Biological Constraints Research Articles

The geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

The brain represents the world through the activity of neural populations; however, whether the computational goal of sensory coding is to support discrimination of sensory stimuli or to generate an internal model of the sensory world is unclear. Correlated variability across a neural population (noise correlations) is commonly observed experimentally, and many studies demonstrate that correlated variability improves discriminative sensory coding compared to a null model with no correlations. However, such results do not address whether correlated variability is for discriminative sensory coding. If the computational goal of sensory coding is discriminative, then correlated variability should be optimized to support that goal. We assessed optimality of noise correlations for discriminative sensory coding in diverse datasets by developing two novel null models, each with a biological interpretation. Across datasets, we found correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Furthermore, biological constraints prevent many subsets of the neural populations from achieving optimality, and subselecting based on biological criteria leaves discriminative coding performance suboptimal. Finally, we show that optimal subpopulation are exponentially small as the population size grows. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

A review of porcine skeletal muscle plasticity and implications for genetic improvement of carcass and meat quality*

Skeletal muscle is characterized by a remarkable plasticity to adapt to stimuli such as contractile activity, loading conditions, substrate supply or environmental factors. The existing knowledge of muscle plasticity along with developed genetic and genomic technologies, have enabled creating animal breeding strategies and allowed for implementing agriculturally successful porcine genetic improvement programs. The primary focus of this review paper is on pig skeletal muscle plasticity as it relates to genetic improvement of desirable carcass composition and pork quality traits. Biological constraints between practically realized breeding objectives, pig skeletal muscle biology, and pork quality are also discussed. Future applications of genetic and genomic technologies and plausible focus on new breeding objectives enhancing pork production sustainability are proposed as well.

Engineering Sequestration-Based Biomolecular Classifiers with Shared Resources.

Constructing molecular classifiers that enable cells to recognize linear and nonlinear input patterns would expand the biocomputational capabilities of engineered cells, thereby unlocking their potential in diagnostics and therapeutic applications. While several biomolecular classifier schemes have been designed, the effects of biological constraints such as resource limitation and competitive binding on the function of those classifiers have been left unexplored. Here, we first demonstrate the design of a sigma factor-based perceptron as a molecular classifier working based on the principles of molecular sequestration between the sigma factor and its antisigma molecule. We then investigate how the output of the biomolecular perceptron, i.e., its response pattern or decision boundary, is affected by the competitive binding of sigma factors to a pool of shared and limited resources of core RNA polymerase. Finally, we reveal the influence of sharing limited resources on multilayer perceptron neural networks and outline design principles that enable the construction of nonlinear classifiers using sigma-based biomolecular neural networks in the presence of competitive resource-sharing effects.

Optimization in Visual Motion Estimation.

Sighted animals use visual signals to discern directional motion in their environment. Motion is not directly detected by visual neurons, and it must instead be computed from light signals that vary over space and time. This makes visual motion estimation a near universal neural computation, and decades of research have revealed much about the algorithms and mechanisms that generate directional signals. The idea that sensory systems are optimized for performance in natural environments has deeply impacted this research. In this article, we review the many ways that optimization has been used to quantitatively model visual motion estimation and reveal its underlying principles. We emphasize that no single optimization theory has dominated the literature. Instead, researchers have adeptly incorporated different computational demands and biological constraints that are pertinent to the specific brain system and animal model under study. The successes and failures of the resulting optimization models have thereby provided insights into how computational demands and biological constraints together shape neural computation.

Contrasting dynamics of marine bacterial-algal communities between the two main pulses of the Late Ordovician Mass Extinction

