A Causal Regulation Modeling Algorithm for Temporal Events with Application to Escherichia coli's Aerobic to Anaerobic Transition.

Time-series experiments are crucial for understanding the transient and dynamic nature of biological phenomena. These experiments, leveraging advanced classification and clustering algorithms, allow for a deep dive into the cellular processes. However, while these approaches effectively identify patterns and trends within data, they often need to improve in elucidating the causal mechanisms behind these changes. Building on this foundation, our study introduces a novel algorithm for temporal causal signaling modeling, integrating established knowledge networks with sequential gene expression data to elucidate signal transduction pathways over time. Focusing on Escherichia coli's (E. coli) aerobic to anaerobic transition (AAT), this research marks a significant leap in understanding the organism's metabolic shifts. By applying our algorithm to a comprehensive E. coli regulatory network and a time-series microarray dataset, we constructed the cross-time point core signaling and regulatory processes of E. coli's AAT. Through gene expression analysis, we validated the primary regulatory interactions governing this process. We identified a novel regulatory scheme wherein environmentally responsive genes, soxR and oxyR, activate fur, modulating the nitrogen metabolism regulators fnr and nac. This regulatory cascade controls the stress regulators ompR and lrhA, ultimately affecting the cell motility gene flhD, unveiling a novel regulatory axis that elucidates the complex regulatory dynamics during the AAT process. Our approach, merging empirical data with prior knowledge, represents a significant advance in modeling cellular signaling processes, offering a deeper understanding of microbial physiology and its applications in biotechnology.

Quantitative analysis of radiosensitizing effect for magnetic hyperthermia-radiation combined therapy on prostate cancer cells.

BACKGROUND: Magnetic hyperthermia (MHT) has emerged as a promising therapeutic approach in the field of radiation oncology due to its superior precision in controlling temperature and managing the heating area compared to conventional hyperthermia. Recent studies have proposed solutions to address clinical safety concerns associated with MHT, which arise from the use of highly concentrated magnetic nanoparticles and the strong magnetic field needed to induce hyperthermic effects. Despite these efforts, challenges remain in quantifying therapeutic outcomes and developing treatment plan systems for combining MHT with radiation therapy (RT). PURPOSE: This study aims to quantitatively measure the therapeutic effect, including radiation dose enhancement (RDE) in the magnetic hyperthermia-radiation combined therapy (MHRT), using the equivalent radiation dose (EQD) estimation method. METHODS: To conduct EQD estimation for MHRT, we compared the therapeutic effects between the conventional hyperthermia-radiation combined therapy (HTRT) and MHRT in human prostate cancer cell lines, PC3 and LNCaP. We adopted a clonogenic assay to validate RDE and the radiosensitizing effect induced by MHT. The data on survival fractions were analyzed using both the linear-quadradic model and Arrhenius model to estimate the biological parameters describing RDE and radiosensitizing effect of MHRT for both cell lines through maximum likelihood estimation. Based on these parameters, a new survival fraction model was suggested for EQD estimation of MHRT. RESULTS: The newly designed model describing the MHRT effect, effectively captures the variations in thermal and radiation dose for both cell lines (R2 > 0.95), and its suitability was confirmed through the normality test of residuals. This model appropriately describes the survival fractions up to 10 Gy for PC3 cells and 8 Gy for LNCaP cells under RT-only conditions. Furthermore, using the newly defined parameter r, the RDE effect was calculated as 29% in PC3 cells and 23% in LNCaP cells. EQDMHRT calculated through this model was 9.47 Gy for PC3 and 4.71 Gy for LNCaP when given 2 Gy and MHT for 30 min. Compared to EQDHTRT, EQDMHRT showed a 26% increase for PC3 and a 20% increase for LNCaP. CONCLUSIONS: The proposed model effectively describes the changes of the survival fraction induced by MHRT in both cell lines and adequately represents actual data values through residual analysis. Newly suggested parameter r for RDE effect shows potential for quantitative comparisons between HTRT and MHRT, and optimizing therapeutic outcomes in MHRT for prostate cancer.

Theoretical evaluation of the impact of diverse treatment conditions by calculation of the tumor control probability (TCP) of simulated cervical cancer Hyperthermia-Radiotherapy (HT-RT) treatments in-silico.

