@article{10812, abstract = {Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.}, author = {Römhild, Roderich and Bollenbach, Mark Tobias and Andersson, Dan I.}, issn = {1740-1534}, journal = {Nature Reviews Microbiology}, keywords = {General Immunology and Microbiology, Microbiology, Infectious Diseases}, pages = {478--490}, publisher = {Springer Nature}, title = {{The physiology and genetics of bacterial responses to antibiotic combinations}}, doi = {10.1038/s41579-022-00700-5}, volume = {20}, year = {2022}, } @article{11341, abstract = {Intragenic regions that are removed during maturation of the RNA transcript—introns—are universally present in the nuclear genomes of eukaryotes1. The budding yeast, an otherwise intron-poor species, preserves two sets of ribosomal protein genes that differ primarily in their introns2,3. Although studies have shed light on the role of ribosomal protein introns under stress and starvation4,5,6, understanding the contribution of introns to ribosome regulation remains challenging. Here, by combining isogrowth profiling7 with single-cell protein measurements8, we show that introns can mediate inducible phenotypic heterogeneity that confers a clear fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal subunit protein Rps22B, which is mediated by an intron in the 5′ untranslated region of its transcript. The two resulting yeast subpopulations differ in their ability to cope with starvation. Low levels of Rps22B protein result in prolonged survival under sustained starvation, whereas high levels of Rps22B enable cells to grow faster after transient starvation. Furthermore, yeasts growing at high concentrations of sugar, similar to those in ripe grapes, exhibit bimodal expression of Rps22B when approaching the stationary phase. Differential intron-mediated regulation of ribosomal protein genes thus provides a way to diversify the population when starvation threatens in natural environments. Our findings reveal a role for introns in inducing phenotypic heterogeneity in changing environments, and suggest that duplicated ribosomal protein genes in yeast contribute to resolving the evolutionary conflict between precise expression control and environmental responsiveness9.}, author = {Lukacisin, Martin and Espinosa-Cantú, Adriana and Bollenbach, Mark Tobias}, issn = {1476-4687}, journal = {Nature}, pages = {113--118}, publisher = {Springer Nature}, title = {{Intron-mediated induction of phenotypic heterogeneity}}, doi = {10.1038/s41586-022-04633-0}, volume = {605}, year = {2022}, } @article{12261, abstract = {Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.}, author = {Angermayr, Andreas and Pang, Tin Yau and Chevereau, Guillaume and Mitosch, Karin and Lercher, Martin J and Bollenbach, Mark Tobias}, issn = {1744-4292}, journal = {Molecular Systems Biology}, keywords = {Applied Mathematics, Computational Theory and Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, Information Systems}, number = {9}, publisher = {Embo Press}, title = {{Growth‐mediated negative feedback shapes quantitative antibiotic response}}, doi = {10.15252/msb.202110490}, volume = {18}, year = {2022}, } @article{10271, abstract = {Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions.}, author = {Qi, Qin and Angermayr, S. Andreas and Bollenbach, Mark Tobias}, issn = {1664-302X}, journal = {Frontiers in Microbiology}, keywords = {microbiology}, publisher = {Frontiers}, title = {{Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli}}, doi = {10.3389/fmicb.2021.760017}, volume = {12}, year = {2021}, } @article{8997, abstract = {Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.}, author = {Kavcic, Bor and Tkačik, Gašper and Bollenbach, Tobias}, issn = {1553-7358}, journal = {PLOS Computational Biology}, keywords = {Modelling and Simulation, Genetics, Molecular Biology, Antibiotics, Drug interactions}, publisher = {Public Library of Science}, title = {{Minimal biophysical model of combined antibiotic action}}, doi = {10.1371/journal.pcbi.1008529}, volume = {17}, year = {2021}, } @article{8037, abstract = {Genetic perturbations that affect bacterial resistance to antibiotics have been characterized genome-wide, but how do such perturbations interact with subsequent evolutionary adaptation to the drug? Here, we show that strong epistasis between resistance mutations and systematically identified genes can be exploited to control spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12 mutant populations in parallel, using a robotic platform that tightly controls population size and selection pressure. We find a global diminishing-returns epistasis pattern: strains that are initially more sensitive generally undergo larger resistance gains. However, some gene deletion strains deviate from this general trend and curtail the evolvability of resistance, including deletions of genes for membrane transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force evolution on inferior mutational paths, not explored in the wild type, and some of these essentially block resistance evolution. This effect is due to strong negative epistasis with resistance mutations. The identified genes and cellular functions provide potential targets for development of adjuvants that may block spontaneous resistance evolution when combined with antibiotics.