Topological augmentation to infer hidden processes in biological systems

Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Selective Translation of Low Abundance and Upregulated Transcripts in Halobacterium salinarum.

When organisms encounter an unfavorable environment, they transition to a physiologically distinct, quiescent state wherein abundant transcripts from the previous active growth state continue to persist, albeit their active transcription is downregulated. In order to generate proteins for the new quiescent physiological state, we hypothesized that the translation machinery must selectively translate upregulated transcripts in an intracellular milieu crowded with considerably higher abundance transcripts from the previous active growth state. Here, we have analyzed genome-wide changes in the transcriptome (RNA sequencing [RNA-seq]), changes in translational regulation and efficiency by ribosome profiling across all transcripts (ribosome profiling [Ribo-seq]), and protein level changes in assembled ribosomal proteins (sequential window acquisition of all theoretical mass spectra [SWATH-MS]) to investigate the interplay of transcriptional and translational regulation in Halobacterium salinarum as it transitions from active growth to quiescence. We have discovered that interplay of regulatory processes at different levels of information processing generates condition-specific ribosomal complexes to translate preferentially pools of low abundance and upregulated transcripts. Through analysis of the gene regulatory network architecture of H. salinarum, Escherichia coli, and Saccharomyces cerevisiae, we demonstrate that this conditional, modular organization of regulatory programs governing translational systems is a generalized feature across all domains of life.IMPORTANCE Our findings demonstrate conclusively that low abundance and upregulated transcripts are preferentially translated, potentially by environment-specific translation systems with distinct ribosomal protein composition. We show that a complex interplay of transcriptional and posttranscriptional regulation underlies the conditional and modular regulatory programs that generate ribosomes of distinct protein composition. The modular regulation of ribosomal proteins with other transcription, translation, and metabolic genes is generalizable to bacterial and eukaryotic microbes. These findings are relevant to how microorganisms adapt to unfavorable environments when they transition from active growth to quiescence by generating proteins from upregulated transcripts that are in considerably lower abundance relative to transcripts associated with the previous physiological state. Selective translation of transcripts by distinct ribosomes could form the basis for adaptive evolution to new environments through a modular regulation of the translational systems

Adaptive Prediction Emerges Over Short Evolutionary Time Scales.

Adaptive prediction is a capability of diverse organisms, including microbes, to sense a cue and prepare in advance to deal with a future environmental challenge. Here, we investigated the timeframe over which adaptive prediction emerges when an organism encounters an environment with novel structure. We subjected yeast to laboratory evolution in a novel environment with repetitive, coupled exposures to a neutral chemical cue (caffeine), followed by a sublethal dose of a toxin (5-FOA), with an interspersed requirement for uracil prototrophy to counter-select mutants that gained constitutive 5-FOA resistance. We demonstrate the remarkable ability of yeast to internalize a novel environmental pattern within 50-150 generations by adaptively predicting 5-FOA stress upon sensing caffeine. We also demonstrate how novel environmental structure can be internalized by coupling two unrelated response networks, such as the response to caffeine and signaling-mediated conditional peroxisomal localization of proteins

Synergistic epistasis enhances the co-operativity of mutualistic interspecies interactions

Early evolution of mutualism is characterized by big and predictable adaptive changes, including the specialization of interacting partners, such as through deleterious mutations in genes not required for metabolic cross-feeding. We sought to investigate whether these early mutations improve cooperativity by manifesting in synergistic epistasis between genomes of the mutually interacting species. Specifically, we have characterized evolutionary trajectories of syntrophic interactions of Desulfovibrio vulgaris (Dv) with Methanococcus maripaludis (Mm) by longitudinally monitoring mutations accumulated over 1000 generations of nine independently evolved communities with analysis of the genotypic structure of one community down to the single-cell level. We discovered extensive parallelism across communities despite considerable variance in their evolutionary trajectories and the perseverance within many evolution lines of a rare lineage of Dv that retained sulfate-respiration (SR+) capability, which is not required for metabolic cross-feeding. An in-depth investigation revealed that synergistic epistasis across pairings of Dv and Mm genotypes had enhanced cooperativity within SR− and SR+ assemblages, enabling their coexistence within the same community. Thus, our findings demonstrate that cooperativity of a mutualism can improve through synergistic epistasis between genomes of the interacting species, enabling the coexistence of mutualistic assemblages of generalists and their specialized variants

Mechanism for microbial population collapse in a fluctuating resource environment.

