diff --git a/spras/analysis/summary.py b/spras/analysis/summary.py
index 2092200f..58123850 100644
--- a/spras/analysis/summary.py
+++ b/spras/analysis/summary.py
@@ -1,3 +1,4 @@
+import json
 from pathlib import Path
 from statistics import median
 from typing import Iterable
@@ -99,8 +100,9 @@ def summarize_networks(file_paths: Iterable[Path], node_table: pd.DataFrame, alg
         # Algorithm parameters have format { algo : { hashcode : { parameter combos } } }
         param_combo = algo_params[algo][hashcode]
         del param_combo['_spras_run_name']
-        # TODO: sort parameters to provide stable summary table output
-        cur_nw_info.append(param_combo)
+        # We use json.dumps to properly serialize enums as strings,
+        # and sort parameters to provide stable summary table output.
+        cur_nw_info.append(json.dumps(param_combo, sort_keys=True))
 
         # Save the current network information to the network summary list
         nw_info.append(cur_nw_info)
diff --git a/spras/omicsintegrator1.py b/spras/omicsintegrator1.py
index d8f07ff8..916b7c45 100644
--- a/spras/omicsintegrator1.py
+++ b/spras/omicsintegrator1.py
@@ -51,11 +51,6 @@ class DummyMode(CaseInsensitiveEnum):
     file = 'file'
     "connect the dummy node to a specific list of nodes provided in a file"
 
-    # To make sure that DummyMode prints as `terminals`, etc.. in JSON dictionaries
-    # (since they use object representation internally.)
-    def __repr__(self) -> str:
-        return f"'{self.name}'"
-
 class OmicsIntegrator1Params(BaseModel):
     dummy_mode: Optional[DummyMode] = None
     mu_squared: bool = False
diff --git a/spras/omicsintegrator2.py b/spras/omicsintegrator2.py
index b20f9c9b..b6c18efd 100644
--- a/spras/omicsintegrator2.py
+++ b/spras/omicsintegrator2.py
@@ -16,8 +16,11 @@
 
 class DummyMode(CaseInsensitiveEnum):
     terminals = 'terminals'
+    "Connect to all terminals."
     others = 'others'
+    "Connect to all nodes except for terminals."
     all = 'all'
+    "Connect to all nodes in the interactome."
 
 class OmicsIntegrator2Params(BaseModel):
     w: float = 5
@@ -29,22 +32,17 @@ class OmicsIntegrator2Params(BaseModel):
     g: float = 3
     "Gamma: multiplicative edge penalty from degree of endpoints"
 
-    noise: Optional[float] = None
+    noise: float = 0.1
     "Standard Deviation of the gaussian noise added to edges in Noisy Edges Randomizations."
 
-    noisy_edges: Optional[int] = None
+    noisy_edges: int = 0
     "An integer specifying how many times to add noise to the given edge values and re-run."
 
-    random_terminals: Optional[int] = None
+    random_terminals: int = 0
     "An integer specifying how many times to apply your given prizes to random nodes in the interactome and re-run"
 
-    dummy_mode: Optional[DummyMode] = None
-    """
-    Tells the program which nodes in the interactome to connect the dummy node to. (default: terminals)
-        "terminals" = connect to all terminals
-        "others" = connect to all nodes except for terminals
-        "all" = connect to all nodes in the interactome.
-    """
+    dummy_mode: DummyMode = DummyMode.terminals
+    "Tells the program which nodes in the interactome to connect the dummy node to."
 
     seed: Optional[int] = None
     "The random seed to use for this run."
@@ -106,7 +104,6 @@ def generate_inputs(data: Dataset, filename_map):
         edges_df.to_csv(filename_map['edges'], sep='\t', index=False, columns=['Interactor1', 'Interactor2', 'cost'],
                         header=['protein1', 'protein2', 'cost'])
 
