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Cellular function depends on heterogeneous dynamic intra-, inter-, and supramolecular structure-function relationships. However, the specific mechanisms by which cellular function is transduced from molecular systems, and by which cellular dysfunction arises from molecular dysfunction are poorly understood. We proposed previously that cellular function manifests as a molecular form of analog computing, in which specific time-dependent state transition fluxes within sets of molecular species (\"molecular differential equations\" (MDEs)) are sped and slowed in response to specific perturbations (inputs). In this work, we offer a theoretical treatment of the molecular mechanisms underlying cellular analog computing (which we refer to as \"biodynamics\"), focusing primarily on non-equilibrium (dynamic) intermolecular state transitions that serve as the principal means by which MDE systems are solved (the molecular equivalent of mathematical \"integration\"). Under these conditions, bound state occupancy is governed by kon and koff, together with the rates of binding partner buildup and decay. Achieving constant fractional occupancy over time depends on: 1) equivalence between kon and the rate of binding site buildup); 2) equivalence between koff and the rate of binding site decay; and 3) free ligand concentration relative to koff/kon (n · Kd, where n is the fold increase in binding partner concentration needed to achieve a given fractional occupancy). Failure to satisfy these conditions results in fractional occupancy well below that corresponding to n · Kd. The implications of biodynamics for cellular function/dysfunction and drug discovery are discussed.

Living cells implement complex computations on the continuous environmental signals that they encounter. These computations involve both analogue- and digital-like processing of signals to give rise to complex developmental programs, context-dependent behaviours and homeostatic activities. In contrast to natural biological systems, synthetic biological systems have largely focused on either digital or analogue computation separately. Here we integrate analogue and digital computation to implement complex hybrid synthetic genetic programs in living cells. We present a framework for building comparator gene circuits to digitize analogue inputs based on different thresholds. We then demonstrate that comparators can be predictably composed together to build band-pass filters, ternary logic systems and multi-level analogue-to-digital converters. In addition, we interface these analogue-to-digital circuits with other digital gene circuits to enable concentration-dependent logic. We expect that this hybrid computational paradigm will enable new industrial, diagnostic and therapeutic applications with engineered cells. Digital and analogue gene circuits each have distinct advantages in natural and engineered cells. Here, Rubens et al . engineer synthetic gene circuits that implement mixed-signal digital and analogue computations in living cells.

In this paper, a logical-based mathematical model of the cellular pathways involved in the COVID-19 infection has been developed to study various drug treatments (single or in combination), in different illness scenarios, providing insights into their mechanisms of action. Drug simulations suggest that the effects of single drugs are limited, or depending on the scenario counterproductive, whereas better results appear combining different treatments. Specifically, the combination of the anti-inflammatory Baricitinib and the anti-viral Remdesivir showed significant benefits while a stronger efficacy emerged from the triple combination of Baricitinib, Remdesivir, and the corticosteroid Dexamethasone. Together with a sensitivity analysis, we performed an analysis of the mechanisms of the drugs to reveal their impact on molecular pathways.

The human ether-a-go-go-related voltage-gated cardiac ion channel (commonly known as hERG) conducts the rapid outward repolarizing potassium current in cardiomyocytes (IKr). Inadvertent blockade of this channel by drug-like molecules represents a key challenge in pharmaceutical R&D due to frequent overlap between the structure-activity relationships of hERG and many primary targets. Building on our previous work, together with recent cryo-EM structures of hERG, we set about to better understand the energetic and structural basis of promiscuous blocker-hERG binding in the context of Biodynamics theory. We propose a two-step blocker binding process consisting of: The initial capture step: diffusion of a single fully solvated blocker copy into a large cavity lined by the intra-cellular cyclic nucleotide binding homology domain (CNBHD). Occupation of this cavity is a necessary but insufficient condition for ion current disruption.The IKr disruption step: translocation of the captured blocker along the channel axis, such that: The head group, consisting of a quasi-rod-shaped moiety, projects into the open pore, accompanied by partial de-solvation of the binding interface.One tail moiety packs along a kink between the S6 helix and proximal C-linker helix adjacent to the intra-cellular entrance of the pore, likewise accompanied by mutual de-solvation of the binding interface (noting that the association barrier is comprised largely of the total head + tail group de-solvation cost).Blockers containing a highly planar moiety that projects into a putative constriction zone within the closed channel become trapped upon closing, as do blockers terminating prior to this region.A single captured blocker copy may conceivably associate and dissociate to/from the pore many times before exiting the CNBHD cavity. Lastly, we highlight possible flaws in the current hERG safety index (SI), and propose an alternate in vivo-relevant strategy factoring in: Benefit/risk.The predicted arrhythmogenic fractional hERG occupancy (based on action potential (AP) simulations of the undiseased human ventricular cardiomyocyte).Alteration of the safety threshold due to underlying disease.Risk of exposure escalation toward the predicted arrhythmic limit due to patient-to-patient pharmacokinetic (PK) variability, drug-drug interactions, overdose, and use for off-label indications in which the hERG safety parameters may differ from their on-label counterparts.

