Complex Systems Summer School 2014-Projects & Working Groups

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Bitcoin

Bitcoin is an online peer-to-peer currency which has gained popularity in recent years. With increased general interest and now more and more companies accepting transactions in Bitcoin, so to has its value and is currently priced at $633 for 1 Bitcoin!

So what is Bitcoin?

The idea behind Bitcoin was to remove the need for a centralised banking system. The way this works is to have all transactions made public and then have the Bitcoin community validate transactions by donating computational power (and rewarded in Bitcoins). For further details see this blog:

http://www.michaelnielsen.org/ddi/how-the-bitcoin-protocol-actually-works/

So why is it interesting?

Usually companies and individuals keep all of their financial records private, but ALL transaction in Bitcoin are publicly available, so this provides a unique opportunity to study financial transactions. While the transactions are public, the owners of Bitcoin remain (relatively) anonymous, and so this has led to associations with criminal activities (e.g. on the silk road) from illegal drugs to hired hit-men. In addition to illegal activities using the currency, there have also been a number of illegal activities against the currency, such as high valued heists of the order of $100 million.

Oh, and of course its a big temporally evolving network.

If you're interested in working on this then add your name below. If there is interest, then we can have a "Bitcoin, beers and blackboard" session to throw some ideas around.

Interested:

Leto
Shai
Flavia (flamarquitti@gmail.com)
Jessica Santana jsant@stanford.edu
Stefan
Ana María
Brais (brais.alvarez@eui.eu)
James (jholdener@mitre.org)
George(qiaozhi827@gmail.com)
Sean Hayes (shaye004@ucr.edu)
Alberto (alberto.antonioni@unil.ch)

Meeting today (Jun 12) 6pm in the main conference room.

MITRE Data Sets

To access the data please contact Juniper she has it on a hard drive. Here is a PDF that explains the datasets and gives some sample challenge questions. MITRE DATA PDF If you have any specific questions about the data you can contact Matt Koehler at mkoehler@mitre.org

Death in physical, biological and social systems

Firms, nation states, human beings and stars all die. Do the causes of "death" in physical, biological and social systems have something in common? If yes, what is it?

Interested

Ana María
Vipin (vipin.veetil at gmail dot com)
Emilia
Luis Martínez (fnxabraxas@gmail.com)

Fractal-like structures in economic data

In the 1960-70s Mandelbrot showed that some economic time series have fractal-like structures, i.e. they look the same at many time scales. The existence of these structures has been debated since. Do economic time series like S&P 500 index have fractal-like structures? If yes, how fractal-like are they?

Interested

Vipin (vipin.veetil at gmail dot com)
Blaz
Qiao Zhi

Microbial Community Data Sets

The Earth Microbiome Project EMP is a massively multidisciplinary effort to analyze microbial communities across the globe. The general premise is to characterize the Earth by environmental parameter space into different biomes and then explore these using samples currently available from researchers across the globe. All data sets are processed in the same way (DNA extraction, PCR primers, sequencing, bioinformatics), making them inter-comparable. You can explore these data sets (including some time series, and a bunch of spatial samplings) at the following link EMP Data (no need to create a login ID, just scroll down to 'Download Public Data'). If you have questions, please contact Sean Gibbons (sgibbons at uchicago dot edu).

Does Larger Memory Capacity Brings about Evolutionary Advantage?

Evolutionary game theory modeling. Agents/players on lattice or networks. A player with n-step memory has responses to all 4^n past game outcomes. Intuitively, a player with longer memory can have more sophisticated strategy, which might be used to exploit player with smaller memory capacity. Yet according to the Prisoner's Dilemma tournament organized by Axelrod, Tit-for-Tat, which can be modeled using only one-step memory, fares better than a number of sophisticated strategies invented by experts in the field of game theory. From the game theory perspective, does smaller memory capacity actually have evolutionary benefits?

