Imagine storing all the information from every movie, book, and website ever created in a container no larger than a sugar cube.
This isn't science fiction—it's the revolutionary potential of encoding digital information in DNA, where a single gram can store 215 petabytes of data. This remarkable application represents just one frontier in the expanding field of biological information science, where the fundamental concepts of information theory intersect with cutting-edge biotechnology.
For decades, scientists have understood that DNA operates much like a sophisticated programming language, complete with encoding, error-correction, and replication systems. What makes this possible is that all known life depends on information—specifically, coded information systems that share remarkable properties with human-designed communication technologies.
Today, companies like Twist Bioscience are leveraging these principles to transform how we store data, develop therapeutics, and understand biological systems. By applying information theory to biology, scientists are learning to read, write, and edit the very code of life itself, opening possibilities that range from anti-idiotypic antibodies that combat disease to designer enzymes that revolutionize manufacturing 3 .
Revolutionary potential for compact, long-term information storage
Mathematical framework for understanding biological information systems
Applications ranging from therapeutics to manufacturing
The mathematical foundation for understanding information was established by Claude Shannon, whose 1948 work "A Mathematical Theory of Communication" laid the groundwork for modern information science.
In Shannon's model, information communicates a decision between alternatives. Each symbol in an alphabet of possibilities can provide a measurable quantity of information, with the basic unit being the bit—which represents a decision between two equally probable choices 4 .
Shannon introduced the concept of entropy (H) as a measure of uncertainty in information systems. Maximum entropy occurs when symbols are equally probable (creating the most uncertainty), while zero entropy indicates the same message is always produced (no uncertainty).
When information theory meets biology, we encounter fascinating questions: Does a bacterium that produces five similar variants of a protein possess more information than one producing a single highly-tuned version?
Scientists like Hubert Yockey and Kirk Durston pioneered the application of Shannon's theories to biology, with Durston introducing the concept of "functional information"—the difference between the entropy of all possible messages and the entropy of those messages that provide a specific biological function 4 .
This framework helps us understand why some proteins tolerate variations in their sequence while others remain virtually unchanged across billions of years of evolution.
Claude Shannon publishes "A Mathematical Theory of Communication"
Hubert Yockey applies information theory to molecular biology
Kirk Durston introduces concept of "functional information"
Information theory applied to DNA data storage and synthetic biology
To understand how scientists optimize biological systems, consider a classic experimental challenge: maximizing the yield of a chemical process where temperature and pH are key factors.
The intuitive approach—One Factor At a Time (OFAT)—involves varying temperature while keeping pH constant, finding the optimal temperature, then varying pH while keeping temperature at this "optimal" setting. This method seems straightforward but contains a critical flaw: it cannot detect interactions between factors 2 .
In our example, an OFAT approach might identify temperature at 30°C and pH at 6.0 as "optimal," yielding 86%. However, this approach misses the twisting relationship between these variables.
Design of Experiments (DOE) provides a powerful alternative framework. For our two-factor scenario, researchers would:
This approach systematically explores the entire experimental space while requiring far fewer resources than testing every possible combination 2 .
The DOE approach revealed a dramatically different relationship between temperature, pH, and yield than the OFAT method detected. The maximum yield (91%) occurred at 45°C and pH 8—significantly higher than the 86% identified through OFAT.
| Experimental Approach | Runs Required | Maximum Yield Found |
|---|---|---|
| OFAT | 13 | 86% |
| DOE | 12 | 91% |
| Temperature | pH | Predicted Yield |
|---|---|---|
| 30°C | 6.0 | 86% |
| 45°C | 8.0 | 91% |
| 45°C | 7.0 | 92% |
Temperature main effect: 28%
pH main effect: 31%
Temperature*pH interaction: 37% (detected by DOE only)
Curvature: 4% (detected by DOE only)
Modern biological research depends on specialized tools and reagents that enable precise interrogation and manipulation of biological information.
| Reagent/Tool | Primary Function | Key Applications |
|---|---|---|
| Twist Standard Hybridization Reagent Kit | Enables efficient and specific binding of panel probes to target DNA regions | Target enrichment for next-generation sequencing; whole exome sequencing 6 |
| Anti-Idiotypic Antibodies | Bind specifically to the unique antigen-binding region (idiotype) of therapeutic antibodies | Pharmacokinetic (PK) assays; anti-drug antibody (ADA) assessments; quality control 3 |
| Custom Antigens | High-quality proteins designed to specifically trigger immune responses | Antibody discovery campaigns; vaccine development; diagnostic assays 3 |
| Twist Library Preparation Kits | Fragment DNA and add sequencing adapters for next-generation sequencing platforms | Whole genome sequencing; comprehensive genomic profiling; population genomics 9 |
| Trinity Freestyle Fast Hybridization Kit | Rapid 1-hour hybridization for on-flow cell enrichment, eliminating multiple steps | Fast next-generation sequencing workflows; reduced hands-on time 9 |
These tools enable researchers to not only read biological information but to write and edit it as well. For instance, anti-idiotypic antibodies serve as critical reagents for monitoring therapeutic antibodies in clinical development, ensuring their safety and efficacy.
The sophistication of these tools continues to evolve. Recent collaborations between companies like Twist Bioscience and Element Biosciences have produced integrated workflows that reduce sample-to-sequencer time to just five hours while maintaining high accuracy across diverse applications 9 .
The intersection of information theory and biology represents one of the most promising frontiers in science.
What began with Shannon's mathematical insights into communication systems has evolved into a sophisticated framework for understanding the fundamental programming of life itself. From designing better experiments that reveal hidden relationships between biological factors to developing reagents that manipulate molecular interactions with exquisite precision, the principles of information science are transforming biotechnology.
Could preserve humanity's knowledge for millennia
May create sustainable alternatives to traditional manufacturing
Might tailor treatments to our individual genetic makeup
The "twist" in our story is both conceptual and corporate—the recognition that information isn't merely descriptive but fundamental to biological function, and the growing capacity of companies like Twist Bioscience to harness this understanding. As we continue to unravel nature's information systems, we move closer to solving some of humanity's most pressing challenges in health, sustainability, and technology.
The future of biological information science promises not just to transform how we understand life, but to enhance life itself through targeted interventions designed with informational precision.