Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform Cristo

Inference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the inference of a GRN is a complex optimization problem that involve processing S-System models, which include large amount of gene expression data from hundreds (even thousands) of genes in multiple time-series (essays). This complexity, along with the amount of data managed, make the inference of GRNs to be a computationally expensive task. Therefore, the genera- tion of parallel algorithmic proposals that operate efficiently on distributed processing platforms is a must in current reconstruction of GRNs. In this paper, a parallel multi-objective approach is proposed for the optimal inference of GRNs, since min- imizing the Mean Squared Error using S-System model and Topology Regularization value. A flexible and robust multi-objective cellular evolutionary algorithm is adapted to deploy parallel tasks, in form of Spark jobs. The proposed approach has been developed using the framework jMetal, so in order to perform parallel computation, we use Spark on a cluster of distributed nodes to evaluate candidate solutions modeling the interactions of genes in biological networks.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Enabling controlling complex networks with local topological information

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulflling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefned state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efciently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio

Stochastic facilitation in heteroclinic communication channels

Biological neural systems encode and transmit information as patterns of activity tracing complex trajectories in high-dimensional state spaces, inspiring alternative paradigms of information processing. Heteroclinic networks, naturally emerging in artificial neural systems, are networks of saddles in state space that provide a transparent approach to generate complex trajectories via controlled switches among interconnected saddles. External signals induce specific switching sequences, thus dynamically encoding inputs as trajectories. Recent works have focused either on computational aspects of heteroclinic networks, i.e., Heteroclinic Computing, or their stochastic properties under noise. Yet, how well such systems may transmit information remains an open question. Here, we investigate the information transmission properties of heteroclinic networks, studying them as communication channels. Choosing a tractable but representative system exhibiting a heteroclinic network, we investigate the mutual information rate (MIR) between input signals and the resulting sequences of states as the level of noise varies. Intriguingly, MIR does not decrease monotonically with increasing noise. Intermediate noise levels indeed maximize the information transmission capacity by promoting an increased yet controlled exploration of the underlying network of states. Complementing standard stochastic resonance, these results highlight the constructive effect of stochastic facilitation (i.e., noise-enhanced information transfer) on heteroclinic communication channels and possibly on more general dynamical systems exhibiting complex trajectories in state space

CoryneCenter – An online resource for the integrated analysis of corynebacterial genome and transcriptome data

Neuweger H, Baumbach J, Albaum S, et al. CoryneCenter: an online resource for the integrated analysis of corynebacterial genome and transcriptome data. BMC Systems Biology. 2007;1(1): 55.Background: The introduction of high-throughput genome sequencing and post-genome analysis technologies, e.g. DNA microarray approaches, has created the potential to unravel and scrutinize complex gene-regulatory networks on a large scale. The discovery of transcriptional regulatory interactions has become a major topic in modern functional genomics. Results: To facilitate the analysis of gene-regulatory networks, we have developed CoryneCenter, a web-based resource for the systematic integration and analysis of genome, transcriptome, and gene regulatory information for prokaryotes, especially corynebacteria. For this purpose, we extended and combined the following systems into a common platform: (1) GenDB, an open source genome annotation system, (2) EMMA, a MAGE compliant application for high-throughput transcriptome data storage and analysis, and (3) CoryneRegNet, an ontology-based data warehouse designed to facilitate the reconstruction and analysis of gene regulatory interactions. We demonstrate the potential of CoryneCenter by means of an application example. Using microarray hybridization data, we compare the gene expression of Corynebacterium glutamicum under acetate and glucose feeding conditions: Known regulatory networks are confirmed, but moreover CoryneCenter points out additional regulatory interactions. Conclusion: CoryneCenter provides more than the sum of its parts. Its novel analysis and visualization features significantly simplify the process of obtaining new biological insights into complex regulatory systems. Although the platform currently focusses on corynebacteria, the integrated tools are by no means restricted to these species, and the presented approach offers a general strategy for the analysis and verification of gene regulatory networks. CoryneCenter provides freely accessible projects with the underlying genome annotation, gene expression, and gene regulation data. The system is publicly available at http://www.CoryneCenter.d

