00-introduction.qmd

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# Introduction {#sec-introduction .unnumbered}

Antimicrobial resistance (AMR) can be considered one of the most
critical health problems of the century. That is, microorganisms'
ability to grow despite exposure to substances designed to inhibit their
growth or kill them. In April 2014, the World Health Organization (WHO)
published its first global report on AMR surveillance
[@EditorialBoard2014]. Taking out of the darkness a common fear, a
possible post-antibiotic future in which common infections or minor
injuries can kill. Therefore, understanding the mechanisms of avoiding
antibiotic action is essential for producing knowledge and developing
strategies that reduce the generation of resistant bacteria.

Bacterial adaptability to hostile environmental conditions can be
explained by different elements, not necessarily exclusive. For
instance, mutational phenomena that allow bacteria to evade the
mechanisms of action of certain antibiotics have been one of the most
studied [@dever1991; @andersson2005]. However, continuous technological
development has allowed us to explore hypotheses where phenotypic
heterogeneity is considered in detail, allowing us to study emergent
behaviors in isogenic populations [@ackermann2015]. Thus, we have gone
from studying bacterial communities as a whole to studying them from
each of the cells that compose them and their emergent properties.

Single-cell microfluidics is one of the technologies that has made it
possible to create and maintain the microenvironments necessary for
studying bacteria [@yin2012]. Among the most outstanding utilities of
microfluidics, we can find the engineering of bacterial systems,
microbial ecology, bacterial cell cycle, homeostasis, cell shape, and
geometry. The latter is one of the characteristics that allow the study
of bacterial filamentation, a phenomenon that occurs when the cell stops
dividing but continues to grow, thus producing elongated cells in the
form of filaments.

Mathematical modeling is among the most common strategies to address the
AMR problem. Mathematical modeling allows one to pose real-life problems
in a space filled with mathematical language, solve them, and test their
solutions in a real-life living system [@verschaffel2002]. Therefore,
this approach can also be used to analyze in detail why a particular
biological phenomenon is occurring, how its behavior can be modified,
and, finally, to design specific experiments to determine their accuracy
and usefulness.

This thesis describes and discusses how and why bacterial filamentation
may be a general mechanism for cell survival upon exposure to toxic
agents, such as antibiotics, based on experimental analyses and
mathematical modeling. We divided this thesis into three chapters that
explain the methodologies used and take us one step closer to
understanding filamentation with each chapter.

@sec-image-processing describes the fundamental process of identifying
and quantifying the properties of each cell over time, for example, its
length, the amount of internal toxin, and the amount of resistance to
the toxin.

@sec-experiment-analysis used the data processed in the previous chapter
to explore bacterial filamentation at the population and single-cell
levels. Data exploration allowed us to simultaneously observe the
behavior of filamentation and its properties in heterogeneous
populations. For reference, one population with an antibiotic resistance
gene located on the chromosome and another on multicopy plasmids.

Finally, in @sec-model-analysis, we postulated a mathematical model that
considers the relationship of cell surface area and volume to the uptake
of a toxic agent diffusing into the medium. This model allowed us to
specifically evaluate the effect of filamentation in an environment
similar to that observed experimentally. Thus, experiments and models
work together to learn more about a biological phenomenon to help
understand and combat the AMR problem.