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bioRxiv2017

Model reduction permits Turing instability analysis of arbitrary reaction-diffusion models

Synthesising a genetic network which generates stable Turing patterns is one of the great challenges of synthetic biology, but a significant obstacle is the disconnect between the mathematical theory and the biological reality. Current mathematical understanding of patterning is typically restricted to systems of 2 or 3 chemical species, for which equations are tractable, but plausible genetic networks typically consist of dozens of interacting species. In this article, we suggest a method for reducing large biochemical systems to systems with 2 or 3 species which can then be studied analytically. We provide conditions to guarantee that the full system forms patterns if the reduced system does, and vice-versa. We confirm our technique with 3 examples: the Brusselator, an example proposed by Turing, and a biochemically plausible patterning system consisting of 17 species. These examples show that our method significantly simplifies the study of pattern formation in large systems.

synthetic biology↗

bioRxiv2018

Evolutionary design of regulatory control. II. Robust error-correcting feedback increases genetic and phenotypic variability

As systems become more robust against perturbations, they can compensate for greater sloppiness in the performance of their components. That robust compensation reduces the force of natural selection on the systems components, leading to component decay. The paradoxical coupling of robustness and decay predicts that robust systems evolve cheaper, lower performing components, which accumulate greater mutational genetic variability and which have greater phenotypic stochasticity in trait expression. Previous work noted the paradox of robustness. However, no general theory for the evolutionary dynamics of system robustness and component decay has been developed. This article takes a first step by linking engineering control theory with the genetic theory of evolutionary dynamics. Control theory emphasizes error-correcting feedback as the single greatest principle in robust system design. Linking control theory to evolution leads to a theory for the evolutionary dynamics of error-correcting feedback, a unifying approach for the evolutionary analysis of robust systems. In this article, I study how, in theory, increasingly robust systems accumulate more genetic variability and greater stochasticity of expression in their components. The theory predicts different levels of variability between different regulatory control architectures and different levels of variability between different components within a particular regulatory control system. Those predictions provide a way to understand the accumulating data on genetic variability and single-cell stochasticity of gene expression. I also show that increasing robustness reduces the frequency of system failures associated with disease and, simultaneously, causes a strong increase in the heritability of disease. Thus, robust error correction in biological regulatory control may partly explain the puzzlingly high heritability of disease and, more generally, the surprisingly high heritability of fitness.

systems biology↗

bioRxiv2017

Challenges in identifying and interpreting organizational modules in morphology

Form is a rich concept that agglutinates information about the proportions and topological arrangement of body parts. Modularity is readily observable in both the variation of proportions (variational modules) and the organization of topology (organizational modules). The study of variational modularity and of organizational modularity faces similar challenges regarding the identification of meaningful modules and the validation of generative processes; however, most studies in morphology focus solely on variational modularity, while organizational modularity is much less understood. A possible cause for this bias is the successful development in the last twenty years of morphometrics, and specially geometric morphometrics, to study patters of variation. This contrasts with the lack of a similar mathematical framework to deal with patterns of organization. Recently, a new mathematical framework has been proposed to study the organization of anatomical parts using tools from Network Theory, so-called anatomical network analysis. This essay explores the potential use of this new framework - and the challenges it faces in identifying and validating biologically meaningful modules in morphological systems -, by providing an example of a complete analysis of modularity of the human skull and upper limb. Finally, we suggest further directions of research that may bridge the gap between variational and organizational modularity studies.

zoology↗

bioRxiv2017

Distorted Biologic Mechanisms in Evolution and Model Extrapolation

Explanations based on low level interacting elements are valuable and powerful since they contribute to identify the key mechanisms of biological functions. However, many dynamic systems based on low level interacting elements with unambiguous, finite and complete information of initial states generate future states that cannot be predicted, implying an increase of complexity and open-ended evolution (oee). Such systems are like Turing machines, that overlap with dynamical systems that cannot halt. We argue that organisms find halting conditions by distorting these mechanisms, creating conditions for a constant creativity that drives evolution. We introduce a modulus of elasticity to measure the changes in these mechanisms in response to changes in the computed environment. We test this concept in a population of predators and predated cells with chemotactic mechanisms and demonstrate how the selection of a given mechanism depends on the entire population. We finally explore this concept in different frameworks and postulate that the identification of predictive mechanisms is only successful with small elasticity modulus.

