Sergei Maslov

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I am a tenured scientific staff member of the Biology Department, Brookhaven National Laboratory located on  Long Island in New York state. 

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. Keep in mind, however, that some of my recent papers didn't make it to the arxiv.

The members of my research group are listed here.

Work address:

Department of Biology, Brookhaven National Laboratory, Upton, New York, 11973
Tel: +1-(631) 344-3742; 
FAX: +1-(631) 344-3407 
E@mail:
WWW: http://www.cmth.bnl.gov/~maslov
http://www.bnl.gov/biology/People/Maslov.asp

Photos:

My recent Picasa albums can be found here. Some older photos are stored here

 

 

Research interests:

Bio-networks Information Networks Older projects

DOE Systems Biology Knowledgebase

I am a co-PI in the Department of Energy Systems Biology Knowledgebase project (KBase) where I lead a team of scientists from  BNL, Cold Spring Harbor Laboratory, and Yale University working on biomolecular networks and systems biology of plants.  In addition to Brookhaven KBase project involves scientists from 3 other national labs: Lawrence Berkeley, Argonne, and Oak Ridge. Kbase is an emergent software and data environment designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses on a scalable computing infrastructure, and model interactions in microbes, plants, and their communities. KBase provides an open, extensible framework for secure sharing of data, tools, and scientific conclusions in predictive and systems biology

Computational Biology

My current research is focused on selected topics in systems, computational, and evolutionary biology with particular emphasis on large-scale properties of complex biomolecular networks. These networks operate inside living cells on multiple levels including protein-protein binding interactions, transcriptional regulation, signaling, metabolic reactions, etc. 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 (PNAS 2011, MSB 2008, PNAS 2007, NJP 2007, Science 2002)?

How they achieve robustness against noise and perturbations (PLoS Comp Bio 2013, PNAS 2007, PRL 2008, Science 2002)? 

How topological properties of these networks affect their functioning inside living cells (PLoS Comp Bio 2013, 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 2013, NAR 2011, PLoS Comb Bio 2011, PNAS 2009, JMB 2009, BMC Evol. Biol. 2004, Nucleic Acids Research 2005, Biol. Direct 2007)? 

Genome-scale networks involve interactions among 1000s of genes/proteins. High-throughput experimental data describing these interactions are often noisy and incomplete. Statistical physics with its emphasis on scaling laws, general trends and correlations has many of the right tools to approach this type of data. In addition to genome-scale network models I am also working on detailed understanding of temporal dynamics of relatively small pathways (tens of genes). For example, together with my collaborators I modeled the time course of SOS response to UV-induced DNA damage in E. coli (PLoS Comp Bio 2007). 

Information Networks

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 (PNAS 2013, 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)? We  proposed a new algorithm, CiteRank, for ranking scientific publications by their relevance to current research directions.

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. 

Older projects

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.

This page was last updated on August 13, 2013