CCSS Complexaton: Win a €1000 cash prize through interdisciplinary team work

Short introduction (YouTube recording)

Representative:

Dr. Gideon Kruseman, International Maize and Wheat Improvement Center (CIMMYT)

Background of the problem:

If we want to provide the growing global population with healthy, affordable and socially acceptable diets while staying within planetary boundaries (Raworth 2017) under climate change, we are facing a huge challenge. Especially in low and lower middle-income countries, affordability is a major issue. Suggestions made by the EAT-Lancet commission (Willett et al. 2019) are not affordable (Hirvonen et al. 2020). Discussions on this topic are filled with emotion while there is a clear need for scientific evidence.                         

Challenges:

There is ample data available from many different sources on separate components of dynamic complex agri-food systems. Bringing them together to provide insight about food system transformation pathways is challenging. The data is often not yet interoperable, partly because the data is from many different sources with different classifications                      

Main questions to be addressed:  

      1. What are the global patterns of consumption?

Changing consumption patterns have been analyzed in isolation at national level in low and middle income countries. Examples include but are not limited to major food staples (Khondoker A Mottaleb et al. 2017; Mottaleb et al. 2018; Khondoker A. Mottaleb, Rahut, and Mishra 2017)

      2. How are these patterns changing over time and space in relation to major drivers of change?

Major primary drivers of change include population growth, income growth, urbanization, climate change. Is it possible to identify tipping points in the past that can provide insight into what happens when there are tipping points in future.

      3. How are dietary transition pathways linked to affordability, health and sustainability?

Complex Systems Science aspects:

Identification of specific emerging patterns and providing forecasts on the possible pathways. The way to do this is open for discussion, this can involve traditional statistical methods (multi stage regression analysis), machine learning (neural networks, random forest) in combination with simulation techniques.   

Possible societal importance/impact:  

The outcomes of this project will feed directly into the current priority setting of international agricultural research of CGIAR, affecting the focus of research for the next decade, and hence have a major impact on food and nutrition security of the resource poor in low and middle-income countries.

Initial literature: 

• Hirvonen, Kalle, Yan Bai, Derek Headey, and William A. Masters. 2020. “Affordability of the EAT–Lancet Reference Diet: A Global Analysis.” The Lancet Global Health.
• Mottaleb, K. A., D. B. Rahut, G. Kruseman, and O. Erenstein. 2018. “Changing Food Consumption of Households in Developing Countries: A Bangladesh Case.” Journal of International Food and Agribusiness Marketing 30(2):156–74.
• Mottaleb, Khondoker A, Dil Bahadur Rahut, Gideon Kruseman, and Olaf Erenstein. 2017. “Changing Food Consumption of Households in Developing Countries: A Bangladesh Case.” Journal of International Food & Agribusiness Marketing 0(0):1–19.
• Mottaleb, Khondoker A., Dil Bahadur Rahut, and Ashok K. Mishra. 2017. “Consumption of Food Away from Home in Bangladesh: Do Rich Households Spend More?” Appetite.
• Raworth, Kate. 2017. “A Doughnut for the Anthropocene: Humanity’s Compass in the 21st Century.” The Lancet Planetary Health.
• Willett, Walter, Johan Rockström, Brent Loken, Marco Springmann, Tim Lang, Sonja Vermeulen, Tara Garnett, David Tilman, Fabrice DeClerck, Amanda Wood, Malin Jonell, Michael Clark, Line J. Gordon, Jessica Fanzo, Corinna Hawkes, Rami Zurayk, Juan A. Rivera, Wim De Vries, Lindiwe Majele Sibanda, Ashkan Afshin, Abhishek Chaudhary, Mario Herrero, Rina Agustina, Francesco Branca, Anna Lartey, Shenggen Fan, Beatrice Crona, Elizabeth Fox, Victoria Bignet, Max Troell, Therese Lindahl, Sudhvir Singh, Sarah E. Cornell, K. Srinath Reddy, Sunita Narain, Sania Nishtar, and Christopher J. L. Murray. 2019. “Food in the Anthropocene: The EAT–Lancet Commission on Healthy Diets from Sustainable Food Systems.” The Lancet.

