Applying the Lessons of Evolvability to Product Formulation

By Eben van Tonder, 15 Feb 2015

Introduction: Leveraging Evolutionary Principles for Faster, More Adaptive Formulation Science

I have been involved in new product development (NPD) for most of my professional life. It was one of the driving forces behind my entry into the meat industry in the early 2000s. I did everything wrong (many will say that I still do 🤣🤣🤣🤣). Over time, I started to realise that I had to develop a toolkit to do more trails, much faster and very cheaply if I was going to make any progress. Much of my current thinking I developed in Nose-to-Tail and Root-to-Tip: Re-Thinking Emulsions under the heading Roadmap (Re-evaluation of the fundamentals – more than NPD)

Recent research by Bhaskar Kumawat et al. (2024) in Proceedings of the National Academy of Sciences (DOI: 10.1073/pnas.2413930121) highlights a fundamental truth: evolvability itself evolves. This means that adaptation is not merely a passive response to environmental changes—it can be actively optimized over time. This insight has direct applications to formulation science, particularly in rapid iteration, strategic variable selection, and predictive modelling. It continued to challenge me in terms of continuing to develop habits and tool-sets that will make me more effective in my work. I share some of those insights with you here.

Key Takeaways from Evolutionary Science for Formulation Scientists

1. Increase Iteration Speed to Accelerate Innovation (Inspired by the Role of Mutation in Evolvability)

Kumawat et al. demonstrated that populations cycling between different environmental pressures evolved a thousandfold increase in beneficial mutations. In formulation, this reinforces the value of running more trials in shorter timeframes to drive continuous improvement.

Application:

  • Develop high-throughput screening methods to test multiple small-batch formulations simultaneously.
  • Use statistical design of experiments (DOE) to systematically evaluate ingredient interactions rather than relying on a linear trial-and-error approach.
  • Apply insights from Meat Emulsions: A Roadmap to Investigations—establishing a robust framework where inexpensive, rapid trials guide product progressions.

2. Identify Key “Mutational Neighborhoods” in Formulation (Inspired by Avida’s Computational Modeling of Adaptation)

The study found that some populations evolved into “zones” where small mutations led to significant advantages in shifting between environmental states. In formulation, this translates to identifying key ingredient substitutions or processing parameters that enhance adaptability with minimal structural disruption.

Application:

  • Identify adaptive formulation points—such as a gelling agent or emulsifier that enables multiple textures without requiring complete reformulation.
  • Use predictive analytics to model ingredient interactions before conducting physical trials.
  • Establish a formulation toolkit that enables flexibility, allowing seamless transitions between different product variants.

3. Utilize Environmental Cycling to Future-Proof Formulations (Inspired by the Red/Blue Berry Model of Specialization vs. Generalization)

Research demonstrated that populations exposed to fluctuating environments became generalists, thriving in both conditions. In formulation, this principle can be applied to designing products that adapt to multiple market demands (e.g., clean label vs. cost-effective solutions, different regulatory frameworks, or shifting consumer preferences).

Application:

  • Develop multi-functional ingredient systems that perform reliably across varying conditions (e.g., pH stability in acidic and neutral environments).
  • Build modular formulation frameworks where base recipes can be easily adapted by swapping key functional components.
  • Ensure adaptability in meat emulsions by leveraging ingredients that maintain functional stability under different thermal and mechanical processing conditions.

4. Predictive Scaling: Double the Trials, Predict the Outcome (Inspired by the Evolutionary Retention of Evolvability)

Once a population evolved higher evolvability, it retained that ability permanently even after selection pressures stabilized. In formulation science, this suggests that past experimentation should inform future design—not just recording successes but analyzing failure patterns to refine predictive models.

Application:

  • Establish a formulation knowledge database to apply insights across different projects.
  • Use AI-driven simulations and machine learning to predict the best ingredient combinations before physical testing.
  • Leverage past learnings from meat emulsions and functional ingredient trials to ensure future iterations build upon prior successes.

5. Parallel Exploration of Formulation Possibilities (Inspired by Evolutionary Search Mechanisms in Nature)

Evolution does not follow a single path—it explores multiple pathways, retaining some options for future shifts. In formulation, this principle supports the development of multiple prototype formulations simultaneously, assessing their potential under different market conditions.

Application:

  • Instead of aiming for a singular “best” formulation, develop a portfolio of variations optimized for different priorities (e.g., shelf-life, texture, cost).
  • Use evolutionary algorithms in product optimization—combining the best traits from multiple trials to generate new candidates.
  • Apply experimental frameworks that test a range of functional ingredient combinations, ensuring adaptability in response to future consumer trends.

Conclusion: Making Formulation a Self-Optimizing Process

The insights from Kumawat et al. (2024) suggest that evolvability is an evolutionary advantage that persists once acquired. The same principle applies to formulation science:

  • The more adaptive and iterative the formulation process, the better equipped it is to anticipate challenges and pivot quickly.
  • Instead of focusing solely on individual formulations, the process of formulation itself should be optimized—treating it as a self-improving system that builds on past learning and accelerates future innovation.
  • By designing highly adaptable formulation processes, formulation scientists can mimic nature’s problem-solving mechanisms, ensuring faster development cycles, resilient products, and the ability to thrive under shifting conditions—just as evolution has done for billions of years.

References

  • Bhaskar Kumawat et al., 2024. Evolution Takes Multiple Paths to Evolvability When Facing Environmental Change. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2413930121
  • Zaman, L., et al. (2024). The Evolution of Evolution Itself: How Evolution Got So Good at Evolving. University of Michigan.
  • Wagner, A. (2014). Arrival of the Fittest: Solving Evolution’s Greatest Puzzle. Penguin.
  • Lenski, R. E. (2017). Experimental Evolution and the Dynamics of Adaptation and Innovation. PNAS, 114(1), 1–8.
  • van Tonder, E. (2024). Nose-to-Tail and Root-to-Tip: Re-Thinking Emulsions under the heading Roadmap (Re-evaluation of the fundamentals – more than NPD)