System-level Properties Research Articles

7.C. Scientific session: Commercial determinants of health: impeding attainment of the Sustainable Development Goals

Abstract The commercial determinants of health (CDOH) and the adverse impacts they have on health equity globally, have recently emerged as an innovative growing field of research in public health. The corporate playbook comprises a myriad of strategies and tactics that aim to enhance profitability for the commercial actors, while harming health and posing grave risks to planetary health for current and future generations. The literature however, rarely explicitly makes the connection between commercial determinants and the impact they have on the attainment of the Sustainable Development Goals (SDGs) set out by the United Nations in 2015. This workshop aims to use the commercial determinants of health framework as a new approach to draw attention to, and make linkages between, commercial determinants and the challenges they pose to the attainment of several if not all the global SDGs. The four presentations showcase exemplars of the commercial determinants to include the firearms industry, fossil fuel industry, food industry and the tobacco industry focusing on impacts in low- and middle-income countries (LMICs). The workshop will start with a brief overview of the CDOH framework, their corporate playbook, and the progress in SDG attainment globally while presenting the first case on the firearms industry trade impacting armed conflicts that impede SDG 3 (good health and wellbeing), 10 (reduced inequalities), and 16 (peace, justice and strong institutions). The second presentation will discuss how the fossil fuel industry uses greenwashing tactics to conceal its harmful activities thereby compromising SDGs 7 (affordable and clean energy), 11 (sustainable cities and communities), and 13 (climate action). The third presentation will address how the tobacco industry impedes the advancement of SDG 3, 8 (decent work and economic growth), 13 (climate action) and 17 (partnerships for the goals) perpetuated by national system level attributes. The last presentation will showcase how the food industry challenges SDG 3 in its production of ultra-processed foods. The workshop will draw on evidence from LMICs, but is also eye opening for high-income countries of Europe which face industry interference in social and health policymaking progress in SGD attainment. The audience will be engaged in a short warm-up exercise at the start of the workshop through an app for real-time feedback accessed through a QR code on the screen, and will interact with the presenters at the end of the workshop if time permits. Key messages • All commercial actors pose a direct or indirect threat to progress in attaining the SDGs. • The commercial determinants of health pose are in direct contradiction to the SDG 17 through their corporate playbook.

Cell-Free Systems Biology: Characterizing Central Metabolism of Clostridium thermocellum with a Three-Enzyme Cascade Reaction.

Genetic approaches have been traditionally used to understand microbial metabolism, but this process can be slow in nonmodel organisms due to limited genetic tools. An alternative approach is to study metabolism directly in the cell lysate. This avoids the need for genetic tools and is routinely used to study individual enzymatic reactions but is not generally used to study systems-level properties of metabolism. Here we demonstrate a new approach that we call "cell-free systems biology", where we use well-characterized enzymes and multienzyme cascades to serve as sources or sinks of intermediate metabolites. This allows us to isolate subnetworks within metabolism and study their systems-level properties. To demonstrate this, we worked with a three-enzyme cascade reaction that converts pyruvate to 2,3-butanediol. Although it has been previously used in cell-free systems, its pH dependence was not well characterized, limiting its utility as a sink for pyruvate. We showed that improved proton accounting allowed better prediction of pH changes and that active pH control allowed 2,3-butanediol titers of up to 2.1 M (189 g/L) from acetoin and 1.6 M (144 g/L) from pyruvate. The improved proton accounting provided a crucial insight that preventing the escape of CO2 from the system largely eliminated the need for active pH control, dramatically simplifying our experimental setup. We then used this cascade reaction to understand limits to product formation in Clostridium thermocellum, an organism with potential applications for cellulosic biofuel production. We showed that the fate of pyruvate is largely controlled by electron availability and that reactions upstream of pyruvate limit overall product formation.

Proteome-wide copy-number estimation from transcriptomics.