5 Vaccine safety remains a crucial point in phase III clinical trials and, in some cases, as per the hepatitis B vaccine, the attrition rate is calculated to be 50% for the transition from phase III to regulatory submission. 5 The risk profiles and the recent need for a way to fast-track vaccine candidates, even among the same company, call for strategies to minimize resources dispersion moving forward only the optimal candidates. 6 Systems biology and in particular quantitative systems pharmacology (QSP) has been proven to be instrumental in reducing in vitro – in vivo disconnections, by generating in silico tools capable of helping in go/no-go decisions. 7 In this work, we developed a mathematical model for mRNA vaccine by adapting and extending a multiscale model of immunogenicity from Chen et al. 1 We maintained unvaried the description of the humoral immune response (T and B cell kinetics) in the draining lymph node, extending the model with a detailed representation of the early events following the injection of the delivery system carrying the mRNA, based on the work of Leonardelli et al. 2021. 8 As illustrated by the model diagram provided in Figure 1 and in the Supplementary Material, we considered the kinetics of four cell types to represent the early events: myeloid dendritic cells (mDCs), plasmacytoid dendritic cells (pDCs), monocytes, and neutrophils. In the LN we also find the adaptive response cells: naïve T (NT); memory T (MT); activated naïve T (ANT); activated memory T (AMT); functional T (FT); naïve B (NB); memory B (MB); activated naïve B (ANB); activated memory B (AMB); short and long-lived plasma cells (respectively, PCS and PCL), which secrete antibody (Ab) Cells recruited at the injection site can be found in four states: naïve, carrying the vector, expressing the protein, and presenting the antigen at different levels. Upon reaching the draining lymph node, the cells will continue to unpack the engulfed vector and express the antigen, and eventually migrate to the bloodstream ( kLN2BLX-Ag). Because the model parameters necessary to quantitatively describe the early events and migration phenomena are not fully available, we used experimental data from Liang et al. 9 to estimate the missing ones ( Supplementary Material).

RNA vaccines represent a milestone in the history of vaccinology. They provide several advantages over more traditional approaches to vaccine development, showing strong immunogenicity and an overall favorable safety profile. While preclinical testing has provided some key insights on how RNA vaccines interact with the innate immune system, their mechanism of action appears to be fragmented amid the literature, making it difficult to formulate new hypotheses to be tested in clinical settings and ultimately improve this technology platform. Here, we propose a systems biology approach, based on the combination of literature mining and mechanistic graphical modeling, to consolidate existing knowledge around mRNA vaccines mode of action and enhance the translatability of preclinical hypotheses into clinical evidence. A Natural Language Processing (NLP) pipeline for automated knowledge extraction retrieved key biological evidences that were joined into an interactive mechanistic graphical model representing the chain of immune events induced by mRNA vaccines administration. The achieved mechanistic graphical model will help the design of future experiments, foster the generation of new hypotheses and set the basis for the development of mathematical models capable of simulating and predicting the immune response to mRNA vaccines.

The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.