INTRODUCTION: Hyperthermia (HT) induces various cellular biological processes, such as repair impairment and direct HT cell killing. In this context, in-silico biophysical models that translate deviations in the treatment conditions into clinical outcome variations may be used to study the extent of such processes and their influence on combined hyperthermia plus radiotherapy (HT + RT) treatments under varying conditions. METHODS: An extended linear-quadratic model calibrated for SiHa and HeLa cell lines (cervical cancer) was used to theoretically study the impact of varying HT treatment conditions on radiosensitization and direct HT cell killing effect. Simulated patients were generated to compute the Tumor Control Probability (TCP) under different HT conditions (number of HT sessions, temperature and time interval), which were randomly selected within margins based on reported patient data. RESULTS: Under the studied conditions, model-based simulations suggested a treatment improvement with a total CEM43 thermal dose of approximately 10 min. Additionally, for a given thermal dose, TCP increased with the number of HT sessions. Furthermore, in the simulations, we showed that the TCP dependence on the temperature/time interval is more correlated with the mean value than with the minimum/maximum value and that comparing the treatment outcome with the mean temperature can be an excellent strategy for studying the time interval effect. CONCLUSION: The use of thermoradiobiological models allows us to theoretically study the impact of varying thermal conditions on HT + RT treatment outcomes. This approach can be used to optimize HT treatments, design clinical trials, and interpret patient data.

Unraveling the complexity of vascular tone regulation: a multiscale computational approach to integrating chemo-mechano-biological pathways with cardiovascular biomechanics.

Vascular tone regulation is a crucial aspect of cardiovascular physiology, with significant implications for overall cardiovascular health. However, the precise physiological mechanisms governing smooth muscle cell contraction and relaxation remain uncertain. The complexity of vascular tone regulation stems from its multiscale and multifactorial nature, involving global hemodynamics, local flow conditions, tissue mechanics, and biochemical pathways. Bridging this knowledge gap and translating it into clinical practice presents a challenge. In this paper, a computational model is presented to integrate chemo-mechano-biological pathways with cardiovascular biomechanics, aiming to unravel the intricacies of vascular tone regulation. The computational framework combines an algebraic description of global hemodynamics with detailed finite element analyses at the scale of vascular segments for describing their passive and active mechanical response, as well as the molecular transport problem linked with chemo-biological pathways triggered by wall shear stresses. Their coupling is accounted for by considering a two-way interaction. Specifically, the focus is on the role of nitric oxide-related molecular pathways, which play a critical role in modulating smooth muscle contraction and relaxation to maintain vascular tone. The computational framework is employed to examine the interplay between localized alterations in the biomechanical response of a specific vessel segment-such as those induced by calcifications or endothelial dysfunction-and the broader global hemodynamic conditions-both under basal and altered states. The proposed approach aims to advance our understanding of vascular tone regulation and its impact on cardiovascular health. By incorporating chemo-mechano-biological mechanisms into in silico models, this study allows us to investigate cardiovascular responses to multifactorial stimuli and incorporate the role of adaptive homeostasis in computational biomechanics frameworks.

Environmental impact of sewage sludge co-digestion with food waste and fat-oil-grease: Integrating plant-wide modeling with life cycle assessment approach.

Anaerobic co-digestion of fat-oil-grease (FOG) and food waste (FW) with sewage sludge (SS) in wastewater treatment plants is a method used to increase biogas production. In this study, digestion scenarios were compared using plant-wide modeling and life cycle assessment: Scenario-0 (mono-digestion of waste-activated sludge (WAS)), Scenario-1 (co-digestion of WAS with FOG), and Scenario-2 (co-digestion of WAS with FW). Scenario-0, with the highest energy use and landfilling of FOG/FW, has the worst environmental impact. Scenario-1 and Scenario-2 minimize the environmental load by energy recovery and avoiding landfilling of organic waste. Scenario-wise, the change in greenhouse gas (GHG) emissions from treatment was negligible. However, due to the impact of landfilling, GHG emissions in Scenario-0 were 21% and 30% higher than in Scenario-1 and 2, respectively. The environmental benefit of anaerobic co-digestion of FOG/FW with SS is not only in the contribution to energy production but also in the recycling of organic waste.

Cross species extrapolation of the disruption of thyroid hormone synthesis by oxyfluorfen using in vitro data, physiologically based pharmacokinetic (PBPK), and thyroid hormone kinetics models.

