Innovation briefing

Metamorphosis:
how converging deep tech will propelbusiness in the new bioeconomy

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

CC in three
your key notes
to take away
1

CC believes that the secrets of successful
metamorphosis to a scaled bio-economy
can be found in the synergistic interactions ofemerging technologies.

2

Interactions of emerging technologies willallow us to help add the predictability thatis imperative for scaling a profitable, resilient
bioeconomy.

3

Business will only be able to realise the fullpotency and sustainability of the bio-revolution
by partnering to harness the collective potentialof emerging technologies, sharing data anddecentralisation.

02© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

Introduction
The bio-revolution is alive with opportunity. Emerging deep tech, including biotechnology andsynthetic biology techniques, is presenting novel ways to create materials, chemicals, foodand medicine. Ambitious innovators are set to redefine their markets with transformative leaps.
Our planet, and the sustainability imperative, stand to benefit. But…
The but is biology. The pace of change is hampered by the complexities of manipulating
and scaling it. Outcomes are hard to predict; timescales and cost are always a hindrance.
This CC Innovation Briefing sows the seeds of a solution.
In it, we fast-forward to the future and present a bold but plausible vision for the new
bioeconomy. We explain how synergistic technology interactions hold the key to scaled,profitable, flexible bioprocesses. And we unpack the reasons why these emerging technologies
will combine to deliver the predictability and certainty that will allow the bioeconomy –and perhaps your business – to flourish.
Our authors draw upon CC’s unique perspective at the intersection of engineering, biology
and advanced computation. Their insights are shaped in an environment where scientists,
technologists and industry experts collaborate to question the status quo. Central to everythingis the interplay of deep tech – in this case synergistic technologies that can be applied at anindividual bioprocess level, a company level or at the level of the entire bioeconomy.

03© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

A vision for the bioeconomy of the future
Converging technologies have accelerated a profitable, scaled,
networked bioeconomy
Let’s spin forward to 2035. You are the bio-based chemical,
material or ingredient manufacturer of the times. Your
bioprocesses are flexible, but still achieve economies ofscale. You exist within the new bioeconomy. It is highly
profitable, operates at scale and is networked – witha collection of real-time interconnected bioeconomy
functions, constantly communicating with each other toshare data and resources. Together, this bioeconomy serves
an expanded, ageing population with the materials andchemicals it needs to thrive, without the environmental cost.

A vision for the bioeconomy in 2035

A flexible network of biological processes making large quantities of valuablebio-based products from a variety of feedstocks (including waste sources)

Agricultural, industrial and post-consumer waste used as feedstock
which means low/free costs, high margins and sustainability benefits

Less volatility in the prices of commodities due toincreased variety of feedstocks and productsPlant emissions have dramatically reduced
due to fewer fossil-based feedstocks

1
Individual
bioprocesses

2
Facility ororganisation

3
Bioeconomy

To be clear, this is not a prediction – it’s designed to be a provocative conversation starter04© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

Adaptable individual bioprocesses
At an individual level, your bioprocesses have overcome
the trade-off between flexibility and economies of scaleby harnessing a multitude of technologies. To illustrate this,let’s imagine that one of your main feedstocks suddenly
becomes unavailable. This might be due to the regulator
limiting fossil extraction, a geopolitical event raising theprice of a fossil input, an extreme weather event wiping
out a particular crop, or a waste stream that you wereusing becoming obsolete. In the past, this would havebeen a business-critical problem – but now it’s only an
inconvenience. You use the following process to adapt:

Stages required to set-up a bioprocess
Organism
High throughput
and feedstock
correlation
selection
testing

1

2

Metabolicengineering3

TEA update

4

Digital twin
simulation

5

Commissioning

6

7

Finance andregulatory
approval

1

Organism and feedstock selection: A database ofavailable feedstocks and prices is fed into an AI which
determines a chassis organism and feedstock basedon the equipment you have available (with input froma metabolic engineering AI which determines what
improvements to yield and titre are likely possible withdifferent chassis and feedstocks).

