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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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Microfluidic Immobilization And Subcellular Imaging Of Developing Caenorhabditis elegans

C. elegans has been an essential model organism in the fields of developmental biology, neuroscience, and aging. However, these areas have been limited by our ability to visualize and track individual C. elegans worms, especially at the subcellular scale, over the course of their lifetime. Here we present a microfluidic device to culture individual C. elegans in parallel throughout post-embryonic development. The device allows for periodic mechanical immobilization of the worm, enabling 3D imaging at subcellular precision. The immobilization is sufficient to enable fluorescence recovery after photobleaching (FRAP) measurements on organelles and other substructures within the same specific cells, throughout larval development, without the use of chemical anesthetics. Using this device, we measure FRAP recovery of two nucleolar proteins in specific intestinal cells within the same worms during larval development. We show that these exhibit different fluorescence recovery as they grow, suggesting differential protein interactions during development. We anticipate that this device will help expand the possible uses of C. elegans as a model organism, enabling its use in addressing fundamental questions at the subcellular scale, including the role of phase transitions in driving spatiotemporal intracellular organization within multicellular organisms.

developmental biology

Reverse-Engineering Biological Networks From Large Data Sets

Much of contemporary systems biology owes its success to the abstraction of a network, the idea that diverse kinds of molecular, cellular, and organismal species and interactions can be modeled as relational nodes and edges in a graph of dependencies. Since the advent of high-throughput data acquisition technologies in fields such as genomics, metabolomics, and neuroscience, the automated inference and reconstruction of such interaction networks directly from large sets of activation data, commonly known as reverse-engineering, has become a routine procedure. Whereas early attempts at network reverse-engineering focused predominantly on producing maps of system architectures with minimal predictive modeling, reconstructions now play instrumental roles in answering questions about the statistics and dynamics of the underlying systems they represent. Many of these predictions have clinical relevance, suggesting novel paradigms for drug discovery and disease treatment. While other reviews focus predominantly on the details and effectiveness of individual network inference algorithms, here we examine the emerging field as a whole. We first summarize several key application areas in which inferred networks have made successful predictions. We then outline the two major classes of reverse-engineering methodologies, emphasizing that the type of prediction that one aims to make dictates the algorithms one should employ. We conclude by discussing whether recent breakthroughs justify the computational costs of large-scale reverse-engineering sufficiently to admit it as a mainstay in the quantitative analysis of living systems.

bioinformatics

Reverse-Correlation Analysis of Mechanosensation Circuit in C. elegans Reveals Temporal and Spatial Encoding

Animals must integrate the activity of multiple mechanoreceptors to navigate complex environments. In Caenorhabditis elegans, the general roles of the mechanosensory neurons have been defined, but most studies involve end-point or single-time-point measurements, and thus lack dynamical information. Here, we formulate a set of unbiased quantitative characterizations of the mechanosensory system by using reverse correlation analysis on behavior. We use a custom tracking, selective illumination, and optogenetics platform to compare two mechanosensory systems: the gentle-touch (TRNs) and harsh-touch (PVD) circuits. This method yields characteristic linear filters that allow for prediction of behavioral responses. The resulting filters are consistent with previous findings, and further provide new insights on the dynamics and spatial encoding of the systems. Our results suggest that the tiled network of the gentle-touch neurons has better resolution for spatial encoding than the harsh-touch neurons. Additionally, linear-nonlinear models accurately predict behavioral responses based only on sensory neuron activity. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that quantitatively characterize these systems. Furthermore, this platform can be extended to capture the behavioral dynamics induced by any neuron or other excitable cells in the animal.\n\nAuthor SummaryAnimals constantly integrate the activity of neurons throughout their bodies to choose the most appropriate behavior. A key goal in quantitative neuroscience is to characterize and predict how neuronal circuits control and modulate behavior. C. elegans, a nematode with a fully mapped connectome, is an ideal model organism for elucidating the links between neuronal circuits and behavior. However, many studies relating activity in neurons to behavior rely on spontaneous behavior and lack information about their dynamics. In this study, we formulate unbiased quantitative characterizations of sensory neurons in C. elegans using with reverse correlation analysis with a white noise stimulus. We use optogenetics to stimulate body touch sensory neurons in freely moving worms, and provide quantitative descriptions that capture the dynamic transformations between sensory neuron activity and behavioral outputs. Our results are consistent with previous findings, and additionally provide new insights on the spatial encoding of these systems. Our system provides a simple platform for characterizing the behavioral output due to specific neurons, and can be extended to any excitable cell in the animal.

bioengineering

Histamine signaling via the metabotropic receptor Trapped in endoderm 1 regulates courtship initiation in Drosophila melanogaster.

