Identification of control targets in Boolean molecular network models via computational algebra

Motivation: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. Experimentally, node manipulation requires technology to completely repress or fully activate a particular gene product while edge manipulations only require a drug that inactivates the interaction between two gene products. Results: This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network.Comment: 12 pages, 4 figures, 2 table

Biological Pathways Based Approaches to Model and Control Gene Regulatory Networks

The aim of effective cancer treatment is to prolong the patients’ life while offering a reasonable quality of life during and after treatment. The treatments must carry their actions/effects in a manner such that a very large percentage of tumor cells die or shift into a state where they stop proliferating. The fundamental issue in systems biology is to model gene interaction via gene regulatory networks (GRN) and hence provide an informatics environment to study the effects of gene mutation as well as derive newer and effective intervention (via drugs) strategies to alter the cancerous state of the network, thereby eradicating the tumor. In this dissertation, we present two approaches to model gene regulatory networks. These approach are different, albeit having a common structure to them. We develop the GRN under a Boolean formalism with deterministic and stochastic framework. The knowledge used to model these networks are derived from biological pathways, which are partial and incomplete. This work is an attempt towards understanding the dynamics of a proliferating cell and to control this system. Initial part (deterministic) of this work focuses on formulating a deterministic model by assuming the pathway regulations to be complete and accurate. Using these models algorithms were developed to pin-point faults (mutations) in the network and design personalized combination therapy depending on the expression signature of specific output genes. To introduce stochastic nature onto the model due to incompleteness in the prior biological knowledge, an uncertainty class of models was defined over the biological network. Two such uncertainty class of models are modeled- one over the state transitions and the other over the node transitions in the system. This knowledge is transferred to priors, and the existing Bayesian theory is used to update and converge to a good model. The Bayesian control theory for Markovian processes is applied to the problem of intervention in Markovian gene regulatory networks, while simultaneously updating the model. Via a toy example, it is shown that effective prior knowledge quantification can significantly help in converging on to the actual model with limited information from the system and take advantage of the optimality promised by Bayesian intervention. These control methods however, suffer from computational and memory complexity issues- Curse of Dimensionality, to be useful for any network size of biological relevance. To counter these issues associated with Dynamic Programming, suboptimal approximate algorithm known as Q-learning and its Bayesian variation are used to save on computational and memory complexities. These sub-optimal approximate algorithms perform very close (but inferior) to optimal policy, but the computational saving, both in terms of time and memory are significant to extend them to networks of larger size

Molecular Science for Drug Development and Biomedicine

With the avalanche of biological sequences generated in the postgenomic age, molecular science is facing an unprecedented challenge, i.e., how to timely utilize the huge amount of data to benefit human beings. Stimulated by such a challenge, a rapid development has taken place in molecular science, particularly in the areas associated with drug development and biomedicine, both experimental and theoretical. The current thematic issue was launched with the focus on the topic of “Molecular Science for Drug Development and Biomedicine”, in hopes to further stimulate more useful techniques and findings from various approaches of molecular science for drug development and biomedicine

Pharmacometric approaches for linking pharmacokinetic and pharmacodynamic models of sunitinib and pazopanib with clinical outcome

Aim of this thesis was to build a modeling framework for patients with metastatic renal cell carcinoma (mRCC) treated with two common first-line therapies, sunitinib and pazopanib. Therefore, as part of the European-wide EuroTARGET project, which aimed at identifying predictive biomarkers in mRCC patients, a pharmacokinetic (PK) phase IV study was conducted in Germany and the Netherlands. Based on a center-specific schedule up to 12 blood samples per patient were collected in conjunction with blood pressure measurements. Plasma concentrations of the respective study drug and the soluble VEGF receptors 2 and 3 were quantified for each time-point using previously established analytical methods. Published pharmacokinetic and pharmacodynamics (PK/PD) models were used as basis for this work. For both sunitinib and pazopanib, reliable individual PK parameters could be obtained and successfully linked to PD models for the potential biomarkers. Covariate analysis of the PK/PD models revealed two single nucleotide polymorphisms with influence on the intrinsic activity of sunitinib on sVEGFR-2 plasma concentrations (VEGFR-3 rs6877011 and ABCB1 rs2032582). The final PK/PD models were then used to establish a link to clinical outcome parameters including progression-free survival and the two most commonly observed adverse events in the mRCC population. In a model-based time-to-event analysis, a high sVEGFR-2 baseline plasma concentration was associated with a worse prognosis for sunitinib patients. In a combined analysis of sunitinib and pazopanib the absolute sVEGFR-2 plasma concentration over time was a potentially predictive factor. Hence, this model allows the prediction of PFS based on the measured sVEGFR-2 plasma concentration. Myelosuppression and fatigue as treatment-associated adverse events were analyzed separately using first-order continuous Markov models. Here, active sunitinib plasma concentration proved to be influential as a higher exposition did result in prolonged time frames of myelosuppression. However, a similar effect was not observed for fatigue. The modeling framework presented in this thesis provides a better understanding of the relationship between the exposure, pharmacological response, and clinical outcome of antiangiogenic drugs and is therefore an important step towards finding optimal dosing schedules and identifying potential predictive biomarkers for both drugs