Operation Of Complex Systems Research Articles

Abstract 6267: Training a machine learning model to predict T cell cytokine secretion function using a novel single-cell flow cytometry method that measures kinetic phenotypes

Advances in high-dimensional single-cell analysis have transformed our understanding of cellular heterogeneity and its role in complex pathologies. Most datasets are acquired through single-cell RNA sequencing, which provides expression of thousands of genes on each cell at a given timepoint. The typical workflow involves identifying potential new cell types via clustering, generation of hypotheses, and followed by post-hoc experiments to test these hypotheses. Technical bottlenecks include the sparse and noisy nature of sequencing data, and the inability to link observations directly to function due to the destructive nature of sequencing. Supervised machine learning (ML) is an increasingly useful approach to generate powerful predictive models of complex processes. This approach requires both high quality and high quantity of training data. An ideal method to acquire this data would be able to follow individual cells over time while measuring changes in phenotype and function. Here, we describe a novel single-cell method using non-destructive optical barcodes that enables acquisition of dynamic high-dimensional data at scale. We apply this method to generate unprecedented predictive models of single-cell function using a human T-cell activation model. Our barcoding approach relies on laser particles (Kwok et al., Nat. Biomed. Eng. 2024) to track individual cells over time. T-cells are barcoded, activated, and measured repeatedly over time using a flow cytometer to capture kinetic changes in expression of protein biomarkers (“kinetic phenotyping”). We link these kinetic phenotypes to cytokine secretion function through a stimulation step and cytokine secretion assay. The single-cell phenotype-to-function datasets are then used to train ML models to predict cytokine secretion prior to stimulation. We observed increased expression of CD25, PD-1 and CD69 in T cells post-activation from 2h to 48h, as expected. However, distinct kinetic patterns were observed. For example, while CD25 increased largely monotonically, there was significant transient expression of PD-1. These observations were consistent across multiple donors. We employed supervised ML (Random Forrest) to train a classification model to predict cytokine secretion from single-cell kinetic phenotypes. The model was validated using 5-fold cross-validation and achieved ROC-AUC values of 0.74 to 0.89 for predicting TNFa, IFNg and IL2 secretion. For each cytokine, we identified the most predictive biomarkers (and timepoint). Finally, we used our classification model to describe T-cell heterogeneity by probability of cytokine secretion and validated our method using conventional analyses. Our approach is expected to be of great utility for predicting single-cell function in complex systems, from immunotherapy development to cancer pathogenesis. Citation Format: Sheldon J.J. Kwok, Yulia Shulga, Emane Rose Assita, Sarah Forward, Trevor Brown, Pratip Chattopadhyay. Training a machine learning model to predict T cell cytokine secretion function using a novel single-cell flow cytometry method that measures kinetic phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6267.

Graph coloring framework to mitigate cascading failure in complex networks

Cascading failures pose a significant threat to the stability and functionality of complex systems, making their mitigation a crucial area of research. While existing strategies aim to enhance network robustness, identifying an optimal set of critical nodes that mediates the cascade for protection remains a challenging task. Here, we present a robust and pragmatic framework that effectively mitigates the cascading failures by strategically identifying and securing critical nodes within the network. Our approach leverages a graph coloring technique to identify the critical nodes using the local network topology, and results in a minimal set of critical nodes to be protected yet maximally effective in mitigating the cascade thereby retaining a large fraction of the network intact. Our method outperforms existing mitigation strategies across diverse network configurations and failure scenarios. An extensive empirical validation using real-world networks highlights the practical utility of our framework, offering a promising tool for enhancing network robustness in complex systems.

