Sergei Maslov

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Starting August 2015 I move to Urbana, U Illinois where I will be a Professor of Bioengineering and Bliss Faculty Scholar at the University of Illinois Urbana-Champaign (UIUC) with appointments at Carl Woese Institute of Genome Biology and National Center for Supercomputing Applications 

I will maintain a joint appointment at  Department of Biological and Environmental Sciences at Brookhaven National Laboratory on Long Island, 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. 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) 327-8222 (cell)
E@mail: ssmaslov at gmail.com
WWW: http://www.cmth.bnl.gov/~maslov

Photos:

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

 

 

Research 

Bio-networks Information Networks Older projects

Computational Biology

My current research is focused on computational systems biology. My collaborators and I analyzed large-scale experimental datasets and developed predictive models which are fundamentally important for a host of applications including Big Data, biomedicine, metabolic engineering for biofuels, emerging "omics" technologies, and synthetic biology. Below I highlight the findings from some of my published and submitted articles:

 In (Science 2002) my collaborators and I proposed general edge rewiring algorithms allowing one to detect and visualize statistically significant topological patterns in large networks. We applied them to yeast PPI and regulatory networks to demonstrate that hubs in these networks rarely directly interact with each other. Our algorithms are currently used and cited to analyze both biomolecular as well as neuroscience (connectome, fMRI) networks.

 In (Nucleic Acids Research 2005) we demonstrated that self-interacting proteins forming homodimers (or homooligomers) are overrepresented in PPI networks and have larger than average degrees. Our observations are important for understanding functional, structural, and evolutionary properties of protein-protein interactions.

  In (PNAS 2007) we proposed and simulated a genome-scale mass action model of the PPI network in baker's yeast taking into account experimentally determined protein concentrations, subcellular localizations, homo- and hetero-dimers and multi-protein complexes. This predictive model allowed us to quantify the cascading effects of changes in protein concentrations affecting stability of mass action steady state of genome-wide PPI networks. Variants of this model are relevant for cancer biology in understanding the control of apoptosis by Bcl-2 family of proteins and for biomedical applications of gene regulation by microRNA/ceRNA.

In (PRL 2008), (MSB 2008; PNAS 2011), and (PLoS Comp Bio 2013) we quantified the effects of, respectively, fluctuations in concentrations, non-specific interactions, and structural stability of proteins on genome-wide mass action dynamics of PPI networks.

 In (PNAS 2009) my collaborators and I proposed the "toolbox" model of co-evolution of metabolic and regulatory networks by Horizontal Gene Transfer (HGT) in bacterial and archaeal genomes. Our model explained a number of trends in properties of these networks with genome size. These insights into modular properties of bacterial genomes and networks are important for bioengineering and biomedical applications.

 In (PLoS Comb Bio 2011) we extended our toolbox model to include anabolic (biosynthetic) pathways. To this end we came up with a computational algorithm predicting the minimal biosynthetic pathway to add to the existing metabolic network of an organism so that it can synthesize a desired target metabolite. Algorithms proposed in this paper are relevant for synthetic biology applications.

  In (NAR 2011; PNAS 2013) we identified functional and evolutionary determinants of sizes of gene families and the frequency with which they are encoded in bacterial genomes. We plan to repeat this analysis on a much larger dataset of ~1018 pairwise comparisons of protein-coding genes in 30,000 sequenced genomes and ~200 metagenomes in the Big Data in Genomics project.

  In (JMB 2009 (journal cover); PNAS 2015) we developed scalable computational algorithms for deriving the basic genome - the consensus genome sequence of a bacterial species excluding variable segments and idiosyncratic genome rearrangements. Basic genome is essential for working with Big Data in bacterial genomics.

  In (PNAS 2015) we developed a suite of computational methods for analyzing Single-Nucleotide Polymorphisms (SNP) within this basic genome and separating vertically inherited, clonal segments from recombined (horizontally transferred) ones. For closely related pairs of E. coli strains, we identified a patchwork of long (10s to 100s kb) recombined segments interspersed among clonally inherited genomic segments. Once sequence divergence between strains exceeds ~1.3% clonal segments virtually disappear. Our results implicate generalized transducing phages in horizontal transfer of genomic segments between strains and suggest their importance in defining the boundaries of bacterial species. Biomedical applications of our findings include understanding the emergence and spread of pathogenic bacterial strains (e.g. E. coli) and of antibiotic resistance in bacterial populations.

In (Sci. Reports, 2015) we identified the optimal ("well-tempered") strategy for lytic-lysogenic transition by phages in fluctuating environments subject to episodic collapses. In (PLoS Comp Bio 2015) we modeled the dynamics of population waves of bacteria triggered by local extinctions of dominant populations e.g. caused by phage predation. Our models are relevant for the analysis of metagenomics data and for bioenergy applications such as e.g. preventing phage infections in bioreactors.

In (JCP 2015) we presented a general theoretical and numerical analysis of the problem of spontaneous emergence of autocatalysis for polymers capable of template-assisted ligation driven by cyclic changes in the environment. Our central result is the existence of the first order transition between the regime dominated by free monomers and that with a self-sustaining population of sufficiently long oligomers. Another key result is the emergence of the kinetically limited optimal overlap length between a template and its two substrates.

DOE Systems Biology Knowledgebase

I am a co-PI of Department of Energy Systems Biology Knowledgebase project (KBase), where I lead a team of scientists from  BNL and Cold Spring Harbor Laboratory.  In addition to Brookhaven KBase project involves scientists from 3 other national labs: Lawrence Berkeley, Argonne, and Oak Ridge, and from several universities. KBase is a 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

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 10, 2015