Thermodynamic Uncertainty Relation in the interlinked cascade of RabGTPases

We model the well-known interlinked cascade of Rab GTPases found in eukaryotic cells by using a network of Markov states to investigate if the universal Thermodynamic Uncertainty Relation (TUR) is obeyed in such non-equilibrium system. First, we prove numerically the TUR in both single species model and double species interlinked model. Moreover, our TUR results show that when two Rab GTPase proteins are interlinked, the thermodynamic cost and hence precision is greatly enhanced as compared to single species switching. This implies that at far from equilibrium, the proteins tries to optimise the precision of their performance of biological processes by forming interlinks in the cascade. Again,our TUR results imply that the interlinked cascade (or oscillator) can achieve a range of tunable rate constants (or frequencies) which suggests a means of maintaining its robustness. Lastly, we highlight a close relation between thermodynamic cost-precision, triangular motifs and cancer biology.Comment: 25 pages, 7 figure

The Metabolic Core and Catalytic Switches Are Fundamental Elements in the Self-Regulation of the Systemic Metabolic Structure of Cells

[Background] Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms. [Methodology/Principal Findings] In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson's correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows. [Conclusions/Significance] We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub –with a high degree of effective connectivity- exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure.Consejo Superior de Investigaciones Cientificas (CSIC),grant 201020I026. Ministerio de Ciencia e Innovacion (MICINN). Programa Ramon y Cajal. Campus de Excelencia Internacional CEI BioTIC GENIL, grant PYR-2010-14. Junta de Andalucia, grant P09-FQM-4682

The role of extrinsic noise in biomolecular information processing systems: an in silico analysis

The intrinsic stochasticity of biomolecular systems is a well studied phe- nomenon. Less attention has been paied to other sources of variability, so called extrinsic noise. While the precise definition of extrinsic noise de- pends on the system in question, it affects all cells and its significance has been demonstrated experimentally. Information theory provides a rigorous mathematical framework for quan- tifying both the amount of information available to a signalling system and its ability to transmit this information. Intracellular signal transduction re- mains a relatively unexplored frontier for the application of information theory. In this thesis, we rely on a metric called mutual information to quantify in- formation flow in models of biochemical signalling systems. After briefly discussing the theoretical background and some of the practical difficulties of estimating mutual information in Chapter 2, we apply it in the context of simplified models of intracellular signalling, referred to as motifs. Using a comprehensive set of two-node motifs we explore the effects of extrin- sic noise, model parameters and various combinations of interaction, on the system’s ability to transmit information about an input signal, repre- sented by a telegraph process. Our results illustrate the importance of the system’s response time and demonstrate a trade-off in transmitting infor- mation about the current state of the input or its average intensity over a period of time. In Chapter 4, we address the problem of determining the magnitude of ex- trinsic noise in the presence of intrinsic stochasticity. Using the Approxi- mate Bayesian Computation - sequential Monte Carlo algorithm, together with published experimental data, we infer parameters describing extrinsic noise in a model of E. coli gene expression. Lastly, in Chapter 5, we construct and analyse models of bacterial two- component signalling, bringing together insights gleaned from earlier work. The results show how the abundances of different molecular species in the system may transmit information about the input signal despite its stochas-tic nature and considerable variation in the numbers of protein molecules present.Open Acces

How mathematical modelling elucidates signalling in Bacillus subtilis

P>Appropriate stimulus perception, signal processing and transduction ensure optimal adaptation of bacteria to environmental challenges. In the Gram-positive model bacterium Bacillus subtilis signalling networks and molecular interactions therein are well-studied, making this species a suitable candidate for the application of mathematical modelling. Here, we review systems biology approaches, focusing on chemotaxis, sporulation, sigma B-dependent general stress response and competence. Processes like chemotaxis and Z-ring assembly depend critically on the subcellular localization of proteins. Environmental response strategies, including sporulation and competence, are characterized by phenotypic heterogeneity in isogenic cultures. The examples of mathematical modelling also include investigations that have demonstrated how operon structure and signalling dynamics are intricately interwoven to establish optimal responses. Our review illustrates that these interdisciplinary approaches offer new insights into the response of B. subtilis to environmental challenges. These case studies reveal modelling as a tool to increase the understanding of complex systems, to help formulating hypotheses and to guide the design of more directed experiments that test predictions

Mathematical models and modular composition rules for synthetic genetic circuits

One major challenge in synthetic biology is how to design genetic circuits with predictable behaviors in various biological contexts. There are two limitations to addressing this challenge in mammalian cells. First, models that can predict circuit behaviors accurately in bacteria cells cannot be directly translated to mammalian cells. Second, upon interconnection, the behavior of a module, the building block of a circuit, may be different from its behavior in a standalone setting. In this thesis, I present a bottom-up modeling framework that can be used to predict circuit behaviors in transiently transfected mammalian cells (TTMC). The first part of the framework is based on a novel bin-dependent ODE model that can describe the behavior of modules in TTMC accurately. The second part of the framework rests upon a method of modular composition that allows model-based design of circuits. The efficacies of the bin-dependent model and the method of modular composition are validated via experimental data. The effects of retroactivity, a loading effect that arises from modular composition, on circuit behaviors are also investigated