City as a system: A systems approach to urban planning and development

The article presents the evolution of a systems approach in socio-economic geography and spatial management from its first application in Polish geography to the times of its gradual use within Complexity Theory. This evolution is accompanied by the transition from the systemic attitude in a cognitive process (geography) to its practical use (spatial management). However, particular emphasis was put on the reconstruction and development of a highly complex functional system which is the city and the use of a systems approach in planning its development. After the general overview of the system as it is understood, the article shows interactional living environment models and a territorial social system. Then, it demonstrates the use of systemic views in relation to the city in the form of urban ecosystem conceptions, a sustainable city model, and also an organicist city model, including the life of the city as an organicist model of its functioning, and city resilience. The final part deals with the perspectives and determinants brought by Complexity Theory in the realm of cognition and practice. What was also evaluated was the possibility of the application of the systems approach (ideas, conceptions, models) with respect to cognition and practice (urban development and planning) in the current state of science and spatial management in Poland.

High-Frequency Tails in Spectral Densities.

Recent advances in numerically exact quantum dynamics methods have brought the dream of accurately modeling the dynamics of chemically complex open systems within reach. Path-integral-based methods, hierarchical equations of motion, and quantum analog simulators all require the spectral density (SD) of the environment to describe its effect on the system. Here, we focus on the decoherence dynamics of electronically excited species in solution in the common case where nonradiative electronic relaxation dominates and is much slower than electronic dephasing. We show that the computed relaxation rate is highly sensitive to the choice of SD representation─such as the Drude-Lorentz or Brownian modes─or strategy used to capture the main SD features, even when early-time dephasing dynamics remains robust. The key reason is that electronic relaxation is dominated by the resonant contribution from the high-frequency tails of the SD, which are orders of magnitude weaker than the main features of the SD and can vary significantly between strategies. This finding highlights an important, yet overlooked, numerical challenge: obtaining an accurate SD requires capturing its structure over several orders of magnitude to ensure correct decoherence dynamics at both early and late times. To address this, we provide a simple transformation that recovers the correct relaxation rates in quantum simulations constrained by algorithmic or physical limitations on the shape of the SD. Our findings enable a comparison of different numerically exact simulation methods and expand the capabilities of analog simulations of open quantum dynamics.

Enhancing Early Systems R&D Capabilities with Systems —Theoretic Process Analysis

ABSTRACTSystems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering‐based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early‐systems R&D discussions. STPA—a ‘top‐down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early‐system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early‐system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. Leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.

Molecularly Woven Polymer Aerogels.

Aerogels with abundant nanopores and large specific surface areas have extensive potential in various applications but are constrained by fragility and difficulty in degradation. Currently, the exploration of adaptive and reprocessing aerogels has become increasingly urgent, as the demand for intelligent and sustainable materials intensifies. Here, we present a molecular weaving strategy to construct molecularly woven polymer aerogels (WPAs) via catalyst-free aldimine condensation between prewoven aldehyde-functionalized Cu(I) bisphenanthroline (Cu(PBD)2) and flexible 4,4'-diaminodibenzyl (DB). The key feature of this system consists entirely of dense woven nodes that can be readily activated by external stimuli, where Cu(I) ions can also be reversibly removed as needed, while preserving porous structures. Consequently, we achieve adjustable mechanical properties of WPAs, with a 10-fold enhancement in elasticity after removing Cu(I) ions. Moreover, the destroyed WPAs demonstrate a straightforward reprocessing capacity rather than tedious monomer recovery due to the dissociation of Cu(I)-coordination bonds, the activation of sequential polymer thread motions, and the accelerated imine bond exchange enabled by adjacent Cu(I) ions. This work offers a new perspective on designing customizable and sustainable aerogels and verifies the feasibility of the emergent molecularly woven technique in a more complex functional material system.

