Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 1 to 50 of 465

1.

Evolution of gene knockout strains of E. coli reveal regulatory architectures governed by metabolism.

McCloskey D, Xu S, Sandberg TE, Brunk E, Hefner Y, Szubin R, Feist AM, Palsson BO.

Nat Commun. 2018 Sep 18;9(1):3796. doi: 10.1038/s41467-018-06219-9.

PMID:
30228271
2.

Genome-scale metabolic reconstructions of multiple Salmonella strains reveal serovar-specific metabolic traits.

Seif Y, Kavvas E, Lachance JC, Yurkovich JT, Nuccio SP, Fang X, Catoiu E, Raffatellu M, Palsson BO, Monk JM.

Nat Commun. 2018 Sep 14;9(1):3771. doi: 10.1038/s41467-018-06112-5.

3.

Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655.

Gao Y, Yurkovich JT, Seo SW, Kabimoldayev I, Dräger A, Chen K, Sastry AV, Fang X, Mih N, Yang L, Eichner J, Cho BK, Kim D, Palsson BO.

Nucleic Acids Res. 2018 Aug 23. doi: 10.1093/nar/gky752. [Epub ahead of print]

PMID:
30137486
4.

Growth Adaptation of gnd and sdhCB Escherichia coli Deletion Strains Diverges From a Similar Initial Perturbation of the Transcriptome.

McCloskey D, Xu S, Sandberg TE, Brunk E, Hefner Y, Szubin R, Feist AM, Palsson BO.

Front Microbiol. 2018 Aug 7;9:1793. doi: 10.3389/fmicb.2018.01793. eCollection 2018.

5.

A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0.

Santos-Zavaleta A, Sánchez-Pérez M, Salgado H, Velázquez-Ramírez DA, Gama-Castro S, Tierrafría VH, Busby SJW, Aquino P, Fang X, Palsson BO, Galagan JE, Collado-Vides J.

BMC Biol. 2018 Aug 16;16(1):91. doi: 10.1186/s12915-018-0555-y.

6.

Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials.

Yurkovich JT, Alcantar MA, Haiman ZB, Palsson BO.

PLoS Comput Biol. 2018 Aug 7;14(8):e1006356. doi: 10.1371/journal.pcbi.1006356. eCollection 2018 Aug.

7.

Multiple Optimal Phenotypes Overcome Redox and Glycolytic Intermediate Metabolite Imbalances in Escherichia coli pgi Knockout Evolutions.

McCloskey D, Xu S, Sandberg TE, Brunk E, Hefner Y, Szubin R, Feist AM, Palsson BO.

Appl Environ Microbiol. 2018 Sep 17;84(19). pii: e00823-18. doi: 10.1128/AEM.00823-18. Print 2018 Oct 1.

PMID:
30054360
8.

COBRAme: A computational framework for genome-scale models of metabolism and gene expression.

Lloyd CJ, Ebrahim A, Yang L, King ZA, Catoiu E, O'Brien EJ, Liu JK, Palsson BO.

PLoS Comput Biol. 2018 Jul 5;14(7):e1006302. doi: 10.1371/journal.pcbi.1006302. eCollection 2018 Jul.

9.

Adaptive laboratory evolution resolves energy depletion to maintain high aromatic metabolite phenotypes in Escherichia coli strains lacking the Phosphotransferase System.

McCloskey D, Xu S, Sandberg TE, Brunk E, Hefner Y, Szubin R, Feist AM, Palsson BO.

Metab Eng. 2018 Jul;48:233-242. doi: 10.1016/j.ymben.2018.06.005. Epub 2018 Jun 15.

PMID:
29906504
10.

Escherichia coli B2 strains prevalent in inflammatory bowel disease patients have distinct metabolic capabilities that enable colonization of intestinal mucosa.

Fang X, Monk JM, Mih N, Du B, Sastry AV, Kavvas E, Seif Y, Smarr L, Palsson BO.

BMC Syst Biol. 2018 Jun 11;12(1):66. doi: 10.1186/s12918-018-0587-5.

11.

The Staphylococcus aureus Two-Component System AgrAC Displays Four Distinct Genomic Arrangements That Delineate Genomic Virulence Factor Signatures.

Choudhary KS, Mih N, Monk J, Kavvas E, Yurkovich JT, Sakoulas G, Palsson BO.

Front Microbiol. 2018 May 25;9:1082. doi: 10.3389/fmicb.2018.01082. eCollection 2018.