Interested:
Degang Wu (dwuab@ust.hk)
Blaz
Brian
Flavia (flamarquitti@gmail.com)
Cole Mathis (cole.mathis@asu.edu)
Claudius
Luis Martinez ((fnxabraxas@gmail.com)

North American Breeding Birds Survey and tropical trees

This dataset contains ~4500 sites where populations of birds (~600 species overall, ~60 species on average in every site) were sampled over the past 44 years. This gives numerous time series of both population sizes and the overall number of species. Some problems with this dataset include large observational errors. A dataset of tropical trees with the exact diameter, identity (from among 300 species) and location of ~250000 trees over 6 censuses is also available. Please contact Michael Kalyuzhny for these.

The Multiplex Networks

How does the structure of social networks affect the emergence and persistence of norms? Why are some norms (like fashion styles) less persistent than others (like religious beliefs)? Is this because different kinds of norms live on different social networks (with same individual participating in different networks)? If yes, how do these social networks interact?

And finally, what do the answers to the above questions tell us about policy interventions? Can certain critical properties of network structures be exploited to change norms, like going to the moon without much fuel? If two networks interact, say religious belief and fashion styles, can interventions in one be used to bring about changes in another?

Meeting: today at 4h15 in this room

Interested

Vipin (vipin.veetil at gmail do com)
Sarah L
Alberto
Francesca
Nhat (nhattdnguyen@gmail.com)
Sanja (sanjakojasanja@gmail.com)
Massimo
Leto
Alireza (alireza.goudarzi at gmail.com)
Claire (lagesse.claire at gmail.com)
Cecilia (ci.andreazzi@gmail.com)
Sean Hayes (shaye004@ucr.edu)
Claudius
Luis Martínez (fnxabraxas@gmail.com)
Brais (brais.alvarez@eui.eu)

Network Tolerance of Failure

How might a network endure non-catastrophic failure without isolating the failing components? Most network failure models consider resilience against failure as a result of isolating failing components. In contrast, is it possible for the network to be robust through "tolerance" of failure? Perhaps, for example, a symbiotic relationship sustains a weakened node until it has recovered its prior performance. Or perhaps a transmitting network retains a connection to an offline node to reduce an anticipated memory load of re-establishing the connection when it comes back online. What are some of the ways in which networks are able to maintain connection to a failing node without failing themselves in the process - how is the cascade halted without isolation? This is meant to be a broad question to generate more specific ideas. Importantly, this question refers to "tolerance of" failure in contrast to "resilience against" failure.
Contact: Jessica Santana jsant@stanford.edu

=> It may be interesting to at least discuss (if not merge) this with the project we're proposing (not yet on here but it involves the optimal design of the isolation approach as a function of a measure comprising the network topology, node properties, node-saving attributes of a regulator etc.). There's also the last project on this page dealing with topology and resilience which could also be incorporated. - Nicolas

Interested:

James Holdener (jholdener@mitre.org)
Junjian Qi
Ells Campbell
Leto
Alireza (alireza.goudarzi at gmail.com)
Claire (lagesse.claire at gmail.com)
Sean Hayes(shaye004@ucr.edu)
Ali Kharrazi
Nicolas K. Scholtes

• For those interested in this project, we will be meeting at 1 pm in the cafeteria with a related project to determine how to split up the groups. Cheers, Jessica*
  

Influence of different types of parasites and pathogens in networks on dynamics and stability of food webs

In nature there is a number of infectious agents which have different evolutionary approaches in way how they influence their hosts. We want to built artificial ecological network and to compare how these evolutionary solutions affect stability of food webs.
Contact: sanjakojasanja@gmail.com

Interested:

Stojan (stojand@mpib-berlin.mpg.de)
Nhat (nhattdnguyen@gmail.com)
Sanja (sanjakojasanja@gmail.com)
Cecilia (ci.andreazzi@gmail.com)
Hey people can you please update your e-mails so we can be easier in contact?

Growth of Cities

Do foraging animals and growing cities utilize resources in the same way? We're interested in building an agent-based model which generates a road network on a map of varying resources by following a set of simple, probabilistic rules. How do the properties of this network evolve through time? How much of city growth can be explained by resource constraints? Do simple rules of growth parallel simple rules of animal foraging behavior? This project will explore agent-based modeling, but will also present opportunities to examine the limits of modeling. Contacts: Diana LaScala-Gruenewald (dianalg11 at gmail.com) and Claire Lagesse (lagesse.claire at gmail.com).

