One among several advantages of measure transport methods is that they allow for a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.
This paper surveys foundation models for AI-enabled biological design, focusing on recent developments in applying large-scale, self-supervised models to tasks such as protein engineering, small molecule design, and genomic sequence design. Though this domain is evolving rapidly, this survey presents and discusses a taxonomy of current models and methods. The focus is on challenges and solutions in adapting these models for biological applications, including biological sequence modeling architectures, controllability in generation, and multi-modal integration. The survey concludes with a discussion of open problems and future directions, offering concrete next-steps to improve the quality of biological sequence generation.
Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research.
Primates exhibit a robust deviation from canonical allometric scaling: at fixed body mass, their lifespans exceed those of non-primate mammals by factors of two to three. A rhesus macaque (8 kg) lives 25-40 years, whereas a cat of similar mass rarely exceeds 18 years. This statistically significant clade-level excess cannot be explained by standard metabolic or ecological models. We provide a thermodynamic explanation within the Principle of Biological Time Equivalence (PBTE), where lifespan is determined by a finite cycle budget governed by entropy production. We show that primates reduce entropy production per physiological cycle through increased neural energy allocation. The neural power fraction acts as a control parameter, extending the effective lifetime cycle count. Three mechanisms, predictive regulation, enhanced repair, and behavioral buffering, jointly suppress dissipation. This yields a quantitative neuro-metabolic multiplier that explains primate longevity and provides testable predictions linking brain energetics, entropy production, and lifespan.
Several interdisciplinary areas have appeared at the interface between biological and physical sciences. In this work, we suggest a complex network-based methodology for analyzing the interrelationships between some of these interdisciplinary areas, including Bioinformatics, Computational Biology, Biochemistry, among others. This approach has been applied over respective data derived from Wikipedia. Related reviews from the scientific literature are also considered as a reference, yielding a respective bipartite hypergraph which can be used to gain insights about the interrelationships underlying the considered interdisciplinary areas. Several interesting results are obtained, including greater interconnection between the considered interdisciplinary areas with biological than with physical sciences. A good agreement was also found between the network obtained from Wikipedia and the interrelationships revealed by the literature reviews. At the same time, the former network was found to exhibit more intricate relationships than in the hypergraph derived from the literature review.
Are biological self-organising systems more ``intelligent'' than artificial intelligence (AI)? If so, why? I address this question using a mathematical framework that defines intelligence in terms of adaptability. Systems are modelled as stacks of abstraction layers (\emph{Stack Theory}) and compared by how effectively they delegate agentic control down their stacks. I illustrate this using computational, biological, military, governmental, and economic systems. Contemporary AI typically relies on static, human-engineered stacks whose lower layers are fixed during deployment. Put provocatively, such systems resemble inflexible bureaucracies that adapt only top-down. Biological systems are more intelligent because they delegate adaptation. Formally, I prove a theorem (\emph{The Law of the Stack}) showing that adaptability at higher layers is bottlenecked by adaptability at lower layers. I further show that, under standard viability assumptions, maximising adaptability is equivalent to minimising variational free energy, implying that delegation is necessary for free-energy minimisation. Generalising bioelectric accounts of cancer as isolation from collective informational structures
The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innova
This study presents the development of the PsyCogMetrics AI Lab (psycogmetrics.ai), an integrated, cloud-based platform that operationalizes psychometric and cognitive-science methodologies for Large Language Model (LLM) evaluation. Framed as a three-cycle Action Design Science study, the Relevance Cycle identifies key limitations in current evaluation methods and unfulfilled stakeholder needs. The Rigor Cycle draws on kernel theories such as Popperian falsifiability, Classical Test Theory, and Cognitive Load Theory to derive deductive design objectives. The Design Cycle operationalizes these objectives through nested Build-Intervene-Evaluate loops. The study contributes a novel IT artifact, a validated design for LLM evaluation, benefiting research at the intersection of AI, psychology, cognitive science, and the social and behavioral sciences.
Biological systems possess negative entropy. In them, one form of order produces another, more organized form of order. We propose a formal scheme to calculate robustness of an entire biological system by quantifying the negative entropy present in it. Our Methodology is based upon a computational implementation of two-person non-cooperative finite zero-sum game between positive (physico-chemical) and negative (biological) entropy, present in the system(TCA cycle, for this work). Biochemical analogue of Nash equilibrium, proposed here, could measure the robustness in TCA cycle in exact numeric terms, whereas the mixed strategy game between these entropies could quantitate the progression of stages of biological adaptation. Synchronization profile amongst macromolecular concentrations (even under environmental perturbations) is found to account for negative entropy and biological robustness. Emergence of synchronization profile was investigated with dynamically varying metabolite concentrations. Obtained results were verified with that from the deterministic simulation methods. Categorical plans to apply this algorithm in Cancer studies and anti-viral therapies are proposed alongsid
Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks -- obtained by aggregating or neglecting temporal or categorical description of biological data -- are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.
This is the proceedings of the 1st International Workshop on Low Carbon Computing (LOCO 2024).