12.

Temperature-Dependent Estimation of Gibbs Energies Using an Updated Group-Contribution Method.

Du B, Zhang Z, Grubner S, Yurkovich JT, Palsson BO, Zielinski DC.

Biophys J. 2018 Jun 5;114(11):2691-2702. doi: 10.1016/j.bpj.2018.04.030.

PMID:
29874618
13.

Adaptation to the coupling of glycolysis to toxic methylglyoxal production in tpiA deletion strains of Escherichia coli requires synchronized and counterintuitive genetic changes.

McCloskey D, Xu S, Sandberg TE, Brunk E, Hefner Y, Szubin R, Feist AM, Palsson BO.

Metab Eng. 2018 Jul;48:82-93. doi: 10.1016/j.ymben.2018.05.012. Epub 2018 May 26.

PMID:
29842925
14.

ChIP-exo interrogation of Crp, DNA, and RNAP holoenzyme interactions.

Latif H, Federowicz S, Ebrahim A, Tarasova J, Szubin R, Utrilla J, Zengler K, Palsson BO.

PLoS One. 2018 May 17;13(5):e0197272. doi: 10.1371/journal.pone.0197272. eCollection 2018.

15.

iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE.

Norsigian CJ, Kavvas E, Seif Y, Palsson BO, Monk JM.

Front Genet. 2018 Apr 10;9:121. doi: 10.3389/fgene.2018.00121. eCollection 2018.

16.

Systems biology as an emerging paradigm in transfusion medicine.

Yurkovich JT, Bordbar A, Sigurjónsson ÓE, Palsson BO.

BMC Syst Biol. 2018 Mar 7;12(1):31. doi: 10.1186/s12918-018-0558-x.

17.

Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions.

Kavvas ES, Seif Y, Yurkovich JT, Norsigian C, Poudel S, Greenwald WW, Ghatak S, Palsson BO, Monk JM.

BMC Syst Biol. 2018 Mar 2;12(1):25. doi: 10.1186/s12918-018-0557-y.

18.

Recon3D enables a three-dimensional view of gene variation in human metabolism.

Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Dräger A, Mih N, Gatto F, Nilsson A, Preciat Gonzalez GA, Aurich MK, Prlić A, Sastry A, Danielsdottir AD, Heinken A, Noronha A, Rose PW, Burley SK, Fleming RMT, Nielsen J, Thiele I, Palsson BO.

Nat Biotechnol. 2018 Mar;36(3):272-281. doi: 10.1038/nbt.4072. Epub 2018 Feb 19.

19.

ssbio: a Python framework for structural systems biology.

Mih N, Brunk E, Chen K, Catoiu E, Sastry A, Kavvas E, Monk JM, Zhang Z, Palsson BO.

Bioinformatics. 2018 Jun 15;34(12):2155-2157. doi: 10.1093/bioinformatics/bty077.

PMID:
29444205
20.

Quantitative -omic data empowers bottom-up systems biology.

Yurkovich JT, Palsson BO.

Curr Opin Biotechnol. 2018 Jun;51:130-136. doi: 10.1016/j.copbio.2018.01.009. Epub 2018 Feb 3. Review.

PMID:
29414439
21.

Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP.

Kim D, Seo SW, Gao Y, Nam H, Guzman GI, Cho BK, Palsson BO.

Nucleic Acids Res. 2018 Apr 6;46(6):2901-2917. doi: 10.1093/nar/gky069.

22.

Modeling the multi-scale mechanisms of macromolecular resource allocation.

Yang L, Yurkovich JT, King ZA, Palsson BO.

Curr Opin Microbiol. 2018 Jan 20;45:8-15. doi: 10.1016/j.mib.2018.01.002. [Epub ahead of print] Review.

PMID:
29367175
23.

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting.

Abdel-Haleem AM, Hefzi H, Mineta K, Gao X, Gojobori T, Palsson BO, Lewis NE, Jamshidi N.

PLoS Comput Biol. 2018 Jan 4;14(1):e1005895. doi: 10.1371/journal.pcbi.1005895. eCollection 2018 Jan.

24.

Metabolic Models of Protein Allocation Call for the Kinetome.

Nilsson A, Nielsen J, Palsson BO.

Cell Syst. 2017 Dec 27;5(6):538-541. doi: 10.1016/j.cels.2017.11.013.

PMID:
29284126
25.

Topological and kinetic determinants of the modal matrices of dynamic models of metabolism.

