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Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the “science of science” needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by “AI Scientists” may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond.

This article proposes a new grand challenge for AI: to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize. There are a series of human cognitive limitations that prevent us from making accelerated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left at the level of a cottage industry. AI systems can transform scientific discoveries into highly efficient practices, thereby enabling us to expand our knowledge in unprecedented ways. Such systems may out‐compute all possible hypotheses and may redefine the nature of scientific intuition, hence the scientific discovery process.

Key Points Robustness is a ubiquitous feature of biological systems. It ensures that specific functions of the system are maintained despite external and internal perturbations. System control, alternative (or fail-safe) mechanisms, modularity and decoupling are the underlying mechanisms that produce robustness. Robustness facilitates the evolvability of complex dynamic systems. Evolution, given enough time, might select a robust trait that is tolerant against environmental perturbations. This interlinks the properties of robustness and evolvability. Robustness is ubiquitous in biological systems that have evolved. There are specific architectural requirements for robust and evolvable systems — genetic buffering, robust modules and bow-tie architecture. These architectural requirements are the basis for the system's robustness against environmental perturbations, but congruent with genetic perturbations; they facilitate generation of a flexible phenotype. Systems that are robust involve intrinsic trade-offs. Enhanced robustness against certain perturbations has to be balanced by extreme fragility elsewhere. This robust yet fragile nature, predicted by the highly optimized tolerance (HOT) theory, is a fundamental property of the system that has been optimally designed or has evolved to cope with perturbations. There are also other trade-offs in the system's performance and resource demands. Diseases can be thought of in terms of the exposed fragility of robust yet fragile systems. The design of effective countermeasures requires proper understanding of a system's behavioural and failure patterns. Diabetes mellitus , cancer and HIV infection represent the typical failure of such a system that requires systematic countermeasures to control robustness of an epidemic state. Countermeasures include systematic intervention to control a system's dynamics, attack fragility or introduce decoys to re-establish control. Developing a theory of biological robustness with a solid mathematical foundation that can realistically represent biological systems is a difficult challenge. Research into non-linear dynamics, control theory and non-equilibrium theory is urgently required, but it has to be careful to capture the essential structural complexity and heterogeneity of biological systems. Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems. Robustness facilitates evolvability and robust traits are often selected by evolution. Such a mutually beneficial process is made possible by specific architectural features observed in robust systems. But there are trade-offs between robustness, fragility, performance and resource demands, which explain system behaviour, including the patterns of failure. Insights into inherent properties of robust systems will provide us with a better understanding of complex diseases and a guiding principle for therapy design.

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.

As coral reefs struggle to survive under climate change, it is crucial to know whether they have the capacity to withstand changing conditions, particularly increasing seawater temperatures. Thermal tolerance requires the integrative response of the different components of the coral holobiont (coral host, algal photosymbiont, and associated microbiome). Here, using a controlled thermal stress experiment across three divergent Caribbean coral species, we attempt to dissect holobiont member metatranscriptome responses from coral taxa with different sensitivities to heat stress and use phylogenetic ANOVA to study the evolution of gene expression adaptation. We show that coral response to heat stress is a complex trait derived from multiple interactions among holobiont members. We identify host and photosymbiont genes that exhibit lineage-specific expression level adaptation and uncover potential roles for bacterial associates in supplementing the metabolic needs of the coral-photosymbiont duo during heat stress. Our results stress the importance of integrative and comparative approaches across a wide range of species to better understand coral survival under the predicted rise in sea surface temperatures. As corals struggle to survive under climate change, it is crucial to know whether they can withstand increasing seawater temperatures. Using a controlled thermal stress experiment across three divergent coral holobionts, this study examines metatranscriptomic responses to heat stress corresponding to the coral host, photosymbionts and associated microbiota.

Cancers are extremely complex, heterogeneous diseases. Many approaches to anticancer treatment have had limited success — cures are still rare. A fundamental hurdle to cancer therapy is acquired tumour 'robustness'. The goal of this article is to present a perspective on cancer as a robust system to provide a framework from which the complexity of tumours can be approached to yield novel therapies.

