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Information theory can be used to describe the gain of evolutionary fitness that an organism obtains from sensing, processing, and acting on environmental information. This paper considers the fitness value of subjective information, i.e., the context-dependent value of different kinds of information. A simplified model is given in which the organism requires two essential nutrients, and can prioritize sensing for one or the other. It is shown that a subjective strategy, in which the organism prioritizes a less abundant nutrient for sensing, leads to higher fitness than a balanced strategy, in which total information is maximized and the meaning of the acquired information is disregarded. Using this model, the fitness advantage of subjective information admits an analytical solution, and it is shown that subjective information is more advantageous when the organism's knowledge of the environment is less precise. 

Information theory has been successfully applied to biology with interesting results and applications, ranging from scientific discovery, to system modeling, and engineering. Novel concepts such as semantic and useful information have been proposed to address the peculiarity of biological systems in contrast to Shannon’s classical theory. In this paper, the concept of subjective information, previously observed as an emergent property in a simulated biological system with determinate char- acteristics, is further explored through the proposal of a novel metric for its quantification. This measure is based on a biological system’s ability to dynamically sense and react to environmental signals to achieve a goal. The novel metric is validated through the simulation of a computational model that enables its correlation with different strategies for information acquisition from the environment and processing. The obtained results indicate that the proposed measure of subjective information is reliable in quantifying the effectiveness of a biological system’s strategy in using information from the environment for its growth and survival. 

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a “subjective information”, i.e., trade-off between a living system’s capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival. 

Information processing has increasingly gained traction as a unifying and holistic concept to characterize biological systems. Current research has obtained important but limited results in applying information to understanding life, mainly because of inherent syntactic constraints embedded in a universally accepted theory, formulated for communication system engineering, rather than a universal characterization of nature. In this paper, we further the notion of "subjective information", which takes into account the relative importance of different information sources for distinct life functions. To this end, we develop a computational model of a microorganism that requires two metabolic substrates to survive and grow. The substrates have different spatial distributions, and the organism acquires information on their environmental concentrations and gradients through a noisy receptor-binding process, ultimately guiding its chemotaxis in the environment to increase the chances of growth and survival. Our simulation results reveal a trade-off between a living system's capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time, suggesting that a form of "subjective information" promotes growth and survival in life processes, rather than the classical, purely syntactic Shannon information. 

Abstract Osteoarthritis is the third most rapidly growing health condition associated with disability, after dementia and diabetes¹. By 2050, the total number of patients with osteoarthritis is estimated to reach 1 billion worldwide². As no disease-modifying treatments exist for osteoarthritis, a better understanding of disease aetiopathology is urgently needed. Here we perform a genome-wide association study meta-analyses across up to 489,975 cases and 1,472,094 controls, establishing 962 independent associations, 513 of which have not been previously reported. Using single-cell multiomics data, we identify signal enrichment in embryonic skeletal development pathways. We integrate orthogonal lines of evidence, including transcriptome, proteome and epigenome profiles of primary joint tissues, and implicate 700 effector genes. Within these, we find rare coding-variant burden associations with effect sizes that are consistently higher than common frequency variant associations. We highlight eight biological processes in which we find convergent involvement of multiple effector genes, including the circadian clock, glial-cell-related processes and pathways with an established role in osteoarthritis (TGFβ, FGF, WNT, BMP and retinoic acid signalling, and extracellular matrix organization). We find that 10% of the effector genes express a protein that is the target of approved drugs, offering repurposing opportunities, which can accelerate translation.