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Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

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Published on: January 7, 2019

Computational medicine: quantitative modeling of complex diseases.

Basant K Tiwary¹

¹Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India.

Briefings in Bioinformatics

|January 31, 2019

Summary

Computational medicine models complex biological systems using high-throughput data for health and disease insights. Advances aid diagnosis and drug target identification, though clinical translation faces challenges.

Keywords:

cancer complex diseases computational medicine diagnosis psychiatric disorders type 2 diabetes

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Area of Science:

Computational medicine
Systems biology
Bioinformatics

Background:

Biological systems are complex, with numerous interacting components across scales.
High-throughput biomedical data enables quantitative modeling of nonlinear biological systems.
Understanding these systems is crucial for health and disease research.

Purpose of the Study:

To discuss advances in computational medicine for complex diseases.
To highlight applications in cancer, psychiatric disorders, and type 2 diabetes.
To explore the translation of computational models from research to clinical practice.

Main Methods:

Network-based studies
Genome-scale metabolic modeling
Kinetic modeling
Support vector machines

Main Results:

Computational models offer insights into complex diseases at genomic, transcriptomic, and proteomic levels.
Recent advances facilitate diagnosis and identification of drug targets.
Specific examples are provided for cancer, psychiatric disorders, and type 2 diabetes.

Conclusions:

Computational medicine is advancing the study and treatment of complex diseases.
Translating these models to clinical practice (bench to bedside) presents ongoing challenges.
Further research is needed to overcome hurdles in clinical implementation.