A hierarchical approach to model decision making: a study in chemotactic behavior ofEscherichia coli

Abstract

Reducing the complex behavior of living entities to its underlying physical and chemical 11 processes is a formidable task in biology. Complex behaviors can be characterized as decision 12 making: the ability to process the incoming information via an intracellular network and act upon 13 this information to choose appropriate strategies. Motility is one such behavior that has been the 14 focus many modeling efforts in the past. Our aim is to reduce the chemotactic behavior in E. coli to 15 its molecular constituents in order to paint a comprehensive and end-to-end picture of this 16 intricate behavior. We utilize a hierarchical approach, consisting of three layers, to achieve this goal: 17 at the first level, chemical reactions involved in chemotaxis are simulated. In the second level, the 18 chemical reactions give rise to the mechanical movement of six independent flagella. At the last 19 layer, the two lower layers are combined to allow a digital bacterium to receive information from its 20 environment and swim through it with verve. Our results are in concert with the experimental 21 studies concerning the motility of E. coli cells. In addition, we show that our detailed model of 22 chemotaxis is reducible to a non-homogeneous Markov process. 23 24 2009). Motility can be viewed as a decision-making process that benefits the living cell by enabling 30 it to find resources in its niche more efficiently (Xie and Wu, 2014). Cell motility requires sensors 31 to monitor the environment, actuators to act upon the incoming information, and an network to 32 process that information. 33 34 The majority of bacteria are motile, swimming being its most common form (Jarrell and McBride, 35 2008; Lauga, 2016). Early studies revealed a substantial amount of variation in motility of clonal 36 cells as they navigate a uniform environment (Dufour et al., 2016). In a homogeneous environment 37 with uniformly-distributed resources, all decisions apropos of motility would be equally likely to be 38 taken -i.e., random walk. Consequently in such circumstances, a motile cell would randomly navigate 39 1 of 18 the environment. Having encountered a non-uniform distribution of resources in the environment, 40 a motile cell will move to more resource-rich areas -i.e., the default random walk turns into biased 41 random walk. 42 43 Chemotaxis, the ability of bacterial cells to sense chemical cues in their environment and move 44 accordingly, predates the divergence of the eubacteria from the archaebacteria (Woese and Fox, 45 1977). The steps taken in chemotactic behavior, to seek attractants and avoid repellents, can be 46 seen as a chain of biased random steps. To illustrate this point, we can focus on Escherichia coli. E. 47 coli detects the concentration of chemoattractants in its vicinity via an array of sensors, processes 48 the sensory data via a sensory network, and swims accordingly using its flagella (Sourjik and 49 Wingreen, 2012; Frankel et al., 2014). Following a trail of chemoattractants to get to their source is 5...