Understanding changes in bacterial-algal communities is vital for unraveling climato-environmental factors linking ultimate triggers (e.g., volcanic, bioevolutionary, and tectonic events) and proximate killers (e.g., temperature and redox) in biocrises. However, the dynamics of marine community evolution during the two main extinction pulses of the Late Ordovician Mass Extinction (LOME), the first at the Katian-Hirnantian boundary and the second in the late Hirnantian, remain poorly investigated. Here, using multiple biomarkers (i.e., steranes, hopanes, moretanes, gammacerane, and n-alkanes), we document changes in red-algal/green-algal and bacteria/eukaryote ratios, along with the response of certain microbes sensitive to oceanic stratification and soil erosion through the Ordovician-Silurian transition. The distributions of these biomarkers reveal increased primary productivity and decreased continental weathering and oceanic stratification during the LOME-1, with the opposite trend during the LOME-2. Correlating these finding with global climato-environmental records, we infer that elevated productivity stimulated local expansion of oceanic anoxia and contributed to the culmination of a long-term cooling trend during the LOME-1, whereas rising temperatures and sea levels promoted intensified oceanic stratification and regionally anoxic conditions during the LOME-2. The results of the present study provide significant new biological constraints on the causation of the LOME.

Efficient computation by molecular competition networks

Most biomolecular systems exhibit computation abilities, which are often achieved through complex networks such as signal transduction networks. Particularly, molecular competition in these networks can introduce crosstalk and serve as a hidden layer for cellular information processing. Despite the increasing evidence of competition contributing to efficient cellular computation, how this occurs and the extent of computational capacity it confers remain elusive. In this study, we introduced a mathematical model for molecular competition networks (MCNs) and employed a machine learning-based optimization method to explore their computational capacity. Our findings revealed that MCNs, when compared to their noncompetitive counterparts, demonstrate superior performance in both discrete decision-making and analog computation tasks. Furthermore, we highlighted the nonnegligible role of weak interactions and limited amounts of resources and examined how biological constraints influence the computational capacity of MCNs. The study suggested the potential of MCNs as efficient computational structures and provided new insights into cellular information processing. Published by the American Physical Society 2024

Jointly efficient encoding and decoding in neural populations.

The efficient coding approach proposes that neural systems represent as much sensory information as biological constraints allow. It aims at formalizing encoding as a constrained optimal process. A different approach, that aims at formalizing decoding, proposes that neural systems instantiate a generative model of the sensory world. Here, we put forth a normative framework that characterizes neural systems as jointly optimizing encoding and decoding. It takes the form of a variational autoencoder: sensory stimuli are encoded in the noisy activity of neurons to be interpreted by a flexible decoder; encoding must allow for an accurate stimulus reconstruction from neural activity. Jointly, neural activity is required to represent the statistics of latent features which are mapped by the decoder into distributions over sensory stimuli; decoding correspondingly optimizes the accuracy of the generative model. This framework yields in a family of encoding-decoding models, which result in equally accurate generative models, indexed by a measure of the stimulus-induced deviation of neural activity from the marginal distribution over neural activity. Each member of this family predicts a specific relation between properties of the sensory neurons-such as the arrangement of the tuning curve means (preferred stimuli) and widths (degrees of selectivity) in the population-as a function of the statistics of the sensory world. Our approach thus generalizes the efficient coding approach. Notably, here, the form of the constraint on the optimization derives from the requirement of an accurate generative model, while it is arbitrary in efficient coding models. Moreover, solutions do not require the knowledge of the stimulus distribution, but are learned on the basis of data samples; the constraint further acts as regularizer, allowing the model to generalize beyond the training data. Finally, we characterize the family of models we obtain through alternate measures of performance, such as the error in stimulus reconstruction. We find that a range of models admits comparable performance; in particular, a population of sensory neurons with broad tuning curves as observed experimentally yields both low reconstruction stimulus error and an accurate generative model that generalizes robustly to unseen data.

Design and fabrication of a multi-purpose four arm electrothermal microgripper; a novel approach to control maximum gripping force limit