}, author = {Lukacisinova, Marta and Fernando, Booshini and Bollenbach, Mark Tobias}, issn = {20411723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance}}, doi = {10.1038/s41467-020-16932-z}, volume = {11}, year = {2020}, } @article{8250, abstract = {Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by “translation bottlenecks”: points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of “continuous epistasis” in bacterial physiology.}, author = {Kavcic, Bor and Tkačik, Gašper and Bollenbach, Tobias}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Mechanisms of drug interactions between translation-inhibiting antibiotics}}, doi = {10.1038/s41467-020-17734-z}, volume = {11}, year = {2020}, } @unpublished{7673, abstract = {Combining drugs can improve the efficacy of treatments. However, predicting the effect of drug combinations is still challenging. The combined potency of drugs determines the drug interaction, which is classified as synergistic, additive, antagonistic, or suppressive. While probabilistic, non-mechanistic models exist, there is currently no biophysical model that can predict antibiotic interactions. Here, we present a physiologically relevant model of the combined action of antibiotics that inhibit protein synthesis by targeting the ribosome. This model captures the kinetics of antibiotic binding and transport, and uses bacterial growth laws to predict growth in the presence of antibiotic combinations. We find that this biophysical model can produce all drug interaction types except suppression. We show analytically that antibiotics which cannot bind to the ribosome simultaneously generally act as substitutes for one another, leading to additive drug interactions. Previously proposed null expectations for higher-order drug interactions follow as a limiting case of our model. We further extend the model to include the effects of direct physical or allosteric interactions between individual drugs on the ribosome. Notably, such direct interactions profoundly change the combined drug effect, depending on the kinetic parameters of the drugs used. The model makes additional predictions for the effects of resistance genes on drug interactions and for interactions between ribosome-targeting antibiotics and antibiotics with other targets. These findings enhance our understanding of the interplay between drug action and cell physiology and are a key step toward a general framework for predicting drug interactions.}, author = {Kavcic, Bor and Tkačik, Gašper and Bollenbach, Tobias}, booktitle = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, title = {{A minimal biophysical model of combined antibiotic action}}, doi = {10.1101/2020.04.18.047886}, year = {2020}, } @article{6046, abstract = {Sudden stress often triggers diverse, temporally structured gene expression responses in microbes, but it is largely unknown how variable in time such responses are and if genes respond in the same temporal order in every single cell. Here, we quantified timing variability of individual promoters responding to sublethal antibiotic stress using fluorescent reporters, microfluidics, and time‐lapse microscopy. We identified lower and upper bounds that put definite constraints on timing variability, which varies strongly among promoters and conditions. Timing variability can be interpreted using results from statistical kinetics, which enable us to estimate the number of rate‐limiting molecular steps underlying different responses. We found that just a few critical steps control some responses while others rely on dozens of steps. To probe connections between different stress responses, we then tracked the temporal order and response time correlations of promoter pairs in individual cells. Our results support that, when bacteria are exposed to the antibiotic nitrofurantoin, the ensuing oxidative stress and SOS responses are part of the same causal chain of molecular events. In contrast, under trimethoprim, the acid stress response and the SOS response are part of different chains of events running in parallel. Our approach reveals fundamental constraints on gene expression timing and provides new insights into the molecular events that underlie the timing of stress responses.}, author = {Mitosch, Karin and Rieckh, Georg and Bollenbach, Mark Tobias}, journal = {Molecular systems biology}, number = {2}, publisher = {Embo Press}, title = {{Temporal order and precision of complex stress responses in individual bacteria}}, doi = {10.15252/msb.20188470}, volume = {15}, year = {2019}, } @article{7026, abstract = {Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper’s transparent peer review process is included in the Supplemental Information.