A logic for biological systems

This paper proposes a specification language, hybrid projection temporal logic of modelling, analyzing and verifying biological systems which can be considered, in general, to be hybrid systems consisting of a non-trivial mixture of discrete and continuous components. The syntax and semantics of the logic are presented, and some examples of hybrid systems are modelled to illustrate the formalism.     

Elementary network reconstruction: A framework for the analysis of regulatory networks in biological systems

Complexity of regulatory networks arises from the high degree of interaction between network components such as DNA, RNA, proteins, and metabolites. We have developed a modeling tool, elementary network reconstruction (ENR), to characterize these networks. ENR is a knowledge-driven, steady state, deterministic, quantitative modeling approach based on linear perturbation theory. In ENR we demonstrate a novel means of expressing control mechanisms by way of dimensionless steady state gains relating input and output variables, which are purely in terms of species abundances (extensive variables). As a result of systematic enumeration of network species in n×n matrix, the two properties of linear perturbation are manifested in graphical representations: transitive property is evident in a special L-shape structure, and additive property is evident in multiple L-shape structures arriving at the same matrix cell. Upon imposing mechanistic (lowest-level) gains, network self-assembly through transitive and additive properties results in elucidation of inherent topology and explicit cataloging of higher level gains, which in turn can be used to predict perturbation results. Application of ENR to the regulatory network behind carbon catabolite repression in Escherichia coli is presented. Through incorporation of known molecular mechanisms governing transient and permanent repressions, the ENR model correctly predicts several key features of this regulatory network, including a 50% downshift in intracellular cAMP level upon exposure to glucose. Since functional genomics studies are mainly concerned with redistribution of species abundances in perturbed systems, ENR could be exploited in the system-level analysis of biological systems.     

A multi-objective genetic algorithm to find active modules in multiplex biological networks

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease.Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: rf.uma-vinu@toduab.siana     

PiNGO: a Cytoscape plugin to find candidate genes in biological networks

PiNGO is a tool to screen biological networks for candidate genes, i.e. genes predicted to be involved in a biological process of interest. The user can narrow the search to genes with particular known functions or exclude genes belonging to particular functional classes. PiNGO provides support for a wide range of organisms and Gene Ontology classification schemes, and it can easily be customized for other organisms and functional classifications. PiNGO is implemented as a plugin for Cytoscape, a popular network visualization platform. AVAILABILITY: PiNGO is distributed as an open-source Java package under the GNU General Public License (http://www.gnu.org/), and can be downloaded via the Cytoscape plugin manager. A detailed user guide and tutorial are available on the PiNGO website (http://www.psb.ugent.be/esb/PiNGO.     

A neuronal network for the logic of Limax learning

We construct a neuronal network to model the logic of associative conditioning as revealed in experimental results using the terrestrial mollusk Limax maximus. We show, in particular, how blocking to a previously conditioned stimulus in the presence of the unconditional stimulus, can emerge as a dynamical property of the network. We also propose experiments to test the new model. Action Editor: G. Bard Ermentrout     

RosettaRemodel: a generalized framework for flexible backbone protein design

We describe RosettaRemodel, a generalized framework for flexible protein design that provides a versatile and convenient interface to the Rosetta modeling suite. RosettaRemodel employs a unified interface, called a blueprint, which allows detailed control over many aspects of flexible backbone protein design calculations. RosettaRemodel allows the construction and elaboration of customized protocols for a wide range of design problems ranging from loop insertion and deletion, disulfide engineering, domain assembly, loop remodeling, motif grafting, symmetrical units, to de novo structure modeling.     