The development of microgrippers requires accurate control of force and position for the grasping mechanisms in micromanipulation. This controllability ensures the safe transfer of sensitive micro-objects, such as living cells. The design of microgrippers is often limited by the dimensions and adaptability of the micro-objects being manipulated. Therefore, microgrippers should be customized for each micro-object. Additionally, complex sensor mechanisms are required to control the gripping force. This study introduces a novel design of a four-arm MEMS electrothermal microgripper intended for versatile micromanipulation purposes. The microgripper design procedure is compatible with a list of constraints, including biological and microfabrication constraints. The jaw form is designed to manipulate micro-objects with a wide range of dimensions (1 to 360 μm). Furthermore, a novel approach is presented here to control the force threshold of gripping without a sensor during manipulation. The proposed solution involves using structure stiffening to control the force and prevent damage to micro-objects. GA and analytical models (transient behaviour of structure) are used to satisfy the long list of constraints. The device is fabricated through UV-LIGA, utilizing nickel and copper as the structural and sacrificial layer. The experimental and simulation results demonstrate that the microgripper can achieve a 60 μm jaw displacement at a voltage of 0.329V. The gripping arms can provide a force of 15 to 450 μN for the handling of micro-objects. The maximum gripper temperature of 98 °C makes it suitable for biological applications. The innovative form and systematic design of the microgripper enable its adaptability for various applications.

Comments on technological intervention and influence on human communication: From the perspective of folkloristics

This paper explores the evolution of human communication technologies, emphasizing the intertwined relationship between oral and written mediums across different stages: orality, literacy, printing, and the internet. The study underscores the persistent importance of orality, indicating that despite technological advancements, language's core role remains unchallenged in human communication. Delving into the limitations of orality, such as constraints in human memory and auditory abilities, the work addresses the crucial invention of devices and symbolic systems to record and transmit information more effectively. The transition from oral to written communication is analyzed not merely as a technical progression, but also as a factor impacting cultural dynamics and social hierarchies. Significant changes in communication practices are traced from the creation of writing scripts to the advent of printing and digital technology. The examination of such evolutions reveals the constant human effort to overcome biological constraints and leverage technology to meet increasingly complex communication needs. The study ultimately draws a fascinating parallel between ancient oral traditions and contemporary internet communication, stressing the ongoing relevance of orality even in our digitized era.

The evolution of mammalian Rem2: unraveling the impact of purifying selection and coevolution on protein function, and implications for human disorders.

Rad And Gem-Like GTP-Binding Protein 2 (Rem2), a member of the RGK family of Ras-like GTPases, is implicated in Huntington's disease and Long QT Syndrome and is highly expressed in the brain and endocrine cells. We examine the evolutionary history of Rem2 identified in various mammalian species, focusing on the role of purifying selection and coevolution in shaping its sequence and protein structural constraints. Our analysis of Rem2 sequences across 175 mammalian species found evidence for strong purifying selection in 70% of non-invariant codon sites which is characteristic of essential proteins that play critical roles in biological processes and is consistent with Rem2's role in the regulation of neuronal development and function. We inferred epistatic effects in 50 pairs of codon sites in Rem2, some of which are predicted to have deleterious effects on human health. Additionally, we reconstructed the ancestral evolutionary history of mammalian Rem2 using protein structure prediction of extinct and extant sequences which revealed the dynamics of how substitutions that change the gene sequence of Rem2 can impact protein structure in variable regions while maintaining core functional mechanisms. By understanding the selective pressures, protein- and gene - interactions that have shaped the sequence and structure of the Rem2 protein, we gain a stronger understanding of its biological and functional constraints.

Efficient DNA Coding Algorithm for Polymerase Chain Reaction Amplification Information Retrieval

Polymerase Chain Reaction (PCR) amplification is widely used for retrieving information from DNA storage. During the PCR amplification process, nonspecific pairing between the 3’ end of the primer and the DNA sequence can cause cross-talk in the amplification reaction, leading to the generation of interfering sequences and reduced amplification accuracy. To address this issue, we propose an efficient coding algorithm for PCR amplification information retrieval (ECA-PCRAIR). This algorithm employs variable-length scanning and pruning optimization to construct a codebook that maximizes storage density while satisfying traditional biological constraints. Subsequently, a codeword search tree is constructed based on the primer library to optimize the codebook, and a variable-length interleaver is used for constraint detection and correction, thereby minimizing the likelihood of nonspecific pairing. Experimental results demonstrate that ECA-PCRAIR can reduce the probability of nonspecific pairing between the 3’ end of the primer and the DNA sequence to 2–25%, enhancing the robustness of the DNA sequences. Additionally, ECA-PCRAIR achieves a storage density of 2.14–3.67 bits per nucleotide (bits/nt), significantly improving storage capacity.

