Top PDF Mutational robustness of gene regulatory networks.

Mutational robustness of gene regulatory networks.

Mutational robustness of gene regulatory networks.

We focused on obtaining evidence for factors influencing robustness against mutations of network structure. The results of our analysis provide several clues towards such factors. An important follow-up would be to investigate in detail the mechanistic aspects of why certain types of networks are more robust than others. Although we will leave that question to a large extent to be addressed by future work, we briefly discuss two relevant issues. One is related to the observation that regulatory mutations in dimer networks have larger effect than in monomeric networks. This might be related to the fact that in dimeric networks, the change in expression of a target gene caused by a regulatory mutation will not only influence other genes via regulatory interactions of that target, but also via protein interactions that it is involved in. We obtained evidence for this by analyzing whether the regulatory mutation mainly had a direct effect (changing expression level of the target gene targeted by the mutation) or mainly an indirect effect (changing expression levels of other genes). In dimeric networks, the indirect effect was much larger than the direct effect, whereas for monomeric networks, the indirect effect was somewhat smaller than the direct effect. Comparing dimeric and monomeric networks, the direct effects were roughly compa- rable, whereas the indirect effect was much larger in the dimeric network (data not shown). Although this should be analyzed in more detail, this analysis indicates a causal mechanism for the observed differences in robustness between dimeric and monomeric net- Figure 4. Cumulative histogram of sequence conservation of DNA contacting residues in human TFs. Sequence entropy was calculated for dimeric (black) and monomeric TFs (red). Lower values indicate more conservation.
Mostrar mais

9 Ler mais

Global analysis of photosynthesis transcriptional regulatory networks.

Global analysis of photosynthesis transcriptional regulatory networks.

The second photosynthesis-related TF characterized for the first time in this study was MppG. Our data showed that MppG functions as a direct transcriptional repressor of photopigment biosynthesis operons, including bchCXYZ and bchFNBHM, with high cellular levels of this protein inhibiting photosynthetic growth. In addition, transcripts from several other operons that encode photosynthesis-related functions were indirectly repressed by MppG. Our data predict that much of this indirect regulation of photosynthesis function is achieved through the direct regulation of the gene that encodes the anti-repressor, AppA, by MppG. Reduced cellular levels of AppA caused by the presence of MppG, would in principle cause accumulation of free PpsR under photosynthetic conditions, which would lead to repression of the photosynthesis-related genes that are PpsR targets (Fig. 5). Given that mppG transcript levels are significantly elevated during photosynthetic growth, its function in repressing photopigment synthesis would appear to be counterintuitive, similar to the observation for the sRNA, PcrZ [28]. Since no significant difference in photosynthetic growth was observed between WT and DMppG cells, the additional pigment produced in the DMppG mutant strain did provide increased fitness, potentially equating to a waste of cellular resources in the production of this extra pigment. In addition, the presence of excess photopigment could be a source of metabolic stress, especially since they can result in production of reactive oxygen species if light is present under microaerophilic conditions in the lab or in nature [42]. Thus, MppG may function as a negative modulator of pigment synthesis to ensure the optimal expression and tight coordination between expression of photopigment biosynthetic pathway genes and those for other components of the photosynthetic apparatus.
Mostrar mais

21 Ler mais

Evolution of networks for body plan patterning; interplay of modularity, robustness and evolvability.

Evolution of networks for body plan patterning; interplay of modularity, robustness and evolvability.

Summarizing, the minimum segment network produces a significant number of domains as a side effect of producing segments, and the minimum domain network performs rather poorly at reproducing the original domain pattern. We conclude that the evolved network is rather non-modular. Instead segments and domains are generated in a highly integrated manner. Indeed, if we compare the two minimum networks, we see that only 2 genes are unique for the minimum segment network and only 3 genes are unique for the minimum domain network (light blue), all other genes are used both for segmentation and domain formation. Thus, to understand the mechanism behind body plan patterning we should look at the core network, which generates segments and domains in an integrated manner. The observation that a complex gene expression transient is translated into a spatial differentiation pattern suggests two things. First, the core network contains multiple attractors allowing for different stable cell types. Indeed, we see a total of 6 positive feedback loops, essential for attractor formation [52–54], in the core network (Figure 6A, top row). Second, the network produces complex and slow expression dynamics, allowing different times of wavefront passage to cause convergence to different attractors. In Text S1 we further explain this developmental mechanism and contrast it with the one described by Francois and Siggia in which a slow timer gene controls a linear sequence of gene activations [43]. Finally, to understand how segments arise as part of this process we study the regulation of the segmentation gene. We see that genes 14 and 15 Figure 5. Architectural modularity scores. Q value frequency
Mostrar mais

16 Ler mais

Functional alignment of regulatory networks: a study of temperate phages.