2

Metabolic engineering: AI undertakes metabolicengineering and optimisation to recommend several
hundred organism variants that would likely produce
your product at the required yield and titre with thebest feedstock. The AI runs on a hybrid system of aconventional and quantum computer to accurately
simulate the reaction sites of the involved enzymes,
improving enzyme activity as it goes. It looks at thedownstream processing steps that will be required andmakes another several hundred variants that miss out
an expensive part of downstream processing. You takethese recommendations to your laboratory where you
use the latest synthetic biology tools and automatedliquid handling equipment to produce these variants.

3

High-throughput correlation testing: In the laboratory,
high-throughput correlation testing (which is an arrayof well-plate sized bioreactors with advanced sensing
and microfluidic feeding) measures how each variant
grows in relation to a number of parameters that willbe critical for scale-up and downstream processing
including oxygen availability, by-product concentrationand pressure changes. It selects variants that give thebest results when scaled up. Some of the results are fed
back into the AI to produce improved recommendations.
This high-throughput correlation testing procedure has
demonstrated a good match to results at larger scales
when fed into a digital twin model.

4

Digital twin simulation: A digital twin of your equipment
is used to predict performance of organisms inlarge scale vessels, using the results from the highthroughput correlation testing and similar processes.
This twin incorporates computational fluid dynamics tounderstand oxygen flow through the fermentation vesselalongside metabolic models to understand proliferationand target product production. Downstream processing
and upstream processing are also included, using
powerful physics engines.

05© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

5

6

7

Flexible, optimised facilities

TEA update: The predictive power of the digital twin (interms of input, titre, yield and downstream processing
efficiency) enables confidence intervals to be given in atechno-economic analysis (TEA). This shows that this newprocess is highly profitable. It also reveals that the main
factors affecting profitability are oxygen availability andthe fermentation time.

Your individual bioprocesses aren’t the only ways in which
the convergence of emerging technologies has affectedyour business. Supply chains have become more volatile
due to climate change and geopolitics, but your business isresilient to this through the combined effects of leveraging
real-time data, modular processes and data fusion.

Finance and regulatory approval: With your estimatedcosts, return on investment and confidence intervals,
you get a good deal on financing the project. The
regulatory approval is straightforward as the regulator
is familiar with these technologies, trusts the predictions
and the regulatory approval process has beenstreamlined accordingly.

 Real-time process data is gathered using conventional

methods but also with advanced photonic methods(science and technology of generating, controlling anddetecting photons) and biosensing (the use of biologicalentities to measure attributes). Artificial intelligence at theedge (local hardware, as opposed to a centralised data
centre or cloud environment) means that a wide varietyof parameters can be inferred, and dynamic control
systems implemented.

Commissioning: When it comes to commissioning
this new process on your existing equipment, thedigital twin has informed you which parameters arelikely to be critical to profitability. You use advanced
sensing modalities to sense these in real-time. Artificial
intelligence monitors these parameters alongside thecritical process attributes to optimise the control system.
The process learns from itself and quickly ramps up
production to optimal levels. It also flags any unusual
events for preventative maintenance.

 Modular processes are unit operations that are

independent but flexibly connected. These processes
are inherently flexible as they can be easily scaled up
or down by removing modules, they can be upgradedand adapted or broken without disrupting the wholesystem and they can be combined with various othermodular processes for diverse functionality. Modularity
is specifically called out as an R&D focus area in the
Schmidt Futures Report – a prominent thought piece on
advancing the US bioeconomy.

Automatic iteration: With the updated TEA, your software
automatically goes back through all the variants tested
in the high throughput correlation testing and performs
a digital twin simulation and the same TEA to give you an
optimal return. This takes an enormous amount of computing
power, but high-performance compute infrastructure isubiquitous now. Indeed, your classical computer may be
integrated with a quantum computer to speed this up.