Spacetime pq theory for AC and DC electric power systems

The 50/60 Hz alternating current (AC) electric power has been the standard and most flexible energy source powering our modern societies for one and a half centuries since the war of the currents: AC versus direct current (DC). A reactive power concept that was introduced at the beginning of the AC power was very useful for circuit/system analysis, design, control, optimization, and ultimately for more efficient and stable generation, transmission, distribution, and consumption. The initial reactive power theory was based on single-phase sinusoidal AC power to capture inductive and capacitive power that yields to net-zero average power over one fundamental cycle. Soon it was expanded to non-sinusoidal AC power and finally to instantaneous three-phase AC power. However, these reactive power theories remain separate and limited to special cases and have never been consolidated and made valid to all cases. Today, more widespread adoption of power electronics and renewable energy is bringing back DC power into the electric grids. The reactive power concept has never been applied to DC power systems. There is no reactive power in DC power systems according to the existing reactive power theories. Do DC power systems really have no reactive power? Capacitors and inductors are widely used in DC just like in AC power systems. Are they not reactive power components? Why are they different from their AC counterparts? Furthermore, are batteries active or reactive power components? What about active devices like power converters (or inverters) with AC (or DC) on one side and DC (or AC) on the other? Do they generate or consume reactive power? Finally, what about AC and DC hybrid power systems? How to define reactive power in such a complex power system that has a multitude of loads, buses, and sources? Is there reactive power between any two loads, any two buses, or any two sources in a power system and what is the total reactive power in such a complex power system as a whole? As the motivation and goal of this paper to answer the above basic questions, to unify the existing AC reactive power theories and to ultimately provide theoretical and insightful guidance for system analysis, design, control, efficiency, optimization, and operation of complex power systems, a concept of spacetime (both spatial and temporal) active and reactive power (pq) theory—the spatiotemporal aspect of active and reactive power—is developed for both AC and DC power systems. The theoretical definitions and physical meanings of the spacetime reactive power will be developed, and real applications and thought experiments/cases/exercises will be explored and discussed. The developed mathematics to define the active (or real) and reactive (or imaginary) power—p and q respectively by dot (scalar) and cross (vector) products of multi-dimension spacetime vectors and time-space mapping principle/law can have some fundamental implications as well.

The Utility of Synthetic Biology in the Treatment of Industrial Wastewaters

Effective treatment of industrial wastewater effluents before discharging them to the soil and water bodies has always been one of the paramount environmental concerns. The pollutants in untreated wastewater effluents have hazardous implications for human health and the ecosystem. Conventional physical and chemical processes of industrial wastewater treatment have many complications and they often fall short in the treatment of new and diverse varieties of pollutants. Several microbial strains in nature have shown their remediation property, but they possess limited efficiency in breaking down pollutants into non-toxic components. Synthetic biology is a perfect amalgam of two fields – biological science and engineering, and it has transformed our ways of understanding the functioning of complex biological systems. Researchers have reported that some engineered microbes can achieve remediation efficiency of up to 100% in specific pollutants such as heavy metals and hydrocarbons. For example, microbes like Pseudomonas veronii have been shown to reduce cadmium concentrations by up to 100%, and Pseudomonas putida has been shown to reduce phenol concentrations by 92%. Synthetic biology-based biosensors are also being developed for pollution monitoring and control of industrial wastewater. In this review, we discuss these advancements of synthetically engineered microorganisms in the treatment of industrial wastewater.

Modern Landscape of Innovative Technologies in Optimizing the Quality of Life of Cancer Patients.

In the era of the intensive development of post-genomic technologies, it is reasonable to review the modern strategy for solving the problems of cancer patients. The current trend of the new paradigm is based on the knowledge and possibilities of correcting molecular genetic processes based on the principles of precision medicine. The key role in implementing such an approach belongs to modern innovative technologies, among which omics technologies occupy a special place. The genesis of the symbiosis of medical-biological and cybernetic technologies aimed at processing information databases becomes the subject of learning the functioning of complex biological systems. Today, for the dynamic development of the implementation of precision medicine based on innovative technologies, it is worth concentrating the efforts on the deep consolidation of transdisciplinary approaches that can form an algorithm of a new market of medical services aimed at improving the quality of life.