The thyroid hormones play key roles in physiological processes such as regulation of the metabolic and cardiac systems as well as the development of the brain and surrounding sympathetic nervous system. Recent efforts to screen environmental chemicals for their ability to alter thyroid hormone synthesis, transport, metabolism and/or function have identified novel chemicals that target key processes in the thyroid pathway. One newly identified chemical, oxyfluorfen, is a diphenyl-ether herbicide used for control of annual broadleaf and grassy weeds in a variety of tree fruit, nut, vine, and field crops. Using in vitro high-throughput screening (HTS) assays, oxyfluorofen was identified to be a potent inhibitor of the thyroidal sodium-iodide symporter (NIS). To quantitatively assess this inhibition mechanism in vivo, we extrapolated in vitro NIS inhibition data to in vivo disruption of thyroid hormones synthesis in rats using physiologically based pharmacokinetic (PBPK) and thyroid hormone kinetics models. The overall computational model (chemical PBPK and THs kinetic sub-models) was calibrated against in vivo data for the levels of oxyfluorfen in thyroid tissue and serum and against serum levels of thyroid hormones triiodothyronine (T3) and thyroxine (T4) in rats. The rat thyroid model was then extrapolated to humans using human in vitro HTS data for NIS inhibition and the chemical specific hepatic clearance rate in humans. The overall species extrapolated PBPK-thyroid kinetics model can be used to predict dose-response (% drop in thyroid serum levels compared to homeostasis) relationships in humans. These relationships can be used to estimate points of departure for health risks related to a drop in serum levels of TH hormones based on HTS assays in vitro to in vivo extrapolation (IVIVE), toxicokinetics, and physiological principles.

Proton versus photon therapy for high-risk prostate cancer with dose escalation of dominant intraprostatic lesions: a preliminary planning study.

Background and purpose: This study aimed to investigate the feasibility of safe-dose escalation to dominant intraprostatic lesions (DILs) and assess the clinical impact using dose-volume (DV) and biological metrics in photon and proton therapy. Biological parameters defined as late grade ≥ 2 gastrointestinal (GI) and genitourinary (GU) derived from planned (D P) and accumulated dose (D A) were utilized. Materials and methods: In total, 10 patients with high-risk prostate cancer with multiparametric MRI-defined DILs were investigated. Each patient had two plans with a focal boost to the DILs using intensity-modulated proton therapy (IMPT) and volumetric-modulated arc therapy (VMAT). Plans were optimized to obtain DIL coverage while respecting the mandatory organ-at-risk constraints. For the planning evaluation, DV metrics, tumor control probability (TCP) for the DILs and whole prostate excluding the DILs (prostate-DILs), and normal tissue complication probability (NTCP) for the rectum and bladder were calculated. Wilcoxon signed-rank test was used for analyzing TCP and NTCP data. Results: IMPT achieved a higher Dmean for the DILs compared to VMAT (IMPT: 68.1 GyRBE vs. VMAT: 66.6 Gy, p < 0.05). Intermediate-high rectal and bladder doses were lower for IMPT (p < 0.05), while the high-dose region (V60 Gy) remained comparable. IMPT-TCP for prostate-DIL were higher compared to VMAT (IMPT: 86%; α/ß = 3, 94.3%; α/ß = 1.5 vs. VMAT: 84.7%; α/ß = 3, 93.9%; α/ß = 1.5, p < 0.05). Likewise, IMPT obtained a moderately higher DIL TCP (IMPT: 97%; α/ß = 3, 99.3%; α/ß = 1.5 vs. VMAT: 95.9%; α/ß = 3, 98.9%; α/ß = 1.5, p < 0.05). Rectal D A-NTCP displayed the highest GI toxicity risk at 5.6%, and IMPT has a lower GI toxicity risk compared to VMAT-predicted Quantec-NTCP (p < 0.05). Bladder D P-NTCP projected a higher GU toxicity than D A-NTCP, with VMAT having the highest risk (p < 0.05). Conclusion: Dose escalation using IMPT is able to achieve a high TCP for the DILs, with the lowest rectal and bladder DV doses at the intermediate-high-dose range. The reduction in physical dose was translated into a lower NTCP (p < 0.05) for the bladder, although rectal toxicity remained equivalent.

Biological modeling in thermoradiotherapy: present status and ongoing developments toward routine clinical use.