Functional alignment of regulatory networks: a study of temperate phages.

phage genome is integrated into the bacterial chromosome and only a few proteins are active. For both phages, three core proteins (CI, Cro, and CII in k, and CI, Apl, and CII in 186) do the main computations, with the switch into lysogeny being coordinated by CII and the reverse switch into the lytic mode initiated by activation of the host SOS response recombination (RecA) protein. The gene-regula- tory networks of all temperate phages have evolved to provide lysogenic and lytic states, and, moreover, to switch from one state to another when particular signals have been received from bacterial proteins, and thus effectively perform the same function.
Mostrar mais

5 Ler mais

Metabolic constraint-based refinement of transcriptional regulatory networks.

Metabolic constraint-based refinement of transcriptional regulatory networks.

There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10 2172 ), significantly better than using gene expression alone. We applied
Mostrar mais

14 Ler mais

GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

The networks with colored edges (Figs. 5D–5F) display the results of a mathematical model, where the expression levels of the individual transcription factors were modeled using mass balance ordinary differential equations with a sigmoidal production function and linear degradation (Dahlquist et al., 2015). Each equation in the model included a production rate, a degradation rate, weights that denote the magnitude and type of influence of the connected transcription factors (activation or repression), and a threshold of expression. The differential equation model was fit to published yeast cold shock microarray data from Schade et al. (2004) using a penalized nonlinear least squares approach. The visualization produced by GRNsight is displaying the results of the optimized weight parameters. Positive weights > 0 represent an activation relationship and are shown by pointed arrowheads. One example is that CIN5 activates the expression of MSN1. Negative weights < 0 represent a repression relationship and are shown by a blunt arrowhead. One example is that ABF1 represses the expression of MSN1. The thicknesses of the edges also vary based on the magnitude of the absolute value of the weight, with larger magnitudes having thicker edges and smaller magnitudes having thinner edges. In Figs. 5D–5F, the edge corresponding to the repression of the expression of MSN1 by ABF1 stands out as the thickest because the absolute value of its weight parameter (-2.97) has the largest magnitude out of all the weights ( Dahlquist et al., 2015). It is noticeable that none of the edges that represent activation are as thick as the ABF1-to- MSN1 edge; only RAP1-to-RPH1 and HAL9-to-MSN4 are close with weights of 1.50 and 1.43, respectively.
Mostrar mais

24 Ler mais

Contribution of Panton-Valentine leukocidin in community-associated methicillin-resistant Staphylococcus aureus pathogenesis.

Contribution of Panton-Valentine leukocidin in community-associated methicillin-resistant Staphylococcus aureus pathogenesis.

Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) strains typically carry genes encoding Panton- Valentine leukocidin (PVL). We used wild-type parental and isogenic PVL-deletion (Dpvl) strains of USA300 (LAC and SF8300) and USA400 (MW2) to test whether PVL alters global gene regulatory networks and contributes to pathogenesis of bacteremia, a hallmark feature of invasive staphylococcal disease. Microarray and proteomic analyses revealed that PVL does not alter gene or protein expression, thereby demonstrating that any contribution of PVL to CA-MRSA pathogenesis is not mediated through interference of global gene regulatory networks. Inasmuch as a direct role for PVL in CA-MRSA pathogenesis remains to be determined, we developed a rabbit bacteremia model of CA-MRSA infection to evaluate the effects of PVL. Following experimental infection of rabbits, an animal species whose granulocytes are more sensitive to the effects of PVL compared with the mouse, we found a contribution of PVL to pathogenesis over the time course of bacteremia. At 24 and 48 hours post infection, PVL appears to play a modest, but measurable role in pathogenesis during the early stages of bacteremic seeding of the kidney, the target organ from which bacteria were not cleared. However, the early survival advantage of this USA300 strain conferred by PVL was lost by 72 hours post infection. These data are consistent with the clinical presentation of rapid-onset, fulminant infection that has been associated with PVL-positive CA- MRSA strains. Taken together, our data indicate a modest and transient positive effect of PVL in the acute phase of bacteremia, thereby providing evidence that PVL contributes to CA-MRSA pathogenesis.
Mostrar mais