Cell-specific genetic expression profile of antennal glia in Drosophila reveals candidate genes in neuron-glia interactions

Understanding the genetic basis of neuron-glia interactions is essential to comprehend the function of glia. Recent studies on Drosophila antennal glia Mz317 has shown their role in olfactory perception. In the antenna, the Mz317-type glia tightly envelops the somas of olfactory sensory neurons and axons already covered by wrapping glia. Here, we investigate candidate genes involved in glial regulation in olfactory reception of Drosophila. Targeted transcriptional profiling reveals that Mz317 glial cells express 21% of Drosophila genes emphasizing functions related to cell junction organization, synaptic transmission, and chemical stimuli response. Comparative gene expression analysis with other glial cell types in both the antenna and brain provides a differential description based on cell type, offers candidate genes for further investigation, and contributes to our understanding of neuron-glia communication in olfactory signaling. Additionally, similarities between the molecular signatures of peripheral glia in Drosophila and vertebrates highlight the utility of model organisms in elucidating glial cell functions in complex systems.

Integrated Assessment of the Quality of Functioning of Local Electric Energy Systems

This research demonstrates the possibility and expediency of forming local electric energy systems (LEESs) based on renewable sources of energy (RSE) as balancing groups in the electric power system (EPS), which can maintain efficiency and provide power supply to consumers in an autonomous mode. The LEES is a part of the EPS of thermal and nuclear power plants and is considered as a separate balancing group. LEESs are designed in such a way that they can operate autonomously in both normal and extreme conditions in the EPS. The sources of electricity in LEESs are small hydroelectric power plants (SHPPs), photovoltaic power plants (PVPPs), and wind power plants (WPPs), whose electricity generation is unstable due to dependence on natural conditions. Therefore, the structure of a LEES with RSE includes an energy storage system with reserves sufficient to compensate for the unstable generation and balancing of the mode. LEESs can differ significantly in terms of key technical and economic indicators (power supply reliability, power losses, and power quality), and therefore, it is necessary to choose the optimal one. It is not advisable to optimize the quality of power supply in a LEES by individual indicators, as improvement of one indicator may lead to deterioration of another. The functional readiness of a LEES should be assessed by the quality of operation, which depends on reliability, power losses, and power quality. To simplify the task of assessing the quality of operation, which is a vector optimization problem, a method for determining the integral indicator as a number that characterizes the LEES and reflects the compromise between the values of reliability, power losses, and power quality has been developed. The integral indicator of the functioning of complex systems is based on a combination of the theory of Markov processes and the criterion method of similarity theory. The value of the integral indicator of the quality of operation of the LEES allows for comparing different variants of power transmission and distribution systems without determining individual components of technical and economic indicators—reliability, power losses, and power quality. The offered integral indicator of the quality of functioning of a LEES with RSE corresponds to the general requirements for such indicators. It reflects the actual operating conditions; allows for assessing the efficiency, quality, and optimality of power supply systems; and can be easily decomposed into partial indicators.

Building a system of dynamic norms for evaluating the functioning of complex systems on the example of the regions of the Central Federal District

In this paper, we present a method of forming norms for evaluating the results of the functioning of complex systems applicable to socio-ecological and economic systems, taking into account the priorities of the development of the regions of the Russian Federation. The methodology involves the selection of normative values from a set of norms based on two methods: the first is based on the construction of econometric models using statistical data for a set of subjects (the first type) and for one selected subject (the second type). The second method uses the methodology of Bayesian intelligent measurements based on the regularizing Bayesian approach (the third and fourth types). Depending on the result of the calculations, a norm is selected that gives a higher (in the case of high priority), average (in the case of medium priority) and lower (in the case of low priority) normative value of the evaluated effective features characterizing the development of the subject. The implementation of the method is demonstrated by the example of the regions of the Central Federal District, including the Tula Region, for which econometric and fuzzy models of the relationship between the volume of gross regional product with the value of fixed assets and the number of employees for sections A (Agriculture, forestry, hunting, fishing and fish farming) and C (Mining) according to OKVED1 are constructed, forming the raw materials sector according to data for 2007–2022. The EFRA and Infoanalyst 2.0 software platforms are used as tools. The results obtained can be used by regional authorities in the formation of norms to assess the results of the functioning of the regions in the short and medium term.