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomicsandtranscriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Bringing a Systems Approach to Living Donor Kidney Transplantation

BackgroundLiving donor kidney transplantation (LDKT) is the best treatment option for patients with kidney failure. Efforts to increase LDKT have focused on micro-level interventions and the need for systems thinking has been highlighted. We aimed to identify and compare health system-level attributes and processes that are facilitators and barriers to LDKT. MethodsWe conducted a qualitative comparative case study analysis of three Canadian provincial healthcare systems with variable LDKT performance (Quebec:low, Ontario:moderate-high, British Columbia:high). Data collection entailed semi-structured interviews (n=91), document review (n=97) and focus groups (n=5 with 40 participants), analyzed using inductive thematic analysis. ResultsOur findings showed a strong relationship between the degree of centralized coordination between governing organizations and the capacity to deliver LDKT: 1) Macro-level coordination between governing organizations in British Columbia and Ontario increased capacities, while Québec was seen as decentralized with little formal coordination; 2) A higher degree of centralized coordination facilitated more effective resource deployment in the form of human resources and initiatives in British Columbia and Ontario, whereas in Québec resource deployment relied on hospital budgets leading to competition for resources and reduced capacity of initiatives; 3) Informal resource sharing through strong communities of practice and local champions facilitated LDKT in Ontario and British Columbia and was limited in Québec. ConclusionOur findings suggest that interventions that account for full-system function, particularly macro-level coordination between governing organizations can improve LDKT delivery. Findings may be used to guide structured organizational change towards increasing LDKT and mitigating the global burden of kidney failure.

An integrated metagenomic, metabolomic and transcriptomic survey of Populus across genotypes and environments

Bridging molecular information to ecosystem-level processes would provide the capacity to understand system vulnerability and, potentially, a means for assessing ecosystem health. Here, we present an integrated dataset containing environmental and metagenomic information from plant-associated microbial communities, plant transcriptomics, plant and soil metabolomics, and soil chemistry and activity characterization measurements derived from the model tree species Populus trichocarpa. Soil, rhizosphere, root endosphere, and leaf samples were collected from 27 different P. trichocarpa genotypes grown in two different environments leading to an integrated dataset of 318 metagenomes, 98 plant transcriptomes, and 314 metabolomic profiles that are supported by diverse soil measurements. This expansive dataset will provide insights into causal linkages that relate genomic features and molecular level events to system-level properties and their environmental influences.

Abstract 5527: Spatiotemporal analysis of growth factor regulation of osteosarcoma AKT and ERK signaling dynamics in an ex vivo lung metastasis model

Abstract In the present study, we investigated the relationships between spatiotemporal distribution of growth factors in the lung microenvironment, metastatic osteosarcoma cell signaling, and intratumoral gene expression heterogeneity. Osteosarcoma is an aggressive bone malignancy that commonly metastasizes to the lungs, resulting in significant patient mortality. During metastasis, disseminated cancer cells are exposed to microenvironment-derived growth factors that result in signaling pathway activation. Two such pathways are MAPK/ERK and PI3K/AKT, which regulate complex transcriptional programs underlying cell proliferation and survival decisions. In this study, we utilized precision cut lung slices (PCLS) obtained from mice with established osteosarcoma metastases to ex vivo model associations between the spatiotemporal distributions of growth factors in the lung, osteosarcoma signaling dynamics, and intratumoral gene expression heterogeneity, with single cell precision. To accomplish this, we engineered osteosarcoma cells that co-expressed genetically-encoded ERK and AKT sensors, engrafted them into PCLS tissues, then recorded sensor dynamics via live-cell microscopy. These tissues were subsequently fixed and immuno-stained for measurement of growth factor expression in resident lung cells and spatial correlation with observed tumor cell ERK-AKT signaling dynamics. Using this approach, we found that growth factor expression was diffusely increased within areas bordering osteosarcoma metastases. FGF2 and IGF1, which we have previously demonstrated to potently induce ERK signaling in osteosarcoma cells, were both significantly upregulated in these regions. However, despite relatively uniform overexpression of growth factors adjacent to tumors, individual osteosarcoma cells displayed stochastics patterns of ERK and AKT signaling, and downstream gene targets. These findings revealed that upregulation of lung growth factors, and the inherent systems-level properties of osteosarcoma signaling networks, can converge to promote intratumoral signaling and gene expression heterogeneity. Together, our results, using a novel PCLS and live-cell biosensor imaging approach, provide new insight into how spatial and temporal changes in microenvironmental signaling composition are integrated by metastatic osteosarcoma cells to promote heterogeneity. Citation Format: Rawan Makkawi, Ryan D. Roberts, Jeremy Copperman, Alexander E. Davies. Spatiotemporal analysis of growth factor regulation of osteosarcoma AKT and ERK signaling dynamics in an ex vivo lung metastasis model [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 5527.