8 Ler mais

Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria

Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria

In addition to experiments directed toward specific pro- teins, large-scale protein-protein interactions are crucial to post- genomic systems biology. HTT applied to proteomics, such as the yeast two-hybrid system, assisted scientists in different fields in the construction of large protein-protein interaction networks (Wallach et al., 2013; Ngounou Wetie et al., 2014). Networks generated from these studies serve to identify potential targets for future biochemical and bioinformatics studies (Kaçar and Gaucher, 2013; Yu et al., 2013). To fill this gap in the field of cyanobacteria, Sato et al. (2007) undertook the first system- atic identification of protein interactions in Synechocystis 6803. Using yeast two-hybrid assays, they screened 1825 genes, dis- covering 3236 independent two-hybrid interactions (Sato et al., 2007). Such interaction data are important for functional anal- yses of genes in Synechocystis 6803, as well as for those con- served in marine cyanobacteria. They are accessible through the CyanoBase website (Nakao et al., 2010) (Table 1). Nowadays, several resources that model protein-protein interaction net- works for cyanobacteria are available for researchers to examine, i.e., generic databases such as STRING, as well as specialized databases such as SynechoNET or InteroPORC. STRING covers more than 1000 organisms, containing experimentally-validated interactions, predicted and transferred interactions, together with interactions obtained through text mining (Franceschini et al., 2013). In contrast, the SynechoNET database is dedicated to Synechocystis 6803, and covers 2930 proteins (i.e., 79% of all predicted proteins in Synechocystis 6803). It includes 109,532 pre- dicted protein-protein interactions extracted from the databases STRING (2658 proteins, 26,805 interactions), PSIMAP-based (1028 proteins, 12,748 interactions), InterDom (1760 proteins, 80,319 interactions) and iPfAM (1541 proteins, 13,448 interac- tions) (Kim et al., 2008). The SynechoNET visualization interface also permits the exploration of a “high confidence” sub-network
Mostrar mais

18 Ler mais

Genome-wide identification of regulatory elements and reconstruction of gene regulatory networks of the green alga Chlamydomonas reinhardtii under carbon deprivation.

Genome-wide identification of regulatory elements and reconstruction of gene regulatory networks of the green alga Chlamydomonas reinhardtii under carbon deprivation.

Many genes have previously been shown to be up-regulated in C. reinhardtii cells during CCM induction. Among them is Cah1, which encodes periplasmic carbonic anhydrase (chromosome_4:1849052-1853308, protein ID 24120; name: estExt_fgenesh1_pm.C_150006, Chlamydomonas genome annotation v.4) [13]. The 5' upstream region of the Cah1 gene contains at least one silencer and one enhancer region [20]. Two putative enhancer elements (EEs), i.e., EE-1 (AGATTTTCACCGGTTGGAAGGAGGT; -293 bp to -269 bp upstream of the translation initiation codon) and EE-2 Figure 4. Cumulative histogram of the approximate distances of FAIRE summits from their next closest annotated gene. Genomic coordinates were used to calculate the distances between FAIRE summits and the start and stop codons of their nearest gene transcripts. The number of transcripts with at least one FAIRE peak assigned was counted and the distances to the start or stop codons computed. The gray line indicates the cumulative percentage of the transcripts to which FAIRE peaks were assigned. As seen, approximately 80% of the FAIRE summits were located within 1.5 kb up- or downstream of the transcript´s start or stop codon. FAIRE peaks were detected using MACS tool v. 1.4.0beta, with a p-value cutoff equal to 10 -05 .
Mostrar mais

16 Ler mais

Phenotypic robustness and the assortativity signature of human transcription factor networks.

Phenotypic robustness and the assortativity signature of human transcription factor networks.

Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs — such as their degree distribution — with the robustness of a TFN’s gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN’s assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN’s assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness.
Mostrar mais

12 Ler mais

Using regulatory and epistatic networks to extend the findings of a genome scan: identifying the gene drivers of pigmentation in merino sheep.

Using regulatory and epistatic networks to extend the findings of a genome scan: identifying the gene drivers of pigmentation in merino sheep.

Despite these considerations, our approach has identified genes which would have remained hidden using either data type in isolation. In what follows, and as a proof of concept validating the biological relevance of our results, we describe how genes located within in the intersecting landscape of the joint network (Figure 3) are known to be involved in pigmentation and more specifically in vitiligo. CD9 is a cell surface protein member of the tetraspanin family mediating signal transduction events that play a role in the regulation of cell development, activation, growth and motility [17] including motility of epidermal keratinocytes [18,19]. The finding that it can trigger platelet activation [20] makes our finding of an epistatic SNP pair connecting CD9 and PDGFRA highly relevant. Indeed, one of our main candidates underpinning the piebald phenotype is PDGFRA taking, along with CD9 and IGFBP7, a prominent hub role in the landscape intersecting the regulatory and the epistatic networks. Xu et al. [21] speculate that the PDGFRA gene may be a candidate susceptibility gene of vitiligo. Furthermore, within the context of livestock species a genome-wide scan [22] found the gene PDGFRA to be under positive selection in Montbe´liarde breed of cattle possibly underlying the primary importance of coat color patterns for herd-book registration. Another intersecting genes (Figure 3) is insulin-like growth factor-binding protein 7 (IGFBP7) which is a secreted protein and functions extracellularly. Hochberg et al. [23] first showed that IGFBP7 is underexpressed in psoriatic epidermis but is inducible by ultraviolet treatment. Since then, IGFBP7 has received particular attention due to its potential to induce apoptosis in human melanoma cell lines [24,25]. Of particular relevance is the fact that both PDGFRA and IGFBP7 are located
Mostrar mais

9 Ler mais

Systematic identification of core transcription factors mediating dysregulated links bridging inflammatory bowel diseases and colorectal cancer.

Systematic identification of core transcription factors mediating dysregulated links bridging inflammatory bowel diseases and colorectal cancer.

approach named network TOM was then proposed to find such a potential clue linking inflammation and cancer, via integrating several expression data sets with different platforms. Indeed, such network integration algorithm was minimally affected by the inter- platform difference [51], as suggested by the hierarchical clustering that, those networks were clearly grouped into three major branches (namely, cancer, inflammation and normal branch). Obvious structure similarity or regulatory similarity was observed among tightly clustered networks in each branch, especially the normal branch as expected although the networks were constructed using expression data from different platforms. It was undoubtedly that we could do much better using a large compendium of expression data sets with unified platform. Additionally, in consideration of the data heterogeneity not only from platform, several other researches have also introduced network integration in stead of directly assembling expression profiles to study the functional links among gene pairs [52,53,54]. Besides the three major successfully clustered branches of normal, inflammation and cancer, a special branch of tightly clustered inflammation and cancer networks was also identified. In order to confirm the robustness of the branch, we also performed permutation tests to construct new clustering results via repeatedly removing one network out. As indicated, the network clustering results generated in network permutation were similar with the clustering using all networks, which also suggested that the platform had minimal effects on the network integration approach. To delineate the links in the intestine, we computed relationships dysregulated with significant gain or loss of mutual information between inflammation and cancer networks, and then generated 24 core TFs together with their connected IC genes.
Mostrar mais

12 Ler mais

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

Once the ensemble is trained, the topology of the gene regulatory network is obtained by applying a second procedure. Considering each gene in the network separately, we pass a value of 1 to the input neuron of the correspondent multilayer perceptron, consequently recording its output values. The continuous output values in the range ½{1,1 represent the expected normalized expression values for the other genes (its neighborhood). This procedure basically aims at verifying the correlation between the input gene and all the others: assuming the input gene maximally expressed (the value 1), an output value of (i.e.) 1 indicates that the correspondent gene will be also maximally expressed, thus indicating perfect correlation between the two genes. An output value of (i.e.) {1 indicates that the correspondent gene will be maximally under-expressed: perfect anti-correlation of the two genes. Thus, the continuous output values in the range ½{1,1 are interpretable in terms of positive correlation (w0), anti-correlation (v0) and no-correlation (0). By cycling this procedure through all the ensemble members in the regression system, we obtain N (one for each of the N genes in the network) vectors of length N{1 of continuos values in ½{1,1. The correlation matrix is obtained by correctly joining the N vectors. It is important to note that all the values of the diagonal of the adjacency matrix are equal to 0 by construction: this procedure does not allow discovering of gene self correlation (regulation) patterns, but only correlation patterns among different genes. Finally the adjacency matrix of the sought gene network is obtained by thresholding the correlation coefficients.
Mostrar mais

19 Ler mais

Global properties and functional complexity of human gene regulatory variation.