Biological modeling for anti-cancer treatments using mathematical models can be very supportive in gaining more insight into dynamic processes responsible for cellular response to treatment, and predicting, evaluating and optimizing therapeutic effects of treatment. This review presents an overview of the current status of biological modeling for hyperthermia in combination with radiotherapy (thermoradiotherapy). Various distinct models have been proposed in the literature, with varying complexity; initially aiming to model the effect of hyperthermia alone, and later on to predict the effect of the combined thermoradiotherapy treatment. Most commonly used models are based on an extension of the linear-quadratic (LQ)-model enabling an easy translation to radiotherapy where the LQ model is widely used. Basic predictions of cell survival have further progressed toward 3 D equivalent dose predictions, i.e., the radiation dose that would be needed without hyperthermia to achieve the same biological effect as the combined thermoradiotherapy treatment. This approach, with the use of temperature-dependent model parameters, allows theoretical evaluation of the effectiveness of different treatment strategies in individual patients, as well as in patient cohorts. This review discusses the significant progress that has been made in biological modeling for hyperthermia combined with radiotherapy. In the future, when adequate temperature-dependent LQ-parameters will be available for a large number of tumor sites and normal tissues, biological modeling can be expected to be of great clinical importance to further optimize combined treatments, optimize clinical protocols and guide further clinical studies.

A Generic Pharmacokinetic Model for Quantifying Mother-to-Offspring Transfer of Lipophilic Persistent Environmental Chemicals.

Lipophilic persistent environmental chemicals (LPECs) can accumulate in a woman's body and transfer to her developing child across the placenta and via breast milk. To assess health risks associated with developmental exposures to LPECs, we developed a pharmacokinetic (PK) model that quantifies mother-to-offspring transfer of LPECs during pregnancy and lactation and facilitates internal dosimetry calculations for offspring. We parameterized the model for mice, rats, and humans using time-varying functions for body mass and milk consumption rates. The only required substance-specific parameter is the elimination half-life of the LPEC in the animal species of interest. We used the model to estimate whole-body concentrations in mothers and offspring following maternal exposures to hexachlorobenzene (HCB) and 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153) and compared these with measured concentrations from animal studies. We also compared estimated concentrations for humans to those generated using a previously published human LPEC PK model. Finally, we compared human equivalent doses (HEDs) calculated using our model and an allometric scaling method. Estimated and observed whole-body concentrations of HCB and PCB 153 in offspring followed similar trends and differed by less than 60%. Simulations of human exposure yielded concentration estimates comparable to those generated using the previously published model, with concentrations in offspring differing by less than 12%. HEDs calculated using our PK model were about 2 orders of magnitude lower than those generated using allometric scaling. Our PK model can be used to calculate internal dose metrics for offspring and corresponding HEDs and thus informs assessment of developmental toxicity risks associated with LPECs.

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Purpose: To develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Methods: Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively. Results: The proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted. Conclusion: The developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.

Comparing the Liver Safety Profiles of 4 Next-Generation CGRP Receptor Antagonists to the Hepatotoxic CGRP Inhibitor Telcagepant Using Quantitative Systems Toxicology Modeling.

Calcitonin gene-related peptide (CGRP) signaling inhibitors have shown efficacy in both the acute and preventive treatment of migraine. Telcagepant, a first-generation CGRP receptor antagonist, was effective but failed in clinical trials due to hepatotoxicity. Subsequently, although 4 next-generation CGRP receptor antagonists (rimegepant, zavegepant, atogepant, and ubrogepant) were being advanced into late-stage clinical trials, due to telcagepant's failure, more confidence in the liver safety of these compounds was needed. DILIsym v6A, a quantitative systems toxicology (QST) model of drug-induced liver injury (DILI), was used to model all 5 compounds and thus to compare the 4 next-generation CGRP receptor antagonists to telcagepant. In vitro experiments were performed to measure the potential for each compound to inhibit bile acid transporters, produce oxidative stress, and cause mitochondrial dysfunction. Physiologically based pharmacokinetic models were produced for each compound in order to appropriately estimate liver exposure. DILIsym predicted clinical elevations of liver enzymes and bilirubin for telcagepant, correctly predicting the observed DILI liability of the first-generation compound. By contrast, DILIsym predicted that each of the 4 next-generation compounds would be significantly less likely to cause DILI than telcagepant. Subsequent clinical trials have validated these predictions for each of the 4 compounds, and all 3 of the compounds submitted to FDA to date (rimegepant, ubrogepant, and atogepant) have since been approved by the FDA with no warning for hepatotoxicity. This work demonstrates the potential for QST modeling to prospectively differentiate between hepatotoxic and nonhepatotoxic molecules within the same class.