Bio-inspired Dynamic Control Systems with Time Delays

The world around us exhibits a rich and ever changing environment of startling, bewildering and fascinating complexity. Almost everything is never as simple as it seems, but through the chaos we may catch fleeting glimpses of the mechanisms within. Throughout the history of human endeavour we have mimicked nature to harness it for our own ends. Our attempts to develop truly autonomous and intelligent machines have however struggled with the limitations of our human ability. This has encouraged some to shirk this responsibility and instead model biological processes and systems to do it for us. This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. We seek to exploit rich temporal dynamics found in physical and biological systems for modelling complex or adaptive behaviour through the artificial evolution of networks to control robots. Throughout, arguments have been presented for the modelling of delays not only to better represent key facets of physical and biological systems, but to increase the computational potential of such systems for the synthesis of control. The thorough investigation of the dynamics of small delayed networks with a wide range of time delays has been undertaken, with a detailed mathematical description of the fixed points of the system and possible oscillatory modes developed to fully describe the behaviour of a single node. Exploration of the behaviour for even small delayed networks illustrates the range of complex behaviour possible and guides the development of interesting solutions. To further exploit the potential of the rich dynamics in such systems, a novel approach to the 3D simulation of locomotory robots has been developed focussing on minimising the computational cost. To verify this simulation tool a simple quadruped robot was developed and the motion of the robot when undergoing a manually designed gait evaluated. The results displayed a high degree of agreement between the simulation and laser tracker data, verifying the accuracy of the model developed. A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. A range of methods has been developed for determining the time delays, including the novel concept of representing the time delays as related to the distance between nodes in a spatial representation of the network. The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. The performance of these systems has been compared and contrasted with the efficacy of evolutionary runs for the same task over the whole range of network and delay types. It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. Experiments in adaptive behaviour, where there is not such a direct link between the enhanced system dynamics and performance, showed no such discernible improvement. Whilst we hypothesise that the ability of such delayed networks to generate switched pattern generating nodes may be useful in Evolutionary Robotics (ER) this was not borne out here. The spatial representation of delays was shown to be more efficient for larger networks, however these techniques restricted the search to lower complexity solutions or led to a significant falloff as the network structure becomes more complex. This would suggest that for anything other than a simple genotype, the direct method for encoding delays is likely most appropriate. With proven benefits for robot locomotion and the open potential for adaptive behaviour delayed dynamic systems for evolved control remain an interesting and promising field in complex systems research

A subgraph isomorphism algorithm and its application to biochemical data

BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms

A subgraph isomorphism algorithm and its application to biochemical data

BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms

Multinuclear NMR spectroscopy and isotopomer distribution analysis applied to metabolic phenotype characterization of complex systems

In the present work, the complex metabolic effects of a rag8 genetic mutation performed on Kluyveromyces lactis yeast cells is investigated with a specific, ‘information-rich’ analytical approach: differential NMR metabolomics. The use of such NMR-based metabolite profiling techniques form the basis for intelligent screening strategies to exploit the biotechnological potentials of yeasts because the rational improvement of K. lactis strains for the production of primary and secondary metabolites requires, first of all, a quantitative understanding of their metabolism, allowing the development of more efficient cell factories through metabolic engineering. Wild-type and mutant cell lines metabolomes are compared each other: through the application of multivariate statistical models a metabolic network is built on statistical basis, describing the metabolite phenotype of the rag8 mutant K. lactis strain. However, the measurement and interpretation of such in vivo metabolite dynamics at a systems level is inherently difficult. Indeed, decipher the intricate web of metabolic networks of a complex system and, particularly, infer something about gene functions only based on metabolite profiling, is one of the greatest challenges in molecular biology which cannot be resolved fully by any metabolomic tool. To resolve and improve the metabolic network description, an extension of the 13C labeling protocol for investigating eukaryotic cellular systems is applied in this work. The resulting labeling pattern of each metabolite reflects the relative importance of the alternative pathways within the metabolic network. This observation underscores the need for acquiring 13C-isotopomer data, instead of just steady-state concentrations, to deduce meaningful relationships between metabolites in related pathways. It was displayed that differential 13C-labeled isotopomer profiles and abundance can serve as a fingerprint of the metabolic networks activity and could reflects both qualitative and quantitative differences in the metabolic pathways that lead to the synthesis of each metabolite. In this way, the role of several metabolic processes could be defined, allowing the exploration of metabolic pathways, leading to qualitative information on the links between labeled precursors and their products and quantitative information on metabolic fluxes. It was demonstrated that in yeasts it has been possible to make significant progress in the analysis of carbon metabolism by using 13C NMR to measure metabolic fluxes in genetically modified cells. This approach, so, can be used for functional genomic analysis of yeast mutants providing detailed quantitative information for the understanding of a biological network useful to identify the key genes for strain improvement. Moreover, an intelligent screening of the large unexploited fungal biodiversity opens the possibility to the development and use of directed genetic modifications of cell factories for the production of novel compounds, that are otherwise difficult to produce by chemical synthesis, and also of new, efficient and environmentally friendly bioprocesses. These possibilities open the way to many comparative functional studies and will certainly change the respective importance of the different yeasts, building up new model yeasts for specific studies