Interested:
Morgan Edwards
Rohan Mehta
Alberto Antonioni
Ernest Liu (yu.liu@math.uu.se)
Michael Kalyuzhny
Hiroshi Ashikaga (hashika1@jhmi.edu)
Bernardo Furtado bernardo.furtado at ipea.gov.br
Luis Martínez (fnxabraxas@gmail.com)
Alex Brummer
James Holdener (jholdener@mitre.org)

• >> For those interested in this project, the next meeting is Thursday 6/12 at 6pm in the cafeteria

Towards a Unified Theory of Biodiversity

The idea is to build on the Unified Neutral Theory of Biodiversity and create a realistic unified theory that incorporates important scaling phenomena following power laws, energetic constraints, and stochasticity that have been previously neglected. Basically, we want to modify the unrealistic assumptions that birth, death, speciation rates are stochastic uniform functions across all species within a given meta-community. In fact, these processes have recently been shown to scale with body size following power laws of the form Y= C * M^alpha, where C is a constant independent of body mass, M, and alpha is the scaling exponent. These constraints will also dictate how much energy is required at different trophic levels and body sizes, ultimately constraining abundance of organisms in natural systems. Stochasticity will still play a role, but it should be first bounded by energetic constraints. We also plan to incorporate environmental noise, that is to say, incorporate in the model the realistic assumption that the environment changes through time and therefore so will the fitness of different species in the meta-community. The ultimate goal is to provide a Unified Theory that can make clear predictions about size-abundance-distributions in natural systems, and, perhaps, also make predictions about speciation-extinction dynamics. We currently hold data to test predictions on an 'ecological' time scale. At this point we are uncertain on whether we could obtain good fossil/contemporary data to calibrate/test models.

Interested:
Diego Barneche Rosado (diego.barneche@mq.edu.au)
Cornelia Metzig (cornelia.metzig@hotmail.com)
Michael Kalyuzhny (michael.kalyuzhny at mail.huji.ac.il)
Ana María

Coupling of different types of networks

We want to develop a concept of how to work with networks of qualitatively different types of relationships or interactions that can influence each other (eg. natural and social). Sign up and come to brainstorm with us :) Contact: sanjakojasanja@gmail.com

For example, people's beliefs about the health benefits/risks of vaccination can be influenced by their social network, and may be studied using belief propagation models. Simultaneously, diseases may spread through a population, which can be studied using diffusion or other epidemiological models. Furthermore, people's beliefs about vaccination may affect their probability of getting infected by a disease, and in turn, getting infected may cause them to re-evaluate their beliefs.

Another example could involve an ecological network expressing predation and competition among species coupled with an environmental network.

Interested:
Sanja
Fahad
Brian
Nhat (nhattdnguyen@gmail.com)
Beth Lusczek
Glen Otero

Co-evolution of Anti-vaccination Sentiment and Flu Infections

People's beliefs about the health benefits/risks of vaccination can be influenced by their social network, and may be studied using belief propagation models. Simultaneously, diseases may spread through a population, which can be studied using diffusion or other epidemiological models. Furthermore, people's beliefs about vaccination may affect their probability of getting infected by a disease, and in turn, getting infected may cause them to re-evaluate their beliefs.

Previous work has separately studied (1) how beliefs propagate and change over time, (2) how diseases spread through a population over time, and (3) the (static) correlation between beliefs about vaccination and infection rates; but possibly not all three simultaneously.

This project has some similarities to the project on "Coupling Different Types of Networks," and it may make sense for the two groups to be in communication with each other, or even to merge the two projects. This can be discussed later as the projects develop.

Interested:
Ells
Nhat
Glen
Andy
Brian
Alberto

Consciousness as an emergent state of matter – what do you think?

You’re conscious right now, reading this. How does subjective experience emerge out of the bundles of particles that we all are?