biophysics↗

bioRxiv2017

Fragmentation Modes And The Evolution Of Life Cycles

Reproduction is a defining feature of living systems. To reproduce, aggregates of biological units (e.g., multicellular organisms or colonial bacteria) must fragment into smaller parts. Fragmentation modes in nature range from binary fission in bacteria to collective-level fragmentation and the production of unicellular propagules in multicellular organisms. Despite this apparent ubiquity, the adaptive significance of fragmentation modes has received little attention. Here, we develop a model in which groups arise from the division of single cells that do not separate but stay together until the moment of group fragmentation. We allow for all possible fragmentation patterns and calculate the population growth rate of each associated life cycle. Fragmentation modes that maximise growth rate comprise a restrictive set of patterns that include production of unicellular propagules and division into two similar size groups. Life cycles marked by single-cell bottlenecks maximise population growth rate under a wide range of conditions. This surprising result offers a new evolutionary explanation for the widespread occurrence of this mode of reproduction. All in all, our model provides a framework for exploring the adaptive significance of fragmentation modes and their associated life cycles.\n\nAuthor SummaryMode of reproduction is a defining trait of all organisms, including colonial bacteria and multicellular organisms. To produce offspring, aggregates must fragment by splitting into two or more groups. The particular way that a given group fragments defines the life cycle of the organism. For instance, insect colonies can reproduce by splitting or by producing individuals that found new colonies. Similarly, some colonial bacteria propagate by fission or by releasing single cells, while others split in highly sophisticated ways; in multicellular organisms reproduction typically proceeds via a single cell bottleneck phase. The space of possibilities for fragmentation is so vast that an exhaustive analysis seems daunting. Focusing on fragmentation modes of a simple kind we parametrise all possible modes of group fragmentation and identify those modes leading to the fastest population growth rate. Two kinds of life cycle dominate: one involving division into two equal size groups, and the other involving production of a unicellular propagule. The prevalence of these life cycles in nature is consistent with our null model and suggests that benefits accruing from population growth rate alone may have shaped the evolution of fragmentation mode.

evolutionary biology↗

bioRxiv2017

Modeling Zika Virus Congenital Eye Disease: Differential Susceptibility of Fetal Retinal Progenitor Cells and iPSC-Derived Retinal Stem Cells to Zika Virus Infection

Zika virus (ZIKV) causes microcephaly and congenital eye disease that is characterized by macular pigment mottling, macular atrophy, and loss of foveal reflex. The cell and molecular basis of congenital ZIKV infection are not well understood. Here, we utilized a biologically relevant cell-based system on human fetal retinal pigment epithelial cells (FRPE) and iPSC-derived retinal stem cells (iRSCs) to model ZIKV-ocular cell injury processes. FRPEs were highly susceptible to ZIKV, resulting in apoptosis and decreased viability, whereas iRSCs showed reduced susceptibility. Transcriptomics and proteomics analyses of infected FRPE cells revealed the activation of innate immune and inflammatory response genes, and dysregulation of cell survival pathways, mitochondrial transmembrane potential, phagocytosis, and particle internalization. Nucleoside analogue drug treatment inhibited ZIKV replication and prevented apoptosis. In conclusion, ZIKV affects ocular cells of different developmental stages resulting in cellular injury and death, further providing molecular insight into the pathogenesis of congenital eye disease.

microbiology↗

bioRxiv2017

Genetic reagents for making split-GAL4 lines in Drosophila

The ability to reproducibly target expression of transgenes to small, defined subsets of cells is a key experimental tool for understanding many biological processes. The Drosophila nervous system contains thousands of distinct cell types and it has generally not been possible to limit expression to one or a few cell types when using a single segment of genomic DNA as an enhancer to drive expression. Intersectional methods, in which expression of the transgene only occurs where two different enhancers overlap in their expression patterns, can be used to achieve the desired specificity. This report describes a set of over 2,800 transgenic lines for use with the split-GAL4 intersectional method.

