Abstract We propose an extension to the framework of reaction systems such that the environment not only plays the role of contributing entities to the successor state, but also influences the actual generation of products. In particular, we consider that an environment can assign valences to entities, be they used as reactants or inhibitors in a reaction, as well as select the entities which can appear in the successor state as product of a reaction. This provides a framework for immersing reaction system specifications into different contexts. We also derive a notion of polarised reaction systems , where entities can appear only as reactants or inhibitors in any given reaction. We compare the expressive power of the models, also showing some equivalences between them, and test them on three case studies derived from physiology, ecology, and chemistry.

Raman spectroscopy offers a powerful, non-destructive tool for pharmaceutical quantification, particularly in environments where traditional techniques like HPLC are limited by throughput and sample preparation demands. However, the quantification of low-concentration compounds remains challenging due to weak Raman scattering and high background interference. This study evaluates the use of portable Raman instrumentation coupled with Support Vector Regression (SVR) to quantify clindamycin across various concentrations. Spectral preprocessing steps included Savitzky–Golay smoothing, Standard Normal Variate (SNV) normalisation, and blank subtraction (ΔSNV) to enhance analyte-specific signal fidelity. Three SVR-based models were developed using full spectra, chemically meaningful fingerprint bands, and coefficient-filtered features. Models were evaluated through grouped cross-validation, bootstrapping, and external testing on formulations prepared in different solvent matrix and derived from distinct clindamycin sources (commercial tablet vs. analytical-grade standard). The top performing model achieved R² values exceeding 0.98 with root mean squared errors below 2.85 mg/mL. Blind sample predictions, made on fully unseen data, fell within 95% confidence intervals of the true concentrations, demonstrating strong model robustness.

The integration of machine learning (ML) techniques with Raman spectroscopy has emerged as a promising strategy for advancing cancer diagnostics through label-free, high-resolution molecular analysis. This review aims to map and synthesize current research directions in this rapidly evolving field by conducting a structured review of existing review articles. Using a curated dataset of 70 reviews retrieved from Scopus and Web of Science, we applied Latent Dirichlet Allocation (LDA) topic modeling to uncover dominant thematic clusters across the literature. Our findings reveal five key research axes: (1) instrumentation and signal acquisition, (2) data preprocessing and spectral denoising, (3) classification models and algorithmic pipelines, (4) biomedical applications in oncology, and (5) emerging trends including deep learning and hybrid methods. This thematic structure highlights both the maturity and fragmentation of the current knowledge landscape. We also discuss the limitations of our approach, including database and article-type restrictions, and the use of LDA as a single modeling method. By identifying underexplored areas and recurring methodological challenges, this review contributes to a clearer understanding of the research gaps and future opportunities at the intersection of ML and Raman spectroscopy for cancer research.

Abstract Reaction systems are a model of computation inspired by the biochemistry exhibited by living cells. This paper introduces the notion of agency as an extension to the reaction systems formalism, leading to distributed reaction systems. Adding agents in the reaction systems setting, allows for the natural modelling and representation of multi-agent and distributed systems. To support the specification of temporal-epistemic properties of distributed reaction systems, we introduce the logic rs ctlk and present experimental results of its associated model checking procedure run on a biological benchmark of within-cell signal transduction networks. The experimental results are encouraging despite the complexity of the rs ctlk model checking problem that is shown to be pspace -complete.

Logical modeling is a powerful tool in biology, offering a system-level understanding of the complex interactions that govern biological processes. A gap that hinders the scalability of logical models is the need to specify the update function of every vertex in the network depending on the status of its predecessors. To address this, we introduce in this paper the concept of strong regulation, where a vertex is only updated to active/inactive if all its predecessors agree in their influences; otherwise, it is set to ambiguous. We explore the interplay between active, inactive, and ambiguous influences in a network. We discuss the existence of phenotype attractors in such networks, where the status of some of the variables is fixed to active/inactive, while the others can have an arbitrary status, including ambiguous.

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.