Scholars of many fields have been deconstructing the mind/body dualism for a while, but consciousness remains a big, hard question. I’m no expert and by proposing this as a topic I’m not expecting that any of us will solve it, but I would be very interested in exchanging on the issue with the smart individuals that you are, grounded in so many backgrounds and unafraid of complex problems.

So... physicians and physicists, social scientists, biologists, mathematicians, philosophers, computer scientists and others… what do you think? Don’t hold back – if beer is necessary for you to address this issue, it can be arranged.

Contact Sarah L (laborde.7@osu.edu)

Interested:
Alireza (alireza.goudarzi at gmail.com)[totally agree with writing down something to figure out what are plausible ways to think about and study this]
Fahad (fahad.khalid@hpi.uni-potsdam.de) [love the topic ... I'm glad someone brought it up ... I might have some ideas to contribute]
Claire (lagesse.claire at gmail.com) [it sounds fascinating... :) ]
Stefan (s.pfenninger12@imperial.ac.uk)
Hiroshi (hashika1@jhmi.edu) [Happy to contribute from a medical and personal point of view]
Sean Hayes(shaye004@ucr.edu)
Cole Mathis (cole.mathis@asu.edu) [The origin and consciousness and the origin of life (my main research interest) have a lot in common, I'm always down to talk about consciousness with some beers, if something novel emerges that's great.]
Emília [happy to contribute from various points of view, but I advance that, to me, it has a lot to do with memory]
Ana María (anamaria.gomezlopez@yale.edu)
Brian (bthompso8784@gmail.com)
Sean Gibbons (sgibbons at uchicago dot edu) [Definitely interested in joining the conversation]
Matthew Ayres (matthew.ayres@growthandinnovation.com.au)
Luis Martínez (fnxabraxas@gmail.com)
Beth Lusczek (lusc0006@umn.edu)
Stojan (stojand@mpib-berlin.mpg.de) (sounds perfect for a beer discussion)
José Aguilar-Rodríguez (jose.aguilar@ieu.uzh.ch)
Bernardo Furtado (bernardo.furtado@ipea.gov.br)(Happy to learn. Consciously on subconsciously.)
Nicolas K. Scholtes (nicolas dot scholtes at unamur dot be) [The science fiction aficionado in me is jumping up and down in his chair!]
Note: this doesn’t have to become a formal project, although it could. Let me know if you’re interested in a chat, writing an interdisciplinary dialogue piece, or anything related to this question.

• An after hour catch-up on Friday evening or this week-end? Here's a basic doodle poll to work out times: http://doodle.com/yi5wp3brbd2z8u5n
  

Tradeoffs between division of labor and stability in networks

The Black Queen Hypothesis BQH describes the evolution of functional dependencies in microbial ecosystems. This process results in a subset of a community providing necessary services for the rest of the community (see link). Organisms that can outsource essential functions escape the cost performing these functions and have more resources for growth and reproduction. However, this process makes the ecosystem more delicate, as the destruction of key species can eliminate their crucial service(s) and lead to system collapse. Thus, there is a tradeoff between the stability of a network (in the face of perturbations), and the degree of cooperation (how many tasks can be outsourced). As a result of this tradeoff, we could expect different community types to arise in stable vs. variable environments. There is likely some critical range between these two modes (high vs. low environmental variability), where some mixed strategy is optimal.

This process may have an analogue in the development of multicellular life (vs. free-living single-celled organisms), where each cell type expresses a subset of the genome and provide a specific set of services to the whole (division of labor). This might also be reflected in social or economic networks (higher stability = more cooperative?). A connection could potentially be made to life-history tradeoffs for individual organisms (r vs. k selection - oligotrophs vs. copiotrophs), or in dissipative chemical systems (e.g. Stat-Mech of Self Replication).

Definitely a work in progress, please add your thoughts if you are interested! Also, please include your contact info alongside your name.

MEETING TIME: June 12 @ 4:15PM - outside lecture hall

• Thoughts:

Vipin: I believe that this mechanism may have much to do with why "business cycle" occur.

