Development of a Novel Method for Biochemical Systems Simulation: Incorporation of Stochasticity in a Deterministic Framework

Heart disease, cancer, diabetes and other complex diseases account for more than half of human mortality in the United States. Other diseases such as AIDS, asthma, Parkinson’s disease, Alzheimer’s disease and cerebrovascular ailments such as stroke not only augment this mortality but also severely deteriorate the quality of human life experience. In spite of enormous financial support and global scientific effort over an extended period of time to combat the challenges posed by these ailments, we find ourselves short of sighting a cure or vaccine. It is widely believed that a major reason for this failure is the traditional reductionist approach adopted by the scientific community in the past. In recent times, however, the systems biology based research paradigm has gained significant favor in the research community especially in the field of complex diseases. One of the critical components of such a paradigm is computational systems biology which is largely driven by mathematical modeling and simulation of biochemical systems. The most common methods for simulating a biochemical system are either: a) continuous deterministic methods or b) discrete event stochastic methods. Although highly popular, none of them are suitable for simulating multi-scale models of biological systems that are ubiquitous in systems biology based research. In this work a novel method for simulating biochemical systems based on a deterministic solution is presented with a modification that also permits the incorporation of stochastic effects. This new method, through extensive validation, has been proven to possess the efficiency of a deterministic framework combined with the accuracy of a stochastic method. The new crossover method can not only handle the concentration and spatial gradients of multi-scale modeling but it does so in a computationally efficient manner. The development of such a method will undoubtedly aid the systems biology researchers by providing them with a tool to simulate multi-scale models of complex diseases

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies

<p>Abstract</p> <p>Background</p> <p>To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem.</p> <p>Results</p> <p>We investigate the modeling process using multiple kinetic equations together with various parameter optimization methods for a well-characterized example network, the biosynthesis of valine and leucine in <it>C. glutamicum</it>. For this purpose, we derive seven dynamic models based on generalized mass action, Michaelis-Menten and convenience kinetics as well as the stochastic Langevin equation. In addition, we introduce two modeling approaches for feedback inhibition to the mass action kinetics. The parameters of each model are estimated using eight optimization strategies. To determine the most promising modeling approaches together with the best optimization algorithms, we carry out a two-step benchmark: (1) coarse-grained comparison of the algorithms on all models and (2) fine-grained tuning of the best optimization algorithms and models. To analyze the space of the best parameters found for each model, we apply clustering, variance, and correlation analysis.</p> <p>Conclusion</p> <p>A mixed model based on the convenience rate law and the Michaelis-Menten equation, in which all reactions are assumed to be reversible, is the most suitable deterministic modeling approach followed by a reversible generalized mass action kinetics model. A Langevin model is advisable to take stochastic effects into account. To estimate the model parameters, three algorithms are particularly useful: For first attempts the settings-free Tribes algorithm yields valuable results. Particle swarm optimization and differential evolution provide significantly better results with appropriate settings.</p

Structure Based Study of CA SPASE-3 and D-Arginine Dehydrogenase

Caspases are important players in programmed cell death. Normal activities of caspases are critical for the cell life cycle and dysfunction of caspases may lead to the development of cancer and neurodegenerative diseases. They have become a popular target for drug design against abnormal cell death. In this study, the recognition of P5 position in substrates by caspase-3, -6 and -7 has been investigated by kinetics, modeling and crystallography. Crystal structures of caspase-3 and -7 in complexes with substrate analog inhibitor Ac-LDESD-CHO have been determined at resolutions of 1.61 and 2.45 Å, respectively, while a model of caspase-6/LDESD is constructed. Enzymatic study and structural analysis have revealed that Caspase-3 and -6 recognize P5 in pentapeptides, while caspase-7 lacks P5-binding residues. D-arginine dehydrogenase catalyzes the flavin-dependent oxidative deamination of D-amino acids to the corresponding imino acids and ammonia. The X-ray crystal structures of DADH and its complexes with several imino acids were determined at 1.03-1.30 Å resolution. The DADH crystal structure comprises a product-free conformation and a product-bound conformation. A flexible loop near the active site forms an “active site lid” and may play an essential role in substrate recognition. The DADH Glu87 forms an ionic interaction with the side chain of iminoarginine, suggesting its importance for DADH preference of positively charged D-amino acids. Comparison of the kinetic data of DADH activity on different D-amino acids and the crystal structures demonstrated that this enzyme is characterized by relatively broad substrate specificity, being able to oxidize positively charged and large hydrophobic D-amino acids bound within a flask-like cavity. Understanding biology at the system level has gained much more attention recently due to the rapid development in genome sequencing and high-throughput measurements. Current simulation methods include deterministic method and stochastic method. Both have their own advantages and disadvantages. Our group has developed a deterministic-stochastic crossover algorithm for simulating biochemical networks. Simulation studies have been performed on biological systems like auto-regulatory gene network and glycolysis system. The new system retains the high efficiency of deterministic method while still reflects the random fluctuations at lower concentration

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation

Optimization Algorithms for Computational Systems Biology

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology