arXiv Quantitative Biology @arxiv_bio@qoto.org

Motor imagery corresponds to the mental practice of simulating visual and kinesthetic aspects of a given motor task. This practice shares a similar neural substrate and correlated temporal scale with motor execution. Besides that, it can lead to performance improvements in the actual execution of the imagined task. Therefore it is important to understand functional differences and equivalences between motor imagery and motor execution. To tackle that we employed a finger-tapping serial reaction time task in two groups of participants, a Motor Execution (n=10) and a Motor imagery (n=10). The sequence of stimuli defining the task had 750 items composed of three distinct auditory stimuli. Also, this sequence had some intrinsic variability making some of the next items unpredictable. Each auditory stimulus was mapped to a single right hand finger in the Motor Imagery group. The Motor imagery group indicated the end of the imagination with a single response using the left hand. The results show improvement in performance of the Motor Imagery group throughout the task and that the duration of the motor imagery, indirectly measured by reaction times, are influenced by distinct factors than those of Motor Execution.

The RNA World hypothesis predicts that self-replicating RNAs evolved before DNA genomes and coded proteins. Despite widespread support for the RNA World, self-replicating RNAs have yet to be identified in a natural context, leaving a key 'missing link' for this explanation of the origin of life. Inspired by recent work showing that condensates of charged polymers can create electrochemical gradients capable of catalyzing hydrolysis, we consider a catalytic RNA condensate as a candidate for the self-replicating RNA. We develop a theoretical framework where an RNA condensate formed by the spontaneous demixing of disordered RNA sequences undergoes self-replicative amplification. Our theory addresses two central problems in the origins of life: (i) the origin of compartmentalization and (ii) the error threshold for the accuracy of templated replication. We show that many of the needed properties of this self-replicating RNA condensate have been realized experimentally in recent studies and can be formalized within a standard polymer physics framework. Specifically, we propose that short, low-complexity RNA polymers formed catalytic condensates capable of templated RNA polymerization. Because the condensate properties depend on the RNA sequences, RNAs that formed condensates with improved polymerization and demixing capacity would be amplified, leading to a 'condensate chain reaction' and evolution by natural selection. We believe this prediction could be tested with current experimental and theoretical tools. Furthermore, we note that the extant nucleolus appears to satisfy many of the requirements of an evolutionary relic for the model we propose. More generally, we suggest that future work on the origin of life would benefit from condensate-centric biophysical models of RNA evolution.

Changing temperature, precipitation regimes, and sea level rise, often associated with climate change, cause salinity intrusion into groundwater and surface water, affecting aquatic ecosystems. This study investigates the impacts of salinity on the physiological traits of freshwater hydrophytes, including Water Hyacinth (Eichhornia crassipes), Buffalo Spinach (Enhydra fluctuans), and Taro (Colocasia esculenta). The plants were exposed to salinity concentrations of 0, 10, 20, and 30 ppt for 48 hours. Parameters such as biomass, stomata density, transpiration rate, chlorophyll content, relative water content, and histo-architectural changes were analyzed. The results showed a decline in biomass, stomatal density, and relative water content with increasing salinity. Taro demonstrated higher salt tolerance compared to other species. Histological observations revealed deformities in root and tuber tissues under saline stress. These findings highlight the critical impacts of climate change-induced salinity on aquatic plant ecosystems.

Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of Hebb's postulate, which suggests that correlated neural activity leads to increases in synaptic strength, to more complex rules that allow bidirectional synaptic updates, ensure stability, or incorporate additional signals like reward or error. At the same time, a range of learning paradigms can be observed behaviorally, from Pavlovian conditioning to motor learning and memory recall. Although it is difficult to directly link synaptic updates to learning outcomes experimentally, computational models provide a valuable tool for building evidence of this connection. In this chapter, we discuss several fundamental learning paradigms, along with the synaptic plasticity rules that might be used to implement them.

With the advent of diffusion models, new proteins can be generated at an unprecedented rate. The \textit{motif scaffolding problem} requires steering this generative process to yield proteins with a desirable functional substructure -- a motif. While models have been trained to take the motif as conditional input, recent techniques in diffusion posterior sampling can be leveraged as zero-shot alternatives whose approximations can be corrected with sequential Monte Carlo (SMC) algorithms. In this work, we introduce a new set of guidance potentials to describe and solve scaffolding tasks by adapting SMC-aided diffusion posterior samplers with an unconditional model, Genie, acting as a prior. Against established benchmarks, we successfully scaffold several single-motif and multi-motif problems. The latter is possible by pairing reconstruction guidance with $\mathrm{SE}(3)$-invariant potentials. In the single-motif case, we find these potentials perform comparably to the conventional masking approach and that reconstruction guidance outperforms replacement methods when aided with SMC. We additionally consider a guidance potential for point symmetry constraints and produce designable internally symmetric monomers with our setup. Overall, this work highlights the capabilities and areas for improvement of zero-shot posterior samplers in motif scaffolding tasks. Code is available at: https://github.com/matsagad/mres-project

Batch effects in omics data obscure true biological signals and constitute a major challenge for privacy-preserving analyses of distributed patient data. Existing batch effect correction methods either require data centralization, which may easily conflict with privacy requirements, or lack support for missing values and automated workflows. To bridge this gap, we developed fedRBE, a federated implementation of limma's removeBatchEffect method. We implemented it as an app for the FeatureCloud platform. Unlike its existing analogs, fedRBE effectively handles data with missing values and offers an automated, user-friendly online user interface (https://featurecloud.ai/app/fedrbe). Leveraging secure multi-party computation provides enhanced security guarantees over classical federated learning approaches. We evaluated our fedRBE algorithm on simulated and real omics data, achieving performance comparable to the centralized method with negligible differences (no greater than 3.6E-13). By enabling collaborative correction without data sharing, fedRBE facilitates large-scale omics studies where batch effect correction is crucial.