Short introduction (YouTube recording)

Representatives: Dr. Rubén Díaz Sierra, Universidad Nacional de Educación a Distancia (UNED); Dr. Mara Baudena, Copernicus Institute of Sustainable Development (UU)

Background of the problem:

The well-known and controversial intermediate disturbance hypothesis (IDH) predicts that intermediate frequency or intensity of perturbations maximize biodiversity, allowing the coexistence of many different species competing for the same resources^1,2. We recently adapted a classical implicit-space model for plant competition and coexistence³ by including stochastic perturbations to model the effects of forest fires on the persistence of ecosystems⁴. More recently, we have developed the mathematical conditions that determine how periodic perturbations alter species coexistence in a two-dimensional impulsive differential system.

Challenges:

The students are expected to perform simulations exploring the effect of frequency and intensity of (or recovery capacity after) perturbations on species coexistence, and how it is affected by colonization rates and competition hierarchy. Students will also attempt to propose generalizations of the coexistence conditions in higher dimensional systems.

Main questions to be addressed:  

1. Is the IDH observed in simulations for large stochastic models?
2. Can conditions in low dimensional systems be generalized for high dimensional models?

Complex Systems Science aspects:

General properties in population ecology are powerful tools in articulating theoretical discussions and inspiring empirical research. Proving them for general systems with partially random elements is a hard task. The proposed modeling scheme allows up-bottom (simulations) and bottom-up (coexistence conditions) approaches to understand the mechanisms behind the IDH.

Possible societal importance/impact:  

The loss of biodiversity is one of our main current societal challenges. Understanding general drivers of species coexistence is very relevant. In particular, perturbations (including fires) are expected to increase, both in frequency and intensity, with climate and land use change, threatening ecosystems diversity, functions and resilience.

Initial literature:  

• Huston, M. A general hypothesis of species diversity. American Naturalist 113, 81-101 (1979).
• Hughes, A. (2010) Disturbance and Diversity: An Ecological Chicken and Egg Problem. Nature Education Knowledge 3(10):48
• Tilman, D. (1994) Competition and biodiversity in spatially-structured habitats. Ecology, 75, 2–16.
• Baudena, M., Santana, V.M., Baeza, M.J., Bautista, S., Eppinga, M.B., Hemerik, L., Garcia Mayor, A., Rodriguez, F., Valdecantos, A., Vallejo, V.R., Vasques, A., Rietkerk, M., 2020. Increased aridity drives post‐fire recovery of Mediterranean forests towards open shrublands. New Phytol. 225, 1500–1515. https://doi.org/10.1111/nph.16252
• Code: https://github.com/baudenam/FireMed-Baudena-et-al-2019-New-Phytologist
• A. Tchuinté Tamen, Y. Dumont, J.J. Tewa, S. Bowong, P. Couteron. A minimalistic model of tree–grass interactions using impulsive differential equations and non-linear feedback functions of grass biomass onto fire-induced tree mortality. Math. Comput. Simul., 133 (2017)

Short introduction (YouTube recording)

Representative: Dr. Bas E. Dutilh, Institute of Biodynamics and Biocomplexity (UU)

Background of the problem:

Bacteria grow by taking up nutrients from their environment, which they convert into building blocks necessary for building new biomass. These conversions are performed by a network of enzymes and can be analyzed by genome-scale metabolic models (GSMMs). GSMMs are considered functional if there is a path from the environmental metabolites to biomass. While we have made great progress in sampling bacteria in their environment through metagenomics, the genomes of these bacteria are often incomplete, so are the GSMMs. The final goal of this project is to predict full GSMMs from an incomplete picture of the genome (subset of genes).

Challenges:

The major challenge is creating a scheme for training and testing that takes into account the complex evolutionary structure of the data. Genes and enzymes in bacteria are evolutionarily related both horizontally and vertically, so different bacteria are not independent observations. This greatly impedes the application of standard machine learning algorithms.

