Metabolic processes are fundamental to life, and understanding their dynamic behavior is crucial for addressing biological questions and advancing applications in medicine and biotechnology. While genome-scale metabolic network reconstructions and constraint-based modeling, such as Flux Balance Analysis (FBA), have provided valuable insights into steady-state metabolism, their inherent limitations in capturing dynamic behavior restrict our ability to predict and manipulate metabolic responses, particularly in complex scenarios like disease. This is especially critical for understanding and treating conditions like cancer. This work addresses this challenge by developing a thermodynamically explicit (TDE) parametrization of kinetic models for metabolic reaction systems, grounded in the principles of equilibrium and non-equilibrium thermodynamics. Specifically, we (1) revisit classical kinetic modeling approaches using Michaelis-Menten and convenience kinetics, (2) leverage thermodynamic principles to derive a TDE parametrization method, and (3) demonstrate the impact of TDE parametrization by comparing simulations of a realistic metabolic model using both classical and TDE approaches. We anticipate that this TDE kinetic approach will be a valuable tool for genome-scale parametrization of dynamic metabolic network models, enabling more accurate and predictive simulations of metabolic behavior under diverse conditions. ### Competing Interest Statement The authors have declared no competing interest.

Protein phosphorylation is one of the most widespread post-translational modifications in biology 1,2 . With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes 3,4 . For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible 3 . Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.

Abstract Reaching population immunity against COVID‐19 is proving difficult even in countries with high vaccination levels. Thus, it is critical to identify limits of control and effective measures against future outbreaks. The effects of nonpharmaceutical interventions (NPIs) and vaccination strategies are analyzed with a detailed community‐specific agent‐based model (ABM). The authors demonstrate that the threshold for population immunity is not a unique number, but depends on the vaccination strategy. Prioritizing highly interactive people diminishes the risk for an infection wave, while prioritizing the elderly minimizes fatalities when vaccinations are low. Control over COVID‐19 outbreaks requires adaptive combination of NPIs and targeted vaccination, exemplified for Germany for January–September 2021. Bimodality emerges from the heterogeneity and stochasticity of community‐specific human–human interactions and infection networks, which can render the effects of limited NPIs uncertain. The authors' simulation platform can process and analyze dynamic COVID‐19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination.

Reaching population immunity against COVID-19 is proving difficult even in countries with high vaccination levels. We demonstrate that this in part is due to heterogeneity and stochasticity resulting from community-specific human-human interaction and infection networks. We address this challenge by community-specific simulation of adaptive strategies. Analyzing the predicted effect of vaccination into an ongoing COVID-19 outbreak, we find that adaptive combinations of targeted vaccination and non-pharmaceutical interventions (NPIs) are required to reach population immunity. Importantly, the threshold for population immunity is not a unique number but strategy and community dependent. Furthermore, the dynamics of COVID-19 outbreaks is highly community-specific: in some communities vaccinating highly interactive people diminishes the risk for an infection wave, while vaccinating the elderly reduces fatalities when vaccinations are low due to supply or hesitancy. Similarly, while risk groups should be vaccinated first to minimize fatalities, optimality branching is observed with increasing population immunity. Bimodality emerges as the infection network gains complexity over time, which entails that NPIs generally need to be longer and stricter. Thus, we analyze and quantify the requirement for NPIs dependent on the chosen vaccination strategy. Our simulation platform can process and analyze dynamic COVID-19 epidemiological situations in diverse communities world-wide to predict pathways to population immunity even with limited vaccination.

Activating PIK3CA mutations are known ‘drivers’ of human cancer and developmental overgrowth syndromes. We recently demonstrated that the ‘hotspot’ PIK3CAH1047R variant exerts unexpected allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs). In this study, we combine high-depth transcriptomics, total proteomics and reverse-phase protein arrays to reveal potentially disease-related alterations in heterozygous cells, and to assess the contribution of activated TGFβ signalling to the stemness phenotype of homozygous PIK3CAH1047R cells. We demonstrate signalling rewiring as a function of oncogenic PI3K signalling strength, and provide experimental evidence that self-sustained stemness is causally related to enhanced autocrine NODAL/TGFβ signalling. A significant transcriptomic signature of TGFβ pathway activation in heterozygous PIK3CAH1047R was observed but was modest and was not associated with the stemness phenotype seen in homozygous mutants. Notably, the stemness gene expression in homozygous PIK3CAH1047R hPSCs was reversed by pharmacological inhibition of NODAL/TGFβ signalling, but not by pharmacological PI3Kα pathway inhibition. Altogether, this provides the first in-depth analysis of PI3K signalling in hPSCs and directly links strong PI3K activation to developmental NODAL/TGFβ signalling. This work illustrates the importance of allele dosage and expression when artificial systems are used to model human genetic disease caused by activating PIK3CA mutations. This article has an associated First Person interview with the first author of the paper.

