Understanding modelling in science and technology: The potential of stories from the field.

1. Understanding modelling in science and technology: The potential of stories from the field. Bev France, FoE, University of Auckland b.france@auckland.ac.nz with Dr Vicki Compton & Professor John Gilbert

2. This presentation will: • Introduce the context • Show differences between science and technology • Present generic models used in science and technology to show differences in purpose, outcome and reasoning that underpins these differences • Analyse how a scientist uses models when working in bio-informatics • Analyse how a biotechnologist uses models when developing vaccines • Argue that understanding modelling has the potential to enhance critical literacies

3. Science explains the natural world through iterative, intellectual and investigative practices that involve observations and controlled manipulations of that world(Compton, 2004) Technology is a purposeful intervention in the world that is specifically designed to meet needs and/or realise opportunities in order to enhance human capability to transform, transport, store and control materials, energy and information. (Compton & France, 2006) Definitions

4. Models in science and technology Formulate and test predictions Inform development Evaluate data Explore consequences Describe phenomena

5. In science models are used to explain or organise observations, then predict and test through further observations (Schwartz & Lederman, 2005). In technology models are used to explore the influences on technological outcomes and their impact in the world as technological developments move from conceptual ideas through to fully realised and implemented technological outcomes(Compton & France, 2006) A model is a representation of a phenomenon initially produced for a specific purpose(Gilbert & Boulter, 2000)

6. In science a model is a representation of the target whose purpose is to predict or explain (Gilbert, 2000) In technology modelling is a process that allows the influences on and impacts in the world to be explored when a concept is developed into reality (Compton & France, 2006) Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase Components of models SOURCE TARGET Formed by process of analogy seeking Object of research

7. In summary: Models in science • are formed by analogy where the target is distinguished from the source • are central to knowledge building • have an empirical foundation based on observation and inference • provide a vehicle for the exploration and explanation of phenomena in the world • allow predictions to be made and tested • are underpinned by inductive reasoning when developing communicative models • are underpinned by deductive reasoning when applying models to specific situations for predictive purposes • The presence of more than one consensus model, the variety • of roles those models can fulfill, and the realisation that models can • evolve provides support for the tentative nature of science • (Van der Valk, Van Driel & De Vos, 2007).

8. In summary: Models in technology • are central to ensuring successful function (epistemological criterion) • are composed of two complementary types: • Functional modelling - testing design concepts prior to the realisation of a technological outcome. • Prototype modelling- - testing of ‘fitness for purpose’ after realisation but prior to implementation. • are underpinned by functional and practical reasoning: • Functional reasoning - explores the technical feasibility of the outcome (‘how to made it happen’) in the functional modelling phase and • (‘how it is happening’) in prototypingphase • Ensures success of function • Practical reasoning - allow exploration of the sociocultural acceptability of outcomes (moral/ethical factors) -. • (‘should it happen”) in functional modelling phase • (‘should it be happening’) in prototyping phase • Supplies the normative element of technology An understanding of the role of models substantiates functionality as an epistemological cornerstone of the nature of technology

9. Conversation with a scientist working in bioinformatics and computational biology • Research area • The use of computational methods in evolutionary biology • Central ideas: • All evidence is empirical • Variation is the raw material for evolution • Understanding genetic history of organisms • Research goals • To understand the evolutionary history of organisms and make inferences about the evolutionary process • Examples of projects • To study the evolution of viruses to infectious forms in order to study control mechanisms.

10. Allen’s story: • Modelling is central to bioinformatics • Models are used to predict and explain phenomena We use models all the time for scientific discovery and inferences • The purpose of evolutionary biologists is to develop a method of constructing a super tree (phylogenetic tree- communicative model) that communicates the best hypothesis of evolutionary relationships that can be inferred (inductive reasoning ) from the data. - Empirical evidence is based on the principles of microevolution where MEPs (Measuring Evolving Populations) are used as populations from which to take molecular sequences at different points in time. - RNA viruses are used as a source of MEPs because they generate high numbers of mutations. For example: evolution of viruses from their endemic form to their infectious forms in order to identify the control mechanisms and ultimately new therapeutic agents.