-    # TODO add reasonable default values
     @staticmethod
     def run(inputs, output_file, args=None, container_settings=None):
         if not container_settings: container_settings = ProcessedContainerSettings()
@@ -195,7 +192,11 @@ def parse_output(raw_pathway_file, standardized_pathway_file, params):
                 df = add_rank_column(df)
                 df = reinsert_direction_col_undirected(df)
                 df.columns = ['Node1', 'Node2', 'Rank', "Direction"]
-            else: # corrupted data
+            else:
+                # We get protein1, protein2, and cost if no edges were inside the solution (as networkx
+                # does not have any edges in the solution to loop over, and therefore never makes the column)
+                # and we get protein1, protein2 if no edges were present in the augmented forest at all:
+                # both of these outcomes should be treated as an empty network.
                 df = pd.DataFrame(columns=['Node1', 'Node2', 'Rank', 'Direction'])
 
             df, has_duplicates = duplicate_edges(df)
diff --git a/test/analysis/expected_output/expected_egfr_summary.txt b/test/analysis/expected_output/expected_egfr_summary.txt
index a2d151a4..bdbaded9 100644
--- a/test/analysis/expected_output/expected_egfr_summary.txt
+++ b/test/analysis/expected_output/expected_egfr_summary.txt
@@ -1,10 +1,10 @@
 Name	Number of nodes	Number of edges	Number of connected components	Density	Max degree	Median degree	Max diameter	Average path length	Nodes in prize	Nodes in sources	Nodes in targets	Nodes in active	Nodes in dummy	Parameter combination
-test/analysis/input/egfr/tps-egfr-domino-params-V3X4RW7_pathway.txt	48	45	3	0.0398936170212766	5	2.0	16	3.882808476926124	27	0	27	27	0	{'module_threshold': 0.05, 'slice_threshold': 0.3}
-test/analysis/input/egfr/tps-egfr-meo-params-GKEDDFZ_pathway.txt	1877	12845	1	0.007295700506524384	469	6.0	6	2.7973618474338107	621	1	620	621	1	{'max_path_length': 3, 'local_search': True, 'rand_restarts': 10}
-test/analysis/input/egfr/tps-egfr-omicsintegrator1-params-3THRXWW_pathway.txt	28	20	8	0.05291005291005291	4	1.0	5	1.306439393939394	28	1	27	28	1	{'dummy_mode': 'file', 'mu_squared': False, 'exclude_terms': False, 'noisy_edges': 0, 'shuffled_prizes': 0, 'random_terminals': 0, 'seed': None, 'w': 0.1, 'b': 10.0, 'd': 10, 'mu': 0.008, 'noise': None, 'g': 0.001, 'r': 0.01}
-test/analysis/input/egfr/tps-egfr-omicsintegrator1-params-5QH767V_pathway.txt	39	31	8	0.04183535762483131	6	1.0	5	1.5084498834498834	39	1	38	39	1	{'dummy_mode': 'file', 'mu_squared': False, 'exclude_terms': False, 'noisy_edges': 0, 'shuffled_prizes': 0, 'random_terminals': 0, 'seed': None, 'w': 0.1, 'b': 2.0, 'd': 10, 'mu': 0.008, 'noise': None, 'g': 0.001, 'r': 0.01}
-test/analysis/input/egfr/tps-egfr-omicsintegrator1-params-ITO5EQS_pathway.txt	14	9	5	0.0989010989010989	4	1.0	2	1.1866666666666668	14	0	14	14	0	{'dummy_mode': 'file', 'mu_squared': False, 'exclude_terms': False, 'noisy_edges': 0, 'shuffled_prizes': 0, 'random_terminals': 0, 'seed': None, 'w': 0.1, 'b': 0.55, 'd': 10, 'mu': 0.008, 'noise': None, 'g': 0.001, 'r': 0.01}