neuroscience↗

bioRxiv2017

A novel domain assembly routine for creating full-length models of membrane proteins from known domain structures

Membrane proteins composed of soluble and membrane domains are often studied one domain at a time. However, to understand the biological function of entire protein systems and their interactions with each other and drugs, knowledge of full-length structures or models is required. Although few computational methods exist that could potentially be used to model full-length constructs of membrane proteins, none of these methods are perfectly suited for the problem at hand. Existing methods either require an interface or knowledge of the relative orientations of the domains, are not designed for domain assembly, and none of them are developed for membrane proteins. Here we describe the first domain assembly protocol specifically designed for membrane proteins that assembles intra- and extracellular soluble domains and the transmembrane domain into models of the full-length membrane protein. Our protocol does not require an interface between the domains and samples possible domain orientations based on backbone dihedrals in the flexible linker regions, created via fragment insertion, while keeping the transmembrane domain fixed in the membrane. Our method, mp_domain_assembly, implemented in RosettaMP samples domain orientations close to the native structure and is best used in conjunction with experimental data to reduce the conformational search space.

biophysics↗

bioRxiv2017

A simple bioreactor-based method to generate kidney organoids from pluripotent stem cells

Kidney organoids generated from human pluripotent stem cells have the potential to revolutionize how kidney development and injury are studied. Current protocols are technically complex and suffer from poor reproducibility and high reagent costs restricting scalability. To overcome these issues, we have established a simple, inexpensive and robust method to grow kidney organoids in bulk from human induced pluripotent stem cells. Our organoids develop tubular structures by day (d) 8 and show optimal tissue morphology at d14. A comparison with fetal human kidney suggests that d14 organoid renal structures most closely resemble capillary loop stage nephrons. We show that deletion of HNF1B, a transcription factor linked to congenital kidney defects, interferes with tubulogenesis, validating our experimental system for studying renal developmental biology. Taken together, our protocol provides a fast, efficient and cost-effective method for generating large quantities of human fetal kidney tissue, enabling the study of normal and aberrant human renal development.

developmental biology↗

bioRxiv2018

Signal-to-Noise ratio made easy: A tool to assess your confocal performance

Confocal microscopy is used today on a daily basis in life science labs. This \"routine\" technique contributes to the progress of scientific projects across many fields by revealing structural details and molecular localization, but researchers need to be aware that detection efficiency and emission light path performance is of major influence in the confocal image quality. By design, a large portion of the signal is discarded in confocal imaging, leading to a decreased signal-to-noise ratio (SNR) which in turn limits resolution. A well-aligned system and high performance detectors are needed in order to generate an image of best quality. However, a convenient method to address system status and performance on the emission side is still lacking. Here, we present a complete method to assess microscope and emission light path performance in terms of SNR, with a comprehensive protocol alongside NoiSee, an easy-to-use macro for Fiji (available via the corresponding update site). We used this method to compare several confocal systems in our facility on biological samples under typical imaging conditions. Our method reveals differences in microscope performance and highlights the various detector types used (multialkali photomultiplier tube (PMT), gallium arsenide phosphide (GaAsP) PMT, and Hybrid detector). Altogether, our method will provide useful information to research groups and facilities to diagnose their confocal microscopes.

biophysics↗

bioRxiv2018

Genomic resources for Goniozus legneri, Aleochara bilineata and Paykullia maculata, representing three independent origins of the parasitoid lifestyle in insects

