Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Toward full-stack in silico synthetic biology: integrating model specification, simulation, verification, and biological compilation

YesWe present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high- performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modelling, simulation, verification and bicompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modelling or specification languages, IBL uniquely blends modelling, verification and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.The work of S.K. is supported by EPSRC (EP/R043787/1). N.K., A.W., and B.B. acknowledge a Royal Academy of Engineering Chair in Emerging Technologies award and an EPSRC programme grant (EP/N031962/1)

Modelling and stochastic simulation of synthetic biological Boolean gates

NoSynthetic Biology aspires to design, compose and engineer biological systems that implement specified behaviour. When designing such systems, hypothesis testing via computational modelling and simulation is vital in order to reduce the need of costly wet lab experiments. As a case study, we discuss the use of computational modelling and stochastic simulation for engineered genetic circuits that implement Boolean AND and OR gates that have been reported in the literature. We present performance analysis results for nine different state-of-the-art stochastic simulation algorithms and analyse the dynamic behaviour of the proposed gates. Stochastic simulations verify the desired functioning of the proposed gate designs

A novel extended Granger Causal model approach demonstrates brain hemispheric differences during face recognition learning

Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learninginduced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning

Comparative lipidomics profiling of human atherosclerotic plaques

Background-We sought to perform a systematic lipid analysis of atherosclerotic plaques using emerging mass spectrometry techniques. Methods and Results-A chip-based robotic nanoelectrospray platform interfaced to a triple quadrupole mass spectrometer was adapted to analyze lipids in tissue sections and extracts from human endarterectomy specimens by shotgun lipidomics. Eighteen scans for different lipid classes plus additional scans for fatty acids resulted in the detection of 150 lipid species from 9 different classes of which 24 were detected in endarterectomies only. Further analyses focused on plaques from symptomatic and asymptomatic patients and stable versus unstable regions within the same lesion. Polyunsaturated cholesteryl esters with long-chain fatty acids and certain sphingomyelin species showed the greatest relative enrichment in plaques compared to plasma and formed part of a lipid signature for vulnerable and stable plaque areas in a systems-wide network analysis. In principal component analyses, the combination of lipid species across different classes provided a better separation of stable and unstable areas than individual lipid classes. Conclusions-This comprehensive analysis of plaque lipids demonstrates the potential of lipidomics for unraveling the lipid heterogeneity within atherosclerotic lesions. (Circ Cardiovasc Genet. 2011; 4: 232-242.

Automated Classification of Brain Tumours from 1H MRS Spectra in INTERPRET, a Multi-Centre Collaboration

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