Global properties and functional complexity of human gene regulatory variation.

In the majority of cases, human disease and complex trait association studies do not identify plausible coding variants implicating a single gene. As a result, post hoc identification of the causal gene(s) that underlie an association signal remains a significant problem that can limit the biological interpretability of disease association study results. However, eQTLs are by definition associated with a specific gene, and trait-associated variants that are also eQTLs can identify potentially causal genes for further functional studies. An early example of the power of this approach was highlighted by the discovery that asthma- associated variants spanning a region of 206 kb (and several gene loci) on the long arm of chromosome 17 [54]. A subsequent eQTL mapping experiment revealed that associated variants were also correlated with expression changes at a specific gene, ORMDL3. Further follow-up studies have suggested that changes in expression of ORMDL3 alter endoplasmic reticulum-mediated calcium signalling, which in turn may effect a change in inflammatory response [55]. Driven by the success of this and other studies, eQTL mapping is now becoming a standard tool for the identification of the genes and regulatory networks that are important for phenotypic variation, with recent applications to psoriasis susceptibility [51], psoriatic arthritis [56], LDL choles- terol levels [57], schizophrenia susceptibility [58], Type 2 diabetes [59], and obesity-related-traits [17] (for a comprehensive review, see [60]).
Mostrar mais

8 Ler mais

Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis.

Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis.

Gene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we have designed a small-sample iterative optimization algorithm (SSIO) to quantitatively model GRNs with nonlinear regulatory relationships. The algorithm utilizes gene expression data as the primary input and it can be applied in case of small-sized samples. Using SSIO, we have quantitatively constructed the dynamic models for the GRNs controlling human and mouse adipogenesis. Compared with two other commonly-used methods, SSIO shows better performance with relatively lower residual errors, and it generates rational predictions on the adipocyte responses to external signals and steady-states. Sensitivity analysis further indicates the validity of our method. Several differences are observed between the GRNs of human and mouse adipocyte differentiations, suggesting the differences in regulatory efficiencies of the transcription factors between the two species. In addition, we use SSIO to quantitatively determine the strengths of the regulatory interactions as well as to optimize regulatory models. The results indicate that SSIO facilitates better investigation and understanding of gene regulatory processes.
Mostrar mais

12 Ler mais

Modelling and Simulation of Biological Regulatory Networks by Stochastic Petri Nets

Modelling and Simulation of Biological Regulatory Networks by Stochastic Petri Nets

approaches devised for modeling the structure of BRNs exist which are used for analysis of dynamical properties [1]. René Thomas Discrete Modelling technique [2] called for representing the change in expression level in a gene or biological entity with a logical function having discrete values. However, it ignores the time taken for the activation or degradation levels. Hybrid Modelling Technique [3] addressed this short coming by associating delays in BRNs by considering the change in levels as piece-wise linear functions thus making it possible to perform model checking and obtain various useful properties. This approach, however, only considers a linear rate of change in the levels and as a result it associates a range of delays within which a certain dynamical behaviour is possible.
Mostrar mais

6 Ler mais

Robust network topologies for generating switch-like cellular responses.

Robust network topologies for generating switch-like cellular responses.

First, across all compositional classes, a significantly larger num- ber of networks demonstrated ultrasensitive behavior than bistable behavior (Fig. 3A), in line with the observation in biological systems that bistability is typically accompanied by ultrasensitivity [5,35,36], but ultrasensitivity can also arise in the absence of bistability [8, 10,37,38]. Second, within a compositional class, a small proportion of networks exhibit switch-like behavior on a large percentage of random parameter sets. The highly skewed nature of robustness score distributions demonstrates that network architecture alone can impact robustness, and that a particular network’s probability of generating switch-like behavior can be dramatically improved with rewiring, and without fine-tuning of kinetic constants such as those associated with binding or catalysis. Third, and most importantly, network composition strongly influences robustness in generating switch-like behavior. Compared to EEE and TTT classes, networks in the hybrid EET and ETT compositional classes yield ultra- sensitive responses on a significantly larger proportion of parameter sets, with the most robust networks achieving ultrasensitivity robustness scores as high as 28%; in contrast, maximum ultra- sensitivity robustness scores in the EEE and TTT classes are 6% and 3%, respectively. For bistability, maximum robustness scores for the EET and ETT compositional classes are approximately 16% and 18%, respectively, while scores for EEE and TTT classes are significantly lower at 3% and 1%, respectively (Fig. 3A). Our findings demonstrate that a particular network topology can yield markedly different robustness scores under different compositional regimes, and suggest that minimal networks composed of an enzyme input component, a transcription output component, and an additional enzyme or transcription regulatory node may be optimal for generating switch-like behavior.
Mostrar mais

12 Ler mais

Ground state robustness as an evolutionary design principle in signaling networks.