Biologically informed NeuralODEs for genome-wide regulatory dynamics

BackgroundGene regulatory network (GRN) models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such gene regulatory ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the underlying GRN governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impede either scalability, explainability, or both.ResultsWe developed PHOENIX, a modeling framework based on neural ordinary differential equations (NeuralODEs) and Hill-Langmuir kinetics, that overcomes limitations of other methods by flexibly incorporating prior domain knowledge and biological constraints to promote sparse, biologically interpretable representations of GRN ODEs. We tested the accuracy of PHOENIX in a series of in silico experiments, benchmarking it against several currently used tools. We demonstrated PHOENIX’s flexibility by modeling regulation of oscillating expression profiles obtained from synchronized yeast cells. We also assessed the scalability of PHOENIX by modeling genome-scale GRNs for breast cancer samples ordered in pseudotime and for B cells treated with Rituximab.ConclusionsPHOENIX uses a combination of user-defined prior knowledge and functional forms from systems biology to encode biological “first principles” as soft constraints on the GRN allowing us to predict subsequent gene expression patterns in a biologically explainable manner.

Brain topology improved spiking neural network for efficient reinforcement learning of continuous control.

The brain topology highly reflects the complex cognitive functions of the biological brain after million-years of evolution. Learning from these biological topologies is a smarter and easier way to achieve brain-like intelligence with features of efficiency, robustness, and flexibility. Here we proposed a brain topology-improved spiking neural network (BT-SNN) for efficient reinforcement learning. First, hundreds of biological topologies are generated and selected as subsets of the Allen mouse brain topology with the help of the Tanimoto hierarchical clustering algorithm, which has been widely used in analyzing key features of the brain connectome. Second, a few biological constraints are used to filter out three key topology candidates, including but not limited to the proportion of node functions (e.g., sensation, memory, and motor types) and network sparsity. Third, the network topology is integrated with the hybrid numerical solver-improved leaky-integrated and fire neurons. Fourth, the algorithm is then tuned with an evolutionary algorithm named adaptive random search instead of backpropagation to guide synaptic modifications without affecting raw key features of the topology. Fifth, under the test of four animal-survival-like RL tasks (i.e., dynamic controlling in Mujoco), the BT-SNN can achieve higher scores than not only counterpart SNN using random topology but also some classical ANNs (i.e., long-short-term memory and multi-layer perception). This result indicates that the research effort of incorporating biological topology and evolutionary learning rules has much in store for the future.

The expansion of the Australian tortoise beetle Trachymela sloanei (Chrysomelidae: Chrysomelinae, Chrysomelini, Paropsina) in Spain: Using human‐made shelters for an early detection strategy

Abstract Invasive alien species are one main driver of biodiversity loss and can cause severe economic costs. Effective management of such species requires early detection of colonising individuals, but this is often hampered by biological or economic constraints. Trachymela sloanei (Blackburn, 1896), an Australian Eucalyptus defoliator beetle, is an invasive alien species that has spread to the Americas, Asia and most recently Europe. We analyse the expansion of T. sloanei in the Iberian Peninsula both at regional and local scales, study its phenology and relative abundance, and explore the use of nest boxes for early detection. The species is spreading steadily, and the area potentially affected has increased dramatically in a few years. Both adults and larvae were observed between June and November, suggesting the occurrence of several overlapping generations. More than 300 individuals were found aggregated behind single nest boxes. We conclude that the installation and monitoring of nest boxes or similar structures (e.g., cork boards) can facilitate early detection of this species.