A deterministic nonlinear ordinary differential equation model for mosquito dynamics in which the mosquitoes can quest for blood either within a human population or within non-human/vertebrate populations is derived and studied. The model captures both the mosquito's aquatic and terrestrial forms and includes a mechanism to investigate the impact of mating on mosquito dynamics. The model uses a restricted form of homogeneous mixing based on the idea that the mosquito has a blood-feeding habit by accounting for the mosquitoes' blood-feeding preferences as well as its gonotrophic cycle. This characterization allows us to compartmentalise the total mosquito population into distinct compartments according to the spatial location of the mosquito (breeding site, resting places and questing places) as well as blood-fed status. Issues of overcrowding and intraspecific competition both within the aquatic and the terrestrial stages of the mosquito's life forms are addressed and considered in the model. Results show that the inclusion of mating induces bi-stability; a phenomenon whereby locally stable trivial and non-trivial equilibria co-exist with an unstable non-zero equilibrium. The local nature of the stable equilibria is demonstrated by numerically showing that the long-term state of the system is sensitive to initial conditions. The bi-stability state is analogous to the phenomenon of the Allee effect that has been reported in population biology. The model's results, including the derivation of the threshold parameter of the system, are comprehensively tested via numerical simulations. The output of our model has direct application to mosquito control strategies, for it clearly shows key points in the mosquito's developmental pathway that can be targeted for control purposes.

Scientific studies in many areas of biology routinely employ evolutionary analyses based on the probabilistic inference of phylogenetic trees from molecular sequence data. Evolutionary processes that act at the molecular level are highly variable, and properly accounting for heterogeneity in evolutionary processes is crucial for more accurate phylogenetic inference. Nucleotide substitution rates and patterns are known to vary among sites in multiple sequence alignments, and such variation can be modeled by partitioning alignments into categories corresponding to different substitution models. Determining $\textit{a priori}$ appropriate partitions can be difficult, however, and better model fit can be achieved through flexible Bayesian infinite mixture models that simultaneously infer the number of partitions, the partition that each site belongs to, and the evolutionary parameters corresponding to each partition. Here, we consider several different types of infinite mixture models, including classic Dirichlet process mixtures, as well as novel approaches for modeling across-site evolutionary variation: hierarchical models for data with a natural group structure, and infinite hidden Markov models that account for spatial patterns in alignments. In analyses of several viral data sets, we find that different types of infinite mixture models emerge as the best choices in different scenarios. To enable these models to scale efficiently to large data sets, we adapt efficient Markov chain Monte Carlo algorithms and exploit opportunities for parallel computing. We implement this infinite mixture modeling framework in BEAST X, a widely-used software package for Bayesian phylogenetic inference.

Protein evolution underpins life, and understanding its behavior as a system is of great importance. However, our current models of protein evolution are arguably too simplistic to allow quantitative interpretation and prediction of evolutionary trajectories. Viewing protein evolution as a complex system has the potential to advance our understanding and ability to model protein evolution. In this perspective, we discuss aspects of protein evolution that are typical of complex systems, from nonlinear dynamics, sensitivity to initial conditions, self-organization, and the emergence of order from chaos and disorder. We discuss how the growth in sequence and structural data, insights from laboratory evolution and new machine learning tools can advance the study of protein evolution and that by treating protein evolution as a complex adaptive system, we may gain new insights into the fundamental principles driving biological innovation and adaptation and apply this to protein engineering and design.

Smooth muscle cells (SMCs) play a fundamental role in the development of atherosclerotic plaques. SMCs may ingest lipids in a similar way to monocyte-derived macrophages (MDMs) in the plaque. This can stimulate SMCs to undergo a phenotypic switch towards a macrophage-like phenotype. We formulate an ordinary differential equation (ODE) model for the populations of SMCs, MDMs and smooth muscle cell-derived macrophages (SDMs) and the internalised lipid load in each population. We use this model to explore the effect on plaque fate of SMC phenotype switching. We find that when SMCs switch to a macrophage-like phenotype, the total lipid contained in cells in the plaque increases. Additionally, removal of SMCs from the plaque via phenotype switching reduces the fibrous plaque cap, increases the lipid in the necrotic core, and increases plaque inflammation. This makes the plaque more vulnerable to rupture, which can lead to heart attacks and strokes. When SDMs are highly proliferative and resistant to cell death, the plaque grows rapidly and becomes highly pathological. The model suggests that plaque dynamics, driven by the switch of SMCs to a macrophage-like phenotype, may drive the development of unstable, vulnerable and pathological plaques.

Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious? Answering this question in a principled manner can only be done on the basis of a theory of consciousness that is grounded in phenomenology and that states the necessary and sufficient conditions for any system, evolved or engineered, to support subjective experience. Here we employ Integrated Information Theory (IIT), which provides principled tools to determine whether a system is conscious, to what degree, and the content of its experience. We consider pairs of systems constituted of simple Boolean units, one of which -- a basic stored-program computer -- simulates the other with full functional equivalence. By applying the principles of IIT, we demonstrate that (i) two systems can be functionally equivalent without being phenomenally equivalent, and (ii) that this conclusion is not dependent on the simulated system's function. We further demonstrate that, according to IIT, it is possible for a digital computer to simulate our behavior, possibly even by simulating the neurons in our brain, without replicating our experience. This contrasts sharply with computational functionalism, the thesis that performing computations of the right kind is necessary and sufficient for consciousness.