Engineering Simulations for Cancer Systems Biology (original) (raw)
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TISON: a next-generation multi-scale modeling theatre for in silico systems oncology
bioRxiv, 2021
Multi-scale models integrating biomolecular data from genetic, transcriptional, and translational levels, coupled with extracellular microenvironments can assist in decoding the complex mechanisms underlying system-level diseases such as cancer. To investigate the emergent properties and clinical translation of such cancer models, we present Theatre for in silico Systems Oncology (TISON, https://tison.lums.edu.pk), a next-generation web-based multi-scale modeling and simulation platform for in silico systems oncology. TISON provides a “zero-code” environment for multi-scale model development by seamlessly coupling scale-specific information from biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries. To compute the temporal evolution of multi-scale models, a simulation engine and data analysis features are also provided. Furthermore, TISON integrates patient-specific gene expression data to evaluate patient-centric models towa...
ELECANS--an integrated model development environment for multiscale cancer systems biology
Bioinformatics, 2013
Motivation: Computational multiscale models help cancer biologists to study the spatiotemporal dynamics of complex biological systems and to reveal the underlying mechanism of emergent properties. Results: To facilitate the construction of such models, we have developed a next generation modelling platform for cancer systems biology, termed 'ELECANS' (electronic cancer system). It is equipped with a graphical user interface-based development environment for multiscale modelling along with a software development kit such that hierarchically complex biological systems can be conveniently modelled and simulated by using the graphical user interface/software development kit combination. Associated software accessories can also help users to perform post-processing of the simulation data for visualization and further analysis. In summary, ELECANS is a new modelling platform for cancer systems biology and provides a convenient and flexible modelling and simulation environment that is particularly useful for those without an intensive programming background. Availability and implementation: ELECANS, its associated software accessories, demo examples, documentation and issues database are freely available at http://sbie.kaist.ac.kr/sub\_0204.php
2018
New biomedical advances at cellular level give the possibility to develop more and more accurate computational models of cells. Moreover, the increasing power of graphical processor units allows the simulation of millions of interacting virtual cells. This paper summarizes efforts made to create multicellular simulators, their attended benefits and their inevitable drawbacks. In particular, it presents the new software SimCells that simulate the dynamics of multicellular systems using a graphically programmable multiagent system. A fully functional example of the tumoral and blood vessel codevelopment is also detailed.
Frontiers in physiology, 2011
Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim(®) and MoBi(®) capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the si...
Oncotarget, 2014
Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce
State of the art in computational modelling of cancer
Mathematical medicine and biology : a journal of the IMA, 2012
Cancer is a complex, multiscale process in which genetic mutations occurring at a subcellular level manifest themselves as functional changes at the cellular and tissue scale. The multiscale nature of cancer requires mathematical modeling approaches that can handle multiple intracellular and extracellular factors acting on different time and space scales. Hybrid models provide a way to integrate both discrete and continuous variables that are used to represent individual cells and concentration or density fields, respectively. Each discrete cell can also be equipped with submodels that drive cell behavior in response to microenvironmental cues. Moreover, the individual cells can interact with one another to form and act as an integrated tissue. Hybrid models form part of a larger class of individualbased models that can naturally connect with tumor cell biology and allow for the integration of multiple interacting variables both intrinsically and extrinsically and are therefore perfectly suited to a systems biology approach to tumor growth.
On the Role of Cell Signaling Models in Cancer Research
Cancer Research, 2009
The main objective of this review is to emphasize the role and importance of the careful mathematical/computational modeling of signaling networks for the understanding of aberrant signaling in cancer and for the development of targeted therapies. [Cancer Res 2009;69(2):400–2]
Cancer systems biology and modeling: Microscopic scale and multiscale approaches
Seminars in Cancer Biology, 2015
Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.
A Grid-Enabled Toolkit for In Silico Oncology Simulations
Proceedings of the First International ICST Conference on Simulation Tools and Techniques for Communications Networks and Systems, 2008
In silico (on the computer) oncology is a multi-disciplinary field that focuses on the examination and modeling of biological mechanisms related to the phenomenon of cancer. In silico oncology simulation model may be used for evaluating and comparing different therapeutic schemes while at the same time considering different values of critical parameters which present substantial inter-patient variability. As the number of the involved parameters and of the considered radiotherapeutic schemes increases, the resulting exponential increase in computational requirements makes the use of a grid environment for the execution of the simulations both a necessity for the involved researchers and an opportunity to make in silico oncology applications available to a wider biomedical and research community. In this paper, we describe a toolkit that enables the execution of in silico oncology simulations on grid infrastructures. This toolkit is designed and developed as a web portal with advanced features that facilitates the execution of in silico oncology simulations in grid environments. Several scenarios of radiotherapy simulations have been performed on the EGEE grid and indicative simulation results, as well as execution times are presented.
Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking
Lecture Notes in Computer Science, 2017
Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-theart biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process.