Many biological processes take a molecular input, carry out some process using molecular or cellular “machinery”, and output a different set of molecules. This observation has seeded a field in which researchers attempt to modify these processes to perform useful computing-like routines. So far, most work uses synthetic biology to build “genetic circuits” equivalent to logic circuits in conventional computers.¹⁹

Future Horizons:

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5-yearhorizon

Parts and processes are standardised

Lab automation makes it possible to conduct experiments at greater scale and AI helps to crunch through the resulting data to provide new insights. Standardisation of biological parts and processes is established, opening the door to commercialisation. This leads to a flowering of computer-aided design tools to help programmers build cellular computers. Biological computing becomes the focus of an increasing number of venture-capital-supported companies exploring the commercial potential of the field using proprietary biological hardware solutions.

10-yearhorizon

New biocomputing pathways are designed and developed

Growing engagement with theoretical computer science results in novel computing paradigms able to harness cells’ unique capabilities. Researchers establish ways to harness a cell’s metabolism to perform computations, with applications in pollution remediation, disease diagnosis and atmospheric sensing. Hybrid models that combine cellular computing with other technologies show promise. Monitoring the mechanisms of bacterial evolution provides inspiration for the design of new biocomputing pathways.

25-yearhorizon

Biocomputing goes beyond Boolean logic

Research catalogues an array of natural biocomputing pathways and creates a new, post-Boolean set of logic operations and design tools for information processing. The full computational power of the cells is formalised. Biological computation combines with quantum biology research to create interesting and potentially fruitful new approaches to information processing.

Cellular computing can go beyond mimicking standard computing, though.²⁰ A cell’s components can be reconfigured in response to external stimuli, and evolution allows populations of cells to adapt to changing environmental circumstances. They also function well in the presence of noise. There are multiple signal pathways within each cell, enabling concurrent, massively parallel information processing.²¹ And communication pathways between cells allow for new forms of distributed computation.²² ²³

This opens up the prospect of performing “whole-cell biocomputations” or even using networks of cells to solve challenges as varied as environmental remediation, drug discovery and medical diagnosis.²⁴ Neuromorphic computing is proving a useful framework for guiding adaptive cellular processes.v And newly engineered plant-microbe communication channels open up the prospect of programming entire ecosystems.²⁶

That said, significant improvements in our ability to manipulate and interface with biological processes, and to understand and measure the state of a cell, are needed to make this a reality. The field also needs deeper engagement with theoretical computer science to give it a firmer conceptual footing.

Cellular Computing - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.