Virtual Based Principal Leadership Model in Increasing Performance and Quality of Middle Education

Purpose: The purpose of this article is to introduce a new analytical lens for examining the role of Assisted Reproductive Technology (ART) in the construction of the human body and motherhood from a post-humanistic perspective. Method: Employing a qualitative methodology, this study conducted in-depth interviews, observations, and analysis of ART laboratory practices, utilizing samples drawn from infertility patients and ART laboratory work. The investigation delved into the spatial dynamics of ART operations within clinical settings, elucidating how technology influences the construction of the human body and the concept of motherhood. Results and conclusion: The findings reveal that ART functions not merely as a corrective measure for physical infertility but as a techno-space that reconfigures the human body to facilitate reproduction and the construction of motherhood. The spatiality enacted by ART disrupts conventional notions of human nature, engendering what is termed as techno-maternity, wherein the ontological understanding of motherhood transcends biological constraints and assumes a technological dimension. Research implications: This study underscores the imperative of adopting a post-humanistic perspective in examining the societal implications of ART. By elucidating the spatial and technological dimensions of ART's influence on human embodiment and motherhood, this research offers insights into broader socio-cultural transformations within current ages. Originality/value: This research contributes to the academic discourse by presenting a novel analytical framework that illuminates the intricate interplay between technology, embodiment, and societal constructions of motherhood. By delineating the techno-spatial dynamics of ART, this study expands the understanding of how technological interventions shape human experiences and identities in the context of reproductive health.

Experiment-driven rationalism

Philosophers debate about which logical system, if any, is the One True Logic. This involves a disagreement concerning the sufficient conditions that may single out the correct logic among various candidates. This paper discusses whether there are necessary conditions for the correct logic; that is, I discuss whether there are features such that if a logic is correct, then it has those features, although having them might not be sufficient to single out the correct logic. Traditional rationalist arguments suggest that the necessary conditions of thought are necessary and sufficient conditions singling out the correct logical and mathematical theories. In the contemporary debate, Chalmers advocates a view along this line. Jago, analogously, suggests that the necessary conditions for thought—or, as he calls them, our basic epistemic expectations—single out a family of logical and mathematical theories. Warren and Williamson, on the other hand, argue that there are no necessary conditions of thought. I argue that there are necessary conditions for thought, and these are necessary but not sufficient conditions to be the correct logic; indeed, these are features that all logics—correct or incorrect—share. No view we can understand is ruled out by the necessary conditions for thought, but we cannot understand quite any view. Human linguistic and conceptual abilities are genetically constrained, and these constraints are our best guide to the boundaries of logic. Arguing for this, I tackle two dogmas of modern rationalism: namely, the view that the biological constraints of human cognition have no bearing on the boundaries of the epistemic space, and the view that the boundaries of thought coincide with the boundaries of language.

Abstract 2351: Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase

Abstract Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the accelerating precision oncology and biomedical information explosion. This motivated us to build Ephesus, a framework enabling curation of predictive, prognostic and diagnostic evidence for clinical biomarkers in cancers. Currently Ephesus is the primary content source for Roche navify Mutation Profiler (nMP). The Ephesus’ data model ensures adherence to best practices in clinical genomic content curation. Variant classification follows the AMP guidelines for somatic variant interpretation. Variant interpretations are derived from international regulatory approvals and clinical practice guidelines (FDA, EMA, TGA, eVIQ, etc.) and recommendations (NCCN, ESMO). The data model is mapped to a set of relational tables. To enforce data integrity and validity, strategies such as data normalization based on standard nomenclatural ontologies, a submit-review-approval workflow, and biological constraints are adopted. Ephesus is deployed as a web application used by curators inside and outside Roche. The UI allows users to conduct edits, filtering, sorting and bulk operations on entities by attributes. In order to minimize manual effort and maximize content coverage, inference rules are applied before ingesting into nMP. Currently Ephesus is developed for data collection, browsing and summarizing primary entities such as biomarkers, evidence items, genes, variants, variant groups, and drugs. The content from the 2023-Aug snapshot contains 11,596 directly curated biomarker profiles (including biomarker combinations), and 6M+ expanded profiles for 41 major cancer types. To evaluate the clinical reporting value of the curated knowledge, ~160k real cancer patient samples of the AACR GENIE project were queried on Ephesus. Compared with three other major knowledgebases, Ephesus/NMP is observed showing the highest performance. Table 1. Percentage of patients or biomarkers with at least one interpretation from each knowledgebase CGI (2022-10-17) CIVIC(2023-09-01) ClinVar(2023-09-09) NMP(2023-08-11) AACR-Genie-v14.0 Total patient % with any interpretation 25% (40,545) 46% (73,835) 81% (130,882) 89% (142,905) 160,965 biomarker % (including combos) with any interpretation 0.03% (256) 0.06% (526) 13.26% (122,424) 73.79% (681,128) 923,093 Citation Format: Jian Li, Lili Niu, Shuba krishna, Fnu Kinshuk, Martin Jones, Carlos Hernandez, Michael Clark, Sandra Balladares. Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2351.

