– My work shows that adaptation is not a fixed process but is dynamically shaped by the structure of genetic interactions. This insight is key to designing more robust and adaptable systems in tech and biology, she says.
Tell us a bit about yourself. Who are you?
– I’m Priyanka Mehra, a researcher in AI and computational biology. My academic journey began with work on various Artificial Intelligence (AI) and bioinformatics projects, including research in federated learning and medical diagnostics. These experiences sparked a deep interest in understanding how complex systems function and evolve over time.
What is your doctoral thesis about?
– My thesis investigates how genetic interactions – epistasis and pleiotropy – shape the evolutionary dynamics of biological systems. I developed a computational framework that extends the traditional NK fitness landscape model by decoupling these genetic properties from ruggedness, allowing them to evolve independently. The work explores how these interactions influence robustness (resistance to mutations) and evolvability (ability to adapt) under static and dynamic environments, offering insights into how organisms navigate complex fitness landscapes.
How have you conducted your studies?
– My study is based entirely on computational modelling as the primary method of investigation. I did not use interviews, surveys, or empirical field/lab experiments.
What have you found?
– The key findings are:
- Epistasis and pleiotropy evolve independently when decoupled from landscape ruggedness.
- Lower epistasis and pleiotropy help avoid “Survival of the Flattest”.
- Epistasis and pleiotropy regulate the trade-off between robustness and evolvability.
- Higher environmental change (V) leads to increased epistasis and pleiotropy, favouring more complex, adaptive mappings.
What is the most significant finding?
– The most significant finding is that evolvability and robustness are context-dependent properties, shaped by the levels of epistasis and pleiotropy. High epistasis and pleiotropy increase evolvability, which is advantageous when populations are in fitness valleys and need to explore new adaptations. Low epistasis and pleiotropy enhance robustness, which is beneficial near fitness peaks where stability is favoured. This shows that the structure of genetic interactions dynamically tunes adaptation based on evolutionary context.
Does your research have connections to education and collaboration at Dalarna University?
– Yes, my research is closely connected to education and collaboration at Dalarna University. It was conducted within the PhD programme in Microdata Analysis and supervised by Professor Arend Hintze, and aligns with the university’s focus on evolutionary computation and complex systems. The work contributed to the academic environment through discussions and seminars. These collaborations enriched the research and supported the development of advanced computational models, strengthening Dalarna University’s profile in artificial life and adaptive systems.
What can the knowledge you have acquired be used for in the future?
– My research is important because it advances our understanding of how genetic systems balance robustness and evolvability, two fundamental yet competing forces in evolution. By showing how epistasis and pleiotropy shape this balance in context-dependent ways, it offers insight into how organisms adapt to complex and changing environments. We have learnt that the structure of genetic interactions is not static but evolves in response to evolutionary pressures, which has broad implications for understanding biological complexity.
Potential future applications include:
- Synthetic biology: designing more adaptable or stable gene circuits.
- Drug resistance modelling: predicting how pathogens might evolve under treatment pressure.
- Evolutionary algorithms: improving machine learning and optimisation systems by incorporating adaptive genetic interaction strategies.
- Personalised medicine: understanding gene-trait dependencies to better predict disease risk or treatment outcomes.
Why did you start researching, and why at Dalarna University?
– I was drawn to understanding complex systems. I chose Dalarna University for its strong focus on computational modelling and for the opportunity to work with Professor Arend Hintze.
What will you do now after the defence?
– I plan to continue in research, focusing on applications of adaptive systems in AI and biology. I’m also open to interdisciplinary roles in academia or research and development.
Who is the target audience for your doctoral thesis?
– Researchers in evolutionary biology, computational modelling, and AI. It may also interest industries working in bioinformatics, synthetic biology, and adaptive algorithms.
Thesis: Epistasis, Pleiotropy, Robustness, and Evolvability: Insights into Evolutionary Dynamics
Article: How Genes Adapt: New Research Sheds Light on Evolution’s Balancing Act - Dalarna University