A Novel Framework for the Optimization of Simultaneous ThermoBrachyTherapy.

In high-dose-rate brachytherapy (HDR-BT) for prostate cancer treatment, interstitial hyperthermia (IHT) is applied to sensitize the tumor to the radiation (RT) dose, aiming at a more efficient treatment. Simultaneous application of HDR-BT and IHT is anticipated to provide maximum radiosensitization of the tumor. With this rationale, the ThermoBrachyTherapy applicators have been designed and developed, enabling simultaneous irradiation and heating. In this research, we present a method to optimize the three-dimensional temperature distribution for simultaneous HDR-BT and IHT based on the resulting equivalent physical dose (EQDphys) of the combined treatment. First, the temperature resulting from each electrode is precomputed. Then, for a given set of electrode settings and a precomputed radiation dose, the EQDphys is calculated based on the temperature-dependent linear-quadratic model. Finally, the optimum set of electrode settings is found through an optimization algorithm. The method is applied on implant geometries and anatomical data of 10 previously irradiated patients, using reported thermoradiobiological parameters and physical doses. We found that an equal equivalent dose coverage of the target can be achieved with a physical RT dose reduction of 20% together with a significantly lower EQDphys to the organs at risk (p-value < 0.001), even in the least favorable scenarios. As a result, simultaneous ThermoBrachyTherapy could lead to a relevant therapeutic benefit for patients with prostate cancer.

Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro.

Human exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous, with mixtures of PFAS detected in drinking water, food, household dust, and other exposure sources. Animal toxicity studies and human epidemiology indicate that PFAS may act through shared mechanisms including activation of peroxisome proliferator activated receptor α (PPARα). However, the effect of PFAS mixtures on human relevant molecular initiating events remains an important data gap in the PFAS literature. Here, we tested the ability of modeling approaches to predict the effect of diverse PPARα ligands on receptor activity using Cos7 cells transiently transfected with a full length human PPARα (hPPARα) expression construct and a peroxisome proliferator response element-driven luciferase reporter. Cells were treated for 24 h with two full hPPARα agonists (pemafibrate and GW7647), a full and a partial hPPARα agonist (pemafibrate and mono(2-ethylhexyl) phthalate), or a full hPPARα agonist and a competitive antagonist (pemafibrate and GW6471). Receptor activity was modeled with three additive approaches: effect summation, relative potency factors (RPF), and generalized concentration addition (GCA). While RPF and GCA accurately predicted activity for mixtures of full hPPARα agonists, only GCA predicted activity for full and partial hPPARα agonists and a full agonist and antagonist. We then generated concentration response curves for seven PFAS, which were well-fit with three-parameter Hill functions. The four perfluorinated carboxylic acids (PFCA) tended to act as full hPPARα agonists while the three perfluorinated sulfonic acids (PFSA) tended to act as partial agonists that varied in efficacy between 28-67 % of the full agonist, positive control level. GCA and RPF performed equally well at predicting the effects of mixtures with three PFCAs, but only GCA predicted experimental activity with mixtures of PFSAs and a mixture of PFCAs and PFSAs at ratios found in the general population. We conclude that of the three approaches, GCA most accurately models the effect of PFAS mixtures on hPPARα activity in vitro. Understanding the differences in efficacy with which PFAS activate hPPARα is essential for accurately predicting the effects of PFAS mixtures. As PFAS can activate multiple nuclear receptors, future analyses should examine mixtures effects in intact cells where multiple molecular initiating events contribute to proximate effects and functional changes.

Targeting chromosome 12q amplification in relapsed glioblastoma: the use of computational biological modeling to identify effective therapy-a case report.