-test/analysis/input/egfr/tps-egfr-omicsintegrator2-params-EHHWPMD_pathway.txt	593	591	2	0.0033669841848593955	32	1.0	30	6.72248989073389	531	1	530	531	1	{'w': 5.0, 'b': 4.0, 'g': 0.0, 'noise': None, 'noisy_edges': None, 'random_terminals': None, 'dummy_mode': None, 'seed': None}
-test/analysis/input/egfr/tps-egfr-omicsintegrator2-params-IV3IPCJ_pathway.txt	704	702	2	0.002836867968446916	35	1.0	24	6.038766691954387	616	1	615	616	1	{'w': 5.0, 'b': 2.0, 'g': 3.0, 'noise': None, 'noisy_edges': None, 'random_terminals': None, 'dummy_mode': None, 'seed': None}
-test/analysis/input/egfr/tps-egfr-pathlinker-params-7S4SLU6_pathway.txt	14	17	1	0.18681318681318682	6	2.0	7	2.857142857142857	6	1	5	6	1	{'k': 10}
-test/analysis/input/egfr/tps-egfr-pathlinker-params-TCEMRS7_pathway.txt	25	32	1	0.10666666666666667	8	2.0	7	3.486666666666667	11	1	10	11	1	{'k': 20}
+test/analysis/input/egfr/tps-egfr-domino-params-V3X4RW7_pathway.txt	48	45	3	0.0398936170212766	5	2.0	16	3.882808476926124	27	0	27	27	0	"{""module_threshold"": 0.05, ""slice_threshold"": 0.3}"
+test/analysis/input/egfr/tps-egfr-meo-params-GKEDDFZ_pathway.txt	1877	12845	1	0.007295700506524384	469	6.0	6	2.7973618474338107	621	1	620	621	1	"{""local_search"": true, ""max_path_length"": 3, ""rand_restarts"": 10}"
+test/analysis/input/egfr/tps-egfr-omicsintegrator1-params-3THRXWW_pathway.txt	28	20	8	0.05291005291005291	4	1.0	5	1.306439393939394	28	1	27	28	1	"{""b"": 10.0, ""d"": 10, ""dummy_mode"": ""file"", ""exclude_terms"": false, ""g"": 0.001, ""mu"": 0.008, ""mu_squared"": false, ""noise"": null, ""noisy_edges"": 0, ""r"": 0.01, ""random_terminals"": 0, ""seed"": null, ""shuffled_prizes"": 0, ""w"": 0.1}"
+test/analysis/input/egfr/tps-egfr-omicsintegrator1-params-5QH767V_pathway.txt	39	31	8	0.04183535762483131	6	1.0	5	1.5084498834498834	39	1	38	39	1	"{""b"": 2.0, ""d"": 10, ""dummy_mode"": ""file"", ""exclude_terms"": false, ""g"": 0.001, ""mu"": 0.008, ""mu_squared"": false, ""noise"": null, ""noisy_edges"": 0, ""r"": 0.01, ""random_terminals"": 0, ""seed"": null, ""shuffled_prizes"": 0, ""w"": 0.1}"
+test/analysis/input/egfr/tps-egfr-omicsintegrator1-params-ITO5EQS_pathway.txt	14	9	5	0.0989010989010989	4	1.0	2	1.1866666666666668	14	0	14	14	0	"{""b"": 0.55, ""d"": 10, ""dummy_mode"": ""file"", ""exclude_terms"": false, ""g"": 0.001, ""mu"": 0.008, ""mu_squared"": false, ""noise"": null, ""noisy_edges"": 0, ""r"": 0.01, ""random_terminals"": 0, ""seed"": null, ""shuffled_prizes"": 0, ""w"": 0.1}"
+test/analysis/input/egfr/tps-egfr-omicsintegrator2-params-EHHWPMD_pathway.txt	593	591	2	0.0033669841848593955	32	1.0	30	6.72248989073389	531	1	530	531	1	"{""b"": 4.0, ""dummy_mode"": ""terminals"", ""g"": 0.0, ""noise"": 0.1, ""noisy_edges"": 0, ""random_terminals"": 0, ""seed"": null, ""w"": 5.0}"
+test/analysis/input/egfr/tps-egfr-omicsintegrator2-params-IV3IPCJ_pathway.txt	704	702	2	0.002836867968446916	35	1.0	24	6.038766691954387	616	1	615	616	1	"{""b"": 2.0, ""dummy_mode"": ""terminals"", ""g"": 3.0, ""noise"": 0.1, ""noisy_edges"": 0, ""random_terminals"": 0, ""seed"": null, ""w"": 5.0}"
