I am a tenured scientific staff member
of the Theory Group, at the Department of
Condensed Matter Physics, Brookhaven National Laboratory located on Long Island in New York.
My publication/citation record
from the ISI Web of Science can be found by following this link:
(roll over the icon for a quick
peek).
The detailed scientific track
record is in my Curriculum Vitae which I am trying to keep up to date.
To quickly search for my recent preprints on arxiv.org
you can follow
this link. Some
of my recent more biological papers didn't make it to arxiv.
The members of my research group are listed here.
Work address:
Department of
Condensed Matter Physics and Materials Science, Brookhaven National Laboratory, Upton, New York,
11973
Tel: +1-(631) 344-3742;
FAX: +1-(631) 344-2918
E@mail: 
WWW: http://www.cmth.bnl.gov/~maslov
Home address:
40 Suffolk Down, Shoreham, New York, 11786
(631) 821-4505 (h);
(631) 579-9559 (cell)
Photos:
My recent Picasa albums can be found here.
Some older photos are stored here.
|
Research interests:

Throughout most of my scientific
career I was working in the highly interdisciplinary field of statistical
physics of complex systems.
My current
research concentrates on topics in systems and
computational biology with particular
emphasis on properties of complex biomolecular
networks. These networks operate inside living cells on multiple
levels including protein-protein binding, transcriptional regulation,
signaling, metabolism. I am interested in a broad range of questions
including:
 |
How
biological networks minimize the undesirable crosstalk and limit the
effects of non-specific interactions (MSB
2008, PNAS 2007, NJP
2007, Science 2002)? |
 |
How they
achieve robustness against noise and perturbations (PNAS
2007, PRL 2008, Science
2002)? |
 |
How
topological properties of these networks affect their functioning inside
living cells (Nucleic Acids Research
2005, Phys.
Biol. 2007, BMC
Bioinformatics 2006, PRL
2004, Science 2002)? |
 |
How
bio-molecular networks and underlying genomes change in the course of
evolution (PNAS 2009, JMB
2009, BMC Evol. Biol. 2004,
Nucleic Acids Research 2005, Biol.
Direct 2007)? |
Understanding
complex biomolecular networks is an essential part of systems
biology defined as
"a comprehensive, quantitative analysis of the manner in which all
components of a biological system interact functionally over time" (Aderem, Cell 2005)
.
Systems biology involves modeling on two scales:
 | Detailed dynamical modeling of small pathways (<10 genes). For
example my collaborators and I modeled the time course of SOS response to
UV-induced DNA damage in
E. coli (PLoS Comp Bio
2007). |
 | "Network biology" investigating interactions among 1000s of genes/proteins.
Experimental data are often noisy and incomplete - connection to statistical
physics through study of scaling laws, general trends and patterns,
correlations. |
I worked on both levels of systems biology with recent detours into
genomics and evolutionary biology.
I am also
interested in emergent properties of large information
networks. These networks connect routers in the Internet, link
webpages, or scientific publications to each other, etc.
My recent research on information networks focused on the following topics:
 |
How to
detect functional units or modules/communities in complex information (or
biomolecular) networks (PRL 2003, Physica
A 2004, Physica
A 2007)? |
 |
How to
efficiently search and rank the information contained in large networks (J.
of_Stat_Phys 2007, J. of Neuroscience
2008, J.
of Informetrics 2007, Physica
A 2007, PRL 2001)? Together
with my graduate students and collaborators from BU I recently proposed
a new algorithm (CiteRank)
ranking scientific publications by their relevance to current research
directions. If interested, here you could look
up the ranking of any paper published between 1893 and 2003 in any of American
Physical Society journals (PR, PRL, PRB, PRC, PRD, PRE, Reviews of Modern
Physics, etc.). |
In my studies I
often (but not always) use the tools of theoretical statistical physics.
Even more important than tools, physics taught me the power of simple models
in revealing the essence of complex phenomena. Simple
models are indispensable if one wants not just to reproduce the complexity of
a system (e.g. by a detailed computer simulations) but to truly understand
it.
Before concentrating on complex
networks I worked on a variety of topics including (in reverse chronological
order) Econophysics, Low-dimensional
magnetism, and Self-Organized Criticality. |