Post-Doc Positions Open:
Evolutionary transitions in Individuality

We are seeking a second post-doctoral research fellow to work on computational modelling of evolutionary systems biology. This position is for 19 months. The applicant will have a PhD in Computer Science, Theoretical Biology or similar, and a demonstrated ability: to build computational models, to reason about evolutionary process in a computational framework, to produce high-quality scientific publications and... for independent thinking.

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The applicant will be based at the University of Southampton (UK) with Richard Watson, and also working with Chris Buckley (University of Sussex) and Mike Levin (Tufts University, USA).

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This position will start Oct 2022 or as soon as possible thereafter.

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The project is funded by The John Templeton Foundation (The Science of Purpose funding initiative).

Salary: £39,739 to £44,706

Closing date: 25th Sept 2022 

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Feel free to contact me directly.

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links:

Jobs.ac.uk advert 

Apply here Soton pages

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About the Project

The truly surprising thing about evolution is not how it makes individuals well adapted to their environment, but how it makes individuals.

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All individuals are made of parts that used to be individuals themselves (e.g. the transition from single-celled life to multicellular organisms). Such evolutionary transitions in individuality have occurred at many levels of biological organisation, and have been fundamental to the origin of biological complexity, but how they occurred is not well understood.

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During a transition, the subject of natural selection changes from adaptations that serve the survival and reproduction of each individual component, to adaptations that serve the development, survival and reproduction of a new individual at higher level of organisation (e.g. the multicellular organism), even when it conflicts with the interests of the component parts.

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How does natural selection produce this rescaling of evolutionary purpose?

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Without an answer to these questions, evolutionary theory can only explain the adaptations that occur within one level of organisation, not how new levels of individuality arise. This project will offer a fundamental advance in evolutionary theory by connecting one level to the next. This has potential to impact 1) fundamental theory in evolutionary, developmental and organismic biology, expanding our understanding of how they interact to create evolutionary purpose, 2) new approaches to manipulating developmental goals and organismic function (e.g. for bioengineering), and 3) big questions about how self-interested individuals (including us) work-together to create something bigger than themselves.

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Methodology

To answer these questions we will build individual-based computational simulations of evolution by natural selection to test specific new hypotheses about the conditions that produce an evolutionary transition in individuality. In particular we will explore the main hypothesis that the conditions that enable natural selection to exhibit such transitions are predicted by the conditions that enable learning systems to induce and exploit deep structure (a.k.a. deep learning) (see 'Design for an individual').

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This builds on a growing body of work that deepens and expands the theoretical unification of evolution, learning, development and cognition ('How Can Evolution Learn'). We aim to show that the relationship between evolutionary transitions and deep learning is not merely a loose analogy but a functional equivalence that opens-up access to an extensive theoretical toolbox, well-developed in learning theory, with specific results and predictions we can test using the computational models.

 

To show that a transition has occurred, it is necessary to demonstrate ‘collective action’ such that multiple individuals (e.g. cells) behave (and evolve) as a coordinated whole. This is tested by exhibiting coordinated change in behaviour or phenotype among multiple individuals, that causes them to arrive at a better combination of behaviours, even though each of the individual changes in behaviour are worse on their own. This requires individuals to evolve a certain kind of relationship structure with one another (i.e. multi-level or ‘deep’ interaction structure). Our models will enable us to test our hypotheses and demonstrate an evolutionary transition in individuality in silico, using only bottom-up natural selection, for the first time.

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Aims and work-packages

Our overall aim is to understand how natural selection rescales evolutionary purpose. i.e., how it organises the relationships between existing individuals to make new individuals that are more than the sum of their parts, acting with unity of purpose at a higher level of organisation, and hence resulting in a rescaling of the evolutionary process.

 

The programme of work has three main stages:

 

Work-package 1: to identify what kind of relationships are needed, and in what organisation.

Producing

• A working computational model of a transition in evolutionary individuality, demonstrating how it can be driven by ‘bottom-up’ selection, for the first time.

• Rigorous characterisation of

  • the type and organisation of relationships required to effect a transition in individuality

  • necessary and sufficient conditions for such transitions to evolve through bottom-up selection.

 

Work-package 2: to explain how evolutionary purpose is thereby rescaled

Producing

• Formal quantification of how transitions change the adaptive capabilities of evolution by natural selection, and how this differs from non-transitional evolution.

• Formal unification of transitional evolution with deep learning principles.

 

Work-package 3: to develop initial connections with other disciplines to explore the broader impact

Producing

• Initial development of new approaches for intervention strategies that manipulate the level of individuality and modify developmental goals (e.g. for bioengineering).

• Initial connections with the broader implications for ethical behaviour and human values (e.g. social relationships that dissolve self-interest and facilitate collective interest).