According to the WHO, cancer is the second most common cause of death worldwide. The social and economic damage caused by cancer is high and rising. In Europe, the annual direct medical expenses alone amount to more than €129 billion. This results in an urgent need for new and sustainable therapeutics, which has currently not been met by the pharmaceutical industry; only 3.4% of cancer drugs entering Phase I clinical trials get to market. Phosphorylation sites are parts of the core machinery of kinase signaling networks, which are known to be dysfunctional in all types of cancer. Indeed, kinases are the second most common drug target yet. However, these inhibitors block all functions of a protein, and they commonly lead to the development of resistance and increased toxicity. To facilitate global and mechanistic modeling of cancer and clinically relevant cell signaling networks, the community will have to develop sophisticated data-driven deep-learning and mechanistic computational models that generate in silico probabilistic predictions of molecular signaling network rearrangements causally implicated in cancer.

Cell signaling is a complex process organized in time and space. Signal transduction is constantly modulated by cell-intrinsic and cell-extrinsic input cues and the resulting phenotypic responses such as morphological can feed back into the system. This provides cells with a responsive, accurate, and rugged system to deal with changes in the surroundings or the genome. Whilst signaling networks (dynamic transient protein-protein interactions modulated by post-translational modifications in response to input cues) have been researched for decades, further analysis of their spatial organization is critical for both basic and disease biology and will benefit from recent advances in computational modeling and image analysis using deep/machine learning and in microscopy and imaging. Furthermore, mathematical modeling with reaction-diffusion approaches on time-varying geometries complements the investigations, allowing to conceptualize the organizational principles of signaling and information transduction in the four dimensions of time and space.

Interactions between humans cause transmission of SARS-CoV-2. We demonstrate that heterogeneity in human-human interactions give rise to non-linear infection networks that gain complexity with time. Consequently, targeted vaccination strategies are challenged as such effects are not accurately captured by epidemiological models assuming homogeneous mixing. With vaccines being prepared for global deployment determining optimality for swiftly reaching population level immunity in heterogeneous local communities world-wide is critical. We introduce a model that predicts the effect of vaccination into an ongoing COVID-19 outbreak using precision simulation of human-human interaction and infection networks. We show that simulations incorporating non-linear network complexity and local heterogeneity can enable governance with performance-quantified vaccination strategies. Vaccinating highly interactive people diminishes the risk for an infection wave, while vaccinating the elderly reduces fatalities at low population level immunity. Interestingly, a combined strategy is not better due to non-linear effects. While risk groups should be vaccinated first to minimize fatalities, significant optimality branching is observed with increasing population level immunity. Importantly, we demonstrate that regardless of immunization strategy non-pharmaceutical interventions are required to prevent ICU overload and breakdown of healthcare systems. The approach, adaptable in real-time and applicable to other viruses, provides a highly valuable platform for the current and future pandemics.

Oncogenic PIK3CA mutations activate phosphoinositide 3-kinase-alpha (PI3Kα) and are among the commonest somatic mutations in cancer. We recently demonstrated that the “hotspot” variant PIK3CAH1047R exerts striking allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs), and found multiple oncogenic PIK3CA copies in a substantial proportion of human cancers. This suggested that the consequences of oncogenic PI3K signaling may differ according to the strength of genetic PIK3CA activation. Here, to identify the stemness-promoting mechanism, we profiled isogenic wild-type, PIK3CAWT/H1047R and PIK3CAH1047R/H1047R iPSCs by high-depth transcriptomics, proteomics and reverse-phase protein arrays (RPPA). We report that the phenotypic switch in homozygous PIK3CAH1047R hPSCs occurs downstream of signaling “rewiring” towards self-sustained TGFβ pathway activation and increased NODAL expression, which was no longer reversible by pharmacological PI3Kα inhibition. Gene expression analysis of PIK3CA-associated human breast cancers in The Cancer Genome Atlas revealed increased expression of NODAL according to tumor stage and PIK3CAH1047R allele dosage. Together with the emerging link between NODAL re-expression and cancer aggressiveness, our data suggest that TGFβ pathway inhibitors warrant investigation in breast tumors stratified by PIK3CAH1047R allele dosage. One-sentence summary Biallelic genetic PI3Kα activation rewires signaling and induces constitutive stemness downstream from self-sustained TGFβ pathway activation.

Small molecule inhibitors of BRAF and MEK have proven effective at inhibiting tumor growth in melanoma patients, however this efficacy is limited due to the almost universal development of drug resistance. To provide advanced insight into the signaling responses that occur following kinase inhibition we have performed quantitative (phospho)-proteomics of human melanoma cells treated with either dabrafenib, a BRAF inhibitor; trametinib, a MEK inhibitor or SCH772984, an ERK inhibitor. Over nine experiments we identified 7827 class I phosphorylation sites on 4960 proteins. This included 54 phosphorylation sites that were significantly down-modulated after exposure to all three inhibitors, 34 of which have not been previously reported. Functional analysis of these novel ERK targets identified roles for them in GTPase activity and regulation, apoptosis and cell-cell adhesion. Comparison of the results presented here with previously reported phosphorylation sites downstream of ERK showed a limited degree of overlap suggesting that ERK signaling responses may be highly cell line and cue specific. In addition we identified 26 phosphorylation sites that were only responsive to dabrafenib. We provide further orthogonal experimental evidence for 3 of these sites in human embryonic kidney cells over-expressing BRAF as well as further computational insights using KinomeXplorer. The validated phosphorylation sites were found to be involved in actin regulation, which has been proposed as a novel mechanism for inhibiting resistance development. These results would suggest that the linearity of the BRAF-MEK-ERK module is at least context dependent.

Human NimA-related kinases (Neks) have multiple mitotic and non-mitotic functions, but few substrates are known. We systematically determined the phosphorylation-site motifs for the entire Nek kinase family, except for Nek11. While all Nek kinases strongly select for hydrophobic residues in the −3 position, the family separates into four distinct groups based on specificity for a serine versus threonine phospho-acceptor, and preference for basic or acidic residues in other positions. Unlike Nek1-Nek9, Nek10 is a dual-specificity kinase that efficiently phosphorylates itself and peptide substrates on serine and tyrosine, and its activity is enhanced by tyrosine auto-phosphorylation. Nek10 dual-specificity depends on residues in the HRD+2 and APE-4 positions that are uncommon in either serine/threonine or tyrosine kinases. Finally, we show that the phosphorylation-site motifs for the mitotic kinases Nek6, Nek7 and Nek9 are essentially identical to that of their upstream activator Plk1, suggesting that Nek6/7/9 function as phospho-motif amplifiers of Plk1 signaling.

We describe a novel Bayesian method for estimating protein concentration and phosphorylation site occupancy ratios from mass spectrometry experiments. Our variance model assigns standard deviations to all quantitative ratios, even when only a single peptide is observed, increasing the number of quantifiable observations in a sample compared to conventional methods. We further demonstrate the application of this method using a dataset investigating the impact of the PRKAR1A-RET gene fusion in immortalized thyroid cells.

Specificity within protein kinase signaling cascades is determined by direct and indirect interactions between kinases and their substrates. While the impact of localization and recruitment on kinase-substrate targeting can be readily assessed, evaluating the relative importance of direct phosphorylation site interactions remains challenging. In this study, we examine the STE20 family of protein serine-threonine kinases to investigate basic mechanisms of substrate targeting. We used peptide arrays to define the phosphorylation site specificity for the majority of STE20 kinases and categorized them into four distinct groups. Using structure-guided mutagenesis, we identified key specificity-determining residues within the kinase catalytic cleft, including an unappreciated role for the kinase β3-αC loop region in controlling specificity. Exchanging key residues between the STE20 kinases p21-activated kinase 4 (PAK4) and Mammalian sterile 20 kinase 4 (MST4) largely interconverted their phosphorylation site preferences. In cells, a reprogrammed PAK4 mutant, engineered to recognize MST substrates, failed to phosphorylate PAK4 substrates or to mediate remodeling of the actin cytoskeleton. In contrast, this mutant could rescue signaling through the Hippo pathway in cells lacking multiple MST kinases. These observations formally demonstrate the importance of catalytic site specificity for directing protein kinase signal transduction pathways. Our findings further suggest that phosphorylation site specificity is both necessary and sufficient to mediate distinct signaling outputs of STE20 kinases and imply broad applicability to other kinase signaling systems.