Lenvatinib is a multiple receptor tyrosine kinase inhibitor targeting mainly vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) receptors. We investigated the immunomodulatory activities of lenvatinib in the tumor microenvironment and its mechanisms of enhanced antitumor activity when combined with a programmed cell death-1 (PD-1) blockade. Antitumor activity was examined in immunodeficient and immunocompetent mouse tumor models. Single-cell analysis, flow cytometric analysis, and immunohistochemistry were used to analyze immune cell populations and their activation. Gene co-expression network analysis and pathway analysis using RNA sequencing data were used to identify lenvatinib-driven combined activity with anti-PD-1 antibody (anti-PD-1). Lenvatinib showed potent antitumor activity in the immunocompetent tumor microenvironment compared with the immunodeficient tumor microenvironment. Antitumor activity of lenvatinib plus anti-PD-1 was greater than that of either single treatment. Flow cytometric analysis revealed that lenvatinib reduced tumor-associated macrophages (TAMs) and increased the percentage of activated CD8+ T cells secreting interferon (IFN)-γ+ and granzyme B (GzmB). Combination treatment further increased the percentage of T cells, especially CD8+ T cells, among CD45+ cells and increased IFN-γ+ and GzmB+ CD8+ T cells. Transcriptome analyses of tumors resected from treated mice showed that genes specifically regulated by the combination were significantly enriched for type-I IFN signaling. Pretreatment with lenvatinib followed by anti-PD-1 treatment induced significant antitumor activity compared with anti-PD-1 treatment alone. Our findings show that lenvatinib modulates cancer immunity in the tumor microenvironment by reducing TAMs and, when combined with PD-1 blockade, shows enhanced antitumor activity via the IFN signaling pathway. These findings provide a scientific rationale for combination therapy of lenvatinib with PD-1 blockade to improve cancer immunotherapy.

In this study, we explore how the Caribbean coral  Orbicella faveolata  recovers after bleaching, using fragments from 13 coral colonies exposed to heat stress (32 °C) for ten days. Biological parameters and coral optical properties were monitored during and after the stress. Increases in both, the excitation pressure over photosystem II ( Qm ) and pigment specific absorption (a* Chl a ) were observed in the stressed corals, associated with reductions in light absorption at the chlorophyll  a  red peak ( D e675 ) and symbiont population density. All coral fragments exposed to heat stress bleached but a fraction of the stressed corals recovered after removing the stress, as indicated by the reductions in  Q m  and increases in  D e675  and the symbiont population observed. This subsample of the experimentally bleached corals also showed blooms of the endolithic algae  Ostreobium  spp. underneath the tissue. Using a numerical model, we quantified the amount of incident light reflected by the coral, and absorbed by the different pigmented components: symbionts, host-tissue and  Ostreobium spp. Our study supports the key contribution of  Ostreobium spp .  blooms near the skeletal surface, to coral recovery after bleaching by reducing skeleton reflectance. Endolithic blooms can thus significantly alleviate the high light stress that affects the remaining symbionts during the stress or when the coral has achieved the bleached phenotype.

Biological robustness is a principle that may shed light on system‐level characteristics of biological systems. One intriguing aspect of the concept of biological robustness is the possible existence of intrinsic trade‐offs among robustness, fragility, performance, and so on. At the same time, whether such trade‐offs hold regardless of the situation or hold only under specific conditions warrants careful investigation. In this paper, we reassess this concept and argue that biological robustness may hold only when a system is sufficiently optimized and that it may not be conserved when there is room for optimization in its design. Several testable predictions and implications for cell culture experiments are presented.

Recognition of pathogen‐associated molecular signatures is critically important in proper activation of the immune system. The toll‐like receptor (TLR) signaling network is responsible for innate immune response. In mammalians, there are 11 TLRs that recognize a variety of ligands from pathogens to trigger immunological responses. In this paper, we present a comprehensive map of TLRs and interleukin 1 receptor signaling networks based on papers published so far. The map illustrates the possible existence of a main network subsystem that has a bow‐tie structure in which myeloid differentiation primary response gene 88 (MyD88) is a nonredundant core element, two collateral subsystems with small GTPase and phosphatidylinositol signaling, and MyD88‐independent pathway. There is extensive crosstalk between the main bow‐tie network and subsystems, as well as feedback and feedforward controls. One obvious feature of this network is the fragility against removal of the nonredundant core element, which is MyD88, and involvement of collateral subsystems for generating different reactions and gene expressions for different stimuli.