+test/analysis/input/egfr/tps-egfr-pathlinker-params-7S4SLU6_pathway.txt	14	17	1	0.18681318681318682	6	2.0	7	2.857142857142857	6	1	5	6	1	"{""k"": 10}"
+test/analysis/input/egfr/tps-egfr-pathlinker-params-TCEMRS7_pathway.txt	25	32	1	0.10666666666666667	8	2.0	7	3.486666666666667	11	1	10	11	1	"{""k"": 20}"
diff --git a/test/analysis/expected_output/expected_example_summary.txt b/test/analysis/expected_output/expected_example_summary.txt
index 78fe74d7..f05b1522 100644
--- a/test/analysis/expected_output/expected_example_summary.txt
+++ b/test/analysis/expected_output/expected_example_summary.txt
@@ -1,13 +1,13 @@
 Name	Number of nodes	Number of edges	Number of connected components	Density	Max degree	Median degree	Max diameter	Average path length	Nodes in prize	Nodes in active	Nodes in dummy	Nodes in sources	Nodes in targets	Parameter combination
 test/analysis/input/example/data0-allpairs-params-BEH6YB2_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{}
-test/analysis/input/example/data0-domino-params-V3X4RW7_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	{'module_threshold': 0.05, 'slice_threshold': 0.3}
-test/analysis/input/example/data0-meo-params-GKEDDFZ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{'max_path_length': 3, 'local_search': True, 'rand_restarts': 10}
-test/analysis/input/example/data0-mincostflow-params-SZPZVU6_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{'flow': 1, 'capacity': 1}
-test/analysis/input/example/data0-omicsintegrator1-params-E3LSEZQ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{'dummy_mode': 'file', 'mu_squared': False, 'exclude_terms': False, 'noisy_edges': 0, 'shuffled_prizes': 0, 'random_terminals': 0, 'seed': None, 'w': 0.0, 'b': 6.0, 'd': 10, 'mu': 0.0, 'noise': None, 'g': 0.001, 'r': 0.0}
-test/analysis/input/example/data0-omicsintegrator1-params-NFIPHUX_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	{'dummy_mode': 'file', 'mu_squared': False, 'exclude_terms': False, 'noisy_edges': 0, 'shuffled_prizes': 0, 'random_terminals': 0, 'seed': None, 'w': 0.0, 'b': 5.0, 'd': 10, 'mu': 0.0, 'noise': None, 'g': 0.001, 'r': 0.0}
-test/analysis/input/example/data0-omicsintegrator1-params-SU2S63Y_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{'dummy_mode': 'file', 'mu_squared': False, 'exclude_terms': False, 'noisy_edges': 0, 'shuffled_prizes': 0, 'random_terminals': 0, 'seed': None, 'w': 5.0, 'b': 5.0, 'd': 10, 'mu': 0.0, 'noise': None, 'g': 0.001, 'r': 0.0}
-test/analysis/input/example/data0-omicsintegrator1-params-V26JBGX_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	{'dummy_mode': 'file', 'mu_squared': False, 'exclude_terms': False, 'noisy_edges': 0, 'shuffled_prizes': 0, 'random_terminals': 0, 'seed': None, 'w': 5.0, 'b': 6.0, 'd': 10, 'mu': 0.0, 'noise': None, 'g': 0.001, 'r': 0.0}
-test/analysis/input/example/data0-omicsintegrator2-params-EHHWPMD_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	{'w': 5.0, 'b': 4.0, 'g': 0.0, 'noise': None, 'noisy_edges': None, 'random_terminals': None, 'dummy_mode': None, 'seed': None}
-test/analysis/input/example/data0-omicsintegrator2-params-IV3IPCJ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{'w': 5.0, 'b': 2.0, 'g': 3.0, 'noise': None, 'noisy_edges': None, 'random_terminals': None, 'dummy_mode': None, 'seed': None}