Parasitoid insects are important model systems for a multitude of biological research topics and widely used as biological control agents against insect pests. While the parasitoid lifestyle has evolved numerous times in different insect groups, research has focused almost exclusively on Hymenoptera from the parasitica clade. The genomes of several members of this group have been sequenced, but no genomic resources are available from any of the other, independent evolutionary origins of the parasitoid lifestyle. Our aim here was to develop genomic resources for three parasitoid insects outside the parasitica. We present draft genome assemblies for Goniozus legneri, a parasitoid Hymenopteran more closely related to the non-parasitoid wasps and bees than to the parasitica wasps, the Coleopteran parasitoid Aleochara bilineata and the Dipteran parasitoid Paykullia maculata. The genome assemblies are fragmented, but complete in terms of gene content. We also provide preliminary structural annotations. We anticipate that these genomic resources will be valuable for testing the generality of findings obtained from parasitica wasps in future comparative studies.\n\nData availabilityThe Whole Genome Shotgun projects have been deposited at DDBJ/ENA/GenBank under the accessions NCVS00000000 (G. legneri), NBZA00000000 (A. bilineata) and NDXZ00000000 (P. maculata). The versions described in this paper are versions NCVS01000000, NBZA01000000 and NDXZ01000000, respectively. Mapped reads and genome annotations are available through http://parasitoids.labs.vu.nl/parasitoids/. This website also includes genome browsers and viroblast instances for each genome.

genomics↗

bioRxiv2018

A biologically driven directional change in susceptibility to global-scale glaciation during the Precambrian-Cambrian transition

Integrated geological evidence suggests that grounded ice sheets occurred at sea level across all latitudes during two intervals within the Neoproterozoic era; the snowball Earth (SBE) events. Glacial events at ~730 and ~650 million years ago (Ma) were probably followed by a less severe but nonetheless global-scale glaciation at ~580Ma, immediately preceding the proliferation of the first fossils exhibiting unambiguous animal-like form. Existing modelling identifies weathering-induced CO2-drawdown as a critical aspect of glacial inception, but ultimately attributes the SBE phenomenon to unusual tectonic boundary conditions. Here we suggest that the evident directional decrease in Earths susceptibility to a SBE suggests that such a-directional abiotic factors are an insufficient explanation for the lack of SBE events since ~580 Ma. Instead we hypothesize that the terrestrial biospheres capacity to sustain a given level of biotic weathering-enhancement under suboptimal/declining temperatures, itself decreased over time: because lichens (with a relatively robust tolerance of sub-optimal temperatures) were gradually displaced on the land surface by more complex photosynthetic life (with a narrower temperature window for growth). We use a simple modelling exercise to highlight the critical (but neglected) importance of the temperature sensitivity of the biotic weathering enhancement factor and discuss the likely values of key parameters in relation to both experiments and the results of complex climate models. We show how the terrestrial biospheres capacity to sustain a given level of silicate-weathering-induced CO2-drawdown is critical to the temperature/greenhouse forcing at which SBE initiation is conceivable. We do not dispute the importance of low degassing rate and other tectonic factors, but propose that the unique feature of the Neoproterozoic was biologys capacity to tip the system over the edge into a runaway ice-albedo feedback; compensating for the self-limiting decline in weathering rate during the temperature decrease on the approach to glaciation. Such compensation was more significant in the Neoproterozoic than the Phanerozoic due, ultimately, to changes in the species composition of the weathering interface over the course of evolutionary time.View Full Text

paleontology↗

bioRxiv2018

Bioorthogonal Antigens Allow The Study Of Intracellular Processing And Presentation Of Post-Translationally Modified Antigens

Proteolysis is fundamental to many biological processes. In the immune system, it underpins the activation of the adaptive immune response: degradation of antigenic material into short peptides and presentation thereof on major histocompatibility complexes, leads to activation of T-cells. This initiates the adaptive immune response against many pathogens.