Understanding the role of genes in directing behavior is one of the primary goals of neuroscience. Mating behavior in Drosophila is controlled by male-specific splicing of the master regulatory gene fruitless (fru), and the male-specific splice form, fruM, is both necessary and sufficient for all aspects of the courtship ritual. We have previously described the role of Trapped in endoderm 1 (Tre1) in courtship behavior. Tre1 encodes an orphan G-protein-coupled receptor that is essential for normal courtship behavior in male flies. We previously found that feminizing Tre1-expressing cells in males via expression of the female-specific splicing factor Transformer (TraF) resulted in rapid courtship initiation. Here we confirm that Tre1 is required in neurons for normal courtship behavior, and present genetic evidence that Tre1 acts through the downregulation of the E-cadherin Shotgun, and that the neurotransmitter histamine is the likely Tre1 ligand. Our findings are the first evidence for metabotropic histamine receptors in Drosophila, and the first to demonstrate a role for histamine in courtship.

genetics

Sensor location optimization and joint angle prediction in hand postures

1. Sensor selection problem 1. Sensor selection problem Grasping of objects (Mason et al. 2001; Santello et al. 2002) or hand shaping during grasping is of interest in robotics, neuroscience, and biomechanics. In animation industry and biomechanics, tracking body and hand movements are essential. For tracking body movements and hand movements, a sizable number of markers are required. As number of marker increase complexity and cost also increase. Researchers have tried various approaches for selecting a set of minimum no. of markers for accurate reproduction of movements. Data-driven approaches have been proposed for classification of various grasps. Some studies have focussed on prediction of joint angles (Kang et al. 2012; Hoyet et al. 2012; Wheatland et al. 2013). Our objective is to predict joint angles from a reduced set of sensors. For preliminary sorting purpose, we used PCA based sensor ranking which is disc ...

bioengineering

Functional consequences of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals

Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (N = 78,308) were meta-analyzed with an extreme-trait cohort of 1,247 individuals with mean IQ [~]170 and 8,185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.

genomics

cytoNet: Network Analysis of Cell Communities

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNets capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin 4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains. Availability and ImplementationQutubLab.org/how | cytoNet contact: cytoNetProject@gmail.com Brain Initiative Alliance Toolmaker cytoNet site: https://www.braininitiative.org/toolmakers/resources/cytonet/ Author / Lay SummarycytoNet provides an online tool to rapidly characterize relationships between objects within images and video frames. To study complex tissue, cell and subcellular topologies, cytoNet integrates vision science with the mathematical technique of graph theory. This allows the method to simultaneously identify environmental effects on single cells and on network topology. cytoNet has versatile use across neuroscience, stem cell biology and regenerative medicine. cytoNet applications described in this study include: (1) characterizing how sensing pain alters neural circuit activity, (2) quantifying how vascular cells respond to neurotrophic stimuli overexpressed in the brain after injury or exercise, (3) delineating features of fat tissue that may confer resistance to obesity and (4) uncovering structure-function relationships of human stem cells as they transform into neurons.

bioengineering

Identifying weak signals in inhomogeneous neuronal images for large-scale tracing of neurites

Reconstructing neuronal morphology across different regions or even the whole brain is important in many areas of neuroscience research. Large-scale tracing of neurites constitutes the core of this type of reconstruction and has many challenges. One key challenge is how to identify a weak signal from an inhomogeneous background. Here, we addressed this problem by constructing an identification model. In this model, empirical observations made from neuronal images are summarized into rules, which are used to design feature vectors that display the differences between the foreground and background, and a support vector machine is used to learn these feature vectors. We embedded this identification model into a tool that we previously developed, SparseTracer, and termed this integration SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace neurites with extremely weak signals (signal-to-background-noise ratio <1.1) against an inhomogeneous background. By testing 12 sub-blocks extracted from a whole imaging dataset, ST-LFV can achieve an average recall rate of 0.99 and precision rate of 0.97, which is superior to that of SparseTracer (which has an average recall rate of 0.93 and average precision rate of 0.86), indicating that this method is well suited to weak signal identification. We applied ST-LFV to trace neurites from large-scale images (approximately 105 GB). During the tracing process, obtaining results equivalent to the ground truth required only one round of manual editing for ST-LFV compared to 20 rounds of manual editing for SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the scale of an entire brain.

