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Who am I?
I am a professor and Bliss Faculty Scholar at the University of Illinois Urbana-Champaign (UIUC) with faculty appointments at Departments of Bioengineering, Department of Physics, Carl R. Woese Institute for Genomic Biology, and National Center for Supercomputing Applications.
I also hold a joint appointment at Argonne National Laboratory, Computing, Environment, and Life Sciences (CELS) directorate, where I work on Deep Neural Networks for cancer drug predictions.
My publication/citation record from
Google scholar is here and that compiled by the ISI Web of Science (Researcher ID) is below:
(roll over the icon for a quick peek or click on it to be redirected to the Researcher ID website).
A complete 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.
I also upload preprints to bioRxiv. Check them out here.
The current and past members of my
research group are listed here.
How to contact me?
Walk or mail:
Office 3406, Carl R. Woese Institute
for Genomic Biology,
1206 West Gregory Dr.,
Urbana IL 61801
For campus mail use mail stop
MC-195
I can also be at my other office:
Office 3226, Digital Computer Lab,
Department of Bioengineering,
1304 W Springfield Ave, Urbana,
IL 61801
For campus mail to that location use
mail stop MC-278.
Call or FAX
(does anyone still uses
FAX ?):
Tel: +1-(217) 265-5705 (o)
FAX: +1-(217) 244-0877
E-mail:
ssmaslov at gmail.com
or maslov at illinois.edu
(Spambots would never
guess to substitute "@" instead of
"at", but you should).
Browse:
http://maslov.bioengineering.illinois.edu
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Current research: Microbial ecosystems, Genome evolution, Biological networks
I work on computational models of microbial ecosystems, genome evolution, and biomolecular networks. I am a physicist by training and often use statistical physics-based modeling techniques. I particularly love simple-yet-rich "bottom-down" models. My research has applications in big data analysis in genomics and systems biology, medicine, synthetic biology and metabolic engineering of biofuels, epidemiology of pathogens and infectious diseases, etc. Below are the highlights from my recent articles in reverse chronological order:
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In (Marriage biorxiv 2017, Byproducts_PRL_2018) we studied the assembly rules of microbial communities using several conceptual models. Each model separately takes into account a general rule contributing to ecosystem’s complexity, diversity, and stability. The new rule explored in (Marriage biorxiv 2017) is that microbes tend to utilize multiple nutrients not simultaneously (as is often assumed in the current models) but sequentially in a preference order determined by their regulatory networks. Stable states in this model can be explained using decades-old game theory research: the "stable marriage" or stable allocation problem awarded the Nobel Prize in economics in 2012.
The model in (Byproducts_PRL_2018), selected as an editors choice in Physical Review Letters (see the APS focus article by Marc Buchanan), is based on the rules of metabolic exchange, where metabolic byproducts excreted by some of the microbes are used as nutrients by others. The complexity of such exchange in the human gut microbiome - a quintessential example of a complex microbial ecosystem - is substantial and involves a network of ~250 metabolites exchanged among ~570 microbial species.
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In (JCP 2015) we presented a general theoretical and numerical analysis of the problem of autocatalysis of polymers capable of template-assisted ligation and 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 polymers. Another key result is the emergence of the kinetically limited optimal sequence overlap length between a template and its two substrates.
This work was continued in (Polymer biorxiv 2017) where we demonstrated that due to the competition for monomers, only a small subset of polymer sequences survives. That is to say, no matter how large is the monomer alphabet size - Z, only 2L out of ZL possible polymer sequences of length L would survive. Mathematically, this rule for polymers is very similar to the competitive exclusion principle in microbial ecosystems, according to which the number of surviving species in a steady state of a single-layer ecosystem cannot be larger than the number of nutrients they feed on. 2-mers (sequences of two letters within a polymer) correspond to microbial species, while 2Z letters on right (Z letters) and left (another Z letters) ends of polymers - to nutrients they use.
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We are interested in collapse-driven population dynamics of microbial ecosystems exposed to predation by their viruses (bacteriophages).
In (Sci. Reports, 2015) we identified the optimal ("well-tempered") strategy for lytic-lysogenic transition used by temperate phages in fluctuating environments.
In (PLoS Comp Bio 2015, Sci Rep 2017) we modeled the dynamics of bacterial populations driven by phage-induced collapses. The emergent properties include "diversity waves" (PLoS Comp Bio 2015) and co-existence of multiple bacterial strains due to Kill-the-Winner dynamics (Sci Rep 2017).
In PRE 2017 we explored how an epidemics of a (possibly benign pathogen) spreading in one species can jump to another and trigger its catastrophic population collapse. The take home message here is that we humans should be afraid of any abundant species with which we share pathogens (think rats, bats, etc.).
This line of research is relevant for the analysis of metagenomics data and for bioenergy applications such as e.g. preventing phage infections in bioreactors. It also has implications for epidemiology.
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In (Genetics 2017) we came up with an evolutionary model in which the competition between clonality and recombination shapes genome diversity. We found two principal regimes in bacterial evolution and identify two composite parameters that dictate the evolutionary fate of bacterial species. In the divergent regime, characterized by either a low recombination frequency or strict barriers to recombination, cohesion due to recombination is not sufficient to overcome the mutational drift. As a consequence, the divergence between pairs of genomes in the population steadily increases in the course of their evolution. The species lacks genetic coherence with sexually isolated clonal sub-populations continuously formed and dissolved. In contrast, in the metastable regime, characterized by a high recombination frequency combined with low barriers to recombination, genomes continuously recombine with the rest of the population. The population remains genetically cohesive and temporally stable. Notably, the transition between these two regimes can be affected by relatively small changes in evolutionary parameters. We classified a number of bacterial species as either divergent or metastable.
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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 thousands bases) 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 E. coli 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.
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In (NAR 2017, PNAS 2013, NAR 2011; ) we identified functional and evolutionary determinants of sizes of gene families and the frequency with which they are encoded in bacterial genomes. We think of repeating this analysis on a much larger dataset of ~1018 pairwise comparisons of protein-coding genes in 30,000 sequenced genomes and ~200 metagenomes.
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In (PNAS 2009) we 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.
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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.
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In (Nucleic Acids Research 2005) we demonstrated that self-interacting proteins forming homodimers (or homooligomers) are overrepresented in Protein-Protein Interaction networks and have larger than average degrees. Our observations are important for understanding functional, structural, and evolutionary properties of protein-protein interactions.
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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.
Links to my networks research
I actively work on topological properties of and dynamics taking place on biomolecular networks. This link summarizes some of my past results.
Information Networks:
I am also interested in the emergent properties of large information or technological networks. These networks connect routers in the Internet (PRL 2003) encode dependencies between Linux software packages (PNAS 2013), hyperlink webpages, or connect scientific publications to each other via citations, etc.
My recent research on information networks addresses the following big questions:
Older projects:
In the past I worked on a variety of topics including (in reverse chronological order) Econophysics, Low-dimensional magnetism, and Self-Organized Criticality.
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