Biosensors

"Decoding biological complexity"

Vision

We envision that by unlocking the synergies between silicon technology, molecular level sensing and biological systems, individual molecules from a tiny amount of a biological sample could be specifically detected and quantified using a silicon chip. These capabilities could potentially lead to powerful, cost effective tools for biomedical research and diagnostics and dramatically accelerate and enable personalized medicine. Using these tools, researchers could profile molecular variations, study efficacy of drug candidates or responses of biological systems to pollutants among other things. There are several modern approaches to reducing the complexity of biological systems both at the level of acquiring system level data and converting it into actionable choices:

Target/signal amplification or single molecule analyses followed by optical detection.
Massively parallel collection of large amounts of data that span the genome-wide or proteome-wide spectrum of possible biological states of interest.
Data processing based on pattern recognition using statistical tools.
Dynamic modeling of biological systems.

Intersection of Silicon and Biology

Significant breakthroughs happen when the power of unrelated disciplines are combined in yet un-imaginable ways. Discovery at the intersection of biology and silicon can help bring together the better of two worlds: significant technical and manufacturing advances of the semiconductor industry combined with the power of biology to impact human health.

Electronic biosensors

“Portable systems to pocket size devices that enable distributed applications at the point of use.” Biosensors are analytical devices incorporating a biological analyte intimately associated with or integrated within a physicochemical transducing micro system. Electronic biosensors usually yield a digital electronic signal which is proportional to the concentration of a specific analyte or group of analytes.

Successful research programs rely on interdisciplinary approaches that include disciplines as diverse as materials, computer science, communications, electronics, molecular biology & bio-informatics. Intel Research is pursuing such an interdisciplinary research program to exploit the growing technical advances to integrate bio-molecules with electronics in order to develop a broad range of functional devices.

Massively parallel molecular sensing

The development of silicon-based sensors for electronic,

label-free detection of bio-molecular interactions could potentially enable massively parallel analysis at the single molecule level. Biosensor arrays made using semi-conductor manufacturing processes have the potential of changing the disease management paradigm by providing increasingly cost effective solutions. One of the goals of the Biosensor effort is to identify and evaluate new approaches to integrating chemistry with silicon-based sensor technologies. The requirements for advanced biosensor technologies will be dependent on the specific application area targeted, e.g. highlyparallel sensor arrays will be required for DNA sequencing or biomarker, discovery, while lower density, robust, sensor arrays would be adequate for diagnostics applications. Current projects are focused on electronic sensor development for bio-molecular analyses. At Intel Research we are collaborating with world leaders in sensor development, chemistry, and applications development.

Opportunity/Challenge:

Understanding molecular interactions at the silicon biology interface.
Enable radical new capabilities with the potential to revolutionize biomedical research, and related disciplines.

Solution:

A silicon sensor array with millions of sensors that are surface-functionalized for molecular sensing
Individual molecules interact or react with functional modifiers on surfaces to create electrical signals to be detected by the sensors
Parallel and sequential molecular interaction/reactions generate biologically relevant data
Data are processed and analyzed, transmitted, stored , and compared real-time