MOTIVATION: There is an increasing amount of data coming from genome-wide studies identifying disease-specific survivability-essential proteins and host factors critical to a cell becoming infected. Targeting such proteins has a strong potential for targeted, precision therapies. Typically however, too few of them are drug targetable. An alternative approach is to influence them through drug targetable proteins upstream of them. Structural target network controllability is a suitable solution to this problem. It aims to discover suitable source nodes (e.g. drug targetable proteins) in a directed interaction network that can control (through a suitable set of input functions) a desired set of targets. RESULTS: We introduce NetControl4BioMed, a free open-source web-based application that allows users to generate or upload directed protein-protein interaction networks and to perform target structural network controllability analyses on them. The analyses can be customized to focus the search on drug targetable source nodes, thus providing drug therapeutic suggestions. The application integrates protein data from HGNC, Ensemble, UniProt, NCBI and InnateDB, directed interaction data from InnateDB, Omnipath and SIGNOR, cell-line data from COLT and DepMap, and drug-target data from DrugBank. AVAILABILITYAND IMPLEMENTATION: The application and data are available online at https://netcontrol.combio.org/. The source code is available at https://github.com/Vilksar/NetControl4BioMed under an MIT license.

Network controllability focuses on the concept of driving the dynamical system associated to a directed network of interactions from an arbitrary initial state to an arbitrary final state, through a well-chosen set of input functions applied in a minimal number of so-called input nodes. In earlier studies we and other groups demonstrated the potential of applying this concept in medicine. A directed network of interactions may be built around the main known drivers of the disease being studied, and then analysed to identify combinations of drug targets controlling survivability-essential genes in the network. This paper takes the next step and focuses on patient data. We demonstrate that comprehensive protein-protein interaction networks can be built around patient genetic data, and that network controllability can be used to identify possible personalised drug combinations. We discuss the algorithmic methods that can be used to construct and analyse these networks.

How robust is a healthcare system? How does a patient navigate the system and what is the cost (e.g., number of medical services required or number of times the medical provider had to be changed to get access to the required medical services) incurred from the first symptoms to getting cured? How will it fare in the wake to a sudden epidemic or a disaster? How are all of these affected by administrative decisions such as allocating/diminishing resources in various areas or centralising services? These are the questions motivating our study on a formal prototype model for a healthcare system. We propose that a healthcare system can be understood as a distributed system with independent nodes (healthcare providers) computing according to their own resources and constraints, with tasks (patient needs) being allocated between the nodes. The questions about the healthcare system become in this context questions about resource availability and distribution between the nodes. We construct in this paper an Event-B model capturing the basic functionality of a simplified healthcare system: patients with different types of medical needs being allocated to suitable medical providers, and navigating between different providers for their turn for multi-step treatments.

BACKGROUND: Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer. RESULT: We develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user's query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php . CONCLUSION: The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.

Computational analysis of the structure of intra-cellular molecular interaction networks can suggest novel therapeutic approaches for systemic diseases like cancer. Recent research in the area of network science has shown that network control theory can be a powerful tool in the understanding and manipulation of such bio-medical networks. In 2011, Liu et al. developed a polynomial time algorithm computing the size of the minimal set of nodes controlling a linear network. In 2014, Gao et al. generalized the problem for target control, minimizing the set of nodes controlling a target within a linear network. The authors developed a Greedy approximation algorithm while leaving open the complexity of the optimization problem. We prove here that the target controllability problem is NP-hard in all practical setups, i.e., when the control power of any individual input is bounded by some constant. We also show that the algorithm provided by Gao et al. fails to provide a valid solution in some special cases, and an additional validation step is required. We fix and improve their algorithm using several heuristics, obtaining in the end an up to 10-fold decrease in running time and also a decrease in the size of solutions.

Constructing large biomodels de-novo is a computationally expensive process, requiring large sets of high-quality data. An alternative approach is to construct them from smaller existing models through various operations such as union, intersection, difference, and refinement. We introduce in this paper a foundational framework for biomodel construction capturing these operations, and we discuss some of their properties.

We consider in this paper the assembly of micronuclear genes in stichotrichous ciliates to their macronuclear form. We represent the micronuclear genes and all their intermediate forms from micro- to macro- as signed permutations, where integer i stands for the i-th MDS of the macronuclear gene and ī stands for the inverted form of that MDS; the macronuclear assembled gene is represented as the sorted permutation 1 2 . . . n, while its micronuclear form is an arbitrary signed permutation. We focus on the elementary gene assembly model consisting of two operations on signed permutations: eh (elementary hairpin inverting) and ed (elementary double recombination); gene assembly is modeled in this framework as a permutation sorting process. The general problem we investigate is to give a characterization of all signed permutations that can be sorted by the elementary operations. We make progress towards a full solution for this problem by relating sequences of eh and ed operations applicable to a given permutation to paths in the dependency graph associated to that permutation.