immunology↗

bioRxiv2018

Linear stability analysis of morphodynamics during tissue regeneration in plants

One of the key characteristics of multicellular organisms is the ability to establish and maintain shapes, or morphologies, under a variety of physical and chemical perturbations. A quantitative description of the underlying morphological dynamics is a critical step to fully understand the self-organising properties of multicellular systems. Although many powerful mathematical tools have been developed to analyse stochastic dynamics, rarely these are applied to experimental developmental biology.\n\nHere, we take root tip regeneration in the plant model system Arabidopsis thaliana as an example of robust morphogenesis in living tissue, and present a novel approach to quantify and model the relaxation of the system to its unperturbed morphology. By generating and analysing time-lapse series of regenerating root tips captured with confocal microscopy, we are able to extract and model the dynamics of key morphological traits at cellular resolution. We present a linear stability analysis of its Markovian dynamics, with the stationary state representing the intact root in the space of morphological traits. We find that the resulting eigenvalues can be classified into two groups, suggesting the co-existence of two distinct temporal scales during the process of regeneration.\n\nWe discuss the possible biological implications of our specific results, and suggest future experiments to further probe the self-organising properties of living tissue.

systems biology↗

bioRxiv2018

Regulation by competition: a hidden layer of gene regulatory network

Molecular competition is ubiquitous, essential and multifunctional throughout diverse biological processes. Competition brings about trade-offs of shared limited resources among the cellular components, and it thus introduce a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. By abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically compare experimental evidences in these processes and analyzed general properties shared behind them. We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. This work uncovered the complexity and generality of molecular competition effect, which might act as a hidden regulatory mechanism with multiple functions throughout biological networks in both natural and synthetic systems.

systems biology↗

bioRxiv2016

An introduction to linear stability analysis for deciphering spatial patterns in signaling networks

Mathematical modeling is now used commonly in the analysis of signaling networks. With advances in high resolution microscopy, the spatial location of different signaling molecules and the spatio-temporal dynamics of signaling microdomains are now widely acknowledged as key features of biochemical signal transduction. Reaction-diffusion mechanisms are commonly used to model such features, often with a heavy reliance on numerical simulations to obtain results. However, simulations are parameter dependent and may not be able to provide an understanding of the full range of the system responses. Analytical approaches on the other hand provide a framework to study the entire phase space. In this tutorial, we provide a largely analytical method for studying reaction-diffusion models and analyzing their stability properties. Using two representative biological examples, we demonstrate how this approach can guide experimental design.

Systems Biology↗

bioRxiv2017

Functional Annotation of Chemical Libraries across Diverse Biological Processes

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compounds mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

systems biology↗

bioRxiv2013

A structural classification of candidate oscillators and multistationary systems

Molecular systems are uncertain: the variability of reaction parameters and the presence of unknown interactions can weaken the predictive capacity of solid mathematical models. However, strong conclusions on the admissible dynamic behaviors of a model can often be achieved without detailed knowledge of its specific parameters. In particular, starting with Thomas' conjectures, loop-based criteria have been largely used to characterize oscillatory and multistationary dynamic outcomes in systems with a sign definite Jacobian.\n\nWe build on the rich literature focused on the identification of potential oscillatory and multistationary behaviors based on parameter-free criteria. We propose a classification for sign-definite non autocatalytic biological networks which summarize several existing results in the literature, adding new results when necessary. We define candidate oscillators and multistationary systems based on their admissible transitions to instability. We introduce four categories: strong/weak candidate oscillatory/multistationary systems, which correspond to networks in which all/some of the existing feedback loops are negative/positive. We provide necessary and sufficient conditions characterizing strong and weak candidate oscillators and multistationary systems based on the exclusive or simultaneous presence of positive and negative loops in their linearized dynamics. We also consider the case in which the overall system is the connection of several stable aggregate monotone components, providing conditions in terms of positive/negative loops in a suitable network with aggregate monotone systems as nodes.\n\nMost realistic examples of biological networks fall in the gray area of systems in which both positive and negative cycles are present: therefore, both oscillatory and bistable behavior are in principle possible. Native systems with a large number of components are often interconnections of monotone modules, where negative/positive loops among modules characterize oscillatory and bistable behaviors, in agreement with our results. Finally, we note that many canonical example circuits exhibiting oscillations or bistability fall in the categories of strong candidate oscillators/multistationary systems.

Systems Biology↗