Computational Modeling, Formal Analysis, and Tools for Systems Biology.

As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science

Detection of subclinical keratoconus using biometric parameters

The validation of innovative methodologies for diagnosing keratoconus in its earliest stages is of major interest in ophthalmology. So far, subclinical keratoconus diagnosis has been made by combining several clinical criteria that allowed the definition of indices and decision trees, which proved to be valuable diagnostic tools. However, further improvements need to be made in order to reduce the risk of ectasia in patients who undergo corneal refractive surgery. The purpose of this work is to report a new subclinical keratoconus detection method based in the analysis of certain biometric parameters extracted from a custom 3D corneal model. This retrospective study includes two groups: the first composed of 67 patients with healthy eyes and normal vision, and the second composed of 24 patients with subclinical keratoconus and normal vision as well. The proposed detection method generates a 3D custom corneal model using computer-aided graphic design (CAGD) tools and corneal surfaces’ data provided by a corneal tomographer. Defined bio-geometric parameters are then derived from the model, and statistically analysed to detect any minimal corneal deformation. The metric which showed the highest area under the receiver-operator curve (ROC) was the posterior apex deviation. This new method detected differences between healthy and sub-clinical keratoconus corneas by using abnormal corneal topography and normal spectacle corrected vision, enabling an integrated tool that facilitates an easier diagnosis and follow-up of keratoconus.This publication has been carried out in the framework of the Thematic Network for Co-Operative Research in Health (RETICS) reference number RD16/0008/0012 financed by the Carlos III Health Institute-General Subdirection of Networks and Cooperative Investigation Centers (R&D&I National Plan 2013–2016) and the European Regional Development Fund (FEDER)

Spatial patterns of Plasmodium vivax transmission explored by multivariate auto-regressive state-space modelling - a case study in Baoshan prefecture in southern China

The transition from the control phase to elimination of malaria in China through the national malaria elimination programme has focussed attention on the need for improvement of the surveillance- response systems. It is now understood that routine passive surveillance is inadequate in the parasite elimination phase that requires supplementation by active surveillance in foci where cluster cases have occurred. This study aims to explore the spatial clusters and temporal trends of malaria cases by the multivariate auto-regressive state-space model (MARSS) along the border to Myanmar in southern China. Data for indigenous cases spanning the period from 2007 to 2010 were extracted from the China's Infectious Diseases Information Reporting Management System (IDIRMS). The best MARSS model indicated that malaria transmission in the study area during 36 months could be grouped into three clusters. The estimation of malaria transmission patterns showed a downward trend across all clusters. The proposed methodology used in this study offers a simple and rapid, yet effective way to categorize patterns of foci which provide assistance for active monitoring of malaria in the elimination phase

Modeling in Biology: looking backward and looking forward

Understanding modeling in biology requires understanding how biology is organized as a discipline and how this organization influences the research practices of biologists. Biology includes a wide range of sub-disciplines, such as cell biology, population biology, evolutionary biology, molecular biology, and systems biology among others. Biologists in sub-disciplines such as cell, molecular, and systems biology believe that the use of a few experimental models allows them to discover biological universals, whereas biologists in sub-disciplines such as ecology and evolutionary biology believe that the use of many different experimental and mathematical models is necessary in order to do this. Many practitioners of both approaches misunderstand best practices of modeling, especially those related to model testing. We stress the need for biologists to better engage with best practices and for philosophers of biology providing normative guidance for biologists to better engage with current developments in biology. This is especially important as biology transitions from a “data-poor” to a “data-rich” discipline. If 21st century biology is going to capitalize on the unprecedented availability of ecological, evolutionary, and molecular data, of computational resources, and of mathematical and statistical tools, biologists will need a better understanding of what modeling is and can be

Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning

Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate ‘objectivization’ and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we highlight the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system

The big and intricate dreams of little organelles: Embracing complexity in the study of membrane traffic

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138421/1/tra12497_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138421/2/tra12497-sup-0001-EditorialProcess.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138421/3/tra12497.pd

Network controllability analysis of three multiple-myeloma patient genetic mutation datasets

Network controllability focuses on the concept of driving the dynamical system associated to a directed network of interactions from an arbitrary initial state to an arbitrary final state, through a well-chosen set of input functions applied in a minimal number of so-called input nodes. In earlier studies we and other groups demonstrated the potential of applying this concept in medicine. A directed network of interactions may be built around the main known drivers of the disease being studied, and then analysed to identify combinations of drug targets controlling survivability-essential genes in the network. This paper takes the next step and focuses on patient data. We demonstrate that comprehensive protein-protein interaction networks can be built around patient genetic data, and that network controllability can be used to identify possible personalised drug combinations. We discuss the algorithmic methods that can be used to construct and analyse these networks.</p

Nonveridical biosemiotics and the Interface Theory of Perception: implications for perception-mediated selection

Recently, the relationship between evolutionary ecology and perceptual science has received renewed attention under perception-mediated selection, a mode of natural selection linking perceptual saliency, rather than veridicality, to fitness. The Interface Theory of Perception (ITP) has been especially prominent in claiming that an organism’s perceptual interface is populated by icons, which arise as a function of evolved, species-specific perceptual interfaces that produce approximations of organisms’ environments through fitness-tuned perceptions. According to perception-mediated selection, perception and behavior calibrate one another as organisms’ capacities to experience and know the objects and properties of their environments lead to responses highlighting certain environmental features selected for survival. We argue this occurs via the Umwelt/Umgebung distinction in ethology, demonstrating that organisms interact with their external environments (Umgebung) through constructed perceptual schema (Umwelt) that produce constrained representations of environmental objects and their properties. Following Peircean semiotics, we claim that ITP’s focus on icons as saliency-simplified markers corresponds to biosemiotics’ understanding of perceptual representations, which manifest as iconic (resembling objects), indexical (referring), or symbolic (arbitrary) modalities, which provide for organisms’ semiotic scaffolding. We argue that ITP provides the computational evidence for biosemiotics’ notion of iconicity, while biosemiotics provides explanation within ITP for how iconicity can build up into indices and symbols. The common contention of these separate frameworks that the process of perception tracks saliency rather than veridicality suggests that digital/dyadic perceptual strategies will be outcompeted by their semiotic/triadic counterparts. This carries implications for evolutionary theory as well as theories of cognition