Subject: [Depfis] Miercoles 6 de Marzo, Seminario Doble en Sistemas Complejos

From: Pablo Balenzuela <[email-masked]>
Date: Thu, 28 Feb 2013 10:14:18 -0300

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: [email-masked]
	Webpage: http://www.df.uba.ar/users/balen/wordpress

---

From: Pablo Balenzuela <[email-masked]>
Date: Tue, 05 Mar 2013 09:55:13 -0300

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

> Complex systems like a cell, the financial market or a society, 
> exhibit non-trivial
> observations or more specifically ZIpf’s law - that states that the 
> size of the k th most
> in physics. Since this is a very special point, this raises the issue 
> of what universal
> for solving both models is dimensional reduction (e.g. data 
> clustering). Different
> tent of different methods and compare them? What is the optimal level 
> of detail
> 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
> 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