Computational Oncology

Computational Oncology

Computational Oncology is a semi-new phrase that is beginning to gain speed in medicine. Computational oncology focuses on the molecular aspects of cancer and utilizes mathematics and computational models to organize tumor growth pathways, tumor biology, bioinformatics, tumor marker profiles, and to develop predictive models for treatments based on all of this information. It also utilizes computer models for population screening, individual cancer cell modeling, and to develop tumor marker analytics useful in the area of precision medicine. This information contributes to the predictability that certain pharmaceuticals or therapeutic approaches will provide long-term solutions to disease in an individual with cancer.

Next-generation sequencing (NGS) has provided so much data about the human genome in healthy and diseased cells. Computational oncology services can take that data and categorize it into a database from which researchers can more seamlessly utilize to benefit their projects. Powered by the quantitative sciences and modern computing, dramatic progress in computational methods, software development practices, cloud computing, and profoundly rich cancer datasets are combining to advance the development of computational oncology.

Our experts have the expertise, educational backgrounds, and solid experience especially in the field of computational biology, bioinformatics, or a combination of computational biology and quantitative genetics. Computational oncology at CD ComputaBio is founded in research excellence and brings to bear computer science, data engineering, machine learning, and software tools to solve the major problems in oncology and cancer biology.

Computational Oncology What do we focus on?

  • Single cell dynamics of tumors

Cover the contexts of drug response, modeling clonal evolution and the tumor microenvironment. We have expertise in computational methods for single-cell omics, cellular imaging, spatial transcriptomics, and proteomics.

  • AI and machine learning applied in oncology

Multimodal data integration (e.g. genomics data), medical and cellular imaging, digital pathology.

  • Cancer Genomics

Somatic genetics, endogenous and exogenous mutational processes, structural variations and genomic instability.

  • Longitudinal analysis of timeseries data

Cell-free DNA, laboratory medicine data, radiologic data.

  • The cancer microbiome research

The collection of microbes that live in and on the human body can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology.

  • Drug sensitivity and resistance mechanisms

As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing.

State-of-the-art Computational Methods

Mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic–pharmacodynamic model, etc.).

Data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.).

CD ComputaBio offers a comprehensive computational oncology services. We have multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit.

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