Luis: Maybe this could be also related with the structure and complexity of institutions and its stability. Successful societies increase their size and develop more complex institutions (with higher level of bureaucracy for example) and that can make them less "flexible" and susceptible to get destroyed under not predicted crisis. I think this is related with "social entropy"...

• Interested:
  

Sean Gibbons (sgibbons at uchicago dot edu)
Rohan Mehta
Stojan (stojand@mpib-berlin.mpib.de)
Emília (emilia.garcia.casademont at gmail dot com)
Ali Kharrazi
Vipin (vipin.veetil at gmail dot com)
Luis Martínez (fnxabraxas@gmail.com)
Ernest Liu (yu.liu@math.uu.se)
Cole Mathis (cole.mathis@asu.edu)
José Aguilar-Rodríguez (jose.aguilar@ieu.uzh.ch)

How can evolutionary game theory be applied to electricity trading

The interest of this project is the following: how can evolutionary game theory be used to find optimal strategies for consumers and/or producers that bid in the electricity market. Also some real data can be considered.
Contact Blaz

Interested:
Degang Wu
Alberto

Information Theory of the Heart

The heart is a complex system with simple rules of operation and minimal central control. Under normal conditions, it orchestrates a self-organized, emergent behavior of 2 to 3 billion heart cells to perform sophisticated, well-timed pumping of the blood. Under abnormal conditions, it can lead to sudden cardiac death due to cardiac arrhythmias, which are also emergent, collective behaviors of a large number of heart cells, where each heart cell doesn't necessarily need to be abnormal.

Each heart cell is a dynamic information processing system, which transmits digital information (0 – resting, 1 – excited) in the form of electrical wave. We aim to establish a theoretical basis to quantify information transmission within the heart using information theory and network theory. Our hypothesis is that arrhythmias following heart attack result from an adaptive mechanism to optimize information transmission in abnormal hearts.

If you want to discuss more please contact Hiroshi (hashika1@jhmi.edu).

Interested:
Hiroshi
Flavia (flamarquitti@gmail.com)
Brian
Bernardo Furtado
José Aguilar-Rodríguez (jose.aguilar@ieu.uzh.ch)

Cells and Software: Is Evolution a Software Engineer?

There appear to be striking similarities between how we design software, and how evolution designed cells. Some of the analogies are:
- The concept of "Encapsulation". In object oriented programs, data inside an object is protected by an interface of functions. Similarly, processes within a cell (intra-cellular signaling cascades) are protected from the extra-cellular messaging activities through membrane-bound receptors acting as the interface.
- Apoptosis (programmed cell death) is similar to proper memory deallocation in programs, while Necrosis is similar to dangling pointers and memory corruption.
- Proteolysis (breaking down of proteins into constituent amino acids) is similar to automatic garbage collection.

There are certain constraints (perhaps physical constraints) on the available solutions in biology. Solutions within this constrained solution space eventually result in the emergence of complex behavior. This emergent behavior has enabled humans to engineer solutions to everyday problems. These artificially engineered solutions are very similar in principal to the solutions that already exist in biological systems.

Are our creative processes ultimately bound by the physical constraints that underlie molecular mechanisms? Or, do we just tend to interpret the phenomena at the molecular level according to our own understanding, which is limited by our senses and neurological processes? Is it all about optimization, and everything else is just a side effect? Can/do “Patterns” transcend disciplines?

Motifs (biological systems) – Patterns (software). Patterns/motifs transcending biological and software systems could perhaps, fill gaps in our knowledge of biological systems, and help us design better software systems. Deterministic patterns could perhaps indicate what is required for high level functions to emerge from molecular interactions. And perhaps these similarities occur at the interface between low level interaction and emergent phenomena.

So let's further explore biological and software systems, and try to find answers to these questions.

For more information, please contact Fahad (fahad.khalid@hpi.uni-potsdam.de).

Interested:
Fahad
Ernest (yu.liu@math.uu.se)
Diana
Degang (dwuab@ust.hk)
Stojan
Ana María
Brian
George (qiaozhi827@gmail.com)
Glen Otero

Susceptibility of Fields of Research to Interdisciplinary Influences (network perspective)

Fields of Research are more often than not isolated from one another in terms of their community, jargon, perspective, research programme (in the sense of Lakatos) and their journals and lots more.

