9 research outputs found
Limits of feedback control in bacterial chemotaxis
Inputs to signaling pathways can have complex statistics that depend on the
environment and on the behavioral response to previous stimuli. Such behavioral
feedback is particularly important in navigation. Successful navigation relies
on proper coupling between sensors, which gather information during motion, and
actuators, which control behavior. Because reorientation conditions future
inputs, behavioral feedback can place sensors and actuators in an operational
regime different from the resting state. How then can organisms maintain proper
information transfer through the pathway while navigating diverse environments?
In bacterial chemotaxis, robust performance is often attributed to the zero
integral feedback control of the sensor, which guarantees that activity returns
to resting state when the input remains constant. While this property provides
sensitivity over a wide range of signal intensities, it remains unclear how
other parameters affect chemotactic performance, especially when considering
that the swimming behavior of the cell determines the input signal. Using
analytical models and simulations that incorporate recent experimental
evidences about behavioral feedback and flagellar motor adaptation we identify
an operational regime of the pathway that maximizes drift velocity for various
environments and sensor adaptation rates. This optimal regime is outside the
dynamic range of the motor response, but maximizes the contrast between run
duration up and down gradients. In steep gradients, the feedback from
chemotactic drift can push the system through a bifurcation. This creates a
non-chemotactic state that traps cells unless the motor is allowed to adapt.
Although motor adaptation helps, we find that as the strength of the feedback
increases individual phenotypes cannot maintain the optimal operational regime
in all environments, suggesting that diversity could be beneficial.Comment: Corrected one typo. First two authors contributed equally. Notably,
there were various typos in the values of the parameters in the model of
motor adaptation. The results remain unchange
Chemotactic smoothing of collective migration
Collective migration -- the directed, coordinated motion of many
self-propelled agents -- is a fascinating emergent behavior exhibited by active
matter that has key functional implications for biological systems. Extensive
studies have elucidated the different ways in which this phenomenon may arise.
Nevertheless, how collective migration can persist when a population is
confronted with perturbations, which inevitably arise in complex settings, is
poorly understood. Here, by combining experiments and simulations, we describe
a mechanism by which collectively migrating populations smooth out large-scale
perturbations in their overall morphology, enabling their constituents to
continue to migrate together. We focus on the canonical example of chemotactic
migration of Escherichia coli, in which fronts of cells move via directed
motion, or chemotaxis, in response to a self-generated nutrient gradient. We
identify two distinct modes in which chemotaxis influences the morphology of
the population: cells in different locations along a front migrate at different
velocities due to spatial variations in (i) the local nutrient gradient and in
(ii) the ability of cells to sense and respond to the local nutrient gradient.
While the first mode is destabilizing, the second mode is stabilizing and
dominates, ultimately driving smoothing of the overall population and enabling
continued collective migration. This process is autonomous, arising without any
external intervention; instead, it is a population-scale consequence of the
manner in which individual cells transduce external signals. Our findings thus
provide insights to predict, and potentially control, the collective migration
and morphology of cell populations and diverse other forms of active matter
The evolution of the bacterial chemotaxis network
Advances in biomolecular technology allow us to sequence entire genomes, but how genes and molecular networks influence the emergence and evolution of phenotypic traits is still unclear. Different fields in biology and medicine are working hard to unravel the relationship between the genome and phenotypes. In this thesis, a new (mechanistic) approach combining systems biology and evolutionary biology is explored to tackle the genotype-phenotype problem. The chemotaxis network of Escherichia coli is used as a model system for its relatively simple network configuration associated with a complex trait such as chemotactic performance. A mathematical model was developed and in silico evolutionary experiments were performed with different environmental conditions. The results show that due to the complexity of the genomic architecture, most individual gene loci have an inconsistent relationship with fitness. In other words, direct relationships between genes and phenotypes are far more complex than just a linear correlation. The reconstruction of the fitness landscape shows that its structure is highly heterogeneous and there are cases in which mutations have unpredictable and inconsistent effects. Another result shows that contrary to static environments, fluctuating environments facilitate the exploration of the fitness landscape. The results in this thesis show the potential of the evolutionary-systems-biology approach, which could help to understand how complex diseases (e.g. cancer or diabetes) develop or how bacteria evolve to become drug resistant