Evolutionary robustness of differentiation in genetic regulatory networks

We investigate the ability of artificial Genetic Regulatory Networks (GRNs) to evolve differentiation. The proposed GRN model supports non-linear interaction between regulating factors, thereby facilitating the realization of complex regulatory logics. As a proof of concept we evolve GRNs of this kind to follow different pathways, producing two kinds of periodic dynamics in response to minimal differences in external input. Furthermore we find that successive increases in environmental pressure for differentiation, allowing a lineage to adapt gradually, compared to an immediate requirement for a switch between behaviors, yields better results on average. Apart from better success there is also less variability in performance, the latter indicating an increase in evolutionary robustness

Evolving spiking neural networks for temporal pattern recognition in the presence of noise

Creative Commons - Attribution-NonCommercial-NoDerivs 3.0 United StatesNervous systems of biological organisms use temporal patterns of spikes to encode sensory input, but the mechanisms that underlie the recognition of such patterns are unclear. In the present work, we explore how networks of spiking neurons can be evolved to recognize temporal input patterns without being able to adjust signal conduction delays. We evolve the networks with GReaNs, an artificial life platform that encodes the topology of the network (and the weights of connections) in a fashion inspired by the encoding of gene regulatory networks in biological genomes. The number of computational nodes or connections is not limited in GReaNs, but here we limit the size of the networks to analyze the functioning of the networks and the effect of network size on the evolvability of robustness to noise. Our results show that even very small networks of spiking neurons can perform temporal pattern recognition in the presence of input noiseFinal Published versio

Bio-logic: gene expression and the laws of combinatorial logic

Original article can be found at: http://www.mitpressjournals.org/ Copyright MIT Press DOI: 10.1162/artl.2008.14.1.121At the heart of the development of fertilized eggs into fully formed organisms and the adaptation of cells to changed conditions are genetic regulatory networks (GRNs). In higher multi-cellular organisms, signal selection and multiplexing is performed at the cis-regulatory domains of genes, where combinations of transcription factors (TFs) regulate the rates at which the genes are transcribed into mRNA. To be able to act as activators or repressors of gene transcription, TFs must first bind to target sequences on the regulatory domains. Two TFs that act in concert may bind entirely independently of each other, but more often binding of the first one will alter the affinity of the other for its binding site. This paper presents a systematic investigation into the effect of TF binding dependencies on the predicted regulatory function of this “bio-logic”. Four extreme scenarios, commonly used to classify enzyme activation and inhibition patterns, for the binding of two TFs were explored: independent (the TFs bind without affecting each other’s affinities), competitive (the TFs compete for the same binding site), ordered (the TFs bind in a compulsory order), and joint binding (the TFs either bind as a preformed complex, or binding of one is virtually impossible in the absence of the other). The conclusions are: 1) the laws of combinatorial logic hold only for systems with independently binding TFs; 2) systems formed according to the other scenarios can mimic the functions of their Boolean logical counterparts, but cannot be combined or decomposed in the same way; and 3) the continuously scaled output of systems consisting of competitively binding activators and repressors can be more robustly controlled than that of single TF or (quasi-) logical multi-TF systems. Keywords: Transcription regulation, Genetic regulatory networks, Enzyme kinetics, Combinatorial logic, Non-Boolean continuous logic, Modelling.Peer reviewe

SBML models and MathSBML

MathSBML is an open-source, freely-downloadable Mathematica package that facilitates working with Systems Biology Markup Language (SBML) models. SBML is a toolneutral,computer-readable format for representing models of biochemical reaction networks, applicable to metabolic networks, cell-signaling pathways, genomic regulatory networks, and other modeling problems in systems biology that is widely supported by the systems biology community. SBML is based on XML, a standard medium for representing and transporting data that is widely supported on the internet as well as in computational biology and bioinformatics. Because SBML is tool-independent, it enables model transportability, reuse, publication and survival. In addition to MathSBML, a number of other tools that support SBML model examination and manipulation are provided on the sbml.org website, including libSBML, a C/C++ library for reading SBML models; an SBML Toolbox for MatLab; file conversion programs; an SBML model validator and visualizer; and SBML specifications and schemas. MathSBML enables SBML file import to and export from Mathematica as well as providing an API for model manipulation and simulation

βCaMKII regulates bidirectional long-term plasticity in cerebellar Purkinje cells by a CaMKII/PP2B switch mechanism

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise statedPeer reviewe

Good Learning and Implicit Model Enumeration

MathSBML is an open-source, freely-downloadable Mathematica package that facilitates working with Systems Biology Markup Language (SBML) models. SBML is a toolneutral,computer-readable format for representing models of biochemical reaction networks, applicable to metabolic networks, cell-signaling pathways, genomic regulatory networks, and other modeling problems in systems biology that is widely supported by the systems biology community. SBML is based on XML, a standard medium for representing and transporting data that is widely supported on the internet as well as in computational biology and bioinformatics. Because SBML is tool-independent, it enables model transportability, reuse, publication and survival. In addition to MathSBML, a number of other tools that support SBML model examination and manipulation are provided on the sbml.org website, including libSBML, a C/C++ library for reading SBML models; an SBML Toolbox for MatLab; file conversion programs; an SBML model validator and visualizer; and SBML specifications and schemas. MathSBML enables SBML file import to and export from Mathematica as well as providing an API for model manipulation and simulation