Distinct Topological Properties of the Reward Anticipation Network in Preadolescent Children With Binge Eating Disorder Symptoms

Few studies have considered the neural underpinnings of binge eating disorder (BED) in children, despite clinical and subclinical symptom presentation occurring in this age group. Symptom presentation at this age is of clinical relevance, as early onset of binge eating is linked to negative health outcomes. Studies in adults have highlighted dysfunction in the frontostriatal reward system as a potential candidate for binge eating pathophysiology, although the exact nature of such dysfunction is currently unclear. Data from 83 children (mean age 9.9 years, SD= 0.60) with symptoms of BED (57% girls) and 123 control participants (mean age 10.0 years, SD= 0.60) (52% girls) were acquired from the 4.0 baseline release of the Adolescent Brain Cognitive Development Study. Task-based graph theoretic techniques were used to analyze data from anticipation trials of the monetary incentive delay task. Network and nodal properties were compared between groups. The BED-S group showed alterations in topological properties associated with the frontostriatal subnetwork, such as reduced nodal efficiency in the superior frontal gyrus, nucleus accumbens, putamen, and in normal sex-difference patterns of these properties, such as diminished girls-greater-than-boys pattern of betweenness-centrality in nucleus accumbens observed in controls. Distinct network properties and sex-difference patterns in preadolescent children with BED-S suggest dysregulation in the reward system compared to those of matched controls. For the first time, these results quantify this dysregulation in terms of systems-level properties during anticipation of monetary reward and significantly inform the early and sex-related brain markers of BED symptoms.

The latent net effectiveness of institutional complexes: a heuristic model.

International institutions strive to achieve their own objectives while operating within a complex network of interdependencies. These interdependencies create an extensive web of relationships that serve as potential pathways for broader institutional impacts. The actions taken by individual institutions, their mutual influences, and the pattern of connectivity collectively shape the overall performance of institutional complexes. Understanding the performance of these complexes is crucial, yet we currently lack a methodology to assess it. To address this gap, we have developed a conceptual framework that integrates three distinct areas of study on three different scales: institutional effectiveness, institutional interlinkages, and institutional networks. This framework enables us to consider what we call the latent net effectiveness, or collective problem-solving potential, of a group of interconnected institutions. To put this framework into practice, we have devised a heuristic model, drawing from the extensive literature on international environmental institutions. As an illustrative example, we have applied this model to a network of 378 multilateral environmental agreements with 810 known issue linkages. Our work underscores the relevance of considering the system-level properties of institutional complexes and emphasizes the need for timely network-based governance and policy interventions to enhance the overall effectiveness of institutional complexes. This article is part of the theme issue 'A complexity science approach to law and governance'.

Fibres-threads of intelligence-enable a new generation of wearable systems.

Fabrics represent a unique platform for seamlessly integrating electronics into everyday experiences. The advancements in functionalizing fabrics at both the single fibre level and within constructed fabrics have fundamentally altered their utility. The revolution in materials, structures, and functionality at the fibre level enables intimate and imperceptible integration, rapidly transforming fibres and fabrics into next-generation wearable devices and systems. In this review, we explore recent scientific and technological breakthroughs in smart fibre-enabled fabrics. We examine common challenges and bottlenecks in fibre materials, physics, chemistry, fabrication strategies, and applications that shape the future of wearable electronics. We propose a closed-loop smart fibre-enabled fabric ecosystem encompassing proactive sensing, interactive communication, data storage and processing, real-time feedback, and energy storage and harvesting, intended to tackle significant challenges in wearable technology. Finally, we envision computing fabrics as sophisticated wearable platforms with system-level attributes for data management, machine learning, artificial intelligence, and closed-loop intelligent networks.

Statistical Brain Network Analysis.

