Biology subjects

Bogdan, P.

Publications and source records attributed to Bogdan, P..

5 recordsLinked to original sources

bioRxiv2026

Distinct Neural Systems Supporting Afferent and Efferent Autonomic Activity

Numerous studies have reported brain correlates of autonomic activity. However, ambiguity exists regarding whether those correlates reflect receiving or sending out autonomic signals. Additionally, little is known about how emotion and aging interact with brain-autonomic coordination. Based on time-varying heart rate variability (HRV) and blood oxygenation level dependent (BOLD) data (N = 104 younger; N = 51 older adults), we found insular and cingulate activity was negatively correlated with HRV during emotion regulation. We further examined the afferent and efferent nature of HRV-BOLD correlations during rest (N = 102 younger; N = 51 older adults). Functionally afferent regions where decreases in HRV were associated with subsequently increased BOLD activity included the posterior insula, postcentral gyrus and frontal pole. Functionally efferent regions where increased activity was associated with subsequently increased HRV included the anterior insula and cingulate. Information appeared to flow in one direction as the afferent regions activity Granger-predicted the efferent regions activity. Together, our findings suggest a feedback loop where decreased HRV increases the afferent regions activity, which activates the efferent regions, leading to increased HRV. Aging seems to affect this system; the efferent and afferent regions scarcely overlapped for younger adults, but did overlap for older adults.

neuroscience↗

bioRxiv2023

Machine learning general transcriptional predictors of plant disease

Plants utilize an innate immune system to defend against all classes of microbial invaders. While we understand specific genetic determinants of host-pathogen interactions, it remains less clear how generalized the immune response is to diverse pathogens. Using a data-driven approach, and utilizing feature selection based on network science and topology, we developed machine learning models that could predict host disease development across diverse pathosystems. These machine learning models identified early transcriptional responses predictive of later disease development, regardless of pathogen class, using a fraction of the host transcriptome. The identified gene sets were not enriched for canonical defense genes, but where statistically enriched for genes previously identified from independent data sets, including those described as representing a general plant stress response. These results highlight novel components of a general plant immune response, and demonstrate the application of machine learning to address biological hypotheses of a complex multigenic outcome. TeaserA machine learning approach can predict plant disease development caused by diverse microbial invaders, and newly identified genes may represent novel components of a general plant response to infection.

plant biology↗

bioRxiv2021

Neuron particles capture network topology and behavior from single units

While networks of neurons, glia and vascular systems enable and support brain functions, to date, mathematical tools to decode network dynamics and structure from very scarce and partially observed neuronal spiking behavior remain underdeveloped. Large neuronal networks contribute to the intrinsic neuron transfer function and observed neuronal spike trains encoding complex causal information processing, yet how this emerging causal fractal memory in the spike trains relates to the network topology is not fully understood. Towards this end, we propose a novel statistical physics inspired neuron particle model that captures the causal information flow and processing features of neuronal spiking activity. Relying on synthetic comprehensive simulations and real-world neuronal spiking activity analysis, the proposed fractional order operators governing the neuronal spiking dynamics provide insights into the memory and scale of the spike trains as well as information about the topological properties of the underlying neuronal networks. Lastly, the proposed model exhibits superior predictions of animal behavior during multiple cognitive tasks.

neuroscience↗

bioRxiv2020

Predicting the Emergence of SARS-CoV-2 Clades

Evolution is a process of change where mutations in the viral RNA are selected based on their fitness for replication and survival. Given that current phylogenetic analysis of SARS-CoV-2 identifies new viral clades after they exhibit evolutionary selections, one wonders whether we can identify the viral selection and predict the emergence of new viral clades? Inspired by the Kolmogorov complexity concept, we propose a generative complexity (algorithmic) framework capable to analyze the viral RNA sequences by mapping the multiscale nucleotide dependencies onto a state machine, where states represent subsequences of nucleotides and state-transition probabilities encode the higher order interactions between these states. We apply computational learning and classification techniques to identify the active state-transitions and use those as features in clade classifiers to decipher the transient mutations (still evolving within a clade) and stable mutations (typical to a clade). As opposed to current analysis tools that rely on the edit distance between sequences and require sequence alignment, our method is computationally local, does not require sequence alignment and is robust to random errors (substitution, insertions and deletions). Relying on the GISAID viral sequence database, we demonstrate that our method can predict clade emergence, potentially aiding with the design of medications and vaccines.

bioinformatics↗

bioRxiv2020

Uncovering new drug properties in target-based drug-drug similarity networks

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach - based on knowledge about the chemical structures - cannot fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug-target interactions to infer drug properties. To this end, we define drug similarity based on drug-target interactions and build a weighted Drug-Drug Similarity Network according to the drug-drug similarity relationships. Using an energy-model network layout, we generate drug communities that are associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them as drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. By using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure, based on molecular docking, to further analyze the repurposing of Azelaic acid and Meprobamate.

bioinformatics↗