A computer-controlled micro-instrumentation biochemical reaction environment system includes an electrically-controllable microfluidic routing system, at least one microreaction chamber, at least one micro-instrumentation device, and at least one electronic microprocessor. The microreaction chamber supports at least one chemical reaction associated with a biological signaling pathway. The micro-instrumentation device measures at least one physical quantity associated with the signaling pathway. The microprocessor transmits electrical control signals, receives electrical measurement signals, and executes at least one software algorithm. The microreaction chamber is connected to the microfluidic routing system to receive and transmit at least a fluid or gas, and the electronic microprocessor receives electrical measurement signals from the micro-instrumentation device via a first electric interface and transmits electrical control signals to electrically-controllable microfluidic routing system via a second electrical interface. The microfluidic routing system can deliver a drug to the microreaction chamber to influence a chemical reaction associated with a biological signaling pathway.

Disclosed are the atomic coordinates of compositions comprising Fc heterodimer proteins in crystalline form derived from high resolution x-ray diffraction. Further disclosed are systems and methods for using all or a portion of these atomic coordinates to identify and design improved Fc heterodimer proteins. Further disclosed are compositions comprising a mixture of (i) a solubilized Fc heterodimer protein and (ii) a mother liquor solution. The mother liquor solution comprises between 2% and 10% (v/v) ethylene glycol, between 10% and 25% (w/v) polyethylene glycol having an average molecular weight of between 2000 Daltons and 10000 Daltons, and between 0.05 M and 0.40 M ammonium iodide. Further disclosed are systems and methods of identifying a mutation which promotes heterodimeric Fc chain pair formation in which structure based modeling is performed to identify a candidate mutation to an Fc chain using all or a portion of the disclosed three-dimensional atomic coordinates.

The present invention provides methods for rapidly and efficiently searching biologically-related data space. More specifically, the present invention provides methods for identifying bio-molecules with desired properties, or which are most suitable for acquiring such properties, from complex bio-molecule libraries or sets of such libraries. The present invention also provides methods for modeling sequence-activity relationships, including but not limited to stepwise addition or subtraction techniques, Bayesian regression, ensemble regression and other methods. The present invention further provides digital systems and software for performing the methods provided herein.

The present invention discloses a construction method for heuristic metabolic co-expression network and the system thereof. Based on the max-dependent criteria, the present invention treats the characterized multivariate mutual information of a plurality of metabolites as mutual function value, and applies an optimization searching for the best feature subset, with a heuristics computational intelligence multimodal optimization algorithm. And by running the optimization process in a plurality of times, combining and studying the results in each time running, a co-expression network structure is built. Finally, a threshold for segmentations is calculated through probability models, and an exact and stable metabolic co-expression network is obtained.

The present invention provides methods for identifying bio-molecules with desired properties, or which are most suitable for acquiring such properties, from complex bio-molecule libraries or sets of such libraries. More specifically, some embodiments of the present invention provide methods for building sequence-activity models comprising multiplicative terms and using the models to guide directed evolution. In some embodiments, the sequence-activity models include one or more interaction terms, each of which including an interaction coefficient representing the contribution to activity of two or more defined residues. In some embodiments, the models describe relation between protein or nucleic acid sequences and protein activities. In some embodiments, the present invention also provides methods for preparing sequence-activity models, including but not limited to stepwise addition or subtraction techniques, Bayesian regression, ensemble regression and other methods. The present invention further provides digital systems and software for performing the methods provided herein.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

A method for calculating interaction energy between a target molecule and a drug candidate molecule, the method including: dividing a trajectory over a total time duration of a molecular dynamic simulation of the target molecule and the drug candidate molecule into groups based on molecular mechanic interaction energy between the target molecule and the drug candidate molecule calculated by molecular mechanics, where the method is a method for calculating the interaction energy between the target molecule and the drug candidate molecule using a calculator.

Systems and methods for creating fully custom products from scratch without exclusive use of off-the-shelf or pre-specified components. A system for creating custom products includes an image capture device for capturing image data and/or measurement data of a user. A computer is communicatively coupled with the image capture device and configured to construct an anatomic model of the user based on the captured image data and/or measurement data. The computer provides a configurable product model and enables preview and automatic or user-guided customization of the product model. A display is communicatively coupled with the computer and displays the custom product model superimposed on the anatomic model or image data of the user. The computer is further configured to provide the customized product model to a manufacturer for manufacturing eyewear for the user in accordance with the customized product model. The manufacturing system is configured to interpret the product model and prepare instructions and control equipment for the manufacturing of the customized product.

The present disclosure provides advantageous systems and methods for identifying molecular vulnerabilities in biological pathways and networks. The present disclosure generally involves conceptualizing a disease/disorder at the molecular level as a faulty physiological system, wherein one or more molecules in the complex intracellular signaling network are dysfunctional. This is accomplished by modeling a given physiological system as a digital logic circuit. More particularly, in exemplarily embodiments, binary logic equations are derived by analyzing the interactions between the input and output nodes of a target biological system. These equations are then used to produce a digital circuit representation for the system. Once a digital circuit representation is created, this circuit may advantageously be analyzed, using fault analysis techniques, in order to determine the vulnerability levels of the molecules of the targeted system.

Drug combinations offer promising treatment for some conditions such as cancer. However, the large number of available drug combinations makes it impractical to try all possible combinations. Machine-learning techniques described in this disclosure train a classification algorithm. Once trained, the classification algorithm uses genomic data from a specific patient to perform in silico tests of drugs and drug combinations against the genomic data to determine which therapies are likely to be effective for treating a condition of the specific patient.