The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind. We have attempted to address this need by developing three complementary statistical frameworks-a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. Here we delineate these approaches, briefly survey related tools, and discuss potential future avenues of research. We hope this review catalyzes further statistical interest and methodological development in the field.

Gene dosage compensation: Origins, criteria to identify compensated genes, and mechanisms including sensor loops as an emerging systems-level property in cancer.

The gene dosage compensation hypothesis presents a mechanism through which the expression of certain genes is modulated to compensate for differences in the dose of genes when additional chromosomes are present. It is one of the means through which cancer cells actively cope with the potential damaging effects of aneuploidy, a hallmark of most cancers. Dosage compensation arises through several processes, including downregulation or overexpression of specific genes and the relocation of dosage-sensitive genes. In cancer, a majority of compensated genes are generally thought to be regulated at the translational or post-translational level, and include the basic components of a compensation loop, including sensors of gene dosage and modulators of gene expression. Post-translational regulation is mostly undertaken by a general degradation or aggregation of remaining protein subunits of macromolecular complexes. An increasingly important role has also been observed for transcriptional level regulation. This article reviews the process of targeted gene dosage compensation in cancer and other biological conditions, along with the mechanisms by which cells regulate specific genes to restore cellular homeostasis. These mechanisms represent potential targets for the inhibition of dosage compensation of specific genes in aneuploid cancers. This article critically examines the process of targeted gene dosage compensation in cancer and other biological contexts, alongside the criteria for identifying genes subject to dosage compensation and the intricate mechanisms by which cells orchestrate the regulation of specific genes to reinstate cellular homeostasis. Ultimately, our aim is to gain a comprehensive understanding of the intricate nature of a systems-level property. This property hinges upon the kinetic parameters of regulatory motifs, which we have termed "gene dosage sensor loops." These loops have the potential to operate at both the transcriptional and translational levels, thus emerging as promising candidates for the inhibition of dosage compensation in specific genes. Additionally, they represent novel and highly specific therapeutic targets in the context of aneuploid cancer.

Irruption Theory: A Novel Conceptualization of the Enactive Account of Motivated Activity.

Cognitive science is lacking conceptual tools to describe how an agent's motivations, as such, can play a role in the generation of its behavior. The enactive approach has made progress by developing a relaxed naturalism, and by placing normativity at the core of life and mind; all cognitive activity is a kind of motivated activity. It has rejected representational architectures, especially their reification of the role of normativity into localized "value" functions, in favor of accounts that appeal to system-level properties of the organism. However, these accounts push the problem of reification to a higher level of description, given that the efficacy of agent-level normativity is completely identified with the efficacy of non-normative system-level activity, while assuming operational equivalency. To allow normativity to have its own efficacy, a new kind of nonreductive theory is proposed: irruption theory. The concept of irruption is introduced to indirectly operationalize an agent's motivated involvement in its activity, specifically in terms of a corresponding underdetermination of its states by their material basis. This implies that irruptions are associated with increased unpredictability of (neuro)physiological activity, and they should, hence, be quantifiable in terms of information-theoretic entropy. Accordingly, evidence that action, cognition, and consciousness are linked to higher levels of neural entropy can be interpreted as indicating higher levels of motivated agential involvement. Counterintuitively, irruptions do not stand in contrast to adaptive behavior. Rather, as indicated by artificial life models of complex adaptive systems, bursts of arbitrary changes in neural activity can facilitate the self-organization of adaptivity. Irruption theory therefore, makes it intelligible how an agent's motivations, as such, can make effective differences to their behavior, without requiring the agent to be able to directly control their body's neurophysiological processes.