Ground state robustness as an evolutionary design principle in signaling networks.

With reference to the robustness principle, our approach is somewhat complementary to that of Shinar et al. [43]. There, the signal-on case is considered in which the output should accurately match the input despite variations in components. However, through our systematic approach we could analyze a particular set of models and highlight topological features which to our knowledge have not been considered before in the context of signaling networks. The generality of our approach required us to make some drastically simplifying assumptions, such as equal rate constants for deactivation processes. Clearly, for many biological systems such simplifications are unrealistic. To overcome the simplifications made on the rate constants, a future prospect could be to combine our notion of robustness with that introduced in [13] to also quantify the parameter ranges of stable behavior for cases for which the analytical results presented here do not hold. Another limitation of our theoretical framework is that we did only analyze positive regulatory structures and it would be an interesting and obvious extension of our approach to also include the stabilizing or destabilizing effects of negative feedback loops.
Mostrar mais

8 Ler mais

Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

and the logic of the interactions (Figure 1). The computa- tional component is based on dynamic growth simulations using regulated Flux Balance Analysis (rFBA), which is a constraints-based approach for bounding allowable steady- state metabolic network flux distributions, coupled with a Boolean logic formalism for transcriptional regulation. The laboratory component has been partially detailed elsewhere [13]. In summary, this experimental procedure employs single or double TF knockout (KO) strains to infer TF–target gene logic rules from a comparison between KO and wild-type gene expression profiles in two growth environments. This procedure is now augmented with TF binding assays to confirm direct TF interaction with target gene promoters [14–16]. In the context of this experimental protocol, an experiment design consists of a growth environment shift and a group of TFs (or ‘‘knockout group’’ [KG]) for which to create deletion strains. The purpose of the algorithm described herein is to produce such experiment designs, and to do so with the primary goal of maximizing the efficiency of the reconstruction process.
Mostrar mais

10 Ler mais

Functional Trade-Offs in Promiscuous Enzymes Cannot Be Explained by Intrinsic Mutational Robustness of the Native Activity.

Functional Trade-Offs in Promiscuous Enzymes Cannot Be Explained by Intrinsic Mutational Robustness of the Native Activity.

The extent to which an emerging new function trades off with the original function is a key characteristic of the dynamics of enzyme evolution. Various cases of laboratory evolution have unveiled a characteristic trend; a large increase in a new, promiscuous activity is often accompanied by only a mild reduction of the native, original activity. A model that associ- ates weak trade-offs with “evolvability” was put forward, which proposed that enzymes pos- sess mutational robustness in the native activity and plasticity in promiscuous activities. This would enable the acquisition of a new function without compromising the original one, reducing the benefit of early gene duplication and therefore the selection pressure thereon. Yet, to date, no experimental study has examined this hypothesis directly. Here, we investi- gate the causes of weak trade-offs by systematically characterizing adaptive mutations that occurred in two cases of evolutionary transitions in enzyme function: (1) from phosphotries- terase to arylesterase, and (2) from atrazine chlorohydrolase to melamine deaminase. Mutational analyses in various genetic backgrounds revealed that, in contrast to the prevail- ing model, the native activity is less robust to mutations than the promiscuous activity. For example, in phosphotriesterase, the deleterious effect of individual mutations on the native phosphotriesterase activity is much larger than their positive effect on the promiscuous ary- lesterase activity. Our observations suggest a revision of the established model: weak trade-offs are not caused by an intrinsic robustness of the native activity and plasticity of the promiscuous activity. We propose that upon strong adaptive pressure for the new activ- ity without selection against the original one, selected mutations will lead to the largest pos- sible increases in the new function, but whether and to what extent they decrease the old function is irrelevant, creating a bias towards initially weak trade-offs and the emergence of generalist enzymes.
Mostrar mais

18 Ler mais

Show all 10000 documents...