School on Physics Applications in Biology

January 22 – 27, 2018

São Paulo, Brazil


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Although new experimental technology is partly responsible for the current revolution in biological research, theoretical models developed by physicists are also playing an important role in changing the way biological research is being performed. The number of theoretical physicists and applied mathematicians migrating into biology has dramatically increased throughout the world, but South America still lags behind.

The one-week school on Physics Application in Biology will feature lectures by physicists and applied mathematicians who have made important contributions to different areas of biological research. This activity will include minicourses, discussion sessions and group exercises on topics including neuroscience, evolutionary dynamics, time- series analysis applied to ecology and epidemiology, collective behavior, and optimization. The school is intended for graduate students and postdoctoral researchers in the physical and biological sciences.

This activity will be preceded by the ‘VII Southern-Summer School on Mathematical Biology’. Candidates may apply either for one or both schools, and preference will be given to PhD students in South America. There is no registration fee and limited funds are available for travel and local expenses.

In order to have an idea of the kind of activities that take place during the course, please visit the home-page of the first edition of this school at


  • Marcus A. M.  Aguiar (UNICAMP, Brazil)
  • Nathan Berkovits (ICTP-SAIFR/IFT-UNESP, Brazil)
  • Marcel Clerc (Universidad de  Chile)
  • Roberto Kraenkel (IFT-UNESP, Brazil)
  • Paulo Inácio Prado (USP/SP, Brazil)


  • William Bialek (Princeton University, USA)

1.Statistical mechanics for networks of real neurons (or, Thinking about a thousand neurons)

We have made some significant progress here:  (a) Showing that we can build Ising models for patterns of activity deep in the mammalian brain – models that connect to many details of the real data, and much more successfully than more complex, “biologically motivated” models. (b) Developing RG-inspired approaches to more complex systems, including methods for analyzing experiments on 1000+ neurons, and seeing hints that real networks are described by non-trivial fixed points.

2.Coding and information flow in a small genetic network 

We have continued to work on the genetic networks relevant in the early fly embryo.  We can now decode the signals that are carried by combinations of gene expression, showing that these signals are of remarkable precision and that the optimal decoding algorithm provides a parameter-free, predictive theory of the distorted body plans that we see in a large class of mutants.  Also, we are developing a deeper layer of analysis – measuring correlations in the system noise, exploring the role of these correlations in making possible an error-correcting code, and thinking about how to make the transition from continuous signals to discrete cellular identities.

The physics of flocking: correlation as a compass from experiments to theory

  • George Sugihara (UC San Diego, USA)

An Introduction to Empirical Dynamics: Equation-Free (Minimalist) Nonlinear Mathematics for a Data-driven Understanding of Nature — Transforming Observations to Insights  

My aim will be to provide a particular perspective that may be of special relevance as we move away from simple 20th century reductionist toy models based on fundamental principles, toward trying to understand how messy natural systems behave. All this is being made possible by the era of Big Data. 21st century holistic science is being enabled by a boon in available data. The math itself is not especially challenging, however the resonance of understanding that can be achieved with a deeper understanding of the implications of simple assumptions like equilibrium, linearity etc. can be significant.



January schools_v2

Online application is now closed



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