Provided that there is no theoretical frame for complex engineered systems (CES) as yet, this paper claims that bio-inspired engineering can help provide such a frame. Within CES bio-inspired systems play a key role. The disclosure from bio-inspired systems and biological computation has not been sufficiently worked out, however. Biological computation is to be taken as the processing of information by living systems that is carried out in polynomial time, i.e., efficiently; such processing however is grasped by current science and research as an intractable problem (for instance, the protein folding problem). A remark is needed here: P versus NP problems should be well defined and delimited but biological computation problems are not. The shift from conventional engineering to bio-inspired engineering needs bring the subject (or problem) of computability to a new level. Within the frame of computation, so far, the prevailing paradigm is still the Turing-Church thesis. In other words, conventional engineering is still ruled by the Church-Turing thesis (CTt). However, CES is ruled by CTt, too. Contrarily to the above, we shall argue here that biological computation demands a more ca
An important part of the analysis of bio-molecular networks is to detect different functional units. Different functions are reflected in a different evolutionary dynamics, and hence in different statistical characteristics of network parts. In this sense, the {\em global statistics} of a biological network, e.g., its connectivity distribution, provides a background, and {\em local deviations} from this background signal functional units. In the computational analysis of biological networks, we thus typically have to discriminate between different statistical models governing different parts of the dataset. The nature of these models depends on the biological question asked. We illustrate this rationale here with three examples: identification of functional parts as highly connected \textit{network clusters}, finding \textit{network motifs}, which occur in a similar form at different places in the network, and the analysis of \textit{cross-species network correlations}, which reflect evolutionary dynamics between species.
Quantum mechanical nonlocality considered as posssible mechanism of long-distance correlations in living organisms and plants, which regulate their coherent development and functioning. It's shown that Doebner-Goldin nonlinear quantum formalism permits to extend nonlocality effects beyond standrd EPR-Bohm scheme and due to it supposedly can describe long-distance correlations in some biological processes. Comparison with some experimental results discussed.
The understanding of the nanoscale physical properties of biomolecules and biomaterials will ultimately promote the research in the biological sciences. In this review, we focused on theory, simulation, and experiments involving nanoscale materials inspired by biological systems. Specifically, self-assembly in living and synthetic materials, bio-functionalized nanomaterials and probing techniques that use nanomaterials are discussed.
This volume presents the proceedings of the 20th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2025), held in Nagaoka, Japan, on December 3-5, 2025. The conference, organized in cooperation with the IEICE Proceedings Series, provides a multidisciplinary forum for researchers in artificial intelligence, knowledge engineering, human-computer interaction, and creativity support systems. The proceedings include peer-reviewed papers accepted through a double-blind review process. Selected papers have been recommended for publication in IEICE Transactions on Information and Systems after an additional peer-review process.
The function of the organism hinges on the performance of its information-processing networks, which convey information via molecular recognition. Many paths within these networks utilize molecular codebooks, such as the genetic code, to translate information written in one class of molecules into another molecular "language" . The present paper examines the emergence and evolution of molecular codes in terms of rate-distortion theory and reviews recent results of this approach. We discuss how the biological problem of maximizing the fitness of an organism by optimizing its molecular coding machinery is equivalent to the communication engineering problem of designing an optimal information channel. The fitness of a molecular code takes into account the interplay between the quality of the channel and the cost of resources which the organism needs to invest in its construction and maintenance. We analyze the dynamics of a population of organisms that compete according to the fitness of their codes. The model suggests a generic mechanism for the emergence of molecular codes as a phase transition in an information channel. This mechanism is put into biological context and demonstrated
COMPUTE is an annual Indian conference supported by ACM India and iSIGCSE. The focus of COMPUTE is to improve the quality of computing education in the country by providing a platform for academicians and researchers to interact and share best practices in teaching, learning, and education in general. The Best Practices Track of COMPUTE 2025 invited Computer Science Educators across the country to submit an experience report for the best practices under multiple categories: 1) Novel classroom activities, 2) Imaginative assignments that promote creativity and problem-solving, 3) Diverse pedagogical approaches (e.g., flipped classrooms, peer teaching, project-based learning), 4) Designing AI-resistant or AI-integrated assessment questions, and 5) Teaching CS to students from other disciplines (e.g., business, humanities, engineering). These proceedings contain papers selected from these submissions for presentation at the conference, as well as a report (written by the editors) from the two best practices sessions where these were presented.
The objective of this short report is to reconsider the subject of bioinformatics as just being a tool of experimental biological science. To do that, we introduce three examples to show how bioinformatics could be considered as an experimental science. These examples show how the development of theoretical biological models generates experimentally verifiable computer hypotheses, which necessarily must be validated by experiments in vitro or in vivo.
This EPTCS volume contains the post-proceedings of the Twelfth International Workshop on Fixed Points in Computer Science, presenting a selection of the works presented during the workshop that took place in Naples (Italy) on the 19th and 20th of February 2024 as a satellite of the International Conference on Computer Science Logic (CSL 2024).