Logical Methods Usage in Diagnostics of the Multi-agents Air-conditioning System

The possibility of cooperative agents usage for the on-board air conditioning system research and diagnosis is considered. A logical model for type I and II faults searching is proposed. This work provides an opportunity to master the practical knowledge and skills the first and second types logical models building to obtaining a minimum test of performance and finding malfunctions and damages the place at complex information systems development, operation and maintenance the stages. An algorithm has been developed that combines the development a generalized I type logical model from the system functional circuit input side and the subsequent construction II type a logical model from its free outputs. The deep learning metod allows to increase the accuracy of the model, the speed of finding faults, predicting and preventing accidents

Life as Paradigm of Knowledge. What Use of Hegel in the Age of the Environmental Crisis?

This paper aims to show to what extent the normativity of organic life that emerged from the natural sciences of Hegel’s time influenced the structuring of his speculative approach. In the first part, the eighteenth-century paradigm shift in the natural sciences is investigated as marking the transition from a physics-based worldview to a biology-based one. This shift argues strongly against the reduction of nature to mechanism and provides an adequate model for analysing the functioning of all other complex systems and, above all, the functioning of reason. In the second part, the consequences of such a shift are evaluated with respect to Hegel’s idealism, especially in relation to the categories of organism and purpose. They are the core elements for understanding not only the mode of living but also that of thinking. In the last part, we identify Hegel’s philosophy as a “living ontology,” an ontology that keeps pace with reality by modifying its categories accordingly. From this point of view, Hegel’s idealism can be compatible with a new idea of the relationship between human beings and the environment, in the direction of a relational ontology. The paper then focuses on the legacy of this re-reading of Hegel’s philosophy in the contemporary debate on ecological thinking that attempts to answer the question raised in the discussion on the environmental crisis and Anthropocene.

The constrained-disorder principle defines the functions of systems in nature.

The Constrained Disorder Principle (CDP) defines all systems in nature by their degree of inherent variability. Per the CDP, the intrinsic variability is mandatory for their proper function and is dynamically changed based on pressures. The CDP defines the boundaries of inherent variability as a mechanism for continuous adaptation to internal and external perturbations, enabling survival and function under dynamic conditions. The laws of nature govern the world's natural phenomena and underlie the function of all systems. Nevertheless, the laws of physics do not entirely explain systems' functionality under pressure, which is essential for determining the correct operation of complex systems in nature. Variability and noise are two broad sources of inherent unpredictability in biology and technology. This paper explores how the CDP defines the function of systems and provides examples from various areas in nature where the CDP applies, including climate, genetic, biology, and human behavioral variabilities. According to the CDP, system malfunction results from inappropriate performance of the boundaries of inherent variability. The environment influences the physiological variability, and species interactions influence eco-evolutionary outcomes. The CDP defines human behavior as being driven by randomness and accounts for malfunctions and their corrections. The paper reviews variability-based CDP algorithms and CDP-based second-generation artificial intelligence systems and their potential for improving systems' prediction and efficiency by using variability.

Imbalance in the duality of criminal activity and its proving

Objective: to verify and confirm (or refute) the hypothesis about the mismatch between the means of proof and the means of criminal activity in terms of strength and diversity, which causes imbalance in the dualism of criminal activity and its proving. Methods: in solving the research problem, we used, first of all, dualistic and hypothetical-deductive methods, as well as methods of functional systems, comparison and other general scientific methods. Results: criminal activity and its proving show the dualism of two complex functional systems, which are opposite, not reducible to each other, equal, but not equipollent. The lack of equipollency is manifested in the inconsistency of the means of proof with the means of criminal activity in terms of strength and diversity. On the one hand, the available means of proof are outdated and limited; on the other hand, they are used ineffectively when forming evidentiary systems of different levels. This leads to imbalance in the dualistic system. This is confirmed by systemic problems of reality – critically low disclosure rate, violation of the rights and freedoms of participants in criminal proceedings, low level of compensation for damages from crimes, protracted procedures of multi-volume criminal cases, etc. Scientific novelty: for the first time, the activity of proving is presented as a complex functional system, which together with criminal activity forms a dualism. In this dualistic system, an imbalance is found, consisting in the non-equipollent relationship between the two functional systems, where the means of proving are inferior to the means of criminal activity both in strength and in diversity. In this regard, the article attempts to reveal the mechanism of proving in order to determine how the two sides in the dualistic system can be balanced. Practical significance: the provisions laid down in the study can in the future act as a methodological basis for improving the means of proving and increasing the effectiveness of forming evidentiary systems of different levels.