This isolation stands in the way of a fruitful merging and interaction of fields. One needs to understand the obstructions. Hence an analysis of the determining factors for 'community inbreeding' is sought after. Eventually also a quantitative measure for the susceptibility of a research field could be formulated.

A clear-cut project could be based on data together with a network type analysis of a precise question around the determining factors. We could look at citation data or journal based data or maybe there is a chance to get hold of data from Jessica's collegues, who works on the diffusion of knowledge.

It would be great also to discuss and gather our ideas and experiences.

Meeting: June 12, 4:20, balcony outside Great Hall.

Interested
Leo (horstmey@mis.mpg.de )
Jennifer Hellmann
Anna Olson
Lin Li
Stefan Pfenninger [other angles include: citation networks/academic generations and how they change through time (Diego's idea); citations between subfields to quantify interdisciplinarity and how it changes over time]
Catherine Bale

Simple case for studying about “from single-cell to multi-cell / species collaboration”

QUESTION: how single-cell evolves to multi-cell, even they do not have so-called “intelligence”?

How to do: Let’s imagine that there is a land where resources A, B, C are located, and a kind of creature which need all of these resources to survive (imagine many creatures live in this land).
One surviving strategy is that every creature gathers A, B and C and eat them on its own. But maybe under some condition, specialization would automatically happen (e.g. one creature gather A and another gather B, and then they share).
The goal is to find the necessary condition if possible (we could think out of many sufficient conditions, but finding necessary conditions seems not so easy).

Interested:
Ernest Liu (yu.liu@math.uu.se)

comment: are you familiar with holland's "echo" stuff ..stojan

COMBINED with project 17 "Tradeoffs between division of labor and stability in networks".

An Analysis of the Hierarchies Present in Modern Economic Theory

The idea of this project is to provide a comprehensive survey of the hierarchical structure of economics (macro -> microeconomics) following the reductionist = constructionist + emergence framework of Anderson (N.B. This equation is my own interpretation of Anderson's paper and is open for discussion). Ideally, we would first investigate the microfoundations literature of macroeconomics and the possibility for ABM to rectify some of the redundancies therein after which we would reverse the direction and see how, given some 'fundamental' laws of economic behaviour, we could couple these with emergent phenomena (whatever they may be) to reconstruct the economy at the macro-level.

Interested
Nicolas ( nicolas dot scholtes at unamur dot be)
Claudius

The Topological Modeling of Infrastructure Networks

There is some disagreement on the topology of power networks in the literature. There are preferential attachment, small-world and random graph models proposed to explain the topology of power networks. There are caveats associated with each of these generative models. For instance, there is the cost of building new transmission lines that may question the validity of preferential models.

The broad question that we attempt to answer here is:

-Can these previous network models create real-world power networks?
-Can we come up with a better model that can replicate a power network or at least one with similar characteristics?
-What about other infrastructure networks, e.g., natural gas network, water piping networks, etc?

Interested: Pooya Rezaei
Tom McAndrew
James Holdener (jholdener@mitre.org)

Functional Networks and their evolution

We are interested in researching (possibly hierarchical) networks that have a function which can be quantified. We are interested in understanding how such networks evolve and may continue to deliver all or part of their function when they are damaged. As of now, we do not have a clear conceptualization of these ideas in formal network language, and our immediate task is to think of the formal network structure we wish to explore (e.g. b-partite, multileveled). Possible concrete cases to apply this formal characterization to are trees (i.e. plants), power grids, or financial networks.