 Data fusion is the combination of different types of data

to infer insights. In the facility of the future, we might
combine data from the scheduling software with realtime dynamic electricity prices, weather predictions,process data and market data to optimise our sites.
This leads to higher utilisation of assets, lower operating
costs and higher revenue. For example, according to a
Capgemini Research Institute report, basic site-wide
digital twins are already bringing average system
performance improvements of over 25% even without
incorporating data from the wider enabling environment.

You’ve rapidly adapted to what used to be a businesscritical problem to achieve economies of scale in the
production of the same product from a new feedstock.
The price of this product has skyrocketed due to theshortage of feedstock, and you are enjoying the returns
enabled by these now well-established technologies. Yourability to provide reliable supply leads to trusted supplier
status, enhancing your industry reputation.

Overall, your multiple bioprocesses achieve economies ofscope (the cost advantages and efficiencies achieved when
producing a variety of products rather than specialising ina single product), are resilient to supply chain volatility andachieve synergies in production, sales and marketing.

06© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

A networked, tokenised bioeconomy
The bioeconomy is responsible for meeting a variety ofhuman needs; from feeding us, to powering our homes, tocleaning our workplaces. Your future bio-based business
operates in a networked bioeconomy where efforts are
co-ordinated to meet these needs. This helps when tacklingthe ‘grand challenges’ that the industry faces, including
moving across regulatory territories, improving sustainability,
and security (which is particularly complicated due to theconvergence of cybersecurity and biosecurity).

In the future bioeconomy, data is shared on a distributed
platform (a system or network in which components andfunctionalities are spread across multiple nodes rather
than one central hub) that combines real-time pricing
information, real-time supply information, future predictions,sustainability best practice and security resources such as
known vulnerabilities.

Tokens facilitating a decentralised bioeconomy
Tokens gained by:

1

Sharing your data

2

Maintaining/improving
the platform

3

Buying tokens

This system is built on open standards and tokenised withbioprocess equipment and market data being interoperable
within the system. This distributed platform has several
advantages:

 No single entity is responsible for resourcing the setup and maintenance of this platform. There is nointermediary, which removes associated costs.

 Companies maintain ownership and control of their data
which reduces data sharing risks.

 Various policy and regulatory bodies can simulate theeffect of their potential policy decisions and regulations
on the industry to choose restrictions and timeframesthat have the smallest impact on companies and the
supply of goods.

Tokens spent by:1

Accessing data

2

Selling/trading tokens

 Transparency in regulation across territories enables

easier planning, collaboration and economies of scale
across borders

 Facilitation of smart contracts – self-executing

contracts where the agreement is written into code andautomatically executed when certain conditions are met.

Smart Contract Example
A company licenses a proprietary enzyme for utilisingcellulosic biomass. The contract uses IoT sensors within
the bioreactor to measure the volume of product madeusing the proprietary enzyme and feeds in real-time
price data to automatically calculate and trigger
payment of the required royalties. Smart contracts
enable automated, trustless, and tamper-resistantexecution of agreements on blockchain, reducing theneed for intermediaries and enhancing efficiency

07© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

How do we achieve this vision?
This idealised future isn’t as far away as it may seem – even
if progress might not be as straightforward as the picture
we’ve painted. The key will be to bring more predictability
to biology and bioprocesses, whilst at the same time
capitalising on varying scales of decentralisation, a trendthat offers a number of benefits to industrial biotechnology.
A collective, community wide approach will also be crucial.
Let’s begin with predictability.

Biology is complex as it has numerous interconnectedparts and variables which often interact in a way whichbrings out emergent properties – where the sum of abehaviour of multiple parts is different than the individual
elements. Biological systems are constantly interactingwith their environment and have evolved according tothis environment over time. This, alongside the inherent
stochasticity of some biological processes mean it canbe very difficult to predict what biology will do. There areseveral ways in which we can overcome these challenges –and we explore them below.

