree of vaso-occlusion. We discovered the existence of a critical cell proliferation/diffusion ratio that separates glioma invasion re- sponses to vaso-modulatory interventions into two distinct regimes. While for tumours, belonging to one regime, vascular modulations reduce the tumour front speed and increase the infiltration width, for those in the other regime the invasion speed increases and infiltration width decreases. We show how these in silico findings can be used to guide individualised approaches of vaso-modulatory treatment strategies and thereby improve success rates

A Review of Mathematical Models for the Formation of\ud Vascular Networks

Mainly two mechanisms are involved in the formation of blood vasculature: vasculogenesis and angiogenesis. The former consists of the formation of a capillary-like network from either a dispersed or a monolayered population of endothelial cells, reproducible also in vitro by specific experimental assays. The latter consists of the sprouting of new vessels from an existing capillary or post-capillary venule. Similar phenomena are also involved in the formation of the lymphatic system through a process generally called lymphangiogenesis.\ud \ud A number of mathematical approaches have analysed these phenomena. This paper reviews the different modelling procedures, with a special emphasis on their ability to reproduce the biological system and to predict measured quantities which describe the overall processes. A comparison between the different methods is also made, highlighting their specific features

The impact of cell crowding and active cell movement on vascular tumour growth

A multiscale model for vascular tumour growth is presented which includes systems of ordinary differential equations for the cell cycle and regulation of apoptosis in individual cells, coupled to partial differential equations for the spatio-temporal dynamics of nutrient and key signalling chemicals. Furthermore, these subcellular and tissue layers are incorporated into a cellular automaton framework for cancerous and normal tissue with an embedded vascular network. The model is the extension of previous work and includes novel features such as cell movement and contact inhibition. We presented a detailed simulation study of the effects of these additions on the invasive behaviour of tumour cells and the tumour's response to chemotherapy. In particular, we find that cell movement alone increases the rate of tumour growth and expansion, but that increasing the tumour cell carrying capacity leads to the formation of less invasive dense hypoxic tumours containing fewer tumour cells. However, when an increased carrying capacity is combined with significant tumour cell movement, the tumour grows and spreads more rapidly, accompanied by large spatio-temporal fluctuations in hypoxia, and hence in the number of quiescent cells. Since, in the model, hypoxic/quiescent cells produce VEGF which stimulates vascular adaptation, such fluctuations can dramatically affect drug delivery and the degree of success of chemotherapy

The Effect of Virotherapy, Chemotherapy, and Immunotherapy to Immune System: Mathematical Modelling Approach

Medical research and therapeutic interventions continue to evolve, and one interesting area of study is the complex interaction among virotherapy, chemotherapy, and the immune system. Each treatment has its own advantages and disadvantages. In this study, a mathematical model was developed to describe how the immune system, tumor cells, and normal cells interact when all three types of therapy are used to treat cancer patients. To determine the effectiveness of various treatments, numerical simulations of eight different treatment strategies were performed. These simulations measured how much the concentration of immune cells, tumor cells, and normal cells decreased as a result of the treatment. Based on the numerical simulations performed, the application of the three types of therapy provided the greatest reduction (99%) in the concentration of tumour cells but also provided a significant reduction (68%) in the concentration of immune cells in the body

Emerging Challenges and Opportunities in Infectious Disease Epidemiology.

