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Lieberman, Tami

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Lieberman

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Tami

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Lieberman, Tami
Author's Publications: search.filters.isAuthorOfPublication.8170677e-fa1c-4dc8-ad70-18ee6d6067cc

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    An objective function exploiting suboptimal solutions in metabolic networks
    (BioMed Central, 2013) Wintermute, Edwin H; Lieberman, Tami; Silver, Pamela
    Background: Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results: We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion: Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network.
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    Genomic insights into bacterial adaptation during infection
    (2014-06-06) Lieberman, Tami; Kishony, Roy; Mitchison, Timothy; Laub, Michael; Sabeti, Padis; Fortune, Sarah
    Bacteria evolve during the colonization of human hosts, yet little is known about the selective pressures and evolutionary forces that shape this evolution. Illumination of these processes may inspire new therapeutic directions for combating bacterial infections and promoting healthy bacteria-host interactions. The advent of high-throughput sequencing has enabled the identification of mutations that occur within the human host, and various tools from computational and evolutionary biology can aid in creating biological understanding from these mutations. Chapter 1 describes recent progress in understanding within-patient bacterial adaption, focusing on insights made from genomic studies.
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    Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures
    (2014) Lieberman, Tami; Flett, Kelly; Yelin, Idan; Martin, Thomas; McAdam, Alexander; Priebe, Gregory; Kishony, Roy
    Advances in sequencing have enabled the identification of mutations acquired by bacterial pathogens during infection1-10. However, it remains unclear whether adaptive mutations fix in the population or lead to pathogen diversification within the patient11,12. Here, we study the genotypic diversity of Burkholderia dolosa within people with cystic fibrosis by re-sequencing individual colonies and whole populations from single sputum samples. Extensive intra-sample diversity reveals that mutations rarely fix within a patient's pathogen population—instead, diversifying lineages coexist for many years. When strong selection is acting on a gene, multiple adaptive mutations arise but neither sweeps to fixation, generating lasting allele diversity that provides a recorded signature of past selection. Genes involved in outer-membrane components, iron scavenging and antibiotic resistance all showed this signature of within-patient selection. These results offer a general and rapid approach for identifying selective pressures acting on a pathogen in individual patients based on single clinical samples.