Interested (please contact us to join the debate):

Alex Brummer (brummera@email.arizona.edu)
Shai Gorsky (shai.gorsky@utah.edu)
Beth Lusczek (lusc0006@umn.edu)
Stefan Pfenninger (s.pfenninger12@imperial.ac.uk)
Jessica Santana (jsant@stanford.edu)
Anna Olson (olsona at cs dot uchicago dot edu)
Nicolas K. Scholtes (nicolas dot scholtes at unamur dot be

Multi-scalar approaches to understand regime shifts in a socio-ecological system

Field-based research, be it qualitative or quantitative, often yields fragmentary and limited data sets when collected in settings with political instability, economic disparity, and rapidly changing environmental conditions. Using the Logone river floodplain in Cameroon as a case study, this project aims to carry out multi-scalar analysis of fragmentary data sets to understand larger social dynamics and ecological regime shifts in this region. Our goal is to address the challenges of network analysis, agent-based modeling, and game theory in this context by examining multiple units of analysis at different scales.

Some of the possible issues to study are: common pool resources problems, influence of (corrupt) institutions, influence of the internal structure of these societies, etc.

We will attempt to meet at 6:00pm today Thursday in the lecture room.

Sarah L
Ana María
Claudius
Luis Martínez (fnxabraxas@gmail.com)
Ali Kharrazi
Francesca Lipari

The architecture of an empirical genotype-phenotype mapping

One of the central open questions in biology is to understand how genotypes map onto phenotypes. While system and developmental biologists are interested in the physical, biochemical and physiological basis of genotype-phenotype maps, evolutionary biologists try to comprehend their evolutionary causes and consequences. Our current knowledge on this question comes from computational models that allow us to rapidly map genotypes to phenotypes for some biological systems, facilitating the systematic exploration of their vast genotype spaces. These models have shown that many genotypes usually map onto the same phenotype. These genotypes form genotype networks, or neutral networks, that spread into genotype space. These networks contain as vertices all the genotypes that share a same phenotype, where genotypes are connected by edges if they differ by a single mutation.

Thanks to several computational models, the formalism of genotype networks has been very successful at providing new insights about the evolution of systems as diverse as RNA, proteins, regulatory networks and metabolism. Payne & Wagner have recently pioneered the application of this formalism to transcription factor (TF) binding sites. TFs are DNA-binding proteins that regulate gene expression by binding to short sequences on DNA — TF binding sites — that are in close physical proximity to the genes’ coding sequence, thus inducing or repressing gene transcription. The set of DNA sites bound by a particular TF can be viewed as a genotype network. This mapping from TF binding sites onto their cognate TFs constitutes the first exhaustive genotype-phenotype map entirely based on experimental data.

Research questions that can be asked in this system:
1. What is the geometry of empirical genotype networks in genotype space and how that geometry affects evolutionary exploration?
2. What is the community structure of these empirical genotype networks? Is this structure determined by the biophysics of TF-DNA binding?
3. How accesible is one phenotype from any other phenotype in this system and how that accessibility defines phenotype space as a topological space?

If you are interested please contact José (jose.aguilar@ieu.uzh.ch).

References:
1. Stadler, B.M., Stadler, P.F., Wagner, G.P., and Fontana, W. (2001). The topology of the possible: formal spaces underlying patterns of evolutionary change. J. Theor. Biol. 213, 241–74.
2. Payne, J.L., and Wagner, A. (2014). The robustness and evolvability of transcription factor binding sites. Science 343, 875–7.

Interested:
Cole
Glen
José
Leto
Massimo
Renske

Network warping via Chaotic Dynamics

There has been an explosion of research in temporal networks, and in Chaos Theory separately. Why not combined them? The idea is this: Generate different network structures (Erdos-Renyi etc.) whos probability of connections depends on a Chaotic attractor.

Are there topological invariants on these networks?

Interested: Tom McAndrew
Sean G. (sgibbons at uchicago dot edu)
Anna Olson (olsona at cs dot uchicago dot edu)

Network robustness as a function of nodes' resilience

Are there characteristics of a network's nodes that make the network more or less robust to failure? Perhaps degree assortativity, or nodes' positions relative to the demands made on them? Given that network failure can affect every node in a network, nodes might, as it were, gain an advantage from being positioned far away from nodes that could catastrophically fail.

Interested:
Anna Olson (olsona at cs.uchicago.edu)
Jenn Hellmann
Leo Horstmeyer