Reaction systems are a formal framework for modeling processes driven by biochemical reactions. They are based on the mechanisms of facilitation and inhibition. A main assumption is that if a resource is available, then it is present in sufficient amounts and as such, several reactions using the same resource will not compete concurrently against each other; this makes reaction systems very different as a modeling framework than traditional frameworks such as ODEs or continuous time Markov chains. We demonstrate in this paper that reaction systems are rich enough to capture the essential characteristics of ODE-based models. We construct a reaction system model for the heat shock response in such a way that its qualitative behavior correlates well with the quantitative behavior of the corresponding ODE model. We construct our reaction system model based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We conclude with a discussion on the expressivity of reaction systems as compared to that of ODE-based models.

To disentangle the numerical contribution of modules to the system-level behavior of a given biomodel, one often considers knock-out mutant models, investigating the change in the model behavior when modules are systematically included and excluded f

In vitro assembly of intermediate filaments from tetrameric vimentin consists of a very rapid phase of tetramers laterally associating into unit-length filaments and a slow phase of filament elongation. We focus in this paper on a systematic quantitative investigation of two molecular models for filament assembly, recently proposed in (Kirmse et al. J. Biol. Chem. 282, 52 (2007), 18563-18572), through mathematical modeling, model fitting, and model validation. We analyze the quantitative contribution of each filament elongation strategy: with tetramers, with unit-length filaments, with longer filaments, or combinations thereof. In each case, we discuss the numerical fitting of the model with respect to one set of data, and its separate validation with respect to a second, different set of data. We introduce a high-resolution model for vimentin filament self-assembly, able to capture the detailed dynamics of filaments of arbitrary length. This provides much more predictive power for the model, in comparison to previous models where only the mean length of all filaments in the solution could be analyzed. We show how kinetic observations on low-resolution models can be extrapolated to the high-resolution model and used for lowering its complexity.

The heat shock response is a well-conserved defence mechanism against the accumulation of misfolded proteins due to prolonged elevated heat. The cell responds to heat shock by raising the levels of heat shock proteins (hsp), which are responsible for chaperoning protein refolding. The synthesis of hsp is highly regulated at the transcription level by specific heat shock (transcription) factors (hsf). One of the regulation mechanisms is the phosphorylation of hsf's. Experimental evidence shows a connection between the hyper-phosphorylation of hsfs and the transactivation of the hsp-encoding genes. In this paper, we incorporate several (de)phosphorylation pathways into an existing well-validated computational model of the heat shock response. We analyze the quantitative control of each of these pathways over the entire process. For each of these pathways we create detailed computational models which we subject to parameter estimation in order to fit them to existing experimental data. In particular, we find conclusive evidence supporting only one of the analyzed pathways. Also, we corroborate our results with a set of computational models of a more reduced size.

The simple intramolecular model for gene assembly in ciliates consists of three molecular operations based on local DNA manipulations. It was shown to predict correctly the assembly of all currently known ciliate gene patterns. Mathematical models in terms of signed permutations and signed strings proved limited in capturing some of the combinatorial details of the simple gene assembly process. A different formalization in terms of overlap-inclusion graphs, recently introduced by Brijder and Hoogeboom, proved well-suited to describe two of the three operations of the model and their combinatorial properties. We introduce in this paper an extension of the framework of Brijder and Hoogeboom in terms of directed overlap-inclusion graphs where more of the linear structure of the ciliate genes is described. We investigate a number of combinatorial properties of these graphs, including a necessary property in terms of forbidden induced subgraphs.

Ciliates have developed a unique nuclear dualism, having two nuclei of different functionality: the germline micronucleus and the somatic macronucleus. The way that ciliates assemble the macronuclear genes after cell mating constitutes one of the most intricate DNA processings in living organisms. This processing is also very interesting from the computational point of view. In this paper, we investigate the operations of loop excision and hairpin excision/reinsertion used in the assembly process. In particular, we consider three levels of formalization of this process, culminating in graph reduction systems.

A notion of semilinearity is introduced for formal power series as a natural generalization of the semilinear sets over a commutative monoid. We prove that the results known for semilinear sets are, in general, preserved in the generalization.