bioinformatics

Optical excitation and detection of neuronal activity

Optogenetics has emerged as an exciting tool for manipulating neural activity, which in turn, can modulate behavior in live organisms. However, detecting the response to the optical stimulation requires electrophysiology with physical contact or fluorescent imaging at target locations, which is often limited by photobleaching and phototoxicity. In this paper, we show that phase imaging can report the intracellular transport induced by optogenetic stimulation. We developed a multimodal instrument that can both stimulate cells with subcellular spatial resolution and detect optical pathlength changes with nanometer scale sensitivity. We found that optical pathlength fluctuations following stimulation are consistent with active organelle transport. Furthermore, the results indicate a broadening in the transport velocity distribution, which is significantly higher in stimulated cells compared to optogenetically inactive cells. It is likely that this label-free, contactless measurement of optogenetic response will provide an enabling approach to neuroscience.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY Quantitative phase imaging enables label-free detection of enhanced intracellular activities induced by optogenetic stimulation.\n\nC_FIG_DISPLAY

bioengineering

The study of animal behaviour as a physical science

Behaviour is the ultimate output of an animals nervous system and choosing the right action at the right time can be critical for survival. The study of the organisation of behaviour in its natural context, ethology, has historically been a primarily qualitative science. A quantitative theory of behaviour would advance research in neuroscience as well as ecology and evolution. However, animal posture typically has many degrees of freedom and behavioural dynamics vary on timescales ranging from milliseconds to years, presenting both technical and conceptual challenges. Here we review 1) advances in imaging and computer vision that are making it possible to capture increasingly complete records of animal motion and 2) new approaches to understanding the resulting behavioural data sets. With the right analytical approaches, these data are allowing researchers to revisit longstanding questions about the structure and organisation of animal behaviour and to put unifying principles on a quantitative footing. Contributions from both experimentalists and theorists are leading to the emergence of a physics of behaviour and the prospect of discovering laws and developing theories with broad applicability. We believe that there now exists an opportunity to develop theories of behaviour which can be tested using these data sets leading to a deeper understanding of how and why animals behave.

animal behavior and cognition

An atlas of Caenorhabditis elegans chemoreceptor expression

One goal of modern day neuroscience is the establishment of molecular maps that assign unique features to individual neuron types. Such maps provide important starting points for neuron classification, for functional analysis and for developmental studies aimed at defining the molecular mechanisms of neuron identity acquisition and neuron identity diversification. In this resource paper, we describe a nervous system-wide map of the potential expression sites of 244 members of the largest gene family in the C. elegans genome, rhodopsin-like (class A) GPCR chemoreceptors, using classic gfp reporter gene technology. We cover representatives of all sequence families of chemoreceptors GPCRs, some of which were previously entirely uncharacterized. Most reporters are expressed in a very restricted number of cells, often just in single cells. We assign GPCR reporter expression to all but two of the 37 sensory neuron classes of the sex-shared, core nervous system. Some sensory neurons express a very small number of receptors, while others, particularly nociceptive neurons, co-express several dozen GPCR reporter genes. GPCR reporters are also expressed in a wide range of inter- and motorneurons, as well as nonneuronal cells, suggesting that GPCRs may constitute receptors not just for environmental signals, but also for internal cues. We observe only one notable, frequent association of coexpression patterns, namely in one nociceptive amphid (ASH) and two nociceptive phasmid sensory neurons (PHA, PHB). We identified GPCRs with sexually dimorphic expression and several GPCR reporters that are expressed in a left/right asymmetric manner. We identified a substantial degree of GPCR expression plasticity; particularly in the context of the environmentally-induced dauer diapause stage when one third of all tested GPCRs alter the cellular specificity of their expression within and outside the nervous system. Intriguingly, in a number of cases, the dauer-specific alterations of GPCR reporter expression in specific neuron classes are maintained during postdauer life and in some case new patterns are induced post-dauer, demonstrating that GPCR gene expression may serve as traits of life history. Taken together, our resource provides an entry point for functional studies and also offers a host of molecular markers for studying molecular patterning and plasticity of the nervous system.\n\nAUTHOR SUMMARYMaps of gene expression patterns in the nervous system provide an important resource for neuron classification, for functional analysis and for developmental studies that ask how different neurons acquire their unique identities. By analyzing transgenic gfp reporter strains, we describe here the expression pattern of 244 putative chemosensory receptor-encoding genes, which constitute the largest gene family in C.elegans. We show that, as expected, chemoreceptor expression is enriched in chemosensory neurons but it is also expressed in a wide range of interneurons, motorneurons, as well as non-neuronal cells, suggesting that putative chemosensory receptors may not just sense environmental signals but also internal cues. We find that each chemoreceptor is expressed in a few neuron types, often just one, but each neuron type can express a large number of chemoreceptors. Interestingly, we uncovered that chemoreceptor expression is remarkably plastic, particularly in the context of the environmentally-induced dauer diapause stage. Taken together, this molecular atlas of chemosensory receptors provides an entry point for functional studies and offers a host of markers for studying neuronal patterning and plasticity.

genetics

A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication

Geneticists use olfactory conditioning in Drosophila to identify learning genes; however, little is known about how these genes are integrated into short-term memory (STM) pathways. Here, we investigated the hypothesis that the STM evidence base is weak. We performed systematic review and meta-analysis of the field. Using metrics to quantify variation between discovery articles and follow-up studies, we found that seven genes were both highly replicated, and highly reproducible. However, ~80% of STM genes have never been replicated. While only a few studies investigated interactions, the reviewed genes could account for >1000% memory. This large summed effect size could indicate irreproducibility, many shared pathways, or that current assay protocols lack the specificity needed to identify core plasticity genes. Mechanistic theories of memory will require the convergence of evidence from system, circuit, cellular, molecular, and genetic experiments; systematic data synthesis is an essential tool for integrated neuroscience.

animal behavior and cognition

Experimental Directory Structure (Exdir): An alternative to HDF5 without introducing a new file format

Natural sciences generate an increasing amount of data in a wide range of formats developed by different research groups and commercial companies. At the same time there is a growing desire to share data along with publications in order to enable reproducible research. Open formats have publicly available specifications which facilitate data sharing and reproducible research. Hierarchical Data Format 5 (HDF5) is a popular open format widely used in neuroscience, often as a foundation for other, more specialized formats. However, drawbacks related to HDF5s complex specification have initiated a discussion for an improved replacement. We propose a novel alternative, the Experimental Directory Structure (Exdir), an open standard for data storage in experimental pipelines which amends drawbacks associated with HDF5 while retaining its advantages. HDF5 stores data and metadata in a hierarchy within a complex binary file which, among other things, is not human-readable, not optimal for version control systems, and lacks support for storing raw data. Exdir, one the other hand, uses file system directories to represent the hierarchy, with metadata stored in human-readable YAML files, datasets stored in binary NumPy files, and raw data stored directly in subdirectories. Furthermore, storing data in multiple files makes it easier to track for version control systems. Exdir is not a file format in itself, but a standard for organizing files in a directory structure. Exdir uses the same abstractions as HDF5 and is compatible with the HDF5 Abstract Data Model. Several research groups are already using data stored in a directory hierarchy as an alternative to HDF5, but no common standard exists in the field. This complicates and limits the opportunity for data sharing and development of common tools for reading, writing, and analyzing data. Exdir facilitates improved data storage, data sharing, reproducible research, and novel insight from interdisciplinary collaboration. With the publication of Exdir, we invite the scientific community to join the development to create an open standard that will serve as many needs as possible and that will serve as a foundation for open access to and exchange of data.\n\nSIGNIFICANCE STATEMENTExperimental Directory Structure (Exdir) is a proposal to standardize a storage solution that has become an increasingly popular alternative to Hierarchical Data Format 5 (HDF5), namely to use directories to define a hierarchy, store data in binary files, and metadata in text files. While this strategy is deployed locally by several research groups, no common standard exists. We envision the establishment of such a standard and present Exdir to the community as a starting point.

bioinformatics

A multi-omic atlas of the human frontal cortex for aging and Alzheimer's disease research

We initiated the systematic profiling of the dorsolateral prefrontal cortex obtained from a subset of autopsied individuals enrolled in the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP), which are jointly designed and belong to a very few prospective studies of aging and dementia with detailed, longitudinal cognitive phenotyping during life and a quantitative, structured neuropathologic examination after death for >3,322 subjects. Here, we outline the first generation of data including genome-wide genotypes (n=2,090), whole genome sequencing (n=1,179), DNA methylation (n=740), chromatin immunoprecipitation with sequencing using an anti-Histone 3 Lysine 9 acetylation (H3K9Ac) antibody (n=712), RNA sequencing (n=638), and miRNA profile (n=702). Generation of other omic data including ATACseq, proteomic and metabolomics profiles is ongoing. Thanks to its prospective design and recruitment of older, non-demented individuals, these data can be repurposed to investigate a large number of syndromic and quantitative neuroscience phenotypes. The many subjects that are cognitively non-impaired at death also offer insights into the biology of the human brain in older non-impaired individuals.

cell biology

Sub-voxel light-sheet microscopy for high-resolution, high-throughput volumetric imaging of large biomedical specimens

A key challenge when imaging whole biomedical specimens is how to quickly obtain massive cellular information over a large field of view (FOV). Here, we report a sub-voxel light-sheet microscopy (SLSM) method enabling high-throughput volumetric imaging of mesoscale specimens at cellular-resolution. A non-axial, continuous scanning strategy is used to rapidly acquire a stack of large-FOV images with three-dimensional (3-D) nanoscale shifts encoded. Then by adopting a sub-voxel-resolving procedure, the SLSM method models these low-resolution, cross-correlated images in the spatial domain and iteratively recovers a 3-D image with improved resolution throughout the sample. This technique can surpass the optical limit of a conventional light-sheet microscope by more than three times, with high acquisition speeds of gigavoxels per minute. As demonstrated by quick reconstruction (minutes to hours) of various samples, e.g., 3-D cultured cells, an intact mouse heart, mouse brain, and live zebrafish embryo, the SLSM method presents a high-throughput way to circumvent the tradeoff between intoto mapping of large-scale tissue (>100 mm3) and isotropic imaging of single-cell (~1-m resolution). It also eliminates the need of complicated mechanical stitching or precisely modulated illumination, using a simple light-sheet setup and fast graphics-processing-unit (GPU)-based computation to achieve high-throughput, high-resolution 3-D microscopy, which could be tailored for a wide range of biomedical applications in pathology, histology, neuroscience, etc.

bioengineering

Standardized Informatics Computing Platform for Advancing Biomedical Discovery Through Data Sharing

ObjectiveThe goal is to develop a standardized informatics computing system that can support end-to-end research data lifecycle management for biomedical research applications.\n\nMaterials and MethodsDesign and implementation of biomedical research informatics computing system (BRICS) is demonstrated. The system architecture is modular in design with several integrated tools: global unique identifier, validation, upload, download and query tools that support user friendly informatics system capability.\n\nResultsBRICS instances were deployed to support research for improvements in diagnosis of traumatic brain injury, biomarker discovery for Parkinsons Disease, the National Ophthalmic Disease Genotyping and Phenotyping network, the informatics core for the Center for Neuroscience and Regenerative Medicine, the Common Data Repository for Nursing Science, Global Rare Diseases Patient Registry, and National Institute of Neurological Disorders and Stroke Clinical Informatics system for trials and research.\n\nDiscussionData deidentification is conducted by using global unique identifier methodology. No personally identifiable information exists on the BRICS supported repositories. The Data Dictionary provides defined Common Data Elements and Unique Data Elements, specific to each of the BRICS instance that enables Query Tool to search through research data. All instances are supported by the Medical Imaging Processing, statistical analysis R, and Visualization software program.\n\nConclusionThe BRICS core modules can be easily adapted for various biomedical research needs thereby reducing cost in developing new instances for additional biomedical research needs. It provides user friendly tools for researchers to query and aggregate genetic, phenotypic, clinical and medical imaging data. Data sets are findable, accessible and reusable for researchers to foster new research on various diseases.

bioinformatics

Persistent Underrepresentation of Women’s Science in High Profile Journals

Yiqin Alicia Shen, Jason M. Webster, Yuichi Shoda, and Ione Fine Department of Psychology, University of Washington Past research has demonstrated an under-representation of female editors and reviewers in top scientific journals, but less is known about the representation of women authors within original research articles. We collected research article publication records from 15 high-profile multidisciplinary and neuroscience journals for 2005-2017 and analyzed the representation of women over time, as well as its relationship with journal impact factor. We find that women authors have been persistently underrepresented in high-profile journals. This under-representation has persisted over more than a decade, with glacial improvement over time. Even within our limited group of high profile journals, the percent of female first and last authors is negatively associated with journal impact factor. Since publishing in high-profile journals is a gateway to academic success, this underrepresentation of women may contribute to the lack of women at the top of the academic ladder.

scientific communication and education

A probabilistic interpretation of PID controllers using active inference

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition within general mathematical frameworks derived from information theory, statistical physics and machine learning. Furthermore, it has been argued that one such proposal, active inference, combines both information and control theory and has its roots in cybernetics studies of the brain. The connections between information and control theory have been discussed since the 1950s by scientists like Shannon and Kalman and have recently risen to prominence in modern stochastic optimal control theory. How-ever, the implications of the confluence of these two theoretical frame-works for the biological sciences have been slow to emerge. Here we argue that if the active inference proposal is to be taken as a general process theory for biological systems, we need to consider how existing control theoretical approaches to biological systems relate to it. In this work we will focus on PID (Proportional-Integral-Derivative) controllers, one of the most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation once we use only simple linear generative models.

systems biology