Growthmediated negative feedback shapes quantitative antibiotic response
A. Angermayr, T.Y. Pang, G. Chevereau, K. Mitosch, M.J. Lercher, M.T. Bollenbach, Molecular Systems Biology 18 (2022).

Intron-mediated induction of phenotypic heterogeneity
M. Lukacisin, A. Espinosa-Cantú, M.T. Bollenbach, Nature 605 (2022) 113–118.

The physiology and genetics of bacterial responses to antibiotic combinations
R. Römhild, M.T. Bollenbach, D.I. Andersson, Nature Reviews Microbiology 20 (2022) 478–490.

Minimal biophysical model of combined antibiotic action
B. Kavcic, G. Tkadik, M.T. Bollenbach, PLOS Computational Biology 17 (2021).

Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli
Q. Qi, S.A. Angermayr, M.T. Bollenbach, Frontiers in Microbiology 12 (2021).

A minimal biophysical model of combined antibiotic action
B. Kavcic, G. Tkadik, M.T. Bollenbach, BioRxiv (2020).

Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance
M. Lukacisinova, B. Fernando, M.T. Bollenbach, Nature Communications 11 (2020).

Mechanisms of drug interactions between translation-inhibiting antibiotics
B. Kavcic, G. Tkadik, M.T. Bollenbach, Nature Communications 11 (2020).

Temporal order and precision of complex stress responses in individual bacteria
K. Mitosch, G. Rieckh, M.T. Bollenbach, Molecular Systems Biology 15 (2019).

Emergent gene expression responses to drug combinations predict higher-order drug interactions
M. Lukacisin, M.T. Bollenbach, Cell Systems 9 (2019) 423-433.e1-e3.

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections
M. de Vos, M.P. Zagórski, A. Mcnally, M.T. Bollenbach, PNAS 114 (2017) 10666–10671.

Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment
K. Mitosch, G. Rieckh, M.T. Bollenbach, Cell Systems 4 (2017) 393–403.

Dendritic cells interpret haptotactic chemokine gradients in a manner governed by signal to noise ratio and dependent on GRK6
J. Schwarz, V. Bierbaum, K. Vaahtomeri, R. Hauschild, M. Brown, I. de Vries, A.F. Leithner, A. Reversat, J. Merrin, T. Tarrant, M.T. Bollenbach, M.K. Sixt, Current Biology 27 (2017) 1314–1325.

Toward a quantitative understanding of antibiotic resistance evolution
M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017) 90–97.

A microfluidic device for measuring cell migration towards substrate bound and soluble chemokine gradients
J. Schwarz, V. Bierbaum, J. Merrin, T. Frank, R. Hauschild, M.T. Bollenbach, S. Tay, M.K. Sixt, M. Mehling, Scientific Reports 6 (2016).

Gradients are shaping up
M.T. Bollenbach, C.-P.J. Heisenberg, Cell 161 (2015) 431–432.

Quantifying the determinants of evolutionary dynamics leading to drug resistance
G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak, M.T. Bollenbach, PLoS Biology 13 (2015).

Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution
M.T. Bollenbach, Current Opinion in Microbiology 27 (2015) 1–9.

Systematic discovery of drug interaction mechanisms
G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015).

Excel file containing the raw data for all figures
G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015).