Advanced data management is starting to crack the hidden world mother nature has created around us. Anthony Finbow, Chief Executive Officer at Eagle Genomics Ltd - a pioneer in applying network science to biology - discusses how.

The agricultural practices we've been using for the last 100 years centre on using fertiliser to optimise the nitrogen available to plants. There have been great benefits, including higher yields and consistent harvests. However, for that control we pay a high price. An over-reliance on nitrogen fertilisers has decimated the soil. An important, but invisible universe - the nano ecosystem in the soil - is in grave danger.

We know very little about the microbial universe of the soil or microbiome. We do know, however, that trees neutralise carbon by capturing carbon monoxide; they then transmit the carbon through to microbes in the soil, where it is fixed. If we could replenish the source microbial universe, we will boost the ground’s ability to capture and retain carbon, and nature can reverse climate change for us - perhaps in as short a time as 30 years.

Meaningful microbiome-based knowledge discovery and analytics

Microsoft has recently gone on record to predict that the food industry “has not even scratched the surface when it comes to understanding the microbiome,” but that once we have, there is a wave of innovation coming from microbiome science. More will be discovered about the interplay of microbes and foods, their impact on human and animal health, as well as establishing what specific soil conditions will drive crop yield and improve animal farming.

What’s helping us understand these processes is sophisticated technology applied to a wide range of scientific data. There are still some practical hurdles to overcome, especially around data standardisation, but modern data management, applied network science using graph data structures and AI-based analytics are allowing everyone from climate change scientists, soil experts and agri-manufacturers to benefit from meaningful microbiome-based knowledge discovery and analytics.

Modern data management technologies enable the unifying of constellations of complex, multi-dimensional data and the exploration and extraction of signals and networks of relationships that are not detectable by humans alone. This facilitates powerful new discoveries about soil biology and how to farm more sustainably. It also moves the global economy towards a secure and sustainable solution to feeding the multiplying populations into the future, while heading off the existential challenge of climate change.

We're witnessing pioneering organisations beginning to make inroads in understanding, for instance, in engineering microbes to fix more nitrogen to plant roots so those plants can grow more effectively, without the damaging environmental impacts of nitrogen fertiliser products.

New generation of agri-tech

Progress is also being made in the application of plant microbiomes for increasing crop yields and improving salt and drought tolerance of crops. Soil microbiomes can be applied as bio-fertilisers for soils and can reduce nitrogen leaching. Non-biodegradable plastics that are the scourge of the oceans may soon have practical alternatives.

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One pioneering enterprise is working to develop hard-printed circuit-board materials which have been manufactured through bioprocesses exploiting microbiome science, to remain stable through their life, but which also degrade very rapidly and appropriately after their useful life.

The start of the third decade in the 21st century is proving to be an exhilarating time to be part of this new generation of agri-tech, as we seek to work more closely with biology. It’s significant news for the environmental and sustainability movement but also for the agriculture and food growing industries.

Bill Gates maintains that understanding the microbiome will be “as big a breakthrough as anything else we will do over the next two decades.” Anyone who investigates the power and the potential of the microbiome has to agree. As a species we can look forward to meeting the challenges of the 21st century with the help of this important micro-world, which advances in data science and AI are opening up for us.

How are advanced data techniques helping unlock the microbiome?

Scientists are using the following techniques to build knowledge and understanding with a view to generating agricultural innovation and environmental sustainability and renewal:

Fit-for-purpose data fabrics
These frameworks ensure that a sufficiently comprehensive set of microbiome-related data is available and accessible, has integrity and value, and can be exchanged, compared and understood in reliable and meaningful ways by non-data scientists. Gartner has made a number of important predictions about fit-for-purpose data fabric features as the demand for complex analytics grows.

Multi-layered hypergraphs
Graph technology provides insight into data relationships, especially in complex scientific projects. The first step up is a hypergraph, a knowledge graph with several interconnected nodes (entities). Going one step further involves the multi-layering of multiple hypergraphs. Stacking graphs allows scientists to structure their thoughts and separate their scientific knowledge in categories (layers), gaining even greater microbiome insight through a better understanding of the context and relationships between the data. A standard graph is a binary representation, while a hypergraph allows for a richer semantic expressiveness. This also helps with reliability and interpretability of data models.

AI-enhanced graph modelling
Artificial intelligence (AI) can help decode patterns that might not be obvious to the human observer. Using AI, life scientists can elevate their microbiome knowledge discovery to a higher level, while not necessarily having to ask data scientists for help to manipulate multi-omics data or consult with statisticians to test hypotheses and decipher connections.

Causal inference programming techniques
To advance understanding, scientists want to understand causality between data points. Advanced application of causal inference programming distils and investigates associations between diverse data, allowing scientists to design new studies to delve deeper into root cause analysis. Causal inference brings great potential in understanding the environmental role of the microbiome.

About the author

Anthony Finbow is Chief Executive Officer at Eagle Genomics Ltd, a UK-based pioneer in applying network science to biology linked to the microbiome. There, he and his team are working with 5 of the top 10 household and personal care companies in the world to create and launch new products that work in harmony with the human and environmental microbiome.