}, author = {Lukacisin, Martin and Bollenbach, Tobias}, issn = {2405-4712}, journal = {Cell Systems}, number = {5}, pages = {423--433.e1--e3}, publisher = {Cell Press}, title = {{Emergent gene expression responses to drug combinations predict higher-order drug interactions}}, doi = {10.1016/j.cels.2019.10.004}, volume = {9}, year = {2019}, } @article{674, abstract = {Navigation of cells along gradients of guidance cues is a determining step in many developmental and immunological processes. Gradients can either be soluble or immobilized to tissues as demonstrated for the haptotactic migration of dendritic cells (DCs) toward higher concentrations of immobilized chemokine CCL21. To elucidate how gradient characteristics govern cellular response patterns, we here introduce an in vitro system allowing to track migratory responses of DCs to precisely controlled immobilized gradients of CCL21. We find that haptotactic sensing depends on the absolute CCL21 concentration and local steepness of the gradient, consistent with a scenario where DC directionality is governed by the signal-to-noise ratio of CCL21 binding to the receptor CCR7. We find that the conditions for optimal DC guidance are perfectly provided by the CCL21 gradients we measure in vivo. Furthermore, we find that CCR7 signal termination by the G-protein-coupled receptor kinase 6 (GRK6) is crucial for haptotactic but dispensable for chemotactic CCL21 gradient sensing in vitro and confirm those observations in vivo. These findings suggest that stable, tissue-bound CCL21 gradients as sustainable “roads” ensure optimal guidance in vivo.}, author = {Schwarz, Jan and Bierbaum, Veronika and Vaahtomeri, Kari and Hauschild, Robert and Brown, Markus and De Vries, Ingrid and Leithner, Alexander F and Reversat, Anne and Merrin, Jack and Tarrant, Teresa and Bollenbach, Tobias and Sixt, Michael K}, issn = {09609822}, journal = {Current Biology}, number = {9}, pages = {1314 -- 1325}, publisher = {Cell Press}, title = {{Dendritic cells interpret haptotactic chemokine gradients in a manner governed by signal to noise ratio and dependent on GRK6}}, doi = {10.1016/j.cub.2017.04.004}, volume = {27}, year = {2017}, } @article{666, abstract = {Antibiotics elicit drastic changes in microbial gene expression, including the induction of stress response genes. While certain stress responses are known to “cross-protect” bacteria from other stressors, it is unclear whether cellular responses to antibiotics have a similar protective role. By measuring the genome-wide transcriptional response dynamics of Escherichia coli to four antibiotics, we found that trimethoprim induces a rapid acid stress response that protects bacteria from subsequent exposure to acid. Combining microfluidics with time-lapse imaging to monitor survival and acid stress response in single cells revealed that the noisy expression of the acid resistance operon gadBC correlates with single-cell survival. Cells with higher gadBC expression following trimethoprim maintain higher intracellular pH and survive the acid stress longer. The seemingly random single-cell survival under acid stress can therefore be predicted from gadBC expression and rationalized in terms of GadB/C molecular function. Overall, we provide a roadmap for identifying the molecular mechanisms of single-cell cross-protection between antibiotics and other stressors.}, author = {Mitosch, Karin and Rieckh, Georg and Bollenbach, Tobias}, issn = {24054712}, journal = {Cell Systems}, number = {4}, pages = {393 -- 403}, publisher = {Cell Press}, title = {{Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment}}, doi = {10.1016/j.cels.2017.03.001}, volume = {4}, year = {2017}, } @article{822, abstract = {Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. }, author = {De Vos, Marjon and Zagórski, Marcin P and Mcnally, Alan and Bollenbach, Mark Tobias}, issn = {00278424}, journal = {PNAS}, number = {40}, pages = {10666 -- 10671}, publisher = {National Academy of Sciences}, title = {{Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections}}, doi = {10.1073/pnas.1713372114}, volume = {114}, year = {2017}, } @article{1027, abstract = {The rising prevalence of antibiotic resistant bacteria is an increasingly serious public health challenge. To address this problem, recent work ranging from clinical studies to theoretical modeling has provided valuable insights into the mechanisms of resistance, its emergence and spread, and ways to counteract it. A deeper understanding of the underlying dynamics of resistance evolution will require a combination of experimental and theoretical expertise from different disciplines and new technology for studying evolution in the laboratory. Here, we review recent advances in the quantitative understanding of the mechanisms and evolution of antibiotic resistance. We focus on key theoretical concepts and new technology that enables well-controlled experiments. We further highlight key challenges that can be met in the near future to ultimately develop effective strategies for combating resistance.