Reactive oxygen species (ROS) such as hydrogen peroxide (H2O2), are now known to play critical roles in signal transduction and in coordinating key cellular processes. However, these species can also covalently damage macromolecules and originate other even more deleterious compounds. At the core of this twine between signaling and defense lays the Peroxiredoxin Thioredoxin Thioredoxin Reductase (PTTR) system. Experimental studies of the PTTRS highlighted many commonalities among different types of cells and organisms, but also intriguing differences in cells’ responses to hydrogen peroxide. The current work aims to study the PTTR system and its characteristics. Using a minimal mathematical model, we seek to uncover the general principles of how organisms exploit the properties of ROS for regulation of other protein while avoiding their deleterious effects. These principles, in the form of relationships among rate constants and species concentrations, are thoroughly supported by experimental observations in a variety of organisms and allow to correlate proteins abundance patterns with the modes of response. Depending on the relative abundances of peroxiredoxins, sulfiredoxin, thioredoxin, thioredoxin reductase and alternative H2O2-consuming proteins, the system is capable of distinct responses to changing hydrogen peroxide supplies, including proportional, ultrasensitive, and hysteretic (toggle switch) ones. The complete characterization of the system however requires the definitions of the operative conditions in which the organism lives. A major and so far not univocally defined value is the maximum attained hydrogen peroxide concentration in vivo. To address this problem were developed a series of sensor with different thresholds and capable of memory functions. The peroxide classifier was then used in an inflammation animal model to measure the maximum attained concentrations. The mathematical model developed in this system and the studies of the general principles underlying the PTTR system together with the experimental application of the H2O2 classifier could be used in clinical research or drug development.

Abstract The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models combined to experimental validations as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity, and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH. Competing Interest Statement The authors have declared no competing interest. * Abbreviations AJ Adherens Junction AKT Protein kinase B BCat Catenin beta-1 CDKN2A Cyclin-Dependent Kinase inhibitor 2A CK1 Casein Kinase 1 CRC Colorectal Cancer CRISPR Clustered Regularly Interspaced Short Palindromic Repeats CSC Cancer Stem-like Cell DELTA Delta-like protein DVL Segment polarity protein dishevelled homolog E2F Transcription factor E2F ECad E-cadherin ECM Extracellular Matrix EGF Epidermal Growth Factor EGFR Epidermal Growth Factor Receptor EMT Epithelial-to-Mesenchymal Transition ERK Extracellular-signal-Regulated Kinase FA Focal Adhesion FAK Focal Adhesion Kinase FAT4 Protocadherin Fat 4 FAT4_L Protocadherin Fat 4 ligand FGFR3 Fibroblast Growth Factor Receptor 3 GoF Gain of Function HCC Hepatocellular Carcinoma HGF Hepatocyte Growth Factor IL6 Interleukin 6 JAK Janus Kinase LATS Large Tumour Suppressor kinase LEF Lymphoid enhancer-binding factor LoF Loss of Function MAPK Mitogen-Activated Protein Kinase MDCK Madin-Darby Canine Kidney miR200 miR-200 superfamily of miRNAs p14 ARF tumour suppressor p21CIP Cyclin-Dependent Kinase Inhibitors p21 p38MAPK p38 Mitogen-Activated Protein Kinases p53 Tumour protein P53 PI3K PhosphoInositide 3-Kinase ROS Reactive oxygen species RPTP Receptor-type tyrosine-protein phosphatase RPTP_L Receptor-type tyrosine-protein phosphatase ligand RPTP-kappa SHH Sonic hedgehog SLUG Zinc finger protein SNAI2 SNAIL Zinc finger protein SNAI1 SRC Proto-oncogene tyrosine-protein kinase STAT Signal Transducer and Activator of Transcription TAZ Transcriptional co-activator with PDZ binding motif TCF Transcription Factor 7 TCGA The Cancer Genome Atlas TGFB Transforming growth factor beta TME Tumour Microenvironment WNT Protein Wnt YAP Yes-Associated Protein ZEB Zinc finger E-box-binding homeobox

Bacterial quorum sensing (QS) is a cell-to-cell communication mechanism through which bacteria share information about cell density, and tune gene expression accordingly. Pathogens exploit QS to orchestrate virulence and regulate the expression of genes related to antimicrobial resistance. Despite the vast literature on QS, the properties of the underlying molecular network are not entirely clear. We compare two synthetic QS circuit architectures: in the first, a single positive feedback loop autoinduces the synthesis of the signal molecule; the second includes an additional positive feedback loop enhancing the synthesis of the signal molecule receptor. Our comprehensive analysis of the two systems and their equilibria highlights the differences in the bistable and hysteretic behaviors of the alternative QS structures. Finally, we investigate three different QS inhibition approaches; numerical analysis predicts their effect on the steady-state behavior of the two different QS models, revealing critical parameter thresholds that guarantee an effective QS suppression.