The Typical Flight Performance of Blowflies: Measuring the Normal Performance Envelope of Calliphora vicina Using a Novel Corner-Cube Arena

Despite a wealth of evidence demonstrating extraordinary maximal performance, little is known about the routine flight performance of insects. We present a set of techniques for benchmarking performance characteristics of insects in free flight, demonstrated using a model species, and comment on the significance of the performance observed. Free-flying blowflies (Calliphora vicina) were filmed inside a novel mirrored arena comprising a large (1.6 m1.6 m1.6 m) corner-cube reflector using a single high-speed digital video camera (250 or 500 fps). This arrangement permitted accurate reconstruction of the flies' 3-dimensional trajectories without the need for synchronisation hardware, by virtue of the multiple reflections of a subject within the arena. Image sequences were analysed using custom-written automated tracking software, and processed using a self-calibrating bundle adjustment procedure to determine the subject's instantaneous 3-dimensional position. We illustrate our method by using these trajectory data to benchmark the routine flight performance envelope of our flies. Flight speeds were most commonly observed between 1.2 ms−1 and 2.3 ms−1, with a maximum of 2.5 ms−1. Our flies tended to dive faster than they climbed, with a maximum descent rate (−2.4 ms−1) almost double the maximum climb rate (1.2 ms−1). Modal turn rate was around 240°s−1, with maximal rates in excess of 1700°s−1. We used the maximal flight performance we observed during normal flight to construct notional physical limits on the blowfly flight envelope, and used the distribution of observations within that notional envelope to postulate behavioural preferences or physiological and anatomical constraints. The flight trajectories we recorded were never steady: rather they were constantly accelerating or decelerating, with maximum tangential accelerations and maximum centripetal accelerations on the order of 3 g

Gaze Strategy in the Free Flying Zebra Finch (Taeniopygia guttata)

Fast moving animals depend on cues derived from the optic flow on their retina. Optic flow from translational locomotion includes information about the three-dimensional composition of the environment, while optic flow experienced during a rotational self motion does not. Thus, a saccadic gaze strategy that segregates rotations from translational movements during locomotion will facilitate extraction of spatial information from the visual input. We analysed whether birds use such a strategy by highspeed video recording zebra finches from two directions during an obstacle avoidance task. Each frame of the recording was examined to derive position and orientation of the beak in three-dimensional space. The data show that in all flights the head orientation was shifted in a saccadic fashion and was kept straight between saccades. Therefore, birds use a gaze strategy that actively stabilizes their gaze during translation to simplify optic flow based navigation. This is the first evidence of birds actively optimizing optic flow during flight

Prospective longitudinal evaluation of treatment-related toxicity and health-related quality of life during the first year of treatment for pediatric acute lymphoblastic leukemia

Background: Pediatric acute lymphoblastic leukemia (ALL) therapy is accompanied by treatment-related toxicities (TRTs) and impaired quality of life. In Australia and New Zealand, children with ALL are treated with either Children’s Oncology Group (COG) or international Berlin-Frankfurt-Munster (iBFM) Study Group-based therapy. We conducted a prospective registry study to document symptomatic TRTs (venous thrombosis, neurotoxicity, pancreatitis and bone toxicity), compare TRT outcomes to retrospective TRT data, and measure the impact of TRTs on children’s general and cancer-related health-related quality of life (HRQoL) and parents’ emotional well-being. Methods: Parents of children with newly diagnosed ALL were invited to participate in the ASSET (Acute Lymphoblastic Leukaemia Subtypes and Side Effects from Treatment) study and a prospective, longitudinal HRQoL study. TRTs were reported prospectively and families completed questionnaires for general (Healthy Utility Index Mark 3) and cancer specific (Pediatric Quality of Life Inventory (PedsQL)-Cancer Module) health related quality of life as well the Emotion Thermometer to assess emotional well-being. Results: Beginning in 2016, 260 pediatric patients with ALL were enrolled on the TRT registry with a median age at diagnosis of 59 months (range 1–213 months), 144 males (55.4%), majority with Pre-B cell immunophenotype, n = 226 (86.9%), 173 patients (66.5%) treated according to COG platform with relatively equal distribution across risk classification sub-groups. From 2018, 79 families participated in the HRQoL study through the first year of treatment. There were 74 TRT recorded, reflecting a 28.5% risk of developing a TRT. Individual TRT incidence was consistent with previous studies, being 7.7% for symptomatic VTE, 11.9% neurotoxicity, 5.4% bone toxicity and 5.0% pancreatitis. Children’s HRQoL was significantly lower than population norms throughout the first year of treatment. An improvement in general HRQoL, measured by the HUI3, contrasted with the lack of improvement in cancer-related HRQoL measured by the PedsQL Cancer Module over the first 12 months. There were no persisting differences in the HRQoL impact of COG compared to iBFM therapy. Conclusions: It is feasible to prospectively monitor TRT incidence and longitudinal HRQoL impacts during ALL therapy. Early phases of ALL therapy, regardless of treatment platform, result in prolonged reductions in cancer-related HRQoL

Identifying Prototypical Components in Behaviour Using Clustering Algorithms

Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural components a challenging problem. We propose an automatic and objective approach for determining and evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and finally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a meaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical movements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze strategy by the set of prototypes being divided into either predominantly translational or rotational movements, respectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be unravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically identify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their quality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from different animals and in different contexts