Managing trade-offs through gene regulation is believed to confer resilience to a microbial community in a fluctuating resource environment. To investigate this hypothesis, we imposed a fluctuating environment that required the sulfate-reduce

Computational approaches to the modelling of topological and dynamical aspects of biochemical networks

Els mecanismes de regulaci o de les c el lules poden ser modelats per controlar i entendre la biologia cel lular. Diferents nivells d'abstracci o s'utilitzen per descriure els processos biol ogics. En aquest treball s'han utilitzat grafs i equacions diferencials per modelar les interaccions cel lulars tant qualitativament com quantitativa. En aquest treball s'han analitzat dades d'interacci o i activitat de diferents organismes, E. coli i S. cerevisiae: xarxes d'interacci o prote na-prote na, de regulaci o de la transcripci o, i metab oliques, aix com per ls d'expressi o gen omica i prote omica. De la rica varietat de mesures de grafs, una variable important d'aquestes xarxes biol ogiques es la distribuci o de grau, i he aplicat eines d'an alisi estad stica per tal de caracteritzar-la. En tots els casos estudiats les distribucions de grau tenen una forma de cua pesada, per o la majoria d'elles presenten difer encies signi catives respecte un model de llei de pot encia, d'acord amb proves estad stiques. D'altra banda, cap de les xarxes podrien ser assignades de forma inequvoca a cap distribuci o testejada. Pel que fa a un nivell m es microsc opic, hem utilitzat equacions diferencials per estudiar la din amica de models de diversos sistemes bioqu mics. En primer lloc, una eina de programari anomenada ByoDyn ha estat creada des de zero. L'eina permet realitzar simulacions deterministes i estoc astiques, analitzar models mitjan cant estimaci o de par ametres, sensibilitat i an alisi d'identi cabilitat, aix com dissenyar optimament experiments. S'ha creat una interf cie web que ofereix la possibilitat d'interactuar amb el programa d'una manera gr a ca, independentment de la con guraci o de l'usuari, permetent l'execuci o del programa en diferents entorns computacionals. Finalment, hem aplicat un protocol de disseny experimental optim en un model multicel lular de l'embriog enesi en vertebrats.Regulatory mechanisms of cells can be modelled to control and under- stand cellular biology. Di erent levels of abstraction are used to describe biological processes. In this work we have used graphs and di erential equations to model cellular interactions qualitatively and quantitatively. From di erent organisms, E. coli and S. cerevisiae, we have analysed data available for they complete interaction and activity networks. At the level of interaction, the protein-protein interaction network, the tran- scriptional regulatory networks and the metabolic network have been studied; for the activity, both gene and protein pro les of the whole or- ganism have been examined. From the rich variety of graph measures, one of primer importance is the degree distribution. I have applied sta- tistical analysis tools to such biological networks in order to characterise the degree distribution. In all cases the studied degree distributions have a heavy-tailed shape, but most of them present signi cant di erences from a power-law model according to a statistical test. Moreover, none of the networks could be unequivocally assigned to any of the tested distribution. On the other hand, in a more ne-grained view, I have used di erential equations to model dynamics of biochemical systems. First, a software tool called ByoDyn has been created from scratch incorporating a fairly complete range of analysis methods. Both deterministic and stochas- tic simulations can be performed, models can be analysed by means of parameter estimation, sensitivity, identi ability analysis, and optimal ex- perimental design. Moreover, a web interface has been created that pro- vides with the possibility interact with the program in a graphical man- ner, independent of the user con guration, allowing the execution of the program at di erent computational environments. Finally, we have ap- plied a protocol of optimal experimental design on a multicellular model of embryogenesis