The Value of Information for Populations in Varying Environments

The notion of information pervades informal descriptions of biological systems, but formal treatments face the problem of defining a quantitative measure of information rooted in a concept of fitness, which is itself an elusive notion. Here, we present a model of population dynamics where this problem is amenable to a mathematical analysis. In the limit where any information about future environmental variations is common to the members of the population, our model is equivalent to known models of financial investment. In this case, the population can be interpreted as a portfolio of financial assets and previous analyses have shown that a key quantity of Shannon's communication theory, the mutual information, sets a fundamental limit on the value of information. We show that this bound can be violated when accounting for features that are irrelevant in finance but inherent to biological systems, such as the stochasticity present at the individual level. This leads us to generalize the measures of uncertainty and information usually encountered in information theory

Multi-objective optimisation, sensitivity and robustness analysis in FBA modelling

In this work, we propose a computational framework to design in silico robust bacteria able to overproduce multiple metabolites. To this end, we search the optimal genetic manipulations, in terms of knockout, which also guarantee the growth of the organism. We introduce a multi-objective optimisation algorithm, called Genetic Design through Multi-Objective (GDMO), and test it in several organisms to maximise the production of key intermediate metabolites such as succinate and acetate. We obtain a vast set of Pareto optimal solutions; each of them represents an organism strain. For each solution, we evaluate the fragility by calculating three robustness indexes and by exploring reactions and metabolite interactions. Finally, we perform the Sensitivity Analysis of the metabolic model, which finds the inputs with the highest influence on the outputs of the model. We show that our methodology provides effective vision of the achievable synthetic strain landscape and a powerful design pipeline.</p

Localization of Large ADP-Ribosylation Factor-Guanine Nucleotide Exchange Factors to Different Golgi Compartments: Evidence for Distinct Functions in Protein Traffic

Activation of several ADP-ribosylation factors (ARFs) by guanine nucleotide exchange factors (GEFs) regulates recruitment of coat proteins (COPs) on the Golgi complex and is generally assumed to be the target of brefeldin A (BFA). The large ARF-GEFs Golgi-specific BFA resistance factor 1 (GBF1) and BFA-inhibited GEFs (BIGs) localize to this organelle but catalyze exchange preferentially on class II and class I ARFs, respectively. We now demonstrate using quantitative confocal microscopy that these GEFs show a very limited overlap with each other (15 and 23%). In contrast, GBF1 colocalizes with the cis-marker p115 (86%), whereas BIGs overlap extensively with TGN38 (83%). Consistent with these distributions, GBF1, but not BIG1, partially relocalized to peripheral sites after incubation at 15°C. The new GBF1 structures represent peripheral vesicular tubular clusters (VTCs) because 88% of structures analyzed stained for both GBF1 and p115. Furthermore, as expected of VTCs, they rapidly reclustered to the Golgi complex in a microtubule-dependent manner upon warm-up. These observations suggest that GBF1 and BIGs activate distinct subclasses of ARFs in specific locations to regulate different types of reactions. In agreement with this possibility, COPI overlapped to a greater extent with GBF1 (64%) than BIG1 (31%), whereas clathrin showed limited overlap with BIG1, and virtually none with GBF1