A framework to evaluate whether to pool or separate behaviors in a multilayer network

A multilayer network approach combines different network layers,which are connected by interlayer edges,to create a single mathematical object.These networks can contain a variety of information types and represent different aspects of a system.However,the process for selecting which information to include is not always straightforward.Using data on 2 agonistic behaviors in a captive population of monk parakeets(Myiopsitta monachus),we developed a framework for investigating how pooling or splitting behaviors at the scale of dyadic relationships(between 2 individuals)affects individual-and group-level social properties.We designed 2 reference models to test whether randomizing the number of interactions across behavior types results in similar structural patterns as the observed data.Although the behaviors were correlated,the first reference model suggests that the 2 behaviors convey different information about some social properties and should therefore not be pooled.However,once we controlled for data sparsity,we found that the observed measures corresponded with those from the second reference model.Hence,our initial result may have been due to the unequal frequencies of each behavior.Overall,our findings support pooling the 2 behaviors.Awareness of how selected measurements can be affected by data properties is warranted,but nonetheless our framework disentangles these efforts and as a result can be used for myriad types of behaviors and questions.This framework will help researchers make informed and data-driven decisions about which behaviors to pool or separate,prior to using the data in subsequent multilayer network analyses.     

Using a network framework to quantitatively select ecological indicators

Ecological indicators are often constructed as an integrated set to represent key information and characteristics of the ecosystem which are tightly linked to management objectives. As an effective tool, ecological indicators play an increasingly important role in ecosystem monitoring, assessment and management. Reasonable selection of an indicator is a prerequisite for effectively using it. A defined protocol with scientific rigor to select ecological indicators is imperative to solve the challenges in ecological indicator selection. This paper compares the Causal Network (CN) with the Ecological Hierarchy Network (EHN) as a framework to select ecological indicators. These frameworks are not exclusive but interdependent in constructing a network framework. Based on the network framework, a quantitative ecological indicator selection method is demonstrated through a theoretical example. In the selection process, the criteria and requirements considering the balance of science and utility are proposed and translated into quantitative constraints of a selection model. By resolving the model under a mathematical operation, the human arbitrary disturbance will be reduced and random selection minimized.     

A framework for models of biological behaviour

Modelling is most clearly understood as a adjunct in the process of deriving predictions from hypotheses. By representing a hypothesised mechanism in a model we hope by manipulating the model to understand the hypotheses' consequences. Eight dimensions on which models of biological behaviour can vary are described: the degree of realism with which they apply to biology; the level of biology they represent; the generality or range of systems the model is supposed to cover; the abstraction or amount of biological detail represented; the accuracy of representation of the mechanisms; the medium in which the model is built; the match of the model behaviour to biological behaviour; and the utility of the model in providing biological understanding and/or technical insight. It is hoped this framework will help to clarify debates over different approaches to modelling, particularly by pointing out how the above dimensions are relatively independent and should not be conflated.     

Computational approaches to find the active binding sites of biological targets against busulfan

Determination of electrophilic and nucleophilic sites of a molecule is the primary task to find the active sites of the lead molecule. In the present study, the active sites of busulfan have been predicted by molecular electrostatic potential surface and Fukui function analysis with the help of dispersion corrected density functional theory. Similarly, the identification of active binding sites of the proteins against lead compound plays a vital role in the field of drug discovery. Rigid and flexible molecular docking approaches are used for this purpose. For rigid docking, Hex 8.0.0 software employing fast Fourier transform (FFT) algorithm has been used. The partial flexible blind docking simulations have been performed with AutoDock 4.2 software; where a Lamarckian genetic algorithm is employed. The results showed that the most electrophilic atoms of busulfan bind with the targets. It is clear from the docking studies that busulfan has inhibition capability toward the targets 12CA and 1BZM.

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Graphical Abstract Docking of ligand and protein

     

A proposed unified framework for biological invasions

There has been a dramatic growth in research on biological invasions over the past 20 years, but a mature understanding of the field has been hampered because invasion biologists concerned with different taxa and different environments have largely adopted different model frameworks for the invasion process, resulting in a confusing range of concepts, terms and definitions. In this review, we propose a unified framework for biological invasions that reconciles and integrates the key features of the most commonly used invasion frameworks into a single conceptual model that can be applied to all human-mediated invasions. The unified framework combines previous stage-based and barrier models, and provides a terminology and categorisation for populations at different points in the invasion process.     