Du B, Zielinski DC, Palsson BO.

PLoS One. 2017 Dec 21;12(12):e0189880. doi: 10.1371/journal.pone.0189880. eCollection 2017.

26.

Dissecting the genetic and metabolic mechanisms of adaptation to the knockout of a major metabolic enzyme in Escherichia coli.

Long CP, Gonzalez JE, Feist AM, Palsson BO, Antoniewicz MR.

Proc Natl Acad Sci U S A. 2018 Jan 2;115(1):222-227. doi: 10.1073/pnas.1716056115. Epub 2017 Dec 18.

27.

Antibiotic-Induced Changes to the Host Metabolic Environment Inhibit Drug Efficacy and Alter Immune Function.

Yang JH, Bhargava P, McCloskey D, Mao N, Palsson BO, Collins JJ.

Cell Host Microbe. 2017 Dec 13;22(6):757-765.e3. doi: 10.1016/j.chom.2017.10.020. Epub 2017 Nov 30.

PMID:
29199098
28.

Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation.

Chen K, Gao Y, Mih N, O'Brien EJ, Yang L, Palsson BO.

Proc Natl Acad Sci U S A. 2017 Oct 24;114(43):11548-11553. doi: 10.1073/pnas.1705524114. Epub 2017 Oct 10.

29.

Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.

Yurkovich JT, Zielinski DC, Yang L, Paglia G, Rolfsson O, Sigurjónsson ÓE, Broddrick JT, Bordbar A, Wichuk K, Brynjólfsson S, Palsson S, Gudmundsson S, Palsson BO.

J Biol Chem. 2017 Dec 1;292(48):19556-19564. doi: 10.1074/jbc.M117.804914. Epub 2017 Oct 13.

PMID:
29030425
30.

iML1515, a knowledgebase that computes Escherichia coli traits.

Monk JM, Lloyd CJ, Brunk E, Mih N, Sastry A, King Z, Takeuchi R, Nomura W, Zhang Z, Mori H, Feist AM, Palsson BO.

Nat Biotechnol. 2017 Oct 11;35(10):904-908. doi: 10.1038/nbt.3956. No abstract available.

PMID:
29020004
31.

A Padawan Programmer's Guide to Developing Software Libraries.

Yurkovich JT, Yurkovich BJ, Dräger A, Palsson BO, King ZA.

Cell Syst. 2017 Nov 22;5(5):431-437. doi: 10.1016/j.cels.2017.08.003. Epub 2017 Oct 4.

PMID:
28988801
32.

Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring.

Long CP, Gonzalez JE, Feist AM, Palsson BO, Antoniewicz MR.

Metab Eng. 2017 Nov;44:100-107. doi: 10.1016/j.ymben.2017.09.012. Epub 2017 Sep 23.

33.

Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities.

Fang X, Sastry A, Mih N, Kim D, Tan J, Yurkovich JT, Lloyd CJ, Gao Y, Yang L, Palsson BO.

Proc Natl Acad Sci U S A. 2017 Sep 19;114(38):10286-10291. doi: 10.1073/pnas.1702581114. Epub 2017 Sep 5.

34.

Revealing genome-scale transcriptional regulatory landscape of OmpR highlights its expanded regulatory roles under osmotic stress in Escherichia coli K-12 MG1655.

Seo SW, Gao Y, Kim D, Szubin R, Yang J, Cho BK, Palsson BO.

Sci Rep. 2017 May 19;7(1):2181. doi: 10.1038/s41598-017-02110-7.

35.

Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies.

Sandberg TE, Lloyd CJ, Palsson BO, Feist AM.

Appl Environ Microbiol. 2017 Jun 16;83(13). pii: e00410-17. doi: 10.1128/AEM.00410-17. Print 2017 Jul 1.

36.

Machine learning in computational biology to accelerate high-throughput protein expression.

Sastry A, Monk J, Tegel H, Uhlen M, Palsson BO, Rockberg J, Brunk E.

Bioinformatics. 2017 Aug 15;33(16):2487-2495. doi: 10.1093/bioinformatics/btx207.

37.

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics.

Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE, Palsson BO.

Sci Rep. 2017 Apr 7;7:46249. doi: 10.1038/srep46249.

38.

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells.

Yurkovich JT, Yang L, Palsson BO.

PLoS Comput Biol. 2017 Mar 6;13(3):e1005424. doi: 10.1371/journal.pcbi.1005424. eCollection 2017 Mar.

