A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

Abstract In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular—powered by breakthroughs in computer vision, large-scale image analysis and machine learning—high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell ‘omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function

Complexity in Developmental Systems: Toward an Integrated Understanding of Organ Formation

During animal development, embryonic cells assemble into intricately structured organs by working together in organized groups capable of implementing tightly coordinated collective behaviors, including patterning, morphogenesis and migration. Although many of the molecular components and basic mechanisms underlying such collective phenomena are known, the complexity emerging from their interplay still represents a major challenge for developmental biology. Here, we first clarify the nature of this challenge and outline three key strategies for addressing it: precision perturbation, synthetic developmental biology, and data-driven inference. We then present the results of our effort to develop a set of tools rooted in two of these strategies and to apply them to uncover new mechanisms and principles underlying the coordination of collective cell behaviors during organogenesis, using the zebrafish posterior lateral line primordium as a model system. To enable precision perturbation of migration and morphogenesis, we sought to adapt optogenetic tools to control chemokine and actin signaling. This endeavor proved far from trivial and we were ultimately unable to derive functional optogenetic constructs. However, our work toward this goal led to a useful new way of perturbing cortical contractility, which in turn revealed a potential role for cell surface tension in lateral line organogenesis. Independently, we hypothesized that the lateral line primordium might employ plithotaxis to coordinate organ formation with collective migration. We tested this hypothesis using a novel optical tool that allows targeted arrest of cell migration, finding that contrary to previous assumptions plithotaxis does not substantially contribute to primordium guidance. Finally, we developed a computational framework for automated single-cell segmentation, latent feature extraction and quantitative analysis of cellular architecture. We identified the key factors defining shape heterogeneity across primordium cells and went on to use this shape space as a reference for mapping the results of multiple experiments into a quantitative atlas of primordium cell architecture. We also propose a number of data-driven approaches to help bridge the gap from big data to mechanistic models. Overall, this study presents several conceptual and methodological advances toward an integrated understanding of complex multi-cellular systems

SeaBase : a multispecies transcriptomic resource and platform for gene network inference

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Oxford University Press for personal use, not for redistribution. The definitive version was published in Integrative and Comparative Biology 54 (2014): 250-263, doi: 10.1093/icb/icu065.Marine and aquatic animals are extraordinarily useful as models for identifying mechanisms of development and evolution, regeneration, resistance to cancer, longevity and symbiosis, among many other areas of research. This is due to the great diversity of these organisms and their wide-ranging capabilities. Genomics tools are essential for taking advantage of these “free lessons” of nature. However, genomics and transcriptomics are challenging in emerging model systems. Here, we present SeaBase, a tool for helping to meet these needs. Specifically, SeaBase provides a platform for sharing and searching transcriptome data. More importantly, SeaBase will support a growing number of tools for inferring gene network mechanisms. The first dataset available on SeaBase is a developmental transcriptome profile of the sea anemone Nematostella vectensis (Anthozoa, Cnidaria). Additional datasets are currently being prepared and we are aiming to expand SeaBase to include user-supplied data for any number of marine and aquatic organisms, thereby supporting many potentially new models for gene network studies.2015-06-0

Combining Network Modeling and Experimental Approaches to Predict Drug Combination Responses

Cancer is a lethal disease and complex at multiple levels of cell biology. Despite many advances in treatments, many patients do not respond to therapy. This is owing to the complexity of cancer-genetic variability due to mutations, the multi-variate biochemical networks within which drug targets reside and existence and plasticity of multiple cell states. It is generally understood that a combination of drugs is a way to address the multi-faceted drivers of cancer and drug resistance. However, the sheer number of testable combinations and challenges in matching patients to appropriate combination treatments are major issues. Here, we first present a general method of network inference which can be applied to infer biological networks. We apply this method to infer different kinds of networks in biological levels where cancer complexity resides-a biochemical network, gene expression and cell state transitions. Next, we focus our attention on glioblastoma and with pharmacological and biological considerations, obtain a ranked list of important drug targets in glioblastoms. We perform drug dose response experiments for 22 blood brain barrier penetrant drugs against 3 glioblastoma cell lines. These methods and experimental results inform a construction of a temporal cell state model to predict and experimentally validate combination treatments for certain drugs. We improve an experimental method to perform high throughput western blots and apply the method to discover biochemical interactions among some important proteins involved in temporal cell state transitions. Lastly, we illustrate a method to investigate potential resistance mechanisms in genome scale proteomic data. We hope that methods and results presented here can be adapted and improved upon to help in the discovery of biochemical interactions, capturing cell state transitions and ultimately help predict effective combination therapies for cancer

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Neuronal Cultures: Exploring Biophysics, Complex Systems, and Medicine in a Dish

Neuronal cultures are one of the most important experimental models in modern interdisciplinary neuroscience, allowing to investigate in a control environment the emergence of complex behavior from an ensemble of interconnected neurons. Here, I review the research that we have conducted at the neurophysics laboratory at the University of Barcelona over the last 15 years, describing first the neuronal cultures that we prepare and the associated tools to acquire and analyze data, to next delve into the different research projects in which we actively participated to progress in the understanding of open questions, extend neuroscience research on new paradigms, and advance the treatment of neurological disorders. I finish the review by discussing the drawbacks and limitations of neuronal cultures, particularly in the context of brain-like models and biomedicine