Energy dependence of signalling dynamics and robustness in bacterial two component systems

Abstract

One of the best known ways bacteria cells understand and respond to the environment are through Two-Component Systems (TCS). These signalling systems are highly diverse in function and can detect a range of physical stimuli including molecular concentrations and temperature, with a range of responses including chemotaxis and anaerobic energy production. TCS exhibit a range of different molecular structures and energy costs, and multiple types co-exist in the same cell. TCSs that incur relatively high energy cost are abundant in biology, despite strong evolutionary pressure to efficiently spend energy. We are motivated to discern what benefits, if any, the more energetically expensive variants had for a cell. We seek to answer this question by modelling energy flow through two variants of TCS. This was accomplished using bond graphs, a physics-based modelling framework that accurately models energy transfer through different physical domains. Our analysis demonstrates that energy availability can affect a cell's signal sensitivity, noise filtering effectiveness, and the stimulus level where cell response is maximal. We also found that these properties are determined not by the molecular parameters themselves, but the reaction rate parameters that govern the reaction systems as a whole. This suggests possible connections between the molecular structure and evolutionary purpose of any two-component system. This opens the door to new synthetic circuit design in systems biology, and we propose new hypotheses about this link between structure and purpose that could be experimentally verified.

Sensitivity analysis of biochemical systems using bond graphs

“…In many cases, biological networks are designed to be robust to noise, independent of parameter values [85,86]. However, this robustness often comes at a cost to resource and energy consumption [41,87], and in some cases, noise is harnessed for biological control [88,89].…”

“…The Python code used to generate the figures in this paper is available as Jupyter notebooks at . Software tools for manipulating biochemical bond graphs are available in Python [57] at and in Julia [58] at . Both sets of tools are designed for modularity and scaleability and can generate both symbolic ODEs and numerical ODEs for simulation.…”

BondGraphs.jl: Composable energy-based modelling in systems biology

“…Bond graph based models of biological networks are biophysics-based and are scalable to large networks of interactions; they satisfy the laws of thermodynamics implicitly, and therefore they enable efficient construction of models that couple interactions between different sub-systems within a biological process. Recent biological bond graph models include mitochondrial respiration , two component bacterial systems (Forrest et al, 2023), and vascular blood flow (Safaei et al, 2018).…”

Fine-grained modelling of ATP dependence of decision-making capacity in genetic regulatory networks

“…The role of ATP in gene expression (Das Neves et al 2010; Mahmoudabadi et al 2019; Phillips and Milo 2009; Flamholz, Phillips, and Milo 2014; Kafri et al 2016), and the fact that substantial cell-to-cell diversity can exist in ATP concentrations in different systems (Yaginuma et al 2014; Takaine et al 2019; Yoshida, Kakizuka, and Imamura 2016; De Col et al 2017) means that ATP variability can lead to cell-to-cell variability in GRN dynamics and behaviour. This mirrors the ATP dependence of other biomolecular pathways (Gawthrop and Crampin 2017; 2014; Qian and Beard 2006) – for example, the detailed role of ATP in shaping the dynamics of signalling cascades has recently been illustrated using quantitative modelling grounded in systems biology and thermodynamics (Forrest et al 2023). In GRNs, theoretical studies have shown that simplified GRN models (Karlebach and Shamir 2008), using coarse-grained descriptions of genes and gene expression processes, exhibit strong energy-dependent diversity in decision-making capacity (Johnston et al 2012; Kerr, Jabbari, and Johnston 2019; Kerr et al 2022).…”