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners

Mathematical modeling of treatment resistance in cancer

Cancer is one of the world’s most lethal diseases. Although our understanding of this disease is expanding continuously, treatments for many types of cancers are still ineffective. The main reason for the high mortality of cancer patients is resistant to therapy. Since resistance to therapy is a complex and dynamical process, an interdisciplinary approach is necessary to understand it. The emergence of a new field called integrative mathematical oncology can tackle many urgent clinical problems in the treatment of cancer that are impossible to address using, for example, an in vitro or in vivo approach. The primary goal of this new field is to translate the biological complexity of a tumor into a precise language, such as mathematical formulas, and to perform model simulations. Therefore, integrative mathematical oncology allows for biological experiments to be performed inexpensively and rapidly. This thesis applies the integrative mathematical oncology approach to investigate resistance to treatment in solid tumors at the molecular and cellular levels. A mathematical model of the most commonly dysregulated pathway in cancer (the p53 signaling pathway) underwent a bifurcation analysis to investigate the possibility of restoring its proper dynamics in two types of cancer: osteosarcoma and breast cancer. Next, a stochastic model of resistance to platinum compounds was developed to improve our understanding of chemo-resistance to this group of drugs in advanced high-grade serous ovarian cancer (HGSOC). Finally, virtual clinical trial simulations (VCTS) were performed to identify a novel drug combination in ovarian cancer. The application of integrative mathematical oncology deepened our understanding of radio- and chemo-resistance in solid tumors. Firstly, the results from the bifurcation analysis of the p53 signaling pathway suggested silencing Mdm2 using siRNA to overcome radio-resistance in breast cancer and osteosarcoma. Next, the stochastic model of platinum resistance was utilized to answer two urgent clinical questions about ovarian cancer: i) how many platinum resistance mechanisms are active at diagnosis, and ii) how many drug-resistance mechanisms must be targeted to improve patient outcomes. Finally, the clinical trial simulations suggested a novel drug combination to overcome platinum resistance in advanced high-grade serous ovarian cancer.Rak jest jedną z najbardziej śmiertelnych chorób na świecie. Pomimo tego, że wiedza na temat tej choroby jest ciągle rozwijana, terapia w przypadku wielu typów nowotworów jest nieefektywna.Głównym powodem wysokiej śmiertelności pacjentów chorych na raka jest oporność na terapię. Jakoże oporność na terapię jest skomplikowanym i dynamicznym procesem, interdyscyplinarne podejście jest niezbędne do jego zrozumienia. Pojawienie się nowej dziedziny zwanej zintegrowaną matematyką onkologiczną jest w stanie rozwiązać wiele palących problemów klinicznych, które są niemożliwe do rozwiązania przy użyciu \textit{in vitro} lub \textit{in vivo}. Głównym celem tej nowej dziedziny jest przetlumaczenie biologicznej złożoności nowotworów na precyzyjny język, taki jak formuły matematyczne, i wykonanie symulacji modelów. Dlatego zintegrowana onkologia matematyczna umożliwia wykonanie eksperymentów biologicznych szybko oraz bez dużych nakładów finansowych. W tej pracy, podejście matematyki onkologicznej zostało wykorzystane do zrozumienia oporności zbitych nowotworów na leczenie na dwóch skalach: molekularnej i komórkowej. Najpierw, model matematyczny najbardziej rozregulowanej ścieżki sygnałowej w raku (ścieżce sygnałowej p53) został poddany analizie bifurkacji celem zbadania możliwości przywrócenia prawidłowej dynamiki tej ścieżki sygnałowej w dwóch nowotworach: kostniakomiessakach i nowotworach piersi. Następnie, stochastyczny model lekooporności na platynę został wykonany celem zrozumienia mechanizmów lekoopornosci na platynę w zaawansowanym surowiczym raku jajnika. Na koniec, virtualne symulacje prób klinicznych zostały wykonane celem zidentyfikowania nowych kombinacji leków w raku jajnika Zasosowanie zintegriwanej matematyki onkologicznej pogłębiło naszą wiedzę o opornosci na radio- i chemo-terapię w nowotworach zbitych. Najpierw, wyniki z analizy bifurkacyjnej ścieżki sygnałowej p53 proponuje wyciszenie Mdm2 przy użyciu siRNA celem pokonania radiooporności w raku piersi i kostniakomięsaku. Następnie, stochastyczny model opornosci na platynę został wykożystany celem odpowiedzi na dwa pilne pytania kliniczne: i) ile mechanizmów oporności na platynę jest aktywnych podczas diagnozy i ii) ile mechanizmów lekoopornosci na platynę nalezy celować celem poprawy przeżywalności. Na koniec, symulacje prób klinicznych sugerują nową kombinację leków celem pokonania lekooporności na platynę w surowiczym raku jajnika

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions