Biology subjects

Pao, G. M.

Publications and source records attributed to Pao, G. M..

6 recordsLinked to original sources

bioRxiv2026

Explainable prediction and simulation of complex system dynamics through networks of manifolds

Complex systems such as brains and other interacting biological and physical processes are difficult to represent because they evolve across many variables, scales, and nonlinear interactions. To capture these multivariate, multiscale interactions we have developed Generative Manifold Networks (GMNs) a machine learning framework consisting of a network of linked dynamical systems. The network is discovered by an interaction function which can focus on causality, shared information, nonlinearity or other metric. Network nodes are low-dimensional data-driven state-space manifolds with generator functions accommodating multiscale dynamics. In contrast to many machine learning approaches GMNs have no latent or randomly initialized variables providing transparent explainability. GMNs generate short term dynamics of chaos on par with echo state networks while outperforming them in long term generation of chaos and neural dynamics, but with a markedly reduced number of dimensions and without sensitive dependence on reservoir parameters or random states. As a result of their holistic, multiscale representation GMNs can learn the complete dynamics of a complex system. We further show that GMNs are universal approximators. GMNs are demonstrated on chaotic dynamics, neural and behavioral recordings of the fruit fly and domestic rat with comparisons to echo state networks and crossformer - a time series transformer. SignificanceA major challenge in machine learning is to model complex systems accurately without losing interpretability. Many methods that succeed in prediction rely on latent variables obscuring mechanistic insight and complicating experimental testing. Generative manifold networks (GMN) construct a network of low-dimensional functional manifolds directly from observed variables with no latent or randomly initialized variables: the model remains transparent and experimentally testable. We prove that GMN are universal approximators showing that high representational power can be achieved without sacrificing explainability. GMN therefore provides a general framework for prediction and simulation in neuroscience and complex systems where unraveling the links between variables in an experimentally testable manner is as important as forecasting their behavior.

neuroscience↗

bioRxiv2026

Existence of Causation without Correlation in Transcriptional Networks

It is commonly assumed that lack of correlation is evidence for lack of causal relationship. Here, however we show that in transcriptional networks, causal linkages can exist in the absence of correlation. We find that a substantial proportion of transcribed genes in yeast and in mouse, show evidence of state-dependent (nonlinear) and temporally coordinated dynamics in their expression patterns (65-77%). Using a test that accommodates this fact, we uncover strong causal relationships that are invisible to correlation-based analyses for both yeast and mouse models. Specifically, for yeast we detect uncorrelated causal relationships for the transcriptional regulators WHI5 and YHP1, and can verify these relationships experimentally. These genes reside at important checkpoints in the cell cycle where multiple signals are integrated at single nodes, giving rise to causal relationships, that despite being uncorrelated, can be accurately detected (71-78%) using a nonlinear causality test.

systems biology↗

bioRxiv2020

Neural basis of opioid-induced respiratory depression and its rescue

Opioid-induced respiratory depression (OIRD) causes death following an opioid overdose, yet the neurobiological mechanisms of this process are not well understood. Here, we show that neurons within the lateral parabrachial nucleus that express the -opioid receptor (PBLOprm1 neurons) are involved in OIRD pathogenesis. PBLOprm1 neuronal activity is tightly correlated with respiratory rate, and this correlation is abolished following morphine injection. Chemogenetic inactivation of PBLOprm1 neurons mimics OIRD in mice, whereas their chemogenetic activation following morphine injection rescues respiratory rhythms to baseline levels. We identified several excitatory G-protein coupled receptors expressed by PBLOprm1 neurons and show that agonists for these receptors restore breathing rates in mice experiencing OIRD. Thus, PBLOprm1 neurons are critical for OIRD pathogenesis, providing a promising therapeutic target for treating OIRD in patients.

neuroscience↗

bioRxiv2020

The D614G mutation in the SARS-CoV2 Spike protein increases infectivity in an ACE2 receptor dependent manner

The SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have a relatively low mutation rate. Nevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection as they are becoming increasingly widespread over time. Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry. The D614G substitution, however, is in linkage disequilibrium with the ORF1b P314L mutation where both mutations almost invariably co-occur, making functional inferences problematic. In addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and an intrinsic genetic property of the virus. Here, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has >1/2 log10 increased infectivity in human cells expressing the human ACE2 protein as the viral receptor. The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells. These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.

microbiology↗

bioRxiv2020

Identification of a novel breathing circuit that controls pain and anxiety

Alleviating pain with controlled breathing has been practiced throughout human history. Despite its wide use and long history, a neural circuit-based understanding of the pain-breathing interaction is largely lacking. Here we report a novel breathing circuit that regulates non-homeostatic breathing rhythm, as well as pain and anxiety. We identify that a cluster of neurons expressing the Oprm1 gene, which encodes the -opioid receptor (MOR) in the lateral subdivision of parabrachial nucleus (PBLOprm1), directly regulates breathing rate in mice by conveying signals from the limbic areas to respiratory rhythm generating neurons in the medullary preBotzinger Complex (preBotC). In addition, we found that pain signals rapidly increase breathing rate by activating these neurons in both awake and anesthetized mice. Inactivating these neurons not only decreases the breathing rate, but it also substantially decreases anxiety-like behaviors and induces strong appetitive behaviors. Furthermore, PBLOprm1 inactivation alleviates pain by attenuating the perception of the affective-motivational aspect of pain. These results suggest that PBLOprm1 neurons play a critical role in the non-homeostatic regulation of breathing and in the regulation of pain and anxiety through breathing.

neuroscience↗

bioRxiv2019

A many-to-one sensory circuit encodes oxygen levels and drives respiratory behaviour in Danio rerio

Zebrafish larvae respond to hypoxia by increasing a number of ventilatory behaviors. During development, these animals switch from skin-resident to gill-resident neuroendocrine cells around 7 days post fertilization (d.p.f.) to detect hypoxia and drive adaptive behaviors. Here, we probe the neural pathways that receive inputs from skin-resident neuroendocrine cells and alter pectoral fin movements. We first show that a 5 d.p.f. larva increases its pectoral fin movements and heart activity upon hypoxia exposure. Next, we map the downstream neural circuitry and show that individual vagal sensory neurons receive inputs from multiple oxygen-sensing neuroendocrine cells. We then use calcium imaging to show that neurons in the second, but not third, vagal sensory ganglia show increases in the magnitude of their hypoxia-evoked responses. Finally, we link purinergic signaling between neuroendocrine cells and second vagal sensory neurons to increases in pectoral fin movements. Collectively, we suggest that vagal sensory neurons transform hypoxic stimuli into respiratory behaviors.

neuroscience↗