Network motifs and hypermotifs in TGFβ-induced epithelial to mesenchymal transition and metastasis

Epithelial to mesenchymal transition (EMT) is a complex, non-linear, dynamic multistep process that plays an integral role in the development of metastatic cancers. A diverse range of signaling molecules, along with their associated pathways, were observed to be involved in promoting EMT and cancer metastasis. Transforming growth factor–β (TGFβ), through its SMAD-dependent and SMAD-independent signaling, orchestrates numerous regulators that converge on key EMT transcription factors (TFs). These TFs further govern the phenotypic transition of cancer cells from epithelial to mesenchymal states. This study explores the TGFβ signaling pathway and its unique network architecture to understand their information processing roles in EMT. Two coherent type 1 feed forward network motifs regulating the expression of SNAIL and N-cadherin were observed. SNAIL, which is one of the crucial regulators of EMT, links both the coherent type 1 feed forward loops (C1FFLs) leading to hypermotif-like structure (Adler and Medzhitov, 2022). Systems modeling and analysis of these motifs and hypermotifs illustrated several interesting emergent information processing roles of the regulators involved. The known roles of these regulators, as described in the literature, were highly correlated with the emergent properties observed. The motifs illustrated persistence detection and noise filtration in regulating the expression of SNAIL and N-cadherin. Along with these system-level properties, the hypermotif architecture also exhibited temporal expression of GLI, SNAIL, ZEB, and N-cadherin. Furthermore, a hypothetical three-layered C1FFL hypermotif was postulated and analyzed. The analysis revealed various interesting system-level properties. However, possible existence of such real biological networks needs further exploration both theoretically and experimentally. Deciphering these network motifs and hypermotifs has provided an additional understanding of the complex biological phenomenon, such as EMT in cancer metastasis.

Disentangling Some Conceptual Knots.

Disentangling Some Conceptual Knots.

Phenotypic pliancy and the breakdown of epigenetic polycomb mechanisms.

Epigenetic regulatory mechanisms allow multicellular organisms to develop distinct specialized cell identities despite having the same total genome. Cell-fate choices are based on gene expression programs and environmental cues that cells experience during embryonic development, and are usually maintained throughout the life of the organism despite new environmental cues. The evolutionarily conserved Polycomb group (PcG) proteins form Polycomb Repressive Complexes that help orchestrate these developmental choices. Post-development, these complexes actively maintain the resulting cell fate, even in the face of environmental perturbations. Given the crucial role of these polycomb mechanisms in providing phenotypic fidelity (i.e. maintenance of cell fate), we hypothesize that their dysregulation after development will lead to decreased phenotypic fidelity allowing dysregulated cells to sustainably switch their phenotype in response to environmental changes. We call this abnormal phenotypic switching phenotypic pliancy. We introduce a general computational evolutionary model that allows us to test our systems-level phenotypic pliancy hypothesis in-silico and in a context-independent manner. We find that 1) phenotypic fidelity is an emergent systems-level property of PcG-like mechanism evolution, and 2) phenotypic pliancy is an emergent systems-level property resulting from this mechanism's dysregulation. Since there is evidence that metastatic cells behave in a phenotypically pliant manner, we hypothesize that progression to metastasis is driven by the emergence of phenotypic pliancy in cancer cells as a result of PcG mechanism dysregulation. We corroborate our hypothesis using single-cell RNA-sequencing data from metastatic cancers. We find that metastatic cancer cells are phenotypically pliant in the same manner as predicted by our model.

A Network-Based Approach for Improving Annotation of Transcription Factor Functions and Binding Sites in Arabidopsis thaliana.

Transcription factors are an integral component of the cellular machinery responsible for regulating many biological processes, and they recognize distinct DNA sequence patterns as well as internal/external signals to mediate target gene expression. The functional roles of an individual transcription factor can be traced back to the functions of its target genes. While such functional associations can be inferred through the use of binding evidence from high-throughput sequencing technologies available today, including chromatin immunoprecipitation sequencing, such experiments can be resource-consuming. On the other hand, exploratory analysis driven by computational techniques can alleviate this burden by narrowing the search scope, but the results are often deemed low-quality or non-specific by biologists. In this paper, we introduce a data-driven, statistics-based strategy to predict novel functional associations for transcription factors in the model plant Arabidopsis thaliana. To achieve this, we leverage one of the largest available gene expression compendia to build a genome-wide transcriptional regulatory network and infer regulatory relationships among transcription factors and their targets. We then use this network to build a pool of likely downstream targets for each transcription factor and query each target pool for functionally enriched gene ontology terms. The results exhibited sufficient statistical significance to annotate most of the transcription factors in Arabidopsis with highly specific biological processes. We also perform DNA binding motif discovery for transcription factors based on their target pool. We show that the predicted functions and motifs strongly agree with curated databases constructed from experimental evidence. In addition, statistical analysis of the network revealed interesting patterns and connections between network topology and system-level transcriptional regulation properties. We believe that the methods demonstrated in this work can be extended to other species to improve the annotation of transcription factors and understand transcriptional regulation on a system level.

