Mechanisms of adaptation in antibiotic resistant bacteria
Mutations responsible for clinically significant, high levels of antibiotic resistance are almost invariably associated with reduced growth rate in laboratory strains, under standard conditions. Despite its apparent relevance for the development of anti-infectious therapeutics as well as for general bacterial physiology, regulation and adaptation, the phenomenon of growth retardation has not been understood or even adequately studied. Recently we began investigating metabolic consequences of the acquired antibiotic resistance using topologically constrained metabolic modelling with the goal of identifying key modulators of the phenotypic variation.

Genomic landscape of starvation-recovery transitions in bacterial populations
Life cycle of bacteria can be viewed as ever repeating phases of starvation and recovery. Genes whose products contribute to optimal entry, survival and exit from starvation, but do not influence exponential growth, are the primary biological focus of this study. Identification of such genes and their relative contribution to fitness under chemically controlled, starvation-recovery conditions will be used to model internally consistent genotype-phenotype relationships.  Using high-throughput sequencing methods in conjunction with saturation transposon mutagenesis we can quantify the effect of every non-essential gene in the Escherichia coli genome on the starvation-related fitness. However, the reliability of fitness measurements depends on the ability to statistically model the high-throughput sequencing data. While intensive research into the best statistical practices in the analysis of transcriptomics data has been pursued in the bioinformatics literature, much less is known about optimal analysis strategies for transposon-saturation data sets. We develop and test experimental designs and statistical approaches to the basic analysis of such data.

Zare H, Khodursky A, Sartorelli V: An evolutionarily biased distribution of miRNA sites toward regulatory genes with high promoter-driven intrinsic transcriptional noise. BMC Evol Biol. 2014 Apr 4;14(1):74.

Edwards AL, Sangurdekar DP, Jeong KS, Khodursky AB, Rybenkov VV: Transient Growth Arrest in Escherichia coli Induced by Chromosome Condensation. PLoS One. 2013 Dec 23;8(12):e84027.

Wang X, Chen M, Khodursky AB, Xiao G: Bayesian joint analysis of gene expression data and gene functional annotations. Statistics in Biosciences 2012, 4 (2): 300-318.

Sangurdekar DP, Zhang Z, Khodursky AB: The association of DNA damage response and nucleotide level modulation with the antibacterial mechanism of the anti-folate drug trimethoprim. BMC Genomics 2011 Nov 28; 12:583.

Zare H, Kaveh M, Khodursky A: Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding. PLoS One 2011; 6(8):e21969. Epub 2011 Aug 12.

Xiao G, Wang X, Khodursky AB: Modeling 3-D chromosomal structures using gene expression data. Journal of the American Statistical Association 2011, 106(493): 61–72.

Xie Y, Pan W, Jeong KS, Xiao G, Khodursky AB: A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data. Stat Med 2010, 29(4):489-503.

Sangurdekar DP, Hamann BL, Smirnov D, Srienc F, Hanawalt PC, Khodursky AB: Thymineless death is associated with loss of essential genetic information from the replication origin. Mol Microbiol 2010 75(6), 1455–1467.