Main questions to be addressed:  

When training & testing machine learning algorithms that predict enzyme content of bacteria from incomplete information,

1. how can the evolutionary relationships between bacteria be taken into account?
2. how can the evolutionary relationships between enzymes be taken into account?
3. how does taking 1 and 2 into account affect the performance of the predictions?

Complex Systems Science aspects:

An incredible diversity in bacterial genomes have emerged over 4 billion years of evolution. Understanding how bacteria grow is important in fields ranging from medicine, agriculture, and biotechnology to fundamental science. Once individual bacteria can be modelled, we can continue with the even more challenging task of understanding their interactions.

Possible societal importance/impact:  

Over the past 20 years, metagenomics has led to the discovery of a vast, unprobed microbial world. This technology has driven the Microbiome field to be one of the fastest growing in the biomedical sciences with impacts ranging from human/animal health, to improving crop yield, to bioremediation of contaminated water/soil.

Initial literature:  

• http://www.nature.com/articles/nmicrobiol201648
• https://academic.oup.com/bioinformatics/article-abstract/36/6/1823/5613176
• https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-018-0593-7
• https://www.biorxiv.org/content/10.1101/2020.03.20.000737v2
• https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007084
• https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003882

Short introduction (YouTube recording)

Representative: Dr. Carla Do Rosario Costa, School of Economics (UU)

Background of the problem:

Knowledge flows within regions are expected to favor the appearance of related industries more than unrelated industries (Neffke et al., 2011). For regions where an industry clusters significantly, most of the knowledge flows are directed into that industry, and it is common to observe high indices of spinoffs (Klepper, 2009; 2010) and entrepreneurship (Feldman, 2001) into that same industry. However, there are cases of regions where more than one cluster succeeds to develop and thrive. We aim to understand such cases and analyze knowledge flows between co-located clusters, unveil their relatedness level, and reveal the types of interchanged skills.

Challenges:

Employees moving across firms have skill profiles. We aim to identify them through machine learning classification. Furthermore, worker mobility allows to determine the network of knowledge flows among firms in the cluster and unsupervised learning algorithms could be used to identify cohesive groups of individuals or firms.

Main questions to be addressed:  

1. What are the skill patterns of workers moving into and out of the cluster industry?
2. Which types of skill profiles move among cohesive groups in the network?

Complex Systems Science aspects:

Embodied knowledge flows in cluster regions are complex to define and categorize, due to the multitude of job classifications, as well as factors such as age, gender, education, experience, tenure, and job function. Identifying patterns is the first step to understanding their impact on firm performance and cluster performance.

Possible societal importance/impact: 

This project aims to deepen our understanding of knowledge flows inside industrial clusters, which contribute to explain firm and cluster performance. Our findings will allow to improve policy designed to foster the creation or development of industrial clusters in economically deprived regions, thus avoiding common wasteful development policy schemes.

Initial literature: 

• Feldman M. The Entrepreneurial Event Revisited: Firm Formation in a Regional Context. Industrial and Corporate Change 2001;10:1-31.
• Hidalgo C., Balland P.-A., Boschma R., Delgado M., Feldman M., Frenken K. et al. The Principle of Relatedness. Papers in Evolutionary Economic Geography (PEEG) 2018;1830.
• Hidalgo C.A., Klinger B., Barabási A.-L., Hausmann R. The Product Space Conditions the Development of Nations. Science 2007;317:482-487.
• Klepper S. Spinoffs: A review and synthesis. European Management Review 2009;6:159-171.
• Klepper S. The origin and growth of industry clusters: The making of Silicon Valley and Detroit. Journal of Urban Economics 2010;67:15-32.
• Neffke F., Henning M., Boschma R. How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions. Economic Geography 2011;87:237-265.
• Zhu S., He C., Zhou Y. How to jump further and catch up? Path-breaking in an uneven industry space. Journal of Economic Geography 2017;17:521-545.

