A5100438110- Mathematical Biology Tumor Growth
- Gene Regulatory Network Analysis
- Microtubule and mitosis dynamics
- Cancer Cells and Metastasis
- Bioinformatics and Genomic Networks
- Cancer Genomics and Diagnostics
- Cell Image Analysis Techniques
- Radiomics and Machine Learning in Medical Imaging
- Gene expression and cancer classification
- Cancer-related molecular mechanisms research
- RNA modifications and cancer
- Computational Drug Discovery Methods
- Neural dynamics and brain function
- MicroRNA in disease regulation
- Hepatocellular Carcinoma Treatment and Prognosis
- Reliability and Maintenance Optimization
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Nonlinear Dynamics and Pattern Formation
- stochastic dynamics and bifurcation
- Chemical Reactions and Isotopes
- Cellular Mechanics and Interactions
- Medicinal Plant Pharmacodynamics Research
- Statistical Distribution Estimation and Applications
- Machine Learning in Bioinformatics
- Probabilistic and Robust Engineering Design
North China University of Technology
2023
The University of Texas MD Anderson Cancer Center
2016-2021
Houston Methodist
2019-2021
Methodist Hospital
2019-2021
Brown Foundation
2016
The University of Texas Health Science Center at Houston
2016
University of New Mexico
2012-2015
Athinoula A. Martinos Center for Biomedical Imaging
2007-2011
Massachusetts General Hospital
2007-2011
Harvard University
2008-2011
To investigate the function of a novel primate-specific long non-coding RNA (lncRNA), named FLANC, based on its genomic location (co-localised with pyknon motif), and to characterise potential as biomarker therapeutic target.FLANC expression was analysed in 349 tumours from four cohorts correlated clinical data. In series multiple vitro vivo models molecular analyses, we characterised fundamental biological roles this lncRNA. We further explored targeting FLANC mouse model colorectal cancer...
Deregulation of noncoding RNAs, including microRNAs (miRs), is implicated in the pathogenesis many human cancers, breast cancer. Through extensive analysis The Cancer Genome Atlas, we found that expression miR-22-3p markedly lower triple-negative cancer (TNBC) than normal tissue. restoration led to significant inhibition TNBC cell proliferation, colony formation, migration, and invasion. We demonstrated reduces eukaryotic elongation factor 2 kinase (eEF2K) by directly binding 3' untranslated...
Abstract Background The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silico modeling considered to be an increasingly promising tool add useful insights into the dynamics of EGFR signal transduction pathway. However, most previous work focused on molecular or cellular level only , neglecting crucial feedback between these scales as well interaction with heterogeneous biochemical microenvironment. Results...
Abstract We present a multiscale agent-based non-small cell lung cancer model that consists of 3D environment with which cells interact while processing phenotypic changes. At the molecular level, transforming growth factor β (TGFβ) has been integrated into our previously developed in silico as second extrinsic input addition to epidermal (EGF). The main aim this study is investigate how effects individual and combinatorial change EGF TGFβ concentrations at level alter tumor dynamics on...
In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require more detailed quantitative understanding the dynamic interactions among tumor cells, and between these cells their biological microenvironment. Data-driven computational models have potential provide experimental biologists such cost-efficient tools generate test hypotheses on progression, infer fundamental...
To date, parameters defining biological properties in multiscale disease models are commonly obtained from a variety of sources. It is thus important to examine the influence parameter perturbations on system behavior, rather than limit model specific set parameters. Such sensitivity analysis can be used investigate how changes input affect outputs. However, cancer require special attention because they generally take longer run does series signaling pathway tasks. In this article, we...
There are two challenges that researchers face when performing global sensitivity analysis (GSA) on multiscale ‘in silico’ cancer models. The first is increased computational intensity, since a model generally takes longer to run than does scale‐specific model. second problem the lack of best GSA method fits all types models, which implies multiple methods and their sequence need be taken into account. In this study, authors therefore propose sampling‐based workflow consisting three phases –...
We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors chemotherapy drug resistance, which are difficult understand solely through experimentation. Experiments cell culture and drug-sensitive (Eµ-myc/Arf-/-) drug-resistant (Eµ-myc/p53-/-) lymphoma lines were conducted calibrate validate a mechanistic model. Inputs inform model include tumor transport characteristics, such as blood...
we present a multiscale agent-based model of Ductal Carcinoma in Situ (DCIS) order to gain detailed understanding the cell-scale population dynamics, phenotypic distributions, and associated interplay important molecular signaling pathways that are involved DCIS ductal invasion into duct cavity (a process refer as advance rate here).DCIS is modeled mathematically through hybridized discrete continuum scale model, which explicitly linked bidirectional feedback mechanism.we find rates occur...
Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly outcomes. We hypothesized that quantitative measures enhancement would more accurately predict clinical...
This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase accuracy of speed up their clinical adaptation, validation, eventual translation. We briefly review current interoperability efforts drawing upon our experiences with development in silico predictive oncology a number European Commission Virtual Physiological Human initiative projects on cancer. A scenario, addressing brain tumor...
Abstract Multiscale modeling is being recognized increasingly as a promising research area in computational cancer systems biology. In the present review, exemplified by two pioneering studies, we attempt to explain why and how such multiscale approach paired with an innovative cross‐scale analytical technique can be useful identifying high‐value molecular therapeutic targets. This novel, integrated has potential offer more effective silico framework for target discovery represents important...
Ductal carcinoma in situ (DCIS) is the most commonly diagnosed form of non-invasive breast cancer, constituting 20% all new cancer cases United States. Although non-invasive, DCIS usually treated surgically through resection. Interestingly, long-term survival studies have shown that patient rates are not significantly impacted by type or resection, indicating increased conservation minimized surgical resection may indeed be possible. This requires a greater understanding disease development,...
Cancer expansion depends on host organ conditions that permit growth. Since such microenvironmental nourishment is limited we argue here an autologous, therapeutically engineered and faster metabolizing cell strain could potentially out-compete native cancer populations for available resources which in turn should contain further This hypothesis aims turning progression, its dependency, into a therapeutic opportunity. To illustrate our concept, developed three-dimensional computational model...
Artificial intelligence (AI) plays a crucial role in genomic analysis, offering great potential for comprehending biological phenomena such as heredity, development, diseases, and evolution. However, the development of AI models needs substantial labeled data, these are typically task-specific with limited generalizability to various applications. Here, we develop Genomics-FM, vocabulary driven foundation model that enables versatile label-efficient functional analysis. Specifically,...