-test/analysis/input/example/data0-pathlinker-params-6SWY7JS_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{'k': 200}
-test/analysis/input/example/data0-pathlinker-params-VQL7BDZ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	{'k': 100}
+test/analysis/input/example/data0-domino-params-V3X4RW7_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	"{""module_threshold"": 0.05, ""slice_threshold"": 0.3}"
+test/analysis/input/example/data0-meo-params-GKEDDFZ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	"{""local_search"": true, ""max_path_length"": 3, ""rand_restarts"": 10}"
+test/analysis/input/example/data0-mincostflow-params-SZPZVU6_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	"{""capacity"": 1, ""flow"": 1}"
+test/analysis/input/example/data0-omicsintegrator1-params-E3LSEZQ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	"{""b"": 6.0, ""d"": 10, ""dummy_mode"": ""file"", ""exclude_terms"": false, ""g"": 0.001, ""mu"": 0.0, ""mu_squared"": false, ""noise"": null, ""noisy_edges"": 0, ""r"": 0.0, ""random_terminals"": 0, ""seed"": null, ""shuffled_prizes"": 0, ""w"": 0.0}"
+test/analysis/input/example/data0-omicsintegrator1-params-NFIPHUX_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	"{""b"": 5.0, ""d"": 10, ""dummy_mode"": ""file"", ""exclude_terms"": false, ""g"": 0.001, ""mu"": 0.0, ""mu_squared"": false, ""noise"": null, ""noisy_edges"": 0, ""r"": 0.0, ""random_terminals"": 0, ""seed"": null, ""shuffled_prizes"": 0, ""w"": 0.0}"
+test/analysis/input/example/data0-omicsintegrator1-params-SU2S63Y_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	"{""b"": 5.0, ""d"": 10, ""dummy_mode"": ""file"", ""exclude_terms"": false, ""g"": 0.001, ""mu"": 0.0, ""mu_squared"": false, ""noise"": null, ""noisy_edges"": 0, ""r"": 0.0, ""random_terminals"": 0, ""seed"": null, ""shuffled_prizes"": 0, ""w"": 5.0}"
+test/analysis/input/example/data0-omicsintegrator1-params-V26JBGX_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	"{""b"": 6.0, ""d"": 10, ""dummy_mode"": ""file"", ""exclude_terms"": false, ""g"": 0.001, ""mu"": 0.0, ""mu_squared"": false, ""noise"": null, ""noisy_edges"": 0, ""r"": 0.0, ""random_terminals"": 0, ""seed"": null, ""shuffled_prizes"": 0, ""w"": 5.0}"
+test/analysis/input/example/data0-omicsintegrator2-params-EHHWPMD_pathway.txt	0	0	0	0.0	0	0.0	0	0.0	0	0	0	0	0	"{""b"": 4.0, ""dummy_mode"": ""terminals"", ""g"": 0.0, ""noise"": 0.1, ""noisy_edges"": 0, ""random_terminals"": 0, ""seed"": null, ""w"": 5.0}"
+test/analysis/input/example/data0-omicsintegrator2-params-IV3IPCJ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	"{""b"": 2.0, ""dummy_mode"": ""terminals"", ""g"": 3.0, ""noise"": 0.1, ""noisy_edges"": 0, ""random_terminals"": 0, ""seed"": null, ""w"": 5.0}"
+test/analysis/input/example/data0-pathlinker-params-6SWY7JS_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	"{""k"": 200}"
+test/analysis/input/example/data0-pathlinker-params-VQL7BDZ_pathway.txt	3	2	1	0.6666666666666666	2	1.0	2	1.3333333333333333	2	2	1	1	1	"{""k"": 100}"