MATHEMATICAL MODELING OF FACTORS AFFECTING THE FUNCTIONING OF COMPLEX SYSTEMS OF TERRITORIAL DEVELOPMENT OF LAND USE IN REGIONS

The necessity of applying methods and models of mathematical modeling of factors affecting the functioning of complex systems of territorial development of land use in regions is determined. It has been proven that the mathematical modeling toolkit is an important element of forming a quantitative basis for the formation and use of factors of territorial development, creating conditions for characterizing their changes in the following investigated periods. The presented toolkit provides opportunities for the development of solutions to increase the efficiency of land use. The research achieved the goal of theoretical and methodological substantiation of mathematical modeling of factors that affect the functioning of complex systems of territorial development of land use in regions. The set tasks were solved: systematization of theoretical provisions regarding the determination of the territorial development of land use in the regions; determination of factors for ensuring the territorial development of land use in the regions; substantiation of directions for mathematical modeling of factors influencing the territorial development of land use in regions. As a result of the systematization of theoretical and methodological provisions, a definition of the territorial development of land use in regions is proposed, which is characterized as a set of spatial, urban planning, investment and environmental factors, the interaction of which leads to the achievement of a qualitatively new state of land relations compared to the past, taking into account social, institutional, management features and the level of interaction of stakeholders operating in the field of regional land use. To ensure territorial development, a mathematical modeling toolkit is used, the use of which allows you to form a quantitative basis for decision-making in the field of land relations. The necessity of applying a set of criteria for the adequacy of the developed mathematical models of the influence of spatial, urban planning, environmental and investment factors on the integral indicator of the level of provision of the territorial development of land use has been established.

Non-Covalent Self-Assembly Behaviors Based on Racemic Binaphthol Scaffolds.

Understanding the fundamental mechanisms involved in the construction and organization of multi-scale structures is crucial for the design and manufacture of complex functional systems with long-range molecular arrangements. In this paper, a series of compounds have been synthesized using racemic binaphthols as the skeleton and a Suzuki coupling reaction for derivatization at the 6,6' positions, which resulted in various structures bearing different functional groups. Control over the self-assembly of these racemic binaphthol derivatives was successfully achieved by adjusting the types and positions of the substituents in the parent binaphthol compound, which revealed the key factors influencing the types of the non-covalent interactions and the self-assembly process. For example, the single-crystal structures of the resulting compounds indicated that assembly structures such as single helix and double helix based on non-traditional hydrogen bond motifs could be obtained, and fascinating non-covalent self-assembly structures such as molecular ladders and catenane discovered.

Advances in the application of network analysis methods in traditional Chinese medicine research

Advances in the application of network analysis methods in traditional Chinese medicine research

Integrating fragment-based screening with targeted protein degradation and genetic rescue to explore eIF4E function

Eukaryotic initiation factor 4E (eIF4E) serves as a regulatory hub for oncogene-driven protein synthesis and is considered a promising anticancer target. Here we screen a fragment library against eIF4E and identify a ligand-binding site with previously unknown function. Follow-up structure-based design yields a low nM tool compound (4, Kd = 0.09 µM; LE 0.38), which disrupts the eIF4E:eIF4G interaction, inhibits translation in cell lysates, and demonstrates target engagement with eIF4E in intact cells (EC50 = 2 µM). By coupling targeted protein degradation with genetic rescue using eIF4E mutants, we show that disruption of both the canonical eIF4G and non-canonical binding sites is likely required to drive a strong cellular effect. This work highlights the power of fragment-based drug discovery to identify pockets in difficult-to-drug proteins and how this approach can be combined with genetic characterization and degrader technology to probe protein function in complex biological systems.