Evolutionarily stable and fragile modules of yeast biochemical network

Gene and protein interaction networks have evolved to precisely specify cell fates and functions. Here, we analyse whether the architecture of these networks affects evolvability. We find evidence to suggest that in yeast these networks are mainly acyclic, and that evolutionary changes in these parts do not affect their global dynamic properties. In contrast, feedback loops strongly influence dynamic behaviour and are often evolutionarily conserved. Feedback loops are often found to reside in a clustered manner by means of coupling and nesting with each other in the molecular interaction network of yeast. In these clusters some feedback mechanisms are biologically vital for the operation of the module and some provide auxiliary functional assistance. We find that the biologically vital feedback mechanisms are highly conserved in both transcription regulation and protein interaction network of yeast. In particular, long feedback loops and oscillating modules in protein interaction networks are found to be biologically vital and hence highly conserved. These data suggest that biochemical networks evolve differentially depending on their structure with acyclic parts being permissive to evolution while cyclic parts tend to be conserved

A Bayesian framework that integrates heterogeneous data for inferring gene regulatory networks

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.European Commission - Seventh Framework Programme (FP7)Science Foundation Irelan

A Bayesian framework that integrates heterogeneous data for inferring gene regulatory networks

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.European Commission - Seventh Framework Programme (FP7)Science Foundation Irelan

A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research

Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.European Commission - Seventh Framework Programme (FP7)Science Foundation IrelandThe Irish Cancer Societ

A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research

Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.European Commission - Seventh Framework Programme (FP7)Science Foundation IrelandThe Irish Cancer Societ

Navigating the Multilayered Organization of Eukaryotic Signaling: A New Trend in Data Integration

The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks are poorly addressed. Here we review recent advances in the development of publically available databases that help us analyze the signal integration and processing by multilayered networks that specify biological responses in model organisms and human cells.European Commission - Seventh Framework Programme (FP7)Science Foundation Irelan

The Cooperative Behavior Of A Human Work

Self managing systems are collective systems capable of accomplishing difficult task in dynamic and varied environment without any guidance and control. Some particular examples of this kind of systems are swarms and self organizing maps. These algorithms are being used to solve a great deal of complex problems like clustering, classification, optimization etc. In this paper we have tried to develop an algorithm to solve such problems easily and in a computationally efficient way. Our algorithm is based on a simulation of the cooperative behavior of a human work group. Like other multi agent systems we also have multiple agents each of which are connected to its topological neighbors. But instead of using any existing popular learning rules like competitive learning etc. here we have developed a simple decision theory based learning process in which each agent can communicate to its neighbors in order to help them to make an useful decision. This paper also contains some experimental results that we have achieved when we applied our algorithm to some image processing applications. Later we have discussed the scope of our algorithm in many different areas of computation

Navigating the multilayered organization of eukaryotic signaling: a new trend in data integration.

The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks are poorly addressed. Here we review recent advances in the development of publically available databases that help us analyze the signal integration and processing by multilayered networks that specify biological responses in model organisms and human cells