}, author = {Lukacisinova, Marta and Bollenbach, Mark Tobias}, journal = {Current Opinion in Biotechnology}, pages = {90 -- 97}, publisher = {Elsevier}, title = {{Toward a quantitative understanding of antibiotic resistance evolution}}, doi = {10.1016/j.copbio.2017.02.013}, volume = {46}, year = {2017}, } @article{1154, abstract = {Cellular locomotion is a central hallmark of eukaryotic life. It is governed by cell-extrinsic molecular factors, which can either emerge in the soluble phase or as immobilized, often adhesive ligands. To encode for direction, every cue must be present as a spatial or temporal gradient. Here, we developed a microfluidic chamber that allows measurement of cell migration in combined response to surface immobilized and soluble molecular gradients. As a proof of principle we study the response of dendritic cells to their major guidance cues, chemokines. The majority of data on chemokine gradient sensing is based on in vitro studies employing soluble gradients. Despite evidence suggesting that in vivo chemokines are often immobilized to sugar residues, limited information is available how cells respond to immobilized chemokines. We tracked migration of dendritic cells towards immobilized gradients of the chemokine CCL21 and varying superimposed soluble gradients of CCL19. Differential migratory patterns illustrate the potential of our setup to quantitatively study the competitive response to both types of gradients. Beyond chemokines our approach is broadly applicable to alternative systems of chemo- and haptotaxis such as cells migrating along gradients of adhesion receptor ligands vs. any soluble cue. }, author = {Schwarz, Jan and Bierbaum, Veronika and Merrin, Jack and Frank, Tino and Hauschild, Robert and Bollenbach, Mark Tobias and Tay, Savaş and Sixt, Michael K and Mehling, Matthias}, journal = {Scientific Reports}, publisher = {Nature Publishing Group}, title = {{A microfluidic device for measuring cell migration towards substrate bound and soluble chemokine gradients}}, doi = {10.1038/srep36440}, volume = {6}, year = {2016}, } @article{1581, abstract = {In animal embryos, morphogen gradients determine tissue patterning and morphogenesis. Shyer et al. provide evidence that, during vertebrate gut formation, tissue folding generates graded activity of signals required for subsequent steps of gut growth and differentiation, thereby revealing an intriguing link between tissue morphogenesis and morphogen gradient formation.}, author = {Bollenbach, Mark Tobias and Heisenberg, Carl-Philipp J}, journal = {Cell}, number = {3}, pages = {431 -- 432}, publisher = {Cell Press}, title = {{Gradients are shaping up}}, doi = {10.1016/j.cell.2015.04.009}, volume = {161}, year = {2015}, } @article{1810, abstract = {Combining antibiotics is a promising strategy for increasing treatment efficacy and for controlling resistance evolution. When drugs are combined, their effects on cells may be amplified or weakened, that is the drugs may show synergistic or antagonistic interactions. Recent work revealed the underlying mechanisms of such drug interactions by elucidating the drugs'; joint effects on cell physiology. Moreover, new treatment strategies that use drug combinations to exploit evolutionary tradeoffs were shown to affect the rate of resistance evolution in predictable ways. High throughput studies have further identified drug candidates based on their interactions with established antibiotics and general principles that enable the prediction of drug interactions were suggested. Overall, the conceptual and technical foundation for the rational design of potent drug combinations is rapidly developing.}, author = {Bollenbach, Mark Tobias}, journal = {Current Opinion in Microbiology}, pages = {1 -- 9}, publisher = {Elsevier}, title = {{Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution}}, doi = {10.1016/j.mib.2015.05.008}, volume = {27}, year = {2015}, } @article{1823, abstract = {Abstract Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome-wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions. Synopsis A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. Drug interactions between antibiotics are highly robust to genetic perturbations. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions. Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways.}, author = {Chevereau, Guillaume and Bollenbach, Mark Tobias}, journal = {Molecular Systems Biology}, number = {4}, publisher = {Nature Publishing Group}, title = {{Systematic discovery of drug interaction mechanisms}}, doi = {10.15252/msb.20156098}, volume = {11}, year = {2015}, } @misc{9711, author = {Chevereau, Guillaume and Lukacisinova, Marta and Batur, Tugce and Guvenek, Aysegul and Ayhan, Dilay Hazal and Toprak, Erdal and Bollenbach, Mark Tobias}, publisher = {Public Library of Science}, title = {{Excel file containing the raw data for all figures}}, doi = {10.1371/journal.pbio.1002299.s001}, year = {2015}, } @misc{9765, author = {Chevereau, Guillaume and Lukacisinova, Marta and Batur, Tugce and Guvenek, Aysegul and Ayhan, Dilay Hazal and Toprak, Erdal and Bollenbach, Mark Tobias}, publisher = {Public Library of Science}, title = {{Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs}}, doi = {10.1371/journal.pbio.1002299.s008}, year = {2015}, }