ARGONAUTE1 (AGO1) binds directly to small regulatory RNA and is a key effector protein of post-transcriptional gene silencing mediated by microRNA (miRNA) and small interfering RNA (siRNA) in Arabidopsis. The formation of an RNA-induced silencing complex (RISC) of AGO1 and small RNA requires the function of the heat shock protein 70/90 chaperone system. Some functions of AGO1 occur in association with endomembranes, in particular the rough endoplasmic reticulum (RER), but proteins interacting with AGO1 in membrane fractions remain unidentified. In this study, we show that the farnesylated heat shock protein 40 homologs, J2 and J3, associate with AGO1 in membrane fractions in a manner that involves protein farnesylation. We also show that three changes in AGO1 function are detectable in mutants in protein farnesylation and J2/J3. First, perturbations of the HSP40/70/90 pathway by mutation of J3, HSP90, and farnesyl transferase affect the amounts of AGO1 associated with membranes. Second, miRNA association with membrane-bound polysomes is increased in farnesyl transferase and farnesylation-deficient J2/J3 mutants. Third, silencing by noncell autonomously acting short interfering RNAs is impaired. These observations highlight the involvement of farnesylated J2/J3 in small RNA-mediated gene regulation, and suggest that the importance of chaperone-AGO1 interaction is not limited to the RISC assembly process.

Cell migration is a central biological process that requires fine coordination of molecular events in time and space. A deregulation of the migratory phenotype is also associated with pathological conditions including cancer where cell motility has a causal role in tumor spreading and metastasis formation. Thus cell migration is of critical and strategic importance across the complex disease spectrum as well as for the basic understanding of cell phenotype. Experimental studies of the migration of cells in monolayers are often conducted with 'wound healing' assays. Analysis of these assays has traditionally relied on how the wound area changes over time. However this method does not take into account the shape of the wound. Given the many options for creating a wound healing assay and the fact that wound shape invariably changes as cells migrate this is a significant flaw. Here we present a novel software package for analyzing concerted cell velocity in wound healing assays. Our method encompasses a wound detection algorithm based on cell confluency thresholding and employs a Bayesian approach in order to estimate concerted cell velocity with an associated likelihood. We have applied this method to study the effect of siRNA knockdown on the migration of a breast cancer cell line and demonstrate that cell velocity can track wound healing independently of wound shape and provides a more robust quantification with significantly higher signal to noise ratios than conventional analyses of wound area. The software presented here will enable other researchers in any field of cell biology to quantitatively analyze and track live cell migratory processes and is therefore expected to have a significant impact on the study of cell migration, including cancer relevant processes. Installation instructions, documentation and source code can be found at http://bowhead.lindinglab.science licensed under GPLv3.

OBJECTIVE:Glycogen synthase kinase 3β (GSK3β) has been implicated in mood disorders. We previously reported associations between a GSK3β polymorphism and hippocampal volume in major depressive disorder (MDD). We then reported similar associations for a subset of GSK3β-regulated genes. We now investigate an algorithm-derived comprehensive list of genes encoding proteins that directly interact with GSK3β to identify a genotypic network influencing hippocampal volume in MDD. PARTICIPANTS AND METHODS:We used discovery (N=141) and replication (N=77) recurrent MDD samples. Our gene list was generated from the NetworKIN database. Hippocampal measures were derived using an optimized Freesurfer protocol. We identified interacting single nucleotide polymorphisms using the machine learning algorithm Random Forest and verified interactions using likelihood ratio tests between nested linear regression models. RESULTS:The discovery sample showed multiple two-single nucleotide polymorphism interactions with hippocampal volume. The replication sample showed a replicable interaction (likelihood ratio test: P=0.0088, replication sample; P=0.017, discovery sample; Stouffer's combined P=0.0007) between genes associated previously with endoplasmic reticulum stress, calcium regulation and histone modifications. CONCLUSION:Our results provide genetic evidence supporting associations between hippocampal volume and MDD, which may reflect underlying cellular stress responses. Our study provides evidence of biological mechanisms that should be further explored in the search for disease-modifying therapeutic targets for depression.

It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. We perform high-content screening of 17 cancer cell lines, generating more than 500 billion data points from similar to 850 million cells. We analyze these data using a deep learning model, resulting in the identification of a continuous 27-dimension space describing all of the observed cell morphologies. From its morphology alone, we could thus predict whether a cell was resistant to ErbB-family drugs, with an accuracy of 74%, and predict the potential mechanism of resistance, subsequently validating the role of MET and insulin-like growth factor 1 receptor (IGF1R) as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.