Thanks to recent increases in research funding for TB 
, substantial progress has been made in our understanding of the basic biology and epidemiology of the disease. Unfortunately, this increased knowledge has not yet had any noticeable impact on the current global trends of TB (). While TB incidence appears to have stabilized in many countries, the total number of cases is still increasing as a function of global human population growth 
. Of particular concern are the ongoing epidemics of multidrug-resistant TB 
, as well as the synergies between TB and the ongoing epidemics of HIV/AIDS and other comorbidities such as diabetes (Box 1).
As our understanding of TB improves, we would like to be able to make better predictions about the future trajectory of the disease and to develop new tools to control the disease better and ultimately reverse global trends. For this to be feasible, TB epidemiology needs to evolve into a more predictive, interdisciplinary endeavour; a discipline we might refer to as “systems epidemiology” (). Systems biology is already a rapidly emerging field, in which cycles of mathematical modelling and experiments using various large-scale “-omics” datasets are integrated in an iterative manner 
. Novel biological processes are being discovered through these systems approaches, which might not have been possible using more traditional methods 
A systems epidemiology approach to TB research.
Last year, Young et al. argued that systems biology approaches will be necessary to elucidate some of the key aspects of host–pathogen interactions in TB 
and to develop new drugs, vaccines, and biomarkers to evaluate new interventions 
. For example, according to another dogma in the TB field, latent TB infections are caused by physiologically dormant bacilli and can thus be differentiated from active disease where MTBC is actively growing and dividing 
. In reality, however, the phenomenon of TB latency most likely reflects a whole spectrum of responses to TB infection, involving phenotypically distinct bacterial subpopulations and spanning various degrees of bacterial burden and associated host immune responses 
. We agree with Young et al. 
that TB latency and similar biological complexities will only be adequately addressed using systems approaches, and we argue further that to comprehend the current TB epidemic as a whole, and to better predict its future trajectory, a complementary systems epidemiology approach will be necessary ().
Mathematical models are already being used extensively to study the epidemiology of TB and to guide control policies 
. Recent applications have shown that socioeconomic factors are key drivers of today's TB epidemic 
. In addition, much theoretical emphasis has been placed on trying to define the impact that drug resistance will have on the global TB epidemic 
. Some of this theoretical work has become more complex by incorporating new biological insights obtained empirically and through targeted experimental studies. Early theoretical studies on the spread of drug-resistant MTBC were based on the assumption that all drug-resistant bacteria had an inherent fitness disadvantage compared to drug-susceptible strains 
; however, as is becoming clear from experimental and molecular epidemiological investigation, substantial heterogeneity exists with respect to the reproductive success of drug-resistant strains 
. Newer mathematical models account for some of this heterogeneity 
One could imagine an expansion of such mathematical approaches—much as systems biology operates—in which epidemiological modelling is combined with more comprehensive biological data related to the host, the pathogen, and their interactions (). Of course, environmental and sociological data would also need to be considered 
. As mathematical models become more finely tuned, they could in turn inform future experimental work to test some of the specific predictions. The genomics revolution now offers the opportunity to study host–pathogen interactions at an unprecedented depth. To be able to make sense out of the current and upcoming deluge of -omics data, however, scientists will have to rely on a mathematically and statistically robust analytical framework. Ideally, some of these theoretical approaches will be able to accommodate increasingly diverse sets of data in order to capture the various biological, environmental, and social aspects of TB.
Among the newly emerging technologies, we believe that next-generation DNA sequencing will play an important role in improving our understanding of TB 
. Whole-genome sequencing could potentially become the new gold standard for strain typing in routine molecular epidemiology 
. For host genetics and TB susceptibility, too, de novo DNA sequencing based approaches could have advantages over traditional SNP typing 
. For example, many of the human populations carrying the largest proportion of the global TB burden have not been sufficiently characterised genetically () 
, and screening for currently limited human SNP collections might have little relevance for these populations 
. Furthermore, comprehensive DNA sequencing of TB patients and controls in various human populations could help unveil rare but biologically relevant mutations 
. Another approach increasingly being used to study both the host and the pathogen is sequence-based transcriptomics, in which gene expression is measured by whole genome sequencing of RNA transcripts; a method referred to as RNA-seq 
. One of the advantages of this approach over existing microarray-based methods is that changes in the expression of noncoding RNAs and other novel transcripts can be easily detected. RNA-seq is particularly useful for genome-wide studies of small regulatory RNAs, as such studies are more difficult to perform using standard DNA microarrays. Recent studies, for example, have reported a role for small regulatory RNAs in M. tuberculosis 
, and there is little doubt more regulatory RNAs will soon be identified by RNA-seq