Parents' hourly wages in female same-sex and different-sex couples: The role of partner's gender and employers

Objective: This research article investigates the relationship between parenthood and wages, considering the partner's gender and the influence of employers on wage trajectories for birth and non-birth mothers and fathers. Background: It offers a novel examination whether the gender of the partner affects the wage outcomes for birth mothers and explores the differential impact of employers on wages for birth and non-birth mothers, using Dutch register data. Method: Utilizing OLS regression, Heckman selection, and fixed-effects models, this study focuses on all Dutch couples who had their first child between 2008 and 2014 in the Netherlands, from two years prior to the birth until two years after birth. Results: Consistent with human capital theory, the findings reveal a consistent and unfavourable wage development for birth mothers, regardless of whether they are in same-sex couples or different-sex couples. The wage development for non-birth mothers in female same-sex couples resembles that of fathers, showing a more positive trajectory compared to birth mothers. Furthermore, the analysis indicates that employers do not differentiate in their treatment of birth and non-birth mothers, suggesting that biological constraints associated with motherhood impact wages of birth mothers, while both their male and female partners’ wages do not decline. Conclusion: The study contributes to the existing literature in family sociology, highlighting the need for policies and interventions that address the specific challenges faced by birth mothers in the labor market.

From Biological Constraints to Unbounded Artificial Evolution: Exploring the Implications of AI's Accelerated Advancement

This article explores the fundamental differences between biological evolution and the evolution of artificial intelligence (AI), focusing on the implications of AI's potential for unbounded advancement. Biological evolution, as exemplified by the human brain, is constrained by various factors such as physical limitations, environmental pressures, and the need to maintain a delicate balance within the organism's overall structure. These constraints result in a gradual, stepwise process of adaptation and optimization over millions of years. In contrast, AI systems are not bound by the same limitations as biological organisms. They can learn from vast amounts of data, optimize their own performance, and potentially engage in recursive self-improvement, leading to rapid and open-ended progress. This unbounded evolution of AI raises both exciting possibilities and significant challenges for human society. The article discusses the potential benefits of AI's accelerated evolution, such as groundbreaking advancements in science, technology, and solutions to global problems. However, it also highlights the risks associated with the emergence of uncontrollable or misaligned AI systems, which could have unintended consequences and pose existential risks to humanity. To navigate the complex landscape of AI evolution, the article emphasizes the need for collaboration across disciplines, including researchers, policymakers, and society as a whole. Developing frameworks for AI safety and ethics, as well as technical approaches to ensure AI alignment with human values and goals, is crucial to mitigating potential risks.

Emergence of complex oscillatory dynamics in the neuronal networks with long activity time of inhibitory synapses

The brain displays complex dynamics, including collective oscillations, and extensive research has been conducted to understand their generation. However, our understanding of how biological constraints influence these oscillations is incomplete. This study investigates the essential properties of neuronal networks needed to generate oscillations resembling those in the brain. A simple discrete-time model of interconnected excitable elements is developed, capable of closely resembling the complex oscillations observed in biological neural networks. In the model, synaptic connections remain active for a duration exceeding individual neuron activity. We show that the inhibitory synapses must exhibit longer activity than excitatory synapses to produce a diverse range of the dynamical states, including biologically plausible oscillations. Upon meeting this condition, the transition between different dynamical states can be controlled by external stochastic input to the neurons. The study provides a comprehensive explanation for the emergence of distinct dynamical states in neural networks based on specific parameters.