Short introduction (YouTube recording)

Representative: Dr. Erik van Sebille,  Institute for Marine and Atmospheric research Utrecht (UU)

Co-supervisor: Ir. Mikael Kaandorp, Institute for Marine and Atmospheric research Utrecht (UU)

Background of the problem                            

The plastic pollution littering our oceans has been one of the most visible signs of our society’s destructive footprint on natural systems. In the ‘Tracking Of Plastics In Our Seas’ (TOPIOS.org) project, we aim to map the whereabouts of this plastic in our ocean. We do this by creating computer simulations of virtual plastics. These simulations, however, need to be trained with data of observed plastic abundances in the real ocean. But these observations are buried in the scientific literature, and no complete and up-to-date databases exist.

Challenges:

The challenge is to build a data mining workflow that trawls through the peer-reviewed scientific literature and returns data of plastic observations in the ocean and on beaches, geo-tagged to their location and time of sampling.

Main questions to be addressed:  

1. How to automatically identify peer-reviewed papers that contain data on observations of plastic in the ocean and on beaches?
2. How to automatically parse that data into a database?
3. How to geo-tag the plastic observations to location and time of sampling?

Complex Systems Science aspects:

To answer the three questions above requires a wide range of skills in computer science and natural language processing, but also in geoscience and data interpretation.

Possible societal importance/impact:  

A publicly available workflow and database of plastic observations can form the basis of a map of the plastics polluting our oceans. Such a map would help with assessing the efficacy of policy/technological solutions to the plastic problem.

Initial literature:  

• Kaandorp et al (2020) on data assimilation of plastic observations into model for the Mediterranean Sea: https://topios.org/mediterranean and associated article at https://pubs.acs.org/doi/10.1021/acs.est.0c01984
• Litterbase portal of curated plastic observations: https://litterbase.awi.de/
• Law (2017) review paper about plastics in the marine environment: https://www.annualreviews.org/doi/abs/10.1146/annurev-marine-010816-060409
• Nasar et al (2018) on Information extraction from scientific articles: a survey https://link.springer.com/article/10.1007/s11192-018-2921-5
• Tkaczyk et al (2015) on automatic extraction of structured metadata from scientific literature https://link.springer.com/article/10.1007/s10032-015-0249-8

Short introduction (YouTube recording)

Representative: Dr. Yuliia Orlova, Van 't Hoff Institute for Molecular Sciences (UvA)

Background of the problem:

Systems chemistry studies transformations with large number of intermediate steps, as in the origin of life problem, for example. Aged oil paintings (think Rembrandt) is a natural laboratory for systems chemistry. They began with several distinct molecules and complexify to include several thousand of chemical species. Our graph automata can automatically ‘discover’ these species and compose a system of non-linear ODEs for reaction kinetics. This reaction network is similar to food web describing how prey and predators coexist in ecosystems: reactant molecules contribute their concentration to the product molecules, similarly to prey contributing its biomass to the biomass of the predators.

Challenges:

Challenges with the reaction network arise from its size. If a reaction network consists of n molecules, then the system of ODEs consists of n equations. The sizes of real world chemical systems grow to several thousands of molecules and few millions of reactions. Moreover, the parameters that describe the speed of reactions (rate parameters) have uncertainty.

Main questions to be addressed:  

1. Can the model be reduced based on the underlying structure of a reaction network?
2. How the uncertainty of rate parameters propagates through the solution of the model and influences final result?

Complex Systems Science aspects:

In this problem we view chemical processes as a complex system represented as a graph and a dynamical system. We provide the reaction network and a dynamical system for further analysis.

Possible societal importance/impact:  

The modeling approach is applicable to other complex chemical and biological processes, such as metabolic processes, protein interactions and prebiotic scenarios. Such systems are big and troublesome when it comes to optimization of system parameters against experimental data. Being able to perform model reduction and uncertainty quantification on such systems may help to improve the match to the experimental data.