一种基于时间自动机的基因网络逻辑模型

最近几年来国外基因组（基因网络）系统逻辑行为的研究新进展——基于有限状态自动机模型的方法,针对该方法的局限性,提出了一种基于时间自动机的基因网络模型,以描述网络行为的时间约束。     

A novel framework for the comparative analysis of biological networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.     

A native to amyloidogenic transition regulated by a backbone trigger

Many polypeptides can self-associate into linear, aggregated assemblies termed amyloid fibers. High-resolution structural insights into the mechanism of fibrillogenesis are elusive owing to the transient and mixed oligomeric nature of assembly intermediates. Here, we report the conformational changes that initiate fiber formation by beta-2-microglobulin (beta2m) in dialysis-related amyloidosis. Access of beta2m to amyloidogenic conformations is catalyzed by selective binding of divalent cations. The chemical basis of this process was determined to be backbone isomerization of a conserved proline. On the basis of this finding, we designed a beta2m variant that closely adopts this intermediate state. The variant has kinetic, thermodynamic and catalytic properties consistent with its being a fibrillogenic intermediate of wild-type beta2m. Furthermore, it is stable and folded, enabling us to unambiguously determine the initiating conformational changes for amyloid assembly at atomic resolution.     

A metacommunity framework for enhancing the effectiveness of biological monitoring strategies

Because of inadequate knowledge and funding, the use of biodiversity indicators is often suggested as a way to support management decisions. Consequently, many studies have analyzed the performance of certain groups as indicator taxa. However, in addition to knowing whether certain groups can adequately represent the biodiversity as a whole, we must also know whether they show similar responses to the main structuring processes affecting biodiversity. Here we present an application of the metacommunity framework for evaluating the effectiveness of biodiversity indicators. Although the metacommunity framework has contributed to a better understanding of biodiversity patterns, there is still limited discussion about its implications for conservation and biomonitoring. We evaluated the effectiveness of indicator taxa in representing spatial variation in macroinvertebrate community composition in Atlantic Forest streams, and the processes that drive this variation. We focused on analyzing whether some groups conform to environmental processes and other groups are more influenced by spatial processes, and on how this can help in deciding which indicator group or groups should be used. We showed that a relatively small subset of taxa from the metacommunity would represent 80% of the variation in community composition shown by the entire metacommunity. Moreover, this subset does not have to be composed of predetermined taxonomic groups, but rather can be defined based on random subsets. We also found that some random subsets composed of a small number of genera performed better in responding to major environmental gradients. There were also random subsets that seemed to be affected by spatial processes, which could indicate important historical processes. We were able to integrate in the same theoretical and practical framework, the selection of biodiversity surrogates, indicators of environmental conditions, and more importantly, an explicit integration of environmental and spatial processes into the selection approach.     

Hidden Markov model approach for identifying the modular framework of the protein backbone

The hidden Markov model (HMM) was used to identify recurrent short 3D structural building blocks (SBBs) describing protein backbones, independently of any a priori knowledge. Polypeptide chains are decomposed into a series of short segments defined by their inter-alpha-carbon distances. Basically, the model takes into account the sequentiality of the observed segments and assumes that each one corresponds to one of several possible SBBs. Fitting the model to a database of non-redundant proteins allowed us to decode proteins in terms of 12 distinct SBBs with different roles in protein structure. Some SBBs correspond to classical regular secondary structures. Others correspond to a significant subdivision of their bounding regions previously considered to be a single pattern. The major contribution of the HMM is that this model implicitly takes into account the sequential connections between SBBs and thus describes the most probable pathways by which the blocks are connected to form the framework of the protein structures. Validation of the SBBs code was performed by extracting SBB series repeated in recoding proteins and examining their structural similarities. Preliminary results on the sequence specificity of SBBs suggest promising perspectives for the prediction of SBBs or series of SBBs from the protein sequences.