[Todos] Reminder: Miercoles 6 de Marzo, Seminario Doble en Sistemas Complejos

[Pablo Balenzuela]

Les recuerdo sobre las dos charlas de mañana,

14-15hs: Matteo Marsilli, Why do complex systems look critical?
15-16hs: Café y masitas
16-17hs: Andrea de Martino, Modeling cell energetics -- Redux

> Dos Charlas sobre Sistemas Complejos
>
> Lugar: Aula Federman, 1er Piso, Pab. 1, Departamento de Física, Ciudad 
> Universitaria
>
> Fecha: Miercoles 6 de Marzo.
>
> De 14 a 15hs: Matteo Marsilli, Why do complex systems look critical?
> De 15 a 16hs: Café y masitas.
> De 16 a 17hs: Andrea de Martino, Modeling cell energetics -- Redux.
>
> Títulos y abstracts:
> Matteo Marsilli*, Why do complex systems look critical?
>
> Complex systems like a cell, the financial market or a society, 
> exhibit non-trivial
> behavior. Often, empirical data obey a scale free distribution in the 
> frequency of
> observations or more specifically ZIpf’s law - that states that the 
> size of the k th most
> frequent observation should be proportional to 1/k. Mora and Bialek 
> translated this
> observation in statistical mechanics terms, by observing that systems 
> that exhibit
> this behavior are akin to critical systems, i.e. systems close to a 
> phase transition
> in physics. Since this is a very special point, this raises the issue 
> of what universal
> mechanism may be responsible for the self-organization to the critical 
> point.
> On the theoretical side, complex systems can be regarded as systems of 
> many
> degrees of freedom, that perform a function (i.e. optimize a goal 
> function). However,
> models can take into account only few variables and the interactions 
> among these.
> They necessarily neglect unknown unknowns. This raises a number of 
> issues: i)
> how can one choose relevant variables, how many should they be? ii) 
> under what
> conditions can the prediction of models match systems’ behavior?
> On the empirical side, one typically faces two problems: i) data are 
> noisy and
> ii) data most often under sample the space of possible states. A 
> convenient strategy
> for solving both models is dimensional reduction (e.g. data 
> clustering). Different
> methods, however, provide different results. Can one measure the 
> information con-
> tent of different methods and compare them? What is the optimal level 
> of detail
> (i.e. number of clusters)?
> After a brief (and biased) review of the problem, we discuss these 
> issues in a
> simple framework inspired by maximum entropy considerations. Our 
> arguments
> suggest that the under sampling regime can be distinguished from the 
> regime where
> the sample becomes informative of system’s behavior. In the 
> under-sampling regime,
> the most informative frequency size distributions have power law 
> behavior and Zipf’s
> law emerges at the crossover between the under sampled regime and the 
> regime where
> the sample contains enough statistics to make inference on the 
> behavior of the system.
> These ideas are illustrated in some applications, showing that they 
> can be used to
> identify relevant variables or to select most informative 
> representations of data, e.g.
> in data clustering.
> Preprint available at http://arxiv.org/abs/1301.3622
>
>
>
> *The Abdus Salam International Centre for Theoretical Physics, Strada 
> Costiera 11, 34014, Trieste, Italy
>
> -----------------------------
>
> Andrea de Martino*
>
> Modeling cell energetics -- Redux
>
> Cells live in contexts with limited resources, and evolution has 
> selected mechanisms that allow them to accomplish the tasks essential 
> for life by making optimal use of these resources. This idea spans 
> from one extreme of complexity of the cellular world (bacteria) to 
> another (cancer cells), and is, in essence, the basis of 
> constraint-based modeling, possibly the most successful approach for 
> unraveling cell energetics. Recent work aimed at correlating 
> energetics with regulatory properties has brought to light a number of 
> crucial facts that are not explained by current theories. To make 
> progress and understand whether evolution has found optimal solutions 
> requires us to dig deeper into the space of possible states of the 
> cell's metabolic networks, and into the nature of the constraints that 
> define it. We shall see how important hints derived from physical and 
> regulatory considerations lead to entirely new classes of models, 
> bearing a high potential for shedding light on the emergence of robust 
> `growth laws' as well as for applications.
>
>
> This is also based on very recent work of mine (basically, it's my 
> other main topic of research besides RNA metabolism); it is also very 
> suited for physicists and biological chemists. For references, see
> http://www.pnas.org/content/early/2009/02/05/0813229106.abstract
> http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0039849 
>
>
> *IPCF-CNR, Dipartimento di Fisica, Sapienza Università di Roma, Roma, 
> Italy.
>


-- 
	Dr. Pablo Balenzuela
	Departamento de Fisica, FCEyN,
	Universidad de Buenos Aires,
	(1428) Ciudad Universitaria,
	Ciudad de Buenos Aires, Argentina.
	TE +54 11 4576 3390 ext 817
	Fax +54 11 4576 3357
	email: balen en df.uba.ar
	Webpage: http://www.df.uba.ar/users/balen/wordpress