基于遗传算法的谷氨酸发酵动力学参数估计

把遗传算法应用于求解谷氨酸分批发酵动力学模型参数,取交叉概率Pc=0．8、变异概率Pm=0．06、初始种群为20、遗传世代数为200代,能进一步提高谷氨酸分批发酵过程状态变量的计算值与实验值的吻合程度。模拟值与实验值对比显示,该动力学模型能很好地反映谷氨酸分批发酵过程。     

放射性药物动态模型建立与参数估计：PET定量分析（一）

正电子放射断层成像技术（Positron Emission Tomography,PET）是广泛应用的功能成像系统,也是分子影像技术之一。PET定量分析为疾病早期诊断、药物疗效评估、疾病发展进程观察提供高灵敏度高精确度的工具。本文介绍PET成像技术中放射性药物动态模型的建立与相关的参数估计分析。     

基于遗传算法的麦角固醇分批发酵动力学参数估算

把遗传算法应用于求解麦角固醇分批发酵动力学模型参数,能进一步提高麦角固醇分批发酵过程状态变量的计算值与实验值的吻合程序,在计算机上对动力学模型进行了拟合,模拟值与实验值对比显示,该动力学模型能很好地反映麦角固醇分批发酵过程。     

A robust methodology for kinetic model parameter estimation for biocatalytic reactions

Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme‐catalyzed reactions generally include several parameters, which are strongly correlated with each other. State‐of‐the‐art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver‐Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches. The parameter estimation problem is decomposed into five hierarchical steps, where the solution of each of the steps becomes the input for the subsequent step to achieve the final model with the corresponding regressed parameters. The model is further used for validating its performance and determining the correlation of the parameters. The final model with the fitted parameters is able to describe both initial rate and dynamic experiments. Application of the methodology is illustrated with a case study using the ω‐transaminase catalyzed synthesis of 1‐phenylethylamine from acetophenone and 2‐propylamine. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: a combined fMRI–EEG study

While there is a growing body of functional magnetic resonance imaging (fMRI) evidence implicating a corpus of brain regions in value-based decision-making in humans, the limited temporal resolution of fMRI cannot address the relative temporal precedence of different brain regions in decision-making. To address this question, we adopted a computational model-based approach to electroencephalography (EEG) data acquired during a simple binary choice task. fMRI data were also acquired from the same participants for source localization. Post-decision value signals emerged 200 ms post-stimulus in a predominantly posterior source in the vicinity of the intraparietal sulcus and posterior temporal lobe cortex, alongside a weaker anterior locus. The signal then shifted to a predominantly anterior locus 850 ms following the trial onset, localized to the ventromedial prefrontal cortex and lateral prefrontal cortex. Comparison signals between unchosen and chosen options emerged late in the trial at 1050 ms in dorsomedial prefrontal cortex, suggesting that such comparison signals may not be directly associated with the decision itself but rather may play a role in post-decision action selection. Taken together, these results provide us new insights into the temporal dynamics of decision-making in the brain, suggesting that for a simple binary choice task, decisions may be encoded predominantly in posterior areas such as intraparietal sulcus, before shifting anteriorly.     

Recruiting machine learning methods for molecular simulations of proteins

Abstract

Molecular dynamics (MD) simulations are critical to understanding the movements of proteins in time. Yet, MD simulations are limited due to the availability of high-resolution protein structures, accuracy of the underlying force-field, computational expense, and difficulty in analysing big data-sets. Machine learning algorithms are now routinely used to circumvent many of these limitations and computational biophysicists are continuously making progress in developing novel applications. Here, we discuss some of these methods, varying from traditional dimensionality reduction approaches to more recent abstractions such as transfer learning and reinforcement learning, and how they have been used to deal with the challenges in MD. We conclude with the prospective issues in the application of machine learning methods in MD, to increase accuracy and efficiency of protein dynamics studies in general.     

A Neuro-Muscular Elasto-Dynamic Model of the Human Arm Part 2: Musculotendon Dynamics and Related Stress Effects

In this paper we develop an elasto-dynamic model of the human arm that includes effects of neuro-muscular control uponelastic deformation in the limb.The elasto-dynamic model of the arm is based on hybrid parameter multiple body systemvariational projection principles presented in the companion paper.Though the technique is suitable for detailed bone and jointmodeling,we present simulations for simplified geometry of the bones,discretized as Rayleigh beams with elongation,whileallowing for large deflections.Motion of the upper extremity is simulated by incorporating muscle forces derived from aHill-type model of musculotendon dynamics.The effects of muscle force are modeled in two ways.In one approach,aneffective joint torque is calculated by multiplying the muscle force by a joint moment ann.A second approach models themuscle as acting along a straight line between the origin and insertion sites of the tendon.Simple arm motion is simulated byutilizing neural feedback and feedforward control.Simulations illustrate the combined effects of neural control strategies,models of muscle force inclusion,and elastic assumptions on joint trajectories and stress and strain development in the bone andtendon.     

基质穿膜传递过程和Monod方程的耦合模型

在分析基质进入细胞穿膜传质机理的基础上,提出了相应的简单传质模型。以此讨论了传递过程对Monod方程的影响,得出了传递过程不影响Monod方程的形式,但影响其动力学参数的结论。这和文献结果和实验数据一致。     

Estimating the Location and Time Course of Synaptic Input from Multi-Site Potential Recordings

A method is introduced that permits accurate and robust extraction of the location and time course of synaptic conductance from potentials recorded on either side of, and perhaps at some distance from, the synapse in question. It is shown that such data permits one to fully overcome the problems typically associated with lack of spaceclamp. The method does not presume anything about the nature of the time course and yet is applicable to branched, active cells receiving simultaneous input from a number of synapses.