Background: Relapsed glioblastoma (GBM) is often an imminently fatal condition with limited therapeutic options. Computation biological modeling, i.e., biosimulation, of comprehensive genomic information affords the opportunity to create a disease avatar that can be interrogated in silico with various drug combinations to identify the most effective therapies. Case Description: We report the outcome of a GBM patient with chromosome 12q amplification who achieved substantial disease remission from a novel therapy using this approach. Following next generation sequencing (NGS) was performed on the tumor specimen. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotype. In silico responses to various drug combinations were biosimulated in the disease network. Efficacy scores representing the computational effect of treatment for each strategy were generated and compared to each other to ascertain the differential benefit in drug response from various regimens. Biosimulation identified CDK4/6 inhibitors, nelfinavir and leflunomide to be effective agents singly and in combination. Upon receiving this treatment, the patient achieved a prompt and clinically meaningful remission lasting 6 months. Conclusions: Biosimulation has utility to identify active treatment combinations, stratify treatment options and identify investigational agents relevant to patients' comprehensive genomic abnormalities. Additionally, the combination of abemaciclib and nelfinavir appear promising for GBM and potentially other cancers harboring chromosome 12q amplification.

A simple model of cardiac mitochondrial respiration with experimental validation.

Cardiac mitochondria are intracellular organelles that play an important role in energy metabolism and cellular calcium regulation. In particular, they influence the excitation-contraction cycle of the heart cell. A large number of mathematical models have been proposed to better understand the mitochondrial dynamics, but they generally show a high level of complexity, and their parameters are very hard to fit to experimental data. We derived a model based on historical free energy-transduction principles, and results from the literature. We proposed simple expressions that allow to reduce the number of parameters to a minimum with respect to the mitochondrial behavior of interest for us. The resulting model has thirty-two parameters, which are reduced to twenty-three after a global sensitivity analysis of its expressions based on Sobol indices. We calibrated our model to experimental data that consists of measurements of mitochondrial respiration rates controlled by external ADP additions. A sensitivity analysis of the respiration rates showed that only seven parameters can be identified using these observations. We calibrated them using a genetic algorithm, with five experimental data sets. At last, we used the calibration results to verify the ability of the model to accurately predict the values of a sixth dataset. Results show that our model is able to reproduce both respiration rates of mitochondria and transitions between those states, with very low variability of the parameters between each experiment. The same methodology may apply to recover all the parameters of the model, if corresponding experimental data were available.

Can the mean linear energy transfer of organs be directly related to patient toxicities for current head and neck cancer intensity-modulated proton therapy practice?

BACKGROUND AND PURPOSE: The relative biological effectiveness (RBE) of proton therapy is predicted to vary with the dose-weighted average linear energy transfer (LETd). However, RBE values may substantially vary for different clinical endpoints. Therefore, the aim of this study was to assess the feasibility of relating mean D⋅LETd parameters to patient toxicity for HNC patients treated with proton therapy. MATERIALS AND METHODS: The delivered physical dose (D) and the voxel-wise product of D and LETd (D⋅LETd) distributions were calculated for 100 head and neck cancer (HNC) proton therapy patients using our TPS (Raystation v6R). The means and covariance matrix of the accumulated D and D⋅LETd of all relevant organs-at-risk (OARs) were used to simulate 2.500 data sets of different sizes. For each dataset, an attempt was made to add mean D⋅LETd parameters to a multivariable NTCP model based on mean D parameters of the same OAR for xerostomia, tube feeding and dysphagia. The likelihood of creating an NTCP model with statistically significant parameters (i.e. power) was calculated as a function of the simulated sample size for various RBE models. RESULTS: The sample size required to have a power of at least 80% to show an independent effect of mean D⋅LETd parameters on toxicity is over 15,000 patients for all toxicities. CONCLUSION: For current clinical practice, it is not feasible to directly model NTCP with both mean D and mean D⋅LETd of OARs. These findings should not be interpreted as a contradiction of previous evidence for the relationship between RBE and LETd.

Combination chemotherapy versus temozolomide for patients with methylated MGMT (m-MGMT) glioblastoma: results of computational biological modeling to predict the magnitude of treatment benefit.

BACKGROUND: A randomized trial in glioblastoma patients with methylated-MGMT (m-MGMT) found an improvement in median survival of 16.7 months for combination therapy with temozolomide (TMZ) and lomustine, however the approach remains controversial and relatively under-utilized. Therefore, we sought to determine whether comprehensive genomic analysis can predict which patients would derive large, intermediate, or negligible benefits from the combination compared to single agent chemotherapy. METHODS: Comprehensive genomic information from 274 newly diagnosed patients with methylated-MGMT glioblastoma (GBM) was downloaded from TCGA. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotypes representing hallmark behavior of cancers. In silico responses to TMZ, lomustine, and combination treatment were biosimulated. Efficacy scores representing the effect of treatment for each treatment strategy were generated and compared to each other to ascertain the differential benefit in drug response. RESULTS: Differential benefits for each drug were identified, including strong, modest-intermediate, negligible, and deleterious (harmful) effects for subgroups of patients. Similarly, the benefits of combination therapy ranged from synergy, little or negligible benefit, and deleterious effects compared to single agent approaches. CONCLUSIONS: The benefit of combination chemotherapy is predicted to vary widely in the population. Biosimulation appears to be a useful tool to address the disease heterogeneity, drug response, and the relevance of particular clinical trials observations to individual patients. Biosimulation has potential to spare some patients the experience of over-treatment while identifying patients uniquely situated to benefit from combination treatment. Validation of this new artificial intelligence tool is needed.