We make biology and bioprocess
predictable

Research

The bio-revolution has often been compared to the siliconrevolution, which led to the proliferation of electronic
devices, personal computing and the digital era. A key
difference between the bio-revolution and the siliconrevolution is that biology is inherently more complex andtherefore less predictable than silicon chips and electronics.

Academic research often focuses on understanding theparts that make up a biological system and using a systems
approach to better predict the output. As the tools we
use to measure biology become more sophisticated, ourunderstanding is rapidly evolving, thus making biology morepredictable. Predictability is specifically called out as one oftwo core research priorities in the Schmidt Report.

Predictability fosters informed business decisions, reduces
risk, ensures consistent product quality and leads to faster
regulatory approval. It enables the transition from fed-batch
to continuous processing through understanding what
impacts phenotype stability. It enables scaled, commerciallyattractive bioprocess by design, minimising trial and error.

Low-sample AI
AI requires many, many samples to be effective (~at least
2,000 per class or 10 times the number of degrees of freedomof a model, which is very high for a string of amino acids).
This is extremely difficult in biology research due to the timeconsuming and expensive nature of biology experiments.
George Church’s lab at Harvard University is changing thatby using ‘protein large language models’ which enable
protein structure and function to be predicted from protein
sequences.

So how do we make biology and bioprocess morepredictable within industrial biotechnology?

Validated simulations
In the earlier ‘individual bioprocess’ section, the conceptsof highly correlated screening and bioprocess digital twins
were explored. Validation of the predictive powers of thesemodel systems is vital in ensuring they can be trusted toassist with business decision making.

Cell-free expression systems
Cell-free expression systems have been developed whichjust contain the transcription and translation machineryof a cell, without the cell membrane or reproduction
apparatus. This reduced complexity could make thesesystems more predictable alongside improving yield andcost-effectiveness. Nonetheless, several barriers remain
including knowledge and resource gaps, the need for bettermeasurements and issues with predicting performance.

08© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

We leverage differing scales ofdecentralisation

We harness the collective potentialof the community

Decentralisation is a trend in industrial biotechnology
because it a) allows manufacture closer to sources of waste,
reducing transport cost and carbon footprint b) enables
supply chain resilience and c) aligns with trends towards
decentralisation and personalisation in consumer markets.

The convergence of technologies is inherently
interdisciplinary – it’s unlikely you can achieve progress
acting alone. Biology is a system. The bioeconomy is asystem. So, we need to be approaching both with a systems
mindset. Rather than focusing on individual elements
in isolation, we need to emphasise the whole picture,understand how entities interrelate and consider thebroader context in which the bioeconomy operates.

In a decentralised bioprocess, the company which owns
the process loses some control over it. There may also be
a variety of fermentation vessel sizes, aeration methods
and feedstock qualities in each of the locations that theprocess is scaled to. For example, if you are putting yourbioprocesses that produces a particular ingredient into
various food manufacturing premises, you may lose accessto the machine entirely.

One of the ways in which we do this is utilising a platform
to collate data, share data and interpret data. In the
provocative vision described above, a distributed platform
was the method for achieving this. In reality, distributed
platforms are the right choice when parties cannot agree ona single trusted third party, or third parties bring prohibitive
extra time, cost and risk.

Better predictive powers make the process of transferring
control much easier as you know which factors are most
likely to impact quality and efficiency, so you can focus
the handover process on these. While other sectors arealso experiencing decentralisation, with bioprocesses, theindividual characteristics of a process have more of an
impact on quality and efficiency than in other sectors suchas manufacturing with plastic.

Whether a centralised or distributed platform is used,
this digital service needs careful design. This must include
a good understanding of how access to data is controlled.
We must also ensure there is enough value in the platform
to encourage a variety of stakeholders to share dataand collaborate.