Building our understanding of social entrepreneurship ecosystems

ABSTRACT While the literature on conventional entrepreneurial ecosystems (EEs) is large and growing, relatively little has been written about social entrepreneurship ecosystems (SEEs). Yet, because social entrepreneurship is distinct from conventional entrepreneurship, it is reasonable to assume that their respective ecosystems differ in significant ways as well. How exactly do they differ, if in fact they do? Do a community’s EE and SEE interact, and, if so, in what ways? What system-level attributes make an SEE vibrant? What role do local social entrepreneurship support organizations play in SEEs, and what is their impact on social entrepreneurs and the social and economic fabric of the community? In this thematic issue, we curate a selection of articles that address these and other questions about SEEs and, in so doing, hopefully inspire more research on this important subject while helping to organize it into a cohesive body of research going forward.

Optimization of Transcription Factor Genetic Circuits.

Simple SummaryTranscription factors (TFs) are proteins that bind to DNA and control the expression of genes, including other TF genes. A common challenge in cellular biology is to link the molecular attributes of TF binding to the system level properties of cellular dynamics. This article introduces a computational method to search for how a TF network might achieve particular functions. For example, how could one link the molecular parameters of TF production rates and DNA binding to a TF network that maintains a cellular circadian rhythm in the absence of external light signals? How could such a system also reset its clock when an external light signal is present? By computationally optimizing such models of TF dynamics, one can formulate hypotheses about how cells achieve particular functions. One can also gain insight into the ways in which cells process information through their TF networks.Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day–night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance.

A Systematic Review of Fault Injection Attacks on IoT Systems

The field of the Internet of Things (IoT) is growing at a breakneck pace and its applications are becoming increasingly sophisticated with time. Fault injection attacks on IoT systems are aimed at altering software behavior by introducing faults into the hardware devices of the system. Attackers introduce glitches into hardware components, such as the clock generator, microcontroller, and voltage source, which can affect software functioning, causing it to misbehave. The methods proposed in the literature to handle fault injection attacks on IoT systems vary from hardware-based attack detection using system-level properties to analyzing the IoT software for vulnerabilities against fault injection attacks. This paper provides a systematic review of the various techniques proposed in the literature to counter fault injection attacks at both the system level and the software level to identify their limitations and propose solutions to address them. Hybrid attack detection methods at the software level are proposed to enhance the security of IoT systems against fault injection attacks. Solutions to the identified limitations are suggested using machine learning, dynamic code instrumentation tools, hardware emulation platforms, and concepts from the software testing domain. Future research possibilities, such as the use of software fault injection tools and supervised machine learning for attack detection at the software level, are investigated.

Spatial patterns in ecological systems: from microbial colonies to landscapes.

Self-organized spatial patterns are ubiquitous in ecological systems and allow populations to adopt non-trivial spatial distributions starting from disordered configurations. These patterns form due to diverse nonlinear interactions among organisms and between organisms and their environment, and lead to the emergence of new (eco)system-level properties unique to self-organized systems. Such pattern consequences include higher resilience and resistance to environmental changes, abrupt ecosystem collapse, hysteresis loops, and reversal of competitive exclusion. Here, we review ecological systems exhibiting self-organized patterns. We establish two broad pattern categories depending on whether the self-organizing process is primarily driven by nonlinear density-dependent demographic rates or by nonlinear density-dependent movement. Using this organization, we examine a wide range of observational scales, from microbial colonies to whole ecosystems, and discuss the mechanisms hypothesized to underlie observed patterns and their system-level consequences. For each example, we review both the empirical evidence and the existing theoretical frameworks developed to identify the causes and consequences of patterning. Finally, we trace qualitative similarities across systems and propose possible ways of developing a more quantitative understanding of how self-organization operates across systems and observational scales in ecology.