Decoding Life: The Bioinformatics Revolution at APBC2009

In the heart of Beijing, over 300 scientists gathered to map the future of biology, one data point at a time.

300+ Scientists

21 Countries

5 Research Areas

Imagine trying to understand a complex machine not by looking at its gears and levers, but by reading the intricate instruction manual that guides its assembly. This is the fundamental challenge of modern biology. Bioinformatics is the field that has risen to this challenge, using computational power to decipher the biological instructions encoded in our molecules.

The Seventh Asia Pacific Bioinformatics Conference (APBC2009), held at Tsinghua University in January 2009, served as a vibrant platform where this decryption mission advanced significantly. It was here that researchers showcased how computational tools were beginning to unravel the profound complexities of life, from the intricate folding of proteins to the vast regulation of genes ¹ .

The Conference: A Global Convergence

APBC2009 was more than just an academic meeting; it was a testament to the collaborative spirit of science. Bringing together over 300 researchers from 21 nations and regions, it represented the largest submission and participation in the conference's history up to that point ¹ ³ .

The event featured talks from leading scientists, including keynote addresses on the molecular evolution of seasonal influenza and novel methods for sequence analysis using Eulerian graphs ¹ .

Research Areas Covered

DNA Sequence Analysis

Including alignment, evolution, and comparative genomics.

Gene Regulation & Expression

Focusing on microarray data and transcriptional regulation.

RNA Structure & Function

Especially non-coding RNAs like microRNAs.

Proteins & Proteomics

Encompassing protein structure, function, and mass spectrometry data processing.

Key Concepts: The Building Blocks of Bioinformatics

The Core Idea

At its heart, bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. It combines computer science, statistics, mathematics, and engineering to analyze and interpret the vast and complex data generated by life science experiments ¹ .

Data Collection

High-throughput technologies generate massive biological datasets

Data Processing

Computational tools clean, normalize, and prepare data for analysis

Data Analysis

Statistical and algorithmic approaches extract meaningful patterns

Biological Interpretation

Results are interpreted in the context of biological systems

The Data Revolution

A major driver for bioinformatics has been the explosion of high-throughput technologies that generate massive datasets. Two key technologies highlighted at APBC2009 were:

Often described as a large parallel Northern blot analysis, microarrays allow researchers to measure the expression levels of thousands of genes simultaneously ⁸ . By extracting mRNA from tissues, reversing transcribing it, and labeling it with fluorescent dyes, scientists can hybridize these samples to a chip containing complementary DNA probes. The resulting fluorescence indicates the abundance of specific mRNA molecules, enabling comparisons between different conditions, such as healthy versus diseased tissue ⁸ .

This technology has evolved into an indispensable tool for protein analysis. Mass spectrometers measure the mass-to-charge ratio of gas-phase ions, allowing researchers to identify proteins, define their interactions, and pinpoint sites of modification ⁵ . The development of new instruments like the LTQ-Orbitrap, which provides high mass accuracy and resolution, has significantly advanced our ability to analyze complex protein mixtures ⁵ .

An In-Depth Look: Tracing Protein Unfolding

Proteins are the workhorses of the cell, but to function properly, they must fold into precise three-dimensional shapes. Understanding how proteins fold and unfold is crucial, as misfolding is linked to numerous diseases. A compelling study presented at APBC2009 explored whether proteins with similar structures follow similar unfolding pathways ² .

Methodology: A Digital Simulation

Researchers used a novel approach to study this process ² :

Model Selection: The study focused on two proteins, Protein G and Protein L, which share similar native topologies but have low sequence identity (only 16%).
Simulating Unfolding: For each protein, forty independent thermal unfolding simulations were performed using computational models.
Constructing a "Property Space": Instead of just tracking atomic positions in Cartesian space, the team constructed a multidimensional "physical property space" based on twelve different physical parameters (e.g., native contact number, radius of gyration, hydrogen bonds). This allowed them to track changes in the proteins' properties during unfolding.
Data Reduction: Using principal component analysis, they reduced this complex twelve-dimensional space into a three-dimensional "essential property subspace" for easier visualization and analysis.

Protein Unfolding Simulation

Visualization of protein unfolding pathways in essential property subspace

Results and Analysis: Mapping the Unfolding Journey

The analysis revealed fascinating insights into the unfolding process ² :

Three Unfolding Pathways: The forty unfolding trajectories for each protein were categorized into three distinct types. The most common was a Type I "umbrella-shaped" pathway, which represented 55% of the trajectories for both proteins. This suggests it is the preferred unfolding route.
A Common Destination: The unfolded state ensembles for both proteins had a similar ellipsoid shape under the essential property subspace, indicating convergent unfolding behaviors despite their sequence differences.
Subtle Differences: While the overall unfolding behavior was similar, key differences emerged. Protein L unfolded faster than Protein G on average, and the distribution of its unfolded states was larger, indicating more structural variability in its unfolded form.

This research demonstrated that native state topology is a key determinant of the unfolding mechanism. The ability to detect these pathways was also a triumph for the "physical property space" approach, which could reveal patterns that might be missed when analyzing structural changes alone ² .

Unfolding Trajectory Types

Protein	Type I (Umbrella)	Type II	Type III
Protein G	22 (55%)	7	11
Protein L	22 (55%)	9	9

Unfolding Simulation Times

Protein	Overall Average	Type I	Type II	Type III
Protein G	2822	2064	3345	4004
Protein L	2134	1367	3391	2763

Time in arbitrary units

The Scientist's Toolkit: Essential Research Resources

The research showcased at APBC2009 relied on a sophisticated array of computational tools, databases, and instruments. The following table details some of the key resources that form the backbone of modern bioinformatics research.

Resource Name	Type	Primary Function
UniProtKB ⁶	Database	A comprehensive protein sequence and functional knowledgebase.
STRING ⁶	Database	Analyzes protein-protein interaction networks and functional enrichment.
SWISS-MODEL ⁶	Software	Provides automated protein structure homology-modelling.
NCBI Resources	Database Suite	A collection of over 40 databases (e.g., Gene, GEO, BLAST) for molecular data.
LTQ-Orbitrap ⁵	Instrument	A high-resolution mass spectrometer for accurate proteomic analysis.
Bioconductor ⁸	Software	An open-source platform for the analysis of genomic data, especially microarrays.
KEGG	Database	A resource for integrating and interpreting large-scale molecular datasets within biological pathways.

Technology Impact Assessment

Microarray Technology 85%

Mass Spectrometry 78%

Computational Modeling 92%

Conclusion: A New Era of Biological Understanding

The Seventh Asia Pacific Bioinformatics Conference was a snapshot of a field in rapid ascent. The work presented—from the intricate simulation of protein dynamics to the statistical frameworks for integrating complex datasets—signaled a fundamental shift in biological science. Biology was no longer a purely observational science but had firmly embraced quantitative, data-driven discovery.

The legacy of conferences like APBC2009 is the ongoing fusion of biology with computational science. This synergy continues to propel us toward a deeper, more systematic understanding of life's processes, ultimately paving the way for breakthroughs in medicine, biotechnology, and our fundamental conception of what it means to be alive.