On the other hand, consolidated biorefineries (with a varietyof bioprocesses working synergistically in one place) bringenhanced flexibility due to being able to have a wide variety
of processes and equipment in one place. It also brings
economies of scale and simplified distribution of end product.
Predictability is also important here for encouraging synergy
across processes (for example, what process might I be able
to use to valorise this side-stream) and understanding howchanges impact the whole system.
By leveraging decentralisation and consolidation
appropriately, the bioeconomy becomes flexible, resilient,
efficient and scalable.

The bioeconomy requires both decentralised facilities and consolidated biorefineries

Decentralised facilities

Consolidated biorefineries

Aligns with consumer
market trends

Valorisation of side/
waste streams

Manufacture close
to feedstock

Synergistic processes
in one place

Supply chain
resilience

Economies of scaleand scope
Enhanced flexibility

09© Cambridge Consultants 2023

Innovation Briefing: Metamorphosis – how converging deep tech will propel business in the new bioeconomy

Conclusion
Certainty

By now, you should be able to anticipate our central
conclusion. Predictability. It is the prerequisite for scale,flexibility and certainty.

A good prediction gives a range rather than a single
value and understands what the prediction is sensitive to.
Certainty builds confidence.

Scale

These three attributes are the keys that allow us to design,
finance and regulate the bioeconomy. How do we achieve
this? A systems approach and the right data and knowledge
sharing infrastructure must be in place to enable change.
Alongside this, partnership is vital. Across disciplines, acrossthe value chain; between industry, deep tech experts, policy
makers and society.

Scale-up of bioprocesses is usually challenging andexpensive. Biology is inherently challenging to predict
due to its complexity and constant interaction with theenvironment. This complexity when we understand and canmanipulate it is powerful. Being able to predict how biology
interacts with our scaled industrial systems is the secret to
unlocking this power.

The powerful predictability that emerges from theconvergence of deep technologies will enable the new
bioeconomy to spread its wings; with the ripples propellingbusinesses to new heights of sustainability and profitability.

Flexibility
Flexibility and modularity demand the ability to take avariable input and produce a fixed output. This might requiredifferent organisms, different vessels, different upstream/
downstream processing. Without prediction, you haveto keep doing the same, rigid thing as the uncertainty is
unworkable.

Continue the conversation
If you’d like to discover more about the subject – or would like to discuss your ambitions for the new bioeconomy –please get in touch with Frances Metcalfe, SVP Global Biotechnology.
frances.metcalfe@cambridgeconsultants.com

10© Cambridge Consultants 2023

Why CC?
Cambridge Consultants (CC), part of Capgemini Invent, is a
global team of 800 bright, talented people – united by the
ambition to turn brilliant and radical ideas into technologies,
products and services that are new to the world. We expand
the boundaries of deep tech innovation by tackling thetough, high-risk challenges that bring defensible competitive
advantage and market leadership for clients. We are trusted
by some of the world’s biggest brands and most ambitious
start-ups to realise their critical technology-based
aspirations – and we’ve been doing it for 60 years.

Rebecca Donaldson,
Technology Strategy Consultant

James Westley,
Capability leader, Industrial Biotech

As part of the strategy, innovation and process group at
Cambridge Consultants, Rebecca helps senior decision
makers understand the technology breakthroughs thatwill have the largest impact on their organisation –and how to capitalise on them, with a particular focus
on biotechnology. She is currently a Fellow at the World
Economic Forum working on the Bioeconomy Initiative.

James is responsible for growing ground-breaking
industrial biotech capabilities and leading multidisciplinary teams to invent radical innovations for
ambitious clients. His background is in thermofluidics
and simulation. He has a passion for challenging current
thinking to find impactful, yet unobvious improvements toall stages of bioprocess development.

UK — USA — SINGAPORE — JAPANwww.cambridgeconsultants.com
Cambridge Consultants is part of Capgemini Invent,the innovation, consulting and transformation brandof the Capgemini Group. www.capgemini.com

Lit-IB-009 v0.5 '2023_EFIB Biotech_Innovation_Briefing'

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