39.

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

Levering J, Dupont CL, Allen AE, Palsson BO, Zengler K.

mSystems. 2017 Feb 14;2(1). pii: e00142-16. doi: 10.1128/mSystems.00142-16. eCollection 2017 Jan-Feb.

40.

A Model for Designing Adaptive Laboratory Evolution Experiments.

LaCroix RA, Palsson BO, Feist AM.

Appl Environ Microbiol. 2017 Mar 31;83(8). pii: e03115-16. doi: 10.1128/AEM.03115-16. Print 2017 Apr 15.

41.

Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism.

Zielinski DC, Jamshidi N, Corbett AJ, Bordbar A, Thomas A, Palsson BO.

Sci Rep. 2017 Jan 25;7:41241. doi: 10.1038/srep41241.

42.

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression.

Ma D, Yang L, Fleming RM, Thiele I, Palsson BO, Saunders MA.

Sci Rep. 2017 Jan 18;7:40863. doi: 10.1038/srep40863.

43.

Whole-Genome Sequencing of Invasion-Resistant Cells Identifies Laminin α2 as a Host Factor for Bacterial Invasion.

van Wijk XM, Döhrmann S, Hallström BM, Li S, Voldborg BG, Meng BX, McKee KK, van Kuppevelt TH, Yurchenco PD, Palsson BO, Lewis NE, Nizet V, Esko JD.

MBio. 2017 Jan 10;8(1). pii: e02128-16. doi: 10.1128/mBio.02128-16.

44.

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion.

King ZA, O'Brien EJ, Feist AM, Palsson BO.

Metab Eng. 2017 Jan;39:220-227. doi: 10.1016/j.ymben.2016.12.004. Epub 2016 Dec 13.

PMID:
27986597
45.

The aldehyde dehydrogenase, AldA, is essential for L-1,2-propanediol utilization in laboratory-evolved Escherichia coli.

Aziz RK, Monk JM, Andrews KA, Nhan J, Khaw VL, Wong H, Palsson BO, Charusanti P.

Microbiol Res. 2017 Jan;194:47-52. doi: 10.1016/j.micres.2016.10.006. Epub 2016 Nov 4.

46.

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis.

Broddrick JT, Rubin BE, Welkie DG, Du N, Mih N, Diamond S, Lee JJ, Golden SS, Palsson BO.

Proc Natl Acad Sci U S A. 2016 Dec 20;113(51):E8344-E8353. doi: 10.1073/pnas.1613446113. Epub 2016 Dec 1.

47.

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism.

Hefzi H, Ang KS, Hanscho M, Bordbar A, Ruckerbauer D, Lakshmanan M, Orellana CA, Baycin-Hizal D, Huang Y, Ley D, Martinez VS, Kyriakopoulos S, Jiménez NE, Zielinski DC, Quek LE, Wulff T, Arnsdorf J, Li S, Lee JS, Paglia G, Loira N, Spahn PN, Pedersen LE, Gutierrez JM, King ZA, Lund AM, Nagarajan H, Thomas A, Abdel-Haleem AM, Zanghellini J, Kildegaard HF, Voldborg BG, Gerdtzen ZP, Betenbaugh MJ, Palsson BO, Andersen MR, Nielsen LK, Borth N, Lee DY, Lewis NE.

Cell Syst. 2016 Nov 23;3(5):434-443.e8. doi: 10.1016/j.cels.2016.10.020.

48.

Principles of proteome allocation are revealed using proteomic data and genome-scale models.

Yang L, Yurkovich JT, Lloyd CJ, Ebrahim A, Saunders MA, Palsson BO.

Sci Rep. 2016 Nov 18;6:36734. doi: 10.1038/srep36734.

49.

Citrate metabolism in red blood cells stored in additive solution-3.

D'Alessandro A, Nemkov T, Yoshida T, Bordbar A, Palsson BO, Hansen KC.

Transfusion. 2017 Feb;57(2):325-336. doi: 10.1111/trf.13892. Epub 2016 Nov 4.

PMID:
27813142
50.

Multi-omic data integration enables discovery of hidden biological regularities.

Ebrahim A, Brunk E, Tan J, O'Brien EJ, Kim D, Szubin R, Lerman JA, Lechner A, Sastry A, Bordbar A, Feist AM, Palsson BO.

Nat Commun. 2016 Oct 26;7:13091. doi: 10.1038/ncomms13091.

Supplemental Content

Loading ...
Support Center