Redistributing Central Target Dose Hot Spots for Hypofractionated Radiosurgery of Large Brain Tumors: A Proof-of-Principle Study.

OBJECTIVE: The present proof-of-principle study investigated radiobiological effects of redistributing central target dose hot spots across different treatment fractions during hypofractionated stereotactic radiosurgery (HSRS) of large intracranial tumors. METHODS: Redistribution of central target dose hot spots during HSRS was simulated, and its effects were evaluated in eight cases of brain metastases. To assess dose variations in the target across N number of treatment fractions, a generalized biologically effective dose (gBED) was formulated. The gBED enhancement ratio was defined as the ratio of gBED in the tested treatment plan (with central target dose hot spot redistributions across fractions) to gBED in the conventional treatment plan (without central target dose hot spot redistributions). RESULTS: At a median α value of 0.3/Gy, the tested treatment plans resulted in average gBED increases of 15.6 ± 3.5% and 8.3 ± 1.8% for α/ß ratios of 2 and 10 Gy, respectively. In comparison with conventional treatment plans, the differences in the Paddick conformity index and gradient index did not exceed 2%. CONCLUSION: Redistributing central target dose hot spots across different treatment fractions during HSRS may be considered promising for enhancing gBED in the target. It may be beneficial for management of large intracranial neoplasms; thus, it warrants further clinical testing.

Possible Overcoming of Tumor Hypoxia with Adaptive Hypofractionated Radiosurgery of Large Brain Metastases: A Biological Modeling Study.

OBJECTIVE: The present biological modeling study evaluated possible application of adaptive hypofractionated stereotactic radiosurgery (HSRS), which involves escalation of the prescription dose according to the gradual decrease in the tumor volume between treatment sessions separated by 2- to 3-week intervals, in the management of large brain metastases. METHODS: To investigate the effects of dose escalation during three-stage adaptive HSRS, a generalized biologically effective dose (gBED) model was applied. Accounting for both a nonuniform dose distribution inside the target and tumor hypoxia was implemented, and normal brain radiation dose distributions were assessed. RESULTS: In comparison with conventional three-stage HSRS (with an identical prescription dose of 10 Gy at each treatment session), adaptive HSRS resulted in a 30-40% increase in gBED. This effect was especially prominent in late-responding targets (with α/ß ratios from 3 to 10 Gy) and in neoplasms containing a high percentage of hypoxic cells. Despite dose escalation in the target, irradiation of the adjacent normal brain tissue was kept within safe limits at a level similar to that applied in conventional three-stage HSRS. CONCLUSION: Adaptive HSRS theoretically results in significant enhancement of gBED in the target and may possibly overcome resistance to irradiation, which is caused by tumor hypoxia. These advantages may translate into higher treatment efficacy in cases of large brain metastases.

Toward a Logic of the Organism: A Process Philosophical Consideration.

Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems biology has focused on models consisting of static systems of differential equations operating with fixed control parameters that are measured or fitted to experimental data. However, the structure of real organisms is a highly dynamic process, the internal causality of which can only be captured by continuously changing systems of equations. In addition, in real physiological settings kinetic parameters can vary by orders of magnitude, i.e., organisms vary the value of internal quantities that in models are represented by fixed control parameters. Both the plasticity of organisms and the state dependence of kinetic parameters adds indeterminacy to the picture and asks for a new statistical perspective. This requirement could be met by the arising Biological Statistical Mechanics project, which promises to do more justice to the nature of real organisms than contemporary modeling. This article concludes that Biological Statistical Mechanics allows for a wider range of organismic ontologies than does the tacitly followed ontology of contemporary theoretical and systems biology, which are implicitly and explicitly based on systems theory.