Initial literature:  

• Y. Orlova, A.A. Gambardella, R.E. Harmon, I. Kryven & Iedema P.D. (2020). Finite representation of reaction kinetics in unbounded biopolymer structures. Chemical Engineering Journal, doi: 10.1016/j.cej.2020.126485.
• Dmitry Yu Zubarev, Dmitrij Rappoport & Ala ́n Aspuru-Guzik (2015) Uncertainty of Prebiotic Scenarios: The Case of the Non-Enzymatic Reverse Tricarboxylic Acid Cycle. Scientific Reports, 5: 8009, doi: 10.1038/srep08009
• Ralph C. Smith. Uncertainty Quantification: Theory, Implementation, and Applications. Volume 12 of Computational Science and Engineering. SIAM, 2013, ISBN: 1611973228, 9781611973228
• Keith Edwards, T.F.Edgar, V.I.Manousiouthakis (1998)  Kinetic model reduction using genetic algorithms. Computers & Chemical Engineering. Volume 22, Issues 1–2, 1998, Pages 239-246, doi: 10.1016/S0098-1354(96)00362-6
• PengZhao, Samuel M.Nackman, Chung K.Law. On the application of betweenness centrality in chemical network analysis: Computational diagnostics and model reduction. Combustion and Flame, Volume 162, Issue 8, August 2015, Pages 2991-2998. Doi: 10.1016/j.combustflame.2015.05.011

Short introduction (YouTube recording)

Representative: Dr. Claudia Wieners, Institute for Marine and Atmospheric research Utrecht (UU)

Background of the problem       

CO2 reduction is hampered by “free-riding” problems: Countries not reducing their emissions still benefit from other countries’ effort, so countries are tempted to do nothing themselves and let others do the effort – resulting in general inaction, unless collaboration arises [1].

By contrast, Geoengineering (large-scale interference with the climate system to limit global warming), while an imperfect “solution”, is considered cheap enough that individual countries could implement it, requiring no collaboration (“free-driving”).

While many fear that the prospect of Geoengineering reduces incentives to decarbonise, others [2] argue that the “threat” of unilateral Geoengineering might actually trigger decarbonization. Which is true?

Challenges:

Investigate the role of Geoengineering in international climate policy!

To this end, construct a simple model (expanding [1]) of several nations optimising their own (if egoistic) or global (if altruistic) welfare. Each nation has two options: CO2 reductions and Geoengineering. What are the possible - and the optimal - policy scenarios?

Main questions to be addressed:  

1. Will the availability of Geoengineering facilitate or prevent decarbonisation?
2. What is the best possible and the most likely policy outcome?
3. What is the effect of inequality in wealth, climate vulnerability, and exposure to geoengineering risk?

Complex Systems Science aspects:

Climate policy is interpreted as an emergent property of the behaviour of interacting agents (states).

Possible societal importance/impact:  

Geoengineering receives increasing attention as possible (partial) solution to climate change. However, it may cause environmental damage, crowd out decarbonization and trigger conflict. This projects aims to chart, in a stylised way, how the option of Geoengineering may affect climate negotiations.

Initial literature: 

• Brede and De Vries, “The energy transition in a climate-constrained world: Regional vs. global optimization”, http://dx.doi.org/10.1016/j.envsoft.2012.07.011 -> a multi-state climate policy model that can serve as starting point
• Fabre and Wagner, “Availability of risky geoengineering can make an ambitious climate mitigation agreement more likely”, https://doi.org/10.1057/s41599-020-0492-6 -> possible effect of geoengineering on climate negotiation in an extremely stylised setting
• Helwegen, Wieners et al., “Complementing CO2 emission reduction by solar radiation management might strongly enhance future welfare”, https://doi.org/10.5194/esd-10-453-2019 ->  possible ways to include geoengineering into climate-economy models
• Robock, “Stratospheric Aerosol Geoengineering”, http://climate.envsci.rutgers.edu/pdf/RobockStratAerosolGeo.pdf -> basic introduction to the most feasible Geoengineering scheme (SAI)
• Reynolds, “The Governance of Solar Geonegineering”, https://doi.org/10.1017/9781316676790 -> background information on Geoengineering, particularly legal aspects