11. Using models in computational biology Our goal is to distil in our representation of the world the most essential points. And to test whether that distilled model makes sense in the light of the data we have collected. Testing a representation of the world to develop understanding TARGET SOURCE Phenomenon being researched Representation of the world as a mathematical model

12. Conversation with a biotechnologist (Site Development Manager) manufacturing sheep vaccines • Technological problem • Campylobacter causes sheep abortions and is responsible for 40% lamb loss. • This company produces and markets Campylobacter vaccine to prevent sheep abortions. • Central ideas: • A vaccine stimulates the protective immunity of an animal by the production of an antibody. • A good vaccine will be inexpensive to produce, store and administer and must be perceived to be safe. • Project goals To increase the market share by: • Improving the vaccination rate • Increasing the number of vaccinations per sheep.

13. Technological modelling to develop a sheep vaccine $ Gate: IRORA (internal range of return analysis) $ Gate: Mathematical modelling - @risk Cost benefit analysis of research designs that provides data to persuade farmers to vaccinate Trialling two-ended spike Prototype Practical reasoning Identify customer benefits Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase

14. Comparisons Different purposes: Allen: to produce the best hypothesis of evolutionary relationships that can be inferred from the data Robert: to make a vaccine that would increase the company’s market share Modelling influenced outcomes Allen: the model enabled the group to predict and explain unexpected properties. Robert: prototyping tested its technical success and indicated further normative aspects that needed to be considered Powered by different cognitive activities Allen: inductive reasoning to develop communicative model and deductive reasoning as they tested its predictive power. Robert: Functional reasoning to explore technical feasibility of the design concept and practical reasoning to determine whether if it was worth changing farmers’ views to multi- vaccinate.

15. Modelling: potential to develop critical literacies • Stories from science and technology provide a rich resource for teachers to explore how modelling is used by • illustrating how scientific knowledge is constructed and communicated within the science community • showing the purpose and power of technological modelling when developing an intervention that is ‘fit for purpose’. • Stories about science and technology can deepen understandings about NOS and NOT. For example • that scientific activities are messy, creative and complex. • that technological modelling provides avenues to explore the technical and normative components that will influence an outcome.

16. The End

17. Using models in computational biology • Characteristics of science research models • ｷNever 100% correct • ｷNever as complex as the original • ｷAre a distillation of the representation to recreate the most essential parts Testing a representation of the world to develop understanding TARGET SOURCE Phenomenon being researched Representation of the world as a mathematical model

18. Using models in computational biology Our goal is to distil in our representation of the world the most essential points. And to test whether that distilled model makes sense in the light of the data we have collected. Is this an adequate representation of our data and experimental observations? Testing a representation of the world to develop understanding TARGET Phenomenon being researched

19. Using models in computational biology Our goal is to distil in our representation of the world the most essential points. And to test whether that distilled model makes sense in the light of the data we have collected. Do viruses that infect a person show a greater degree of selection acting on them than viruses found in their endemic host? Testing a representation of the world to develop understanding SOURCE Representation of the world as a mathematical model

20. Technological modelling to develop a sheep vaccine $ Gate: IRORA (internal range of return analysis) When we are developing a new product we determine whether there was a dollar to be made out of it. We would model the financials e.g. R&D, cost, price, market size, market date, profit. Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase

21. Technological modelling to develop a sheep vaccine $ Gate: Mathematical modelling - @risk We used a statistical modelling technique (@Risk) to compare financial variables in order to calculate the % chance of making a buck. Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase

22. Technological modelling to develop a sheep vaccine We use modelling to identify features and benefits of technology. Features are design elements built into the product but customers need to perceive the feature as a benefit. For example a new vaccine with three strains was not perceived by customers as a benefit. Identify customer benefits Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase

23. Technological modelling to develop a sheep vaccine We had a problem in that we needed to provide data that would convince sheep farmers that they should annually revaccinate their sheep against Campylobacter. Needed to develop a cost benefit research design using knowledge of farmer/vet and experts (vet, scientists, sales rep, marketing person) to evaluate these research designs. Cost benefit analysis of research designs that provides data to persuade farmers to vaccinate Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase

24. Technological modelling to develop a sheep vaccine Autovet was an invention that was too novel. It was a mechanism for delivering chemicals intravaginally and had a microchip to control the delivery rate as well as a heat detector to signal when the cow was on heat. Practical reasoning Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase

25. Technological modelling to develop a sheep vaccine We produced a prototype of a little plastic two-ended spike that connected two bottles together. We trialed it, refined it and launched it. Trialling two-ended spike Prototype Development over Time Pre-Realisation ofOutcome Post-Realisation ofOutcome Transition Phase