Producing functional microbial consortia

One or more non-transitory computer-readable media storing computer-executable instructions that upon execution cause one or more processors to perform acts comprising:

• generating a machine learning model that based on training data that includes the identification information of one or more microbes, the machine learning model at least correlating one or more environmental sample variable values of multiple environmental samples with microbial variable values of one or more microbial species and one or more microbial consortia that are present in the multiple environmental samples;
• receiving a request for information related to one or more variable values;
• and
• applying the machine learning model to the one or more variable values to at least one of identifying one or more microbial species that are associated with the one or more variable values, identifying one or more environmental characteristics that are associated with the one or more variable values, or identifying at least one microbial consortium that is associated with the one or more variable values.

A computing device, comprising:

• one or more processors;
• and
• memory including a plurality of computer-executable components that are executable by the one or more processors to perform a plurality of actions, the plurality of actions comprising:
• generating a machine learning model that, based on training data that includes the identification information of one or more microbes, the machine learning model at least correlates one or more environmental sample variable values of multiple environmental samples with microbial variable values of one or more microbial species and one or more microbial consortia that are present in the multiple environmental samples;
• receiving a request for information related to one or more variable values;
• and
• applying the machine learning model to the one or more variable values to at least one of identifying one or more microbial species that are associated with the one or more variable values, identifying one or more environmental characteristics that are associated with the one or more variable values, or identifying at least one microbial consortium that is associated with the one or more variable values.

A method, comprising:

• obtaining an environmental sample comprising organic matter for microbial biomining;
• homogenizing the environmental sample to produce an input sample;
• culturing the input sample in an environment that includes one or more environmental conditions;
• and
• in response to determining based on one or more variable measurements that resulted from the culturing that the input sample produced a successful microbial biomining result, obtaining identification information for microbes that are present in a corresponding microbial consortium of the input sample.

One or more non-transitory computer-readable media storing computer-executable instructions that upon execution cause one or more processors to perform acts comprising:

• generating a machine learning model that based on training data that includes the identification information of one or more microbes, the machine learning model at least correlating one or more environmental sample variable values of an environmental sample with microbial variable values of one or more microbial species and one or more microbial consortia that are present in the environmental sample;
• receiving a request for information related to one or more variable values;
• and
• applying the machine learning model to the one or more variable values to at least one of:
• identifying one or more microbial species that are associated with the one or more variable values;
• identifying one or more environmental characteristics that are associated with the one or more variable values;
• and/or identifying at least one microbial consortium that is associated with the one or more variable values.

A computing device, comprising:

• one or more processors;
• and
• a memory including a plurality of computer-executable components that are executable by the one or more processors to perform a plurality of actions, the plurality of actions comprising:
• generating a machine learning model that, based on training data that includes the identification information of one or more microbes, the machine learning model at least correlates one or more environmental sample variable values of an environmental sample with microbial variable values of one or more microbial species and one or more microbial consortia that are present in the environmental sample;
• receiving a request for information related to one or more variable values;
• and
• applying the machine learning model to the one or more variable values to at least one of:
• identifying one or more microbial species that are associated with the one or more variable values;
• identifying one or more environmental characteristics that are associated with the one or more variable values;
• and/or identifying at least one microbial consortium that is associated with the one or more variable values.

A method for producing a microbial consortium that performs a specified function, the method comprising:

• providing a sample comprising a plurality of microorganisms;
• inoculating a first volume of a growth medium with a portion of said sample to provide a first culture;
• growing the first culture under a set of selective conditions;
• producing a first taxonomic classification of microorganisms in the first culture;
• inoculating a second volume of the growth medium with a portion of the first culture to provide a second culture;
• growing the second culture under the set of selective conditions;
• producing a second taxonomic classification of microorganisms in the second culture;
• deriving a measure of microbial community stability of the second culture with respect to the first culture using the second taxonomic classification and the first taxonomic classification.

A method for producing a microbial consortium that performs a specified function, the method comprising:

• a) providing a sample comprising a plurality of microorganisms;
• b) inoculating an Nth volume of a growth medium with a portion of said sample to provide an Nth culture;
• c) growing the Nth culture under a set of selective conditions;
• d) producing an Nth taxonomic classification of microorganisms in the Nth culture;
• e) inoculating a N+1th volume of the growth medium with a portion of the Nth culture;
• growing the N+1th culture under the set of selective conditions;
• g) producing a N+1th taxonomic classification of microorganisms in the N+1th culture;
• h) deriving a measure of microbial community stability of the N+1th culture with respect to the Nth culture using the N+1th taxonomic classification and the Nth taxonomic classification;
• i) repeating iteratively steps (e) to (h) with the N+1th culture acting as the Nth culture until the measure of microbial community stability reaches a plateau value;
